Next Article in Journal
Alexithymia in the Narratization of Romantic Relationships: The Mediating Role of Fear of Intimacy
Next Article in Special Issue
Many Models, Little Adoption—What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection?
Previous Article in Journal
Is the Absence of Manual Lymphatic Drainage-Based Treatment in Lymphedema after Breast Cancer Harmful? A Randomized Crossover Study
Previous Article in Special Issue
Prediction of Blood Risk Score in Diabetes Using Deep Neural Networks
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Digital Therapeutics for Improving Effectiveness of Pharmaceutical Drugs and Biological Products: Preclinical and Clinical Studies Supporting Development of Drug + Digital Combination Therapies for Chronic Diseases

by
Zack Biskupiak
1,
Victor Vinh Ha
1,
Aarushi Rohaj
1,2 and
Grzegorz Bulaj
1,*
1
Department of Medicinal Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
2
The Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT 84113, USA
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(2), 403; https://doi.org/10.3390/jcm13020403
Submission received: 30 November 2023 / Revised: 8 January 2024 / Accepted: 9 January 2024 / Published: 11 January 2024

Abstract

:
Limitations of pharmaceutical drugs and biologics for chronic diseases (e.g., medication non-adherence, adverse effects, toxicity, or inadequate efficacy) can be mitigated by mobile medical apps, known as digital therapeutics (DTx). Authorization of adjunct DTx by the US Food and Drug Administration and draft guidelines on “prescription drug use-related software” illustrate opportunities to create drug + digital combination therapies, ultimately leading towards drug–device combination products (DTx has a status of medical devices). Digital interventions (mobile, web-based, virtual reality, and video game applications) demonstrate clinically meaningful benefits for people living with Alzheimer’s disease, dementia, rheumatoid arthritis, cancer, chronic pain, epilepsy, depression, and anxiety. In the respective animal disease models, preclinical studies on environmental enrichment and other non-pharmacological modalities (physical activity, social interactions, learning, and music) as surrogates for DTx “active ingredients” also show improved outcomes. In this narrative review, we discuss how drug + digital combination therapies can impact translational research, drug discovery and development, generic drug repurposing, and gene therapies. Market-driven incentives to create drug–device combination products are illustrated by Humira® (adalimumab) facing a “patent-cliff” competition with cheaper and more effective biosimilars seamlessly integrated with DTx. In conclusion, pharma and biotech companies, patients, and healthcare professionals will benefit from accelerating integration of digital interventions with pharmacotherapies.

1. Introduction

A high prevalence of chronic diseases has challenged healthcare systems and public health [1]. The most effective way to reduce the impact of chronic medical conditions is to integrate disease management and prevention with pharmacological and digital health innovations [2]. Clinical benefits of pharmaceutical drugs and biologics are confronted by such limitations as drug-resistance, medication non-adherence, adverse effects, affordability, accessibility, and inadequate efficacy. Drug-related morbidities and mortality also contribute to increased healthcare spending [3]. Clinical benefits of digital health technologies are balanced by issues related to cybersecurity, privacy, engagement and attrition rates, the reimbursement process, evolving regulatory process, and rapid advances in technology that can outpace their implementation into health care. The pharmaceutical industry has embraced digital transformation, further accelerated by artificial intelligence (AI) [4,5].
DTx are mobile medical apps that have received the US Food and Drug Administration (FDA), or other regulatory agency, authorization for treating, or preventing, specific medical conditions through a “software as a medical device” (SaMD) regulatory pathway [5,6,7,8,9]. Since DTx are medical devices, their integration with drugs and biologics can follow the drug–device combination product guidelines. The FDA Office of Combination Products defines a combination product as “A product comprised of two or more regulated components, i.e., drug/device, biologic/device, drug/biologic, or drug/device/biologic, that are physically, chemically, or otherwise combined or mixed and produced as a single entity” [10]. To support development efforts towards marketing of drug + digital combination therapies, the FDA draft guideline, “Regulatory Considerations for Prescription Drug Use-Related Software”, describes regulatory solutions for integrating a mobile app with prescription drugs and biologics [11].
Combining clinical benefits of drug- and digital-based therapies can outweigh their limitations, while simultaneously offering personalized therapies for people living with chronic diseases [12,13,14,15]. For example, the FDA authorization of reSET-O, an adjunct DTx in combination with buprenorphine for opioid use disorder, illustrates one strategy to create drug + digital combination therapies [16,17]. The use of reSET-O in combination with buprenorphine significantly increased opioid abstinence and treatment retention [18]. Digital interventions improve opioid-based analgesia [19] and medication adherence [20,21]. Digital platforms delivering disease self-management and remote patient monitoring (e.g., Huma®, BlueStar®, Propeller®, HelloBetter®) offer means to improve pharmacotherapy outcomes via drug + digital combination therapies. Diverse combinations of digital health technologies and pharmacological treatments are illustrated in Figure 1.
Creating drug + digital combination products was proposed to improve control of seizures in people with refractory epilepsy, and to increase the value proposition of branded and generic drugs by expanding their intellectual property protection [13]. The design of a DTx prototype for epilepsy to be combined with an antiseizure medication (ASM) as a drug–device combination product illustrated a means to decrease dosing of a pharmaceutical drug without compromising clinical efficacy [22]. Sverdlov and colleagues discussed how drug + digital combination therapies can increase clinical efficacy of pharmacotherapies [12]. Development of drug + digital combination therapies can be accomplished through a two-step process: (1) development of DTx using the “software as a medical device” regulatory pathways (e.g., 510k clearance, de novo, premarket authorization (PMA)), and (2) development of DTx-Rx combination product, whereas DTx is a medical device.
For preclinical studies, our group described an approach to evaluate DTx “active ingredients” (audiogenic stimulation, cognitive stimulation, physical activities) in combination with pharmaceutical drugs [23], and proposed a preclinical strategy to evaluate drug + digital combination therapies in animal models of human diseases, using environmental enrichment (EE) as a surrogate for digital interventions (Table 3 in [23]). While research studies show clinical and cost-effectiveness benefits of digital interventions for diverse chronic conditions, to the best of our knowledge, there are no published studies on integrating pharmacological and digital interventions via drug + digital combination products [14,19,24,25].
In this narrative review, we summarize preclinical studies on EE and DTx “active ingredients”, as well as clinical studies on digital interventions across selected examples of chronic diseases, namely Alzheimer’s disease, dementia, rheumatoid arthritis, cancer, chronic pain, depression and anxiety, and epilepsy. A rationale for randomly choosing these diverse neurological, neurodegenerative, mental, inflammatory, and autoimune conditions was to review evidence supporting development of drug + digital combination therapies as a universal strategy for treating chronic disorders. A keyword-based search in PubMed, Google Scholar, and Embase databases was performed by three authors to identify systematic reviews, meta-analyses, randomized controlled trials, and preclinical studies on DTx-compatible interventions and EE. Each section of this review is organized by a specific chronic disease and provides examples of clinical effects of digital interventions, followed by preclinical evidence of EE and individual non-pharmacological modalities as a surrogate for DTx “active ingredients”. We also discuss the impact of drug + digital combination therapies on the innovation of generic drugs and biosilimars, drug repurposing, and gene therapies.
The main objective of this review is to encourage translational research on drug–device combination products. Herein, we summarize preclinical and clinical studies that bridge pharmacological and digital interventions. The focus on preclinical studies relevant to testing DTx “active ingredients” highlights novel approaches to improve drug-discovery outcomes when evaluating investigational new drug (IND) candidates. The focus on reviewing clinical studies of digital health technologies and DTx-compatible non-pharmacological interventions for chronic diseases highlights new opportunities for pharma/biotech companies and patients to increase clinically meaningful benefits via drug + digital combination therapies.

2. Alzheimer’s Disease and Dementia

Alzheimer’s disease (AD) is a progressive neurodegenerative disease that can lead to dementia. As AD and dementia progress, patients and caregivers are burdened with an increased demand for managing and providing care [26]. The slow progress towards effective pharmacological treatments for AD was recently disrupted by the FDA approvals of monoclonal antibodies (mAbs) such as aducanumab and lecanemab intended to reduce amyloid-β in the brain [27,28,29]. Another biologic, donanemab, is also expected to receive regulatory approval [30,31,32,33]. These new biologics add to a repertoire of pharmacological agents for AD and dementia, such as cholinesterase inhibitors (donepezil, rivastigmine, and galantamine) and NMDA antagonists (memantine). More drug candidates against AD are currently undergoing clinical trials [34].
From wearables that monitor physical and mental health to video games that improve cognitive functions, digital health technologies can improve therapy outcomes for AD and dementia patients [9,35,36,37,38,39]. One example of a mobile app for dementia patients is iWander which delivers audible prompts and improves patient–caregiver communications [40]. Another example is Backup Memory, a mobile app developed by Samsung for AD patients where patients go through daily reminders of past events to help slow down the progression of their disease. The WhatMatters app provides personalized support for dementia patients through caregivers [41]. Recommended mobile apps for people with AD and dementia were reviewed elsewhere [42,43,44].
Research studies on digital interventions such as virtual reality (VR) and mobile apps for people with AD and dementia support patient care benefits [45,46,47,48,49]. Systematic review and meta-analysis (SR/MA) studies suggest that VR interventions can improve cognitive functions and ability to perform daily activities in AD patients with mild cognitive impairment (MCI) [45,49]. In addition to therapeutic effects [50], digital interventions can be useful for diagnosis, monitoring AD prognosis [47], improving communications [46], and preventing loneliness and social isolation [51]. Promises and challenges of digital health technologies for older people have been recently reviewed [38]. Since non-pharmacologic interventions, such as physical exercises and music, offer clinical benefits for AD and dementia patients [52,53,54,55,56,57], a combination of these modalities with pharmacotherapies can further improve patient care. Benefits of integrating digital and pharmacological interventions are summarized in Figure 2.
As illustrated in Figure 3, integration of digital and pharmacological therapies for AD patients can address some disadvantages of mAbs targeting amyloid-β protein, such as requiring intravenous administration (IV) every 2–4 weeks, limited efficacy, and the development of amyloid-related imaging abnormalities (ARIA) [29]. In between IV infusions, AD patients taking these biologics would benefit from digital interventions delivering non-pharmacological treatments, similar to those AD patients who received NMDA antagonists, acetylcholine esterase inhibitors, and internet-delivered multimodal treatments [58]. Daily digital interventions could include daily physical exercises, listening to music, and cognitive stimulation activities.
Animal studies in AD and dementia models showed that EE and physical activity improved spatial and working memories, and reduced levels of amyloid-β and tau proteins [59,60]. EE also improved cognitive functions in vascular dementia rats [61,62], and had positive effects on the cognitive reserve [63]. Zhang and colleagues [64] discovered that voluntary physical exercise ameliorated cognitive impairment in transgenic male APP/PS1 and wild-type mice. The running group had significantly shorter escape latency, better discrimination in the new object recognition test, and lower amyloid plaque deposition than sedentary AD mice. This finding is in accord with another study showing that both physical activity and cognitive stimulation can restore spatial memory, recognition, and motor deficits in the Tg4-42 AD mouse model [60]. Alzheimer’s rats that had both EE and donepezil showed significant improvement in performance on the Morris Water Maze tests compared to having either EE or donepezil alone, or neither [65].
Preclinical studies of the FDA-approved drugs for AD such as acetylcholinesterase inhibitors and NMDA antagonists can reduce cognitive decline and levels of amyloid-β protein in AD animal models [66]. Based on the effects of EE, testing drug-like compounds in the context of physical exercise, cognitive stimulation, music, and social interactions can further improve therapy outcomes, as compared to testing compounds under “standard” conditions. For example, EE and physical exercise increase neural plasticity, spatial and working memory [59], improve cognitive flexibility [67], increase hippocampal neurogenesis and expression of brain-derived neurotropic factor (BDNF) and nerve-growth factor (NGF) [68], and even reverse cognitive decline [63]. As illustrated in Figure 4, such EE-based preclinical studies may accelerate discovery and development of drug + digital combination therapies comprising non-pharmacological interventions with drugs targeting neurodegenerative pathways.

3. Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic disease that causes painful joint swelling and inflammation. Current pharmacological drugs used for arthritis include nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, and disease modifying anti-rheumatic drugs (DMARDs) [69]. Two main objectives of the therapy are pain relief and slowing the progression of joint damage [70]. NSAIDS are important for arthritis pain management [71], while DMARDs are immunomodulators (e.g., methotrexate and biologics such as certolizumab and adalimumab). Biological DMARDs can differ in their effectiveness [72,73], and can also increase the risk of serious infections [74].
Arthritis self-management programs improve therapy outcomes [75], and can be transformed into digital interventions [76,77,78,79,80,81]. Mobile apps for people living with RA (e.g., CareHand, LiveWith, The RAISE, RA Healthline, and ArthritisPower) differ in their content and quality [82,83,84,85]. These apps aim to improve patient outcomes with a variety of methods, such as tracking disease progression, providing patient education, encouraging healthy habits like physical exercise, and promoting social interactions and better nutrition. Users of the application LiveWith had higher scores on the patient self-efficacy of managing symptoms (P-SEMS) scale [86]. Patients with higher P-SEMS scores also tended to have lower levels of pain and increased levels of patient activation. Rodriguez and colleagues conducted a trial with the CareHand app, which included personalized exercise regimens, social functions, and patient education [80]. In their study, 53% of patients were also receiving concurrent drug treatments. Their findings showed that the group using the app + drug combination fared significantly better in their recovery compared to the group provided only with an exercise program. Despite promising clinical studies, real-world acceptance and adoption of digital interventions for RA is challenging [87,88].
Given that non-pharmacological interventions like physical exercise, quality sleep, optimized nutrition, social interactions, and patient education can improve patient outcomes, the combination of these methods with NSAIDs or DMARDs could further compound patient benefits [89,90]. Non-pharmacological management of pain, fatigue, inflammation, disability, and mental comorbidities is recommended for difficult-to-treat RA patients [89,91]. As an example of clinical benefits of combining pharmaceutical drugs with non-pharmacological interventions, a recent SR/MA suggested that exercise therapy was a better treatment option than NSAIDs and opioid analgesics for knee osteoarthritis pain [92]. As illustrated in Figure 5, integrating digital interventions with pharmacotherapies offers personalized therapies that aim to improve disease prognosis, as compared to “drug-alone” treatments.
Improving drug efficacy in animal models of arthritis can be achieved by testing compounds in the presence of EE (Figure 4). A number of preclinical studies in animal model of arthritis evaluated the effects of physical exercise [93,94,95,96]. Arthritic mice treated with exercise showed slower disease progression, thicker knee cartilage, and lower TNF-α levels compared to a control group [94]. The benefits of physical exercise on joint pathophysiology were reviewed by Derue and Ribero-da-Silva [93]. Running wheels, treadmills, or other exercise-based interventions in animal models improved preserved bone structure, downregulation of inflammatory signaling, improvement in weight asymmetry, and reduced pain compared to sedentary mice [93]. EE in the form of larger cages, running wheels, toys, and other enrichments ameliorated inflammatory changes, reduced acute edema, and increased expression of BDNF in the hippocampus among arthritic mice [97]. Preclinical studies showing positive effects of EE in animal arthritis models support EE-enhanced testing of new pharmacological compounds to improve the therapeutic window of potential IND candidates.

4. Cancer

Cancer is a chronic disease characterized by abnormal cells dividing uncontrollably and impacting healthy parts of the body. In addition to killing cancer cells, oncology patients often need to navigate pain, changes in their daily habits, mental and physical fatigue, and other symptoms related to both cancer and anti-cancer therapies. Current treatments for cancer include chemotherapy, immunotherapy, surgery, radiation therapy, hormone therapy, and cryoablation. For patients and healthcare professionals, the main clinical challenges are treatment adherence, symptom monitoring, symptom management, social support, and self-efficacy. Empowering oncology patients using digital technologies has been recognized as a promising strategy to improve therapy outcomes [98,99,100,101,102,103,104].
An early example of digital interventions for oncology patients is Re-Mission, a video game developed by Hope Labs that was shown to increase treatment adherence, cancer self-efficacy, and knowledge of cancer among younger patients [105,106]. Another example is an exercise-empowerment video game “Empower Stars”, which aimed to support children with cancer undergoing chemotherapy [107]. For adult oncology patients, a “LivingWith®” app delivers self-management interventions that reduced medical office visits [101]. Kaiku Health is a digital patient monitoring platform that supports cancer care, where patients can report symptoms, connect with their healthcare team, and receive self-care instructions to help detect cancer signs, symptoms, and relapses. This technology was also used to collect patient-reported outcomes during chemotherapy treatments [108,109,110]. It is noteworthy that the use of a web-based app to monitor symptoms and initiate palliative care significantly increased survival of lung cancer patients [111].
In a narrative review, Gussoni and colleagues summarized commercially available DTx for oncology indications [112]. The majority of these mobile apps are focused on symptom monitoring and management, and improving quality of life (QoL). Digital interventions can improve psychological outcomes [113], adherence to chemotherapy [106,114], and cancer pain management [115,116]. For example, Pain Guard app offers medication reminders, patient education, and treatment with the use of soothing music [117]. Using this app was associated with increased instances of pain remission, improved medication adherence, and reduced breakthrough pain [117]. Similarly, VR-based applications can reduce pain, fatigue, depression, and anxiety among cancer patients [118,119,120].
Mobile apps promoting physical activity can improve cancer-related fatigue, sedentary lifestyle, and psychosocial outcomes [121], also through personalized home exercise programs [122,123]. As cancer mortality declines, digital interventions delivering physical exercise interventions for cancer survivors are of equal importance [124]. A mobile app iCanFit was designed for cancer survivors to facilitate physical activity by tracking goals, finding resources, and providing peer-support and health education [125]. After 2–3 months of using iCanFit, the treatment group showed a significant increase in QoL and engagement in physical exercise [126]. Digitally delivered, personalized exercise programs, additionally supported by online health education, improved physical health among cancer survivors [127].
As illustrated in Figure 6, combining digital interventions with chemotherapy and immunotherapy agents is a rational strategy to improve cancer prognosis. For example, integrating cancer-specific DTx with pembrolizumab (Keytruda®) or paclitaxel as a drug–device combination product can offer more personalized treatments that maintain anti-cancer effectiveness, reduce drug side effects and cancer pain, improve psychosocial outcomes and health-related QoL, and support overall cancer care including communications with HCPs.
Animal models of cancer provide an opportunity to accelerate preclinical development of drug + digital combination therapies by testing anti-cancer drug candidates in the presence of DTx “active ingredients”. Studies show that EE in the form of physical exercise, social interactions, and cognitive and sensory stimulation can enhance anti-tumor immunity, increase lifespan, reduce tumor volume and cancer progression, and decrease cancer pain and chemotherapy-related toxicity [128,129,130,131,132]. The effects of physical exercise on cancer growth and treatment efficacy are generally positive [133,134]. Physical exercise was shown to enhance anti-PD-1 immunotherapies [135,136] and the efficacy of checkpoint inhibitors [137], and reduce doxorubicin-mediated cardiotoxicity in mice [138]. Stretching exercises for 10 min every day for 4 weeks in breast cancer mice models significantly reduced tumor volume and growth, as compared to the control group [139]. Kutz and colleagues discussed an exercise-oncology strategy to improve cancer treatments [136].
The promise of EE to improve cancer therapies is illustrated by an increased lifespan in colon cancer mouse model [129]. EE in the form of cages with running wheels, toys, and social interactions slowed tumor size and growth in pancreatic cancer mice [132]. Even simpler EE conditions such as providing an ‘igloo’ in the mice’s cage increased the NK cytotoxicity against Yac-1 lymphoma cells and decrease the number of tumors [131]. EE intervention in lung cancer mice reduced metastasis while increasing the number of lung-infiltrating NK cells and T and B lymphocytes [140]. EE can also include sensory stimulation, e.g., exposure of rats with bone cancer to music for two weeks showed lower tumor volumes and pain scores [128]. Music was also shown to mitigate a stress-induced increase in metastatic nodules in lungs of rats injected with carcinosarcoma cells [141]. These preclinical studies suggest that testing novel anti-cancer compounds in the presence of EE can increase their efficacy and decrease toxicity, thus widening their therapeutic window.

5. Chronic Pain

Chronic pain is defined as “pain that persists or recurs for more than 3 months” [142]. It is estimated that 25–30% of the human population is affected by pain [143,144], while inadequate pain treatment can lead to disability, mental health comorbidities, substance use disorder, and public health crisis [145,146,147]. NSAIDs, opioids, muscle relaxants, and other analgesic drugs are common pharmacological treatments for chronic pain. However, these medications provide short term pain relief, while causing adverse effects, gastrointestinal and cardiovascular toxicities, tolerance, and addiction. Non-pharmacological treatments for pain include physical exercise, psychological therapies, mindfulness and meditation, music, education, self-management, digital interventions, and other multimodal treatments [148,149,150,151,152,153,154]. A multimodal approach that integrates pharmacotherapy and non-pharmacological interventions enables more efficient and personalized pain management [14,24,155].
Since pioneering efforts to develop a VR-based technology for burn pain [156,157,158], DTx, such as RelieVRx, Kaia Health, and Hello Better Chronic Pain have expanded pain indications to other chronic conditions [24,159,160,161]. Digital therapeutic programs such as RelieVRx and Hello Better Chronic Pain are multi-week digital interventions that deliver patient education, mindfulness- and distraction-based practices, immersive environments, relaxation, breathing and physical exercises. RelieVRx received FDA authorization to market this prescription adjunt DTx treatment to adults with moderate to severe chronic low back pain, while Hello Better Chronic Pain is CE-certified as a medical device and DiGA-approved prescription app available in Germany. Kaia Health mobile technology can analyze body movements and recommend personalized physical therapy, as well as offering patient education, relaxation techniques, and consultations with coaches and medical providers. The Kaia Health app was shown to reduce non-specific lower back pain [159,162] and improve sleep in back pain patients [163]. This DTx is indicated for musculoskeletal pain, and is available in the US and Europe.
Clinical studies confirm the effectiveness of VR and mobile apps for acute, perioperative, and chronic pain [9,154,164,165,166,167,168,169,170]. These technologies deliver such “active ingredients” as physical exercises, psychotherapies, education, relaxation, self-management, and empowerment, while offering the convenience of at-home use [160]. Early post-marketing studies suggest an overall safety profile with a very low rate of adverse effects [171]. Challenges in developing DTx for chronic pain include meeting such primary care needs as patient–provider communications and counseling [172].
The benefits of integrating digital interventions with analgesic drugs are illustrated in Figure 7. Of particular importance for patients taking opioids are DTx that can lead to drug tapering [173,174,175]. Since patient education is gaining recognition as an “active ingredient” for pain relief and management, digital technologies are being explored to scale up such interventions [160,176,177]. Given the analgesic properties of music [149,150,178,179,180,181,182], this non-pharmacological modality is underutilized as an adjunct digital intervention [183,184]. The compatibility of DTx with other pharmacological and non-pharmacological treatments as drug + digital combination therapies for chronic pain was highlighted in Figure 6 and Figure 7 in the perspective article [24].
For preclinical studies on drug + digital combination therapies for pain, our group proposed the use of EE as a surrogate for testing DTx “active ingredients” in combination with analgesic drugs [23]. In the carrageenan model of inflammatory pain in mice, the sensory stimulation (3-week exposure to music) significantly enhanced ibuprofen-based analgesia [23]. In the music-treated mice, cannabidiol and galanin-based NAX-5055 significantly reduced paw edema, suggesting positive interactions between the stimuli and drug treatments [23]. Music-induced analgesic effects were reported in a rat model of bone cancer pain [128], while other studies with mice produced mixed results [185,186,187]. Analgesic activities of physical exercises in rodents were reviewed elsewhere [188]. Another non-pharmacological modality tested in animal pain models is the exposure to specific light [189,190,191]. A light-emitting diode (LED) producing green light elicited antinociceptive effects in both neuropathic pain and postsurgical pain models in rats [189,191]. The light-induced analgesia was mediated by a release of endogenous opioid neuropeptides and reduced neuroinflammation [191,192]. The authors emphasized translational aspects of their findings to improve pain relief and reduce opioid use [191].
The effects of EE in animal pain models are well documented [193,194,195,196,197,198], including a wide range of nociception-related responses like reducing levels of inflammatory cytokines (IL-1β) and enhancing production of anti-inflammatory cytokines (IL-10), endogenous opioids, and BDNF [193]. Positive effects of EE on neuropathic pain were observed in a mouse model of chronic constriction injury (CCI) [194]. EE-mediated analgesic effects, reduction of depression-like phenotype, and memory deficits in the CCI mice were explained by involvement of neuronal PAS domain 4 protein and lowered levels of TNFα in the hippocampus [194]. EE also decreased stress-induced visceral pain and anxiety/depression-like phenotypes, while upregulating expression of IL-10 and downregulating expression of TNFα and IL-1β in specific parts of the mouse brain [199].
Pleiotropic effects of EE on pain-related physiology and behavior can modulate the activity of analgesic compounds. A combination of EE and ketamine was more effective than ketamine alone in reducing nociception in spinal cord injury model in rats [195]. Green LED light exposure enhanced the analgesic activities of morphine and ibuprofen in postsurgical pain model in rats [191]. Voluntary wheel running lowered doses of analgesic drugs needed to alleviate complete Freund’s adjuvant (CFA)-induced pain in mice [200]. The use of a running wheel to screen analgesic compounds was proposed [201]. The aforementioned studies suggest that EE containing running wheels, green light-emitting diodes, and music can improve the efficacy of drug candidates being evaluated for the treatment of pain.

6. Depression and Anxiety

Depression and anxiety are common mental health conditions that can impact an individual’s health-related QoL and can lead to disability and suicide [202,203]. Both disorders can affect mood, appetite, ability to engage socially, enjoyment of life, and the ability to take care of one’s self or their work. Depression and anxiety are treated with antidepressant and anxiolytic medications, as well as psychotherapies. Challenges with drug-based treatments are onset of action, non-adherence, drug-resistance, adverse effects, and abuse [204,205,206,207]. Challenges with psychotherapies, such as cognitive behavioral therapies (CBT), are accessibility, affordability, and effectiveness [208,209,210].
Digital health technologies are helpful for monitoring and treatment of anxiety and depression [211,212]. An early success in reducing depressive symptoms with a mobile app SuperBetter [213] and a computer game SPARX [214] opened doors to many mental health mobile apps [215,216]. Examples of DTx for depression include Deprexis® [217,218,219], SparkRx® [220], HelloBetter [221,222], and Daylight for anxiety [223]. Some mental well-being apps were shown to reduce depressive and anxiety symptoms in RCTs, for example Headspace [224], MoodHacker [225], or MoodGym [226,227]. In addition to mobile apps, VR-based apps are also effective in treating depressive and anxiety symptoms [228,229]. Most digital interventions for mental disorders employ such “active ingredients” as CBT, patient education, physical exercises, self-management, mindfulness practices, encouraging social interactions, and promoting healthy lifestyles [230,231,232,233,234]. Personalizing digital therapies for depression and anxiety is important to optimize their effectiveness [235,236]. Adjunct digital interventions for drug-based treatment of refractory depression appeared more effective, as compared to drug-alone treatment, illustrating the benefits of drug + digital combination therapies [237,238]. Opportunities to combine antidepressants with adjunct digital therapies were illustrated using software delivering non-pharmacological modalities shown to reduce depressive symptoms [239,240,241].
Preclinical testing of drug candidates and DTx “active ingredients” in EE-enhanced animal models for depression and anxiety can accelerate development of drug + digital combination therapies [242,243,244]. The need to innovate preclinical psychopharmacology through the “use of disease-relevant experimental perturbations” [245] was addressed by Branchi and colleagues who applied a drug-EE model for testing the efficacy of fluoxetine under either enrichment or stressful conditions [242,244]. Mice were exposed to interchanged stressful and EE living conditions, followed by 21-day treatments with either fluoxetine/EE or fluoxetine/stress [242]. Mice treated with fluoxetine/EE had significantly lower depressive symptoms, higher hippocampal and hypothalamic BDNF levels, and lower levels of cortisol compared to the “standard-cage” mice [242]. While fluoxetine and EE can reduce depression-like behaviors, they elicit distinct gene expression patterns in the amygdala, suggesting potential benefits of the fluoxetine/EE combination, instead of mono-therapies [246]. Another research group showed that EE reduced onset of action of a serotonin-norepinephrine reuptake inhibitor (SNRI) drug venlafaxine in mice, and these effects could be accounted for by parvalbumin interneurons in the hippocampus [247].
Animal studies show positive effects of EE-based treatments for depression and anxiety [248,249,250,251,252]. EE intervention in depression-induced male rat pups through administration of clomipramine reversed depression-like phenotype, depression-induced dentate gyrus hypotrophy, and basolateral amygdala hypertrophy [253]. Antidepressant and anti-anxiety effects of music were shown in chronic unpredictable mild stress in mice [252] and in a maternal separation rat model of early-life stress [254]. Anxiolytic effects of music were observed in knock-in transgenic mice (BDNFMet/Met) that exhibited fluoxetine-resistant anxiety symptoms [255]. Another DTx “active ingredient”, namely physical exercise, when tested in mice showed similar antidepressant and neuroregenerative effects as fluoxetine [256]. Physical exercise showed better outcomes than fluoxetine when comparing depressive behaviors and promoting hippocampal myelination [257]. From translational research and clinical perspectives, drug + digital combination therapies may offer improved effectiveness of psychopharmacology (Figure 8).

7. Epilepsy

Epilepsy is a neurological disorder characterized by patients having spontaneous epileptic seizures [258]. Epilepsy impacts cognitive and psychological functions, with higher prevalence of anxiety, depression, and migraine as comorbidities [259]. People with epilepsy experience higher incidence of body injuries, disability, diminished quality of life (QoL), and higher mortality rates [260]. Treatment options include antiseizure medications (ASMs) [261] and neuromodulation devices [262], while brain surgery remains an option for refractory epilepsy [263]. The multiple challenges with pharmacological management of epilepsy are drug resistance [264], drug adverse effects [265], medication non-adherence [266], polypharmacy [267], and drug shortages [268]. Notably, only 50% of newly diagnosed epilepsy patients become seizure free for one year, or longer, following their initial ASM treatment [269]. Given apparent limitations of ASMs, a rationale for integrating epilepsy self-management and pharmacological treatments via drug + digital combination therapies was proposed [13].
Mobile apps for people with epilepsy deliver self-management tools, including a seizure diary, medication reminders, stress and sleep management, patient education, and communication with a healthcare team [270,271,272]. An early example of online self-management programs was the WebEase platform which focused on medication, sleep, and stress management [273,274,275,276]. A 12-week RCT of a mobile app delivering medication reminders, seizure diary, healthy habits checklist (sleep, exercise, and stress), and health education showed increased medication adherence and self-efficacy [277]. EpApp is an epilepsy self-management app intended for adolescents, and it was shown to increase epilepsy knowledge and medication management; however, there was no significant difference in seizure burden after 4-week use [278]. In one RCT, the 6-month use of a self-management mobile app resulted in significant reduction of seizure frequency and improved self-management [279]. A web-based prototype DTx for the treatment of epilepsy was designed based on behavioral and music-based interventions that were previously shown to reduce seizures [22]. The “active ingredients” in this digital intervention included management of sleep, stress, and emotions; medication adherence; patient education; self-esteem; avoiding seizure triggers; and listening to specific music compositions [280,281,282,283,284,285].
Preclinical studies showed that EE and individual non-pharmacological interventions can reduce epileptic seizures in animal models of epilepsy [286,287,288,289]. EE intervention yielded disease-modifying (antiepileptogenic) effects by delaying an onset of seizures in a rat model of absence epilepsy [288]. These EE effects were transgenerational, since the next generation of the animals had reduced seizure frequency, as compared to the control offspring group [288]. Delayed kindling epileptogenesis via EE was observed in another rat model of epilepsy [290]. In post-status epilepticus TLE rat model of epilepsy, EE intervention was able to restore neurogenesis and cognitive functions and decrease the duration of spontaneous EEG seizures [291]. In the same TLE model of epilepsy, another group showed that EE reduced seizures and depressive symptoms [292]. In addition to preclinical findings on reducing epileptic seizures, EE was able to restore epilepsy-induced sleep and cognitive and behavioral impairments [293,294,295].
A promising DTx “active ingredient” for epilepsy is specific music [280,281,296,297,298,299,300,301], and the clinical effects were also reproduced in preclinical studies [23,302,303]. Xu and colleagues showed that exposure of TLE mice to a specific music composition enhanced the anti-seizure activity of sub-effective doses of valproic acid or levetiracetam [303]. In the corneal kindling mouse model of epilepsy, the same music composition reduced cumulative seizure burden and mortality rates in the music-treated group [23]. In the spontaneous absence epilepsy rat model, music exposure reduced both seizure frequency and spontaneous high-rhythmic spike discharges [302]. Another possible DTx “active ingredient” for epilepsy management is physical activity [304,305,306,307]. Preclinical studies show that physical exercise can reduce epileptic seizures [308,309,310] and enhance the efficacy of ASMs, such as carbamazepine and valproate [311,312]. Translational aspects of physical exercises in epilepsy suggest such benefits as antiepileptogenesis and neuroprotection [313,314]. As illustrated in Figure 9, the combination of ASMs with digitally delivered non-pharmacological modalities can offer better seizure control, as compared to drug-alone interventions.

8. Other Indications and Applications

Drug + digital combination therapies can benefit people living with cardiometabolic disorders. Digital therapies for the treatment of diabetes type 2 were pioneered with the development of DTx BlueStar® [5,315,316], and showed efficacy in reducing HbA1c [317,318]. Diverse diabetes digital health technologies include such DTx as glucose tracking/monitoring systems apps, self-management, and lifestyle support apps (e.g., d-Nav, Glooko, mySugr, Dexcom, and Dario) [319,320]. New opportunities exist to combine DTx with automated close-loop insulin delivery systems [321]. DTx for hypertension and obesity can be integrated with beta blockers to improve blood pressure management, or with semaglutide (Ozempic®, Wegovy®) for weight loss, respectively [322,323,324].
Drug-based management of chronic infections (e.g., dolutegravir for HIV/AIDS, or sofosbuvir for hepatitis C) can be integrated with DTx that improve therapy outcomes through medication tracking and diverse self-management interventions [325,326,327]. Notably, positive effects of EE and physical exercise on the innate and adaptive immune functions and viral infections in rodents were reported [328,329,330], opening translational opportunities to develop combination therapies for chronic infections [331].
Gene therapy is another example where combinations with DTx can improve therapy outcomes. Since gene therapies aim to improve symptoms after only one injection, developing DTx as an adjunct digital intervention or/and “biologic + digital” combination product may support a patient’s journey before and after the correction of a mutated gene. In the case of the treatments for amyotrophic lateral sclerosis with tofersen [332] and spinal muscular atrophy with onasemnogege abeparvovec [333], these patients could use digital technologies for monitoring therapy outcomes and delivering neurorehabilitation exercises [334,335,336,337]. “One-time treatment” gene therapies for indications where self-management and self-efficacy can improve therapy outcomes can be developed together with DTx that provide clinically meaningful benefits beyond the injection of DNA vectors [338].
Due to software flexibility in delivering just-in-time adaptive interventions through DTx, drug + digital combination therapies can redefine precision medicine by providing digital therapy content tailored to a patient’s needs and disease progression [14]. Given advances in biomarker research for metabolic or neurodegenerative conditions, drug + digital combination therapy can start with digital-first care [339,340]. This approach is applicable for the treatment of osteoarthritis [341,342]. For rheumatoid arthritis and other chronic inflammatory conditions associated with flares [343], drug + digital combination therapies offer the flexibility of tapering DMARDs between longer periods of remission. For people living with chronic pain or depression, personalized drug + digital combination therapies can adjust drug-based management of symptoms after remission. Figure 10 illustrates diverse scenarios for patient-centered care in which pharmacotherapies are adjusted based on a disease activity status and prognosis.
Another application of drug + digital combination therapies and products is drug repurposing, which is recognized as an innovative way to expand indications for existing drugs [344,345,346]. Computational, molecular, and cellular screening approaches aim to match drug phenotypes with a new therapeutic target. Once a new indication is identified, preclinical validation of a repurposed drug in a new target disease animal model can include both the “standard” testing conditions, as well as the EE conditions that include disease-relevant surrogate ingredients for DTx (Figure 11). Similarly, adjunct digital intervention during clinical validation of a repurposed drug may offer better primary endpoint outcomes, since such combination therapy can include new disease-specific self-management digital content. Example applications for chronic conditions include repurposing anti-inflammatory drugs for cardiovascular [347], psychiatric [348], neurological [349], and autoimmune disorders [350].

9. “Patent Cliff” as an Incentive for Developing Drug + Digital Combination Therapies

Opportunities to develop drug + digital combination therapies can be illustrated by adalimumab (Humira®) indicated for rheumatoid arthritis and other autoimmune and inflammatory disorders. While facing a “patent cliff” for this commercially successful biologic and competition from several FDA-approved biosimilars, AbbVie engaged with diverse strategies to extend the US market exclusivity beyond 2023 [351,352]. However, Humira-based treatments have not been innovated by developing drug–device combination products comprising adalimumab and DTx that could provide additional clinical benefits [79,80,81]. Such a drug + digital combination product approach could offer copyright-protected therapy that could improve both Humira’s market dominance and patient outcomes. Instead, AbbVie continues to offer a mobile app “Humira Complete”, delivering medication reminder, injection instructions, symptom trackers, creating personal goals, and connecting with a Nurse Ambassador, among other features.
Transforming the “Humira Complete” app into DTx would require (1) expansion of disease self-management and empowerment interventions and (2) clinical validation of its efficacy in RCT. The FDA’s draft guidelines illustrate innovative opportunities for marketing adalimumab plus a mobile app for which “use of the prescription drug use-related software with the product results in a meaningful improvement in a clinical outcome as compared to use of the product without the prescription drug use-related software” [11]. Therefore, after ending the market exclusivity, brand-name Humira® may compete with cheaper and more effective biosimilars seamlessly integrated with DTx that would deliver clinically meaningful benefits.
As shown in Figure 12, these opportunities to advance drug + digital combination products apply to many blockbuster drugs that face a “patent cliff”. Pharma and biotech companies that own patent-protected market exclusivity for brand-name drugs and biologics can face new challenges when more effective combination therapies with respective generics enter patient-driven competition. We hypothesize that anticipation of marketing “more innovative” drug–device combination products from generic drug competitors will motivate development of drug + digital combination therapies.

10. Limitations of This Review

While this review is focused on translational aspects of drug + digital combination therapies, it has several limitations, including (1) a lack of reviewing research on mechanisms of action (MOA) of digital and EE interventions, (2) restricting overview of existing studies to only several chronic diseases, (3) not discussing regulatory aspects, patient privacy and security protections, interoperability, standards, and cost-effectiveness considerations of DTx, and (4) literature selection bias of a narrative review. Pleiotropic MOA of EE interventions was reviewed elsewhere [193,353,354]. It is also noteworthy that a diversity of EE experimental protocols precludes generalization for MOA [355]. A lack of data for physiological outcomes of DTx interventions is likely due to an initial focus on the efficacy studies rather than to delineate MOA. Regarding the second limitation of this review, we acknowledge that drug + digital combination therapies are applicable to other chronic conditions not discussed here. For example, advances in DTx for Parkinson’s disease, including the MedRhythm’s technology [356], support their combinations with levodopa. Digital interventions for bipolar disorder are developed by MindPax and others [357,358]. DTx for insomnia, such as Somryst®, Sleepio®, Somzz®, and HelloBetter® Sleep, can be integrated with drug-based treatments for sleep [359].
Regulatory aspects for DTx and drug + digital combination products have been omitted in this review, due to the complexity of evolving regulations across the FDA, EMA, and other country-specific agencies [6]. The FDA draft guidelines support integration of mobile apps with prescription drugs and biologics, opening a new frontier for pharma and biotech to advance drug + digital combination therapies. Cost-effectiveness studies support financial benefits of DTx [360,361,362]. However, there are also multiple barriers to a broader implementation of DTx in healthcare systems [88,363]. Insights from early adopters of DTx, for example Germany, can be helpful for healthcare stakeholders in other countries to navigate both opportunities and challenges of bringing digital and drug + digital combination therapies to patients [364].
This narrative review also has the innate limitation of summarizing relevant articles without the rigor of a systematic review. An apparent selection bias can impact both an objective analysis of published literature for individual chronic diseases, and the validity of the authors’ conclusions.

11. Conclusions

Clinical and preclinical studies support translational research on integrating digital interventions with pharmacotherapies. Available evidence for digital interventions varies from disease to disease while showing clinically meaningful benefits for patients living with the chronic diseases reviewed here. Academic and industry groups focused on drug discovery and preclinical development may consider evaluating their lead compounds in the presence of DTx “active ingredients” delivered as EE intervention, hence increasing the odds of advancing IND candidates to clinical studies. When studying new compounds in animal disease models, this “EE-pharmacology” approach will require more standardized testing conditions. The observed diversity in experimental design in animal studies of EE + drug interventions warrants establishing preclinical guidelines for investigating DTx “active ingredients” that support future co-development of drug + digital combination therapies.
Developing drug + digital combination therapies is still in its infancy, despite apparent opportunities to improve effectiveness of pharmaceutical drugs and biologics using digital interventions [12,13,22,23]. In conclusion, a quote from Helen Keller, “Alone we can do so little; together we can do so much”, can serve as encouragement for translational and clinical research to develop drug + digital combination therapies, including drug–device combination products for a personalized treatment of chronic diseases.

Author Contributions

G.B. conceived the research. G.B., Z.B., V.V.H. and A.R. performed the literature search, wrote and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This project was founded by a grant from the ALSAM Foundation.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

G.B. would like to thank the ALSAM Foundation for supporting his work. The authors would like to thank Cameron Metcalf, Eric Schmidt, and Steve White for helpful comments on the manuscript, and the University of Utah Print and Mail Services for graphic design assistance with preparation of figures. The names of many companies and their products described in this article are protected by registered trademarks and copyright.

Conflicts of Interest

G.B. is a founder and owner of OMNI Self-care, LLC, a health promotion and consulting company expanding commercial applications of evidence-based self-management practices. GB is a co-inventor on two issued US patents, 9,569,562 and 9,747,423, “Disease Therapy Game Technology”, and a patent-pending application “Multimodal Platform for Treating Epilepsy”. These patents are related to digital health technologies and are owned by the University of Utah. All remaining authors declare the absence of potential conflicts of interest.

References

  1. Disease, G.B.D.; Injury, I.; Prevalence, C. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018, 392, 1789–1858. [Google Scholar] [CrossRef]
  2. Bauer, U.E.; Briss, P.A.; Goodman, R.A.; Bowman, B.A. Prevention of chronic disease in the 21st century: Elimination of the leading preventable causes of premature death and disability in the USA. Lancet 2014, 384, 45–52. [Google Scholar] [CrossRef]
  3. Watanabe, J.H.; McInnis, T.; Hirsch, J.D. Cost of Prescription Drug-Related Morbidity and Mortality. Ann. Pharmacother. 2018, 52, 829–837. [Google Scholar] [CrossRef]
  4. Wang, C.; Lee, C.; Shin, H. Digital therapeutics from bench to bedside. NPJ Digit. Med. 2023, 6, 38. [Google Scholar] [CrossRef]
  5. Patel, N.A.; Butte, A.J. Characteristics and challenges of the clinical pipeline of digital therapeutics. NPJ Digit. Med. 2020, 3, 159. [Google Scholar] [CrossRef]
  6. Shuren, J.; Patel, B.; Gottlieb, S. FDA Regulation of Mobile Medical Apps. JAMA 2018, 320, 337–338. [Google Scholar] [CrossRef]
  7. Shafai, G.; Aungst, T.D. Prescription digital therapeutics: A new frontier for pharmacists and the future of treatment. J. Am. Pharm. Assoc. 2023, 63, 1030–1034. [Google Scholar] [CrossRef]
  8. Ribba, B.; Peck, R.; Hutchinson, L.; Bousnina, I.; Motti, D. Digital Therapeutics as a New Therapeutic Modality: A Review from the Perspective of Clinical Pharmacology. Clin. Pharmacol. Ther. 2023, 114, 578–590. [Google Scholar] [CrossRef]
  9. Abbadessa, G.; Brigo, F.; Clerico, M.; De Mercanti, S.; Trojsi, F.; Tedeschi, G.; Bonavita, S.; Lavorgna, L. Digital therapeutics in neurology. J. Neurol. 2022, 269, 1209–1224. [Google Scholar] [CrossRef]
  10. FDA. Combination Products. Available online: https://www.fda.gov/combination-products/about-combination-products/combination-product-definition-combination-product-types (accessed on 5 October 2023).
  11. FDA. Regulatory Considerations for Prescription Drug Use-Related Software. Available online: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/regulatory-considerations-prescription-drug-use-related-software (accessed on 1 October 2023).
  12. Sverdlov, O.; van Dam, J.; Hannesdottir, K.; Thornton-Wells, T. Digital Therapeutics: An Integral Component of Digital Innovation in Drug Development. Clin. Pharmacol. Ther. 2018, 104, 72–80. [Google Scholar] [CrossRef]
  13. Bulaj, G. Combining non-pharmacological treatments with pharmacotherapies for neurological disorders: A unique interface of the brain, drug-device, and intellectual property. Front. Neurol. 2014, 5, 126. [Google Scholar] [CrossRef]
  14. Bulaj, G.; Clark, J.; Ebrahimi, M.; Bald, E. From Precision Metapharmacology to Patient Empowerment: Delivery of Self-Care Practices for Epilepsy, Pain, Depression and Cancer Using Digital Health Technologies. Front. Pharmacol. 2021, 12, 612602. [Google Scholar] [CrossRef]
  15. Rafiei, R.; Williams, C.; Jiang, J.; Aungst, T.D.; Durrer, M.; Tran, D.; Howald, R. Digital Health Integration Assessment and Maturity of the United States Biopharmaceutical Industry: Forces Driving the Next Generation of Connected Autoinjectable Devices. JMIR mHealth uHealth 2021, 9, e25406. [Google Scholar] [CrossRef]
  16. Maricich, Y.A.; Gerwien, R.; Kuo, A.; Malone, D.C.; Velez, F.F. Real-world use and clinical outcomes after 24 weeks of treatment with a prescription digital therapeutic for opioid use disorder. Hosp. Pract. 2021, 49, 348–355. [Google Scholar] [CrossRef]
  17. Kawasaki, S.; Mills-Huffnagle, S.; Aydinoglo, N.; Maxin, H.; Nunes, E. Patient-and provider-reported experiences of a Mobile Novel Digital Therapeutic in People with Opioid Use Disorder (reSET-O): Feasibility and acceptability study. JMIR Form. Res. 2022, 6, e33073. [Google Scholar] [CrossRef]
  18. Maricich, Y.A.; Bickel, W.K.; Marsch, L.A.; Gatchalian, K.; Botbyl, J.; Luderer, H.F. Safety and efficacy of a prescription digital therapeutic as an adjunct to buprenorphine for treatment of opioid use disorder. Curr. Med. Res. Opin. 2021, 37, 167–173. [Google Scholar] [CrossRef]
  19. Giravi, H.Y.; Biskupiak, Z.; Tyler, L.S.; Bulaj, G. Adjunct Digital Interventions Improve Opioid-Based Pain Management: Impact of Virtual Reality and Mobile Applications on Patient-Centered Pharmacy Care. Front. Digit. Health 2022, 4, 884047. [Google Scholar] [CrossRef]
  20. Al-Arkee, S.; Mason, J.; Lane, D.A.; Fabritz, L.; Chua, W.; Haque, M.S.; Jalal, Z. Mobile Apps to Improve Medication Adherence in Cardiovascular Disease: Systematic Review and Meta-analysis. J. Med. Internet Res. 2021, 23, e24190. [Google Scholar] [CrossRef]
  21. Cazeau, N. Mobile Health Interventions: Examining Medication Adherence Outcomes Among Patients With Cancer. Clin. J. Oncol. Nurs. 2021, 25, 431–438. [Google Scholar] [CrossRef] [PubMed]
  22. Afra, P.; Bruggers, C.S.; Sweney, M.; Fagatele, L.; Alavi, F.; Greenwald, M.; Huntsman, M.; Nguyen, K.; Jones, J.K.; Shantz, D.; et al. Mobile Software as a Medical Device (SaMD) for the Treatment of Epilepsy: Development of Digital Therapeutics Comprising Behavioral and Music-Based Interventions for Neurological Disorders. Front. Hum. Neurosci. 2018, 12, 171. [Google Scholar] [CrossRef]
  23. Metcalf, C.S.; Huntsman, M.; Garcia, G.; Kochanski, A.K.; Chikinda, M.; Watanabe, E.; Underwood, T.; Vanegas, F.; Smith, M.D.; White, H.S.; et al. Music-Enhanced Analgesia and Antiseizure Activities in Animal Models of Pain and Epilepsy: Toward Preclinical Studies Supporting Development of Digital Therapeutics and Their Combinations with Pharmaceutical Drugs. Front. Neurol. 2019, 10, 277. [Google Scholar] [CrossRef]
  24. Rohaj, A.; Bulaj, G. Digital Therapeutics (DTx) Expand Multimodal Treatment Options for Chronic Low Back Pain: The Nexus of Precision Medicine, Patient Education, and Public Health. Healthcare 2023, 11, 1469. [Google Scholar] [CrossRef]
  25. McGowan, R. Digital Combination Products and Software. In The Combination Products Handbook; CRC Press: Boca Raton, FL, USA, 2023; pp. 425–440. [Google Scholar]
  26. Bodner, K.A.; Goldberg, T.E.; Devanand, D.P.; Doraiswamy, P.M. Advancing Computerized Cognitive Training for MCI and Alzheimer’s Disease in a Pandemic and Post-pandemic World. Front. Psychiatry 2020, 11, 557571. [Google Scholar] [CrossRef]
  27. Leisher, S.; Bohorquez, A.; Gay, M.; Garcia, V.; Jones, R.; Baldaranov, D.; Rafii, M.S. Amyloid-Lowering Monoclonal Antibodies for the Treatment of Early Alzheimer’s Disease. CNS Drugs 2023, 37, 671–677. [Google Scholar] [CrossRef]
  28. Dickson, S.P.; Hennessey, S.; Nicodemus Johnson, J.; Knowlton, N.; Hendrix, S.B. Avoiding future controversies in the Alzheimer’s disease space through understanding the aducanumab data and FDA review. Alzheimer’s Res. Ther. 2023, 15, 98. [Google Scholar] [CrossRef]
  29. Cummings, J.; Apostolova, L.; Rabinovici, G.D.; Atri, A.; Aisen, P.; Greenberg, S.; Hendrix, S.; Selkoe, D.; Weiner, M.; Petersen, R.C.; et al. Lecanemab: Appropriate Use Recommendations. J. Prev. Alzheimer’s Dis. 2023, 10, 362–377. [Google Scholar] [CrossRef]
  30. Rashad, A.; Rasool, A.; Shaheryar, M.; Sarfraz, A.; Sarfraz, Z.; Robles-Velasco, K.; Cherrez-Ojeda, I. Donanemab for Alzheimer’s Disease: A Systematic Review of Clinical Trials. Healthcare 2022, 11, 32. [Google Scholar] [CrossRef]
  31. van der Flier, W.M.; Tijms, B.M. Treatments for AD: Towards the right target at the right time. Nat. Rev. Neurol. 2023, 19, 581–582. [Google Scholar] [CrossRef]
  32. Loeffler, D.A. Antibody-Mediated Clearance of Brain Amyloid-β: Mechanisms of Action, Effects of Natural and Monoclonal Anti-Aβ Antibodies, and Downstream Effects. J. Alzheimer’s Dis. Rep. 2023, 7, 873–899. [Google Scholar] [CrossRef]
  33. Sims, J.R.; Zimmer, J.A.; Evans, C.D.; Lu, M.; Ardayfio, P.; Sparks, J.; Wessels, A.M.; Shcherbinin, S.; Wang, H.; Monkul Nery, E.S.; et al. Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial. JAMA 2023, 330, 512–527. [Google Scholar] [CrossRef]
  34. Peng, Y.; Jin, H.; Xue, Y.H.; Chen, Q.; Yao, S.Y.; Du, M.Q.; Liu, S. Current and future therapeutic strategies for Alzheimer’s disease: An overview of drug development bottlenecks. Front. Aging Neurosci. 2023, 15, 1206572. [Google Scholar] [CrossRef] [PubMed]
  35. Elfaki, A.O.; Alotaibi, M. The role of M-health applications in the fight against Alzheimer’s: Current and future directions. Mhealth 2018, 4, 32. [Google Scholar] [CrossRef]
  36. Al-Salah, R.; Salam, A.; Alzamil, M.; Alaskr, R.; Alyemni, M.; Alahmdi, M.; Alqahtani, B. Thakirni Application: An Assistive Application for Alzheimer Patients. Int. J. Online Biomed. Eng. 2020, 16, 121–131. [Google Scholar] [CrossRef]
  37. Øksnebjerg, L.; Woods, B.; Ruth, K.; Lauridsen, A.; Kristiansen, S.; Holst, H.D.; Waldemar, G. A tablet app supporting self-management for people with dementia: Explorative study of adoption and use patterns. JMIR mHealth uHealth 2020, 8, e14694. [Google Scholar] [CrossRef]
  38. Chen, C.; Ding, S.; Wang, J. Digital health for aging populations. Nat. Med. 2023, 29, 1623–1630. [Google Scholar] [CrossRef]
  39. Shuren, J.; Doraiswamy, P. Digital therapeutics for MCI and Alzheimer’s disease: A regulatory perspective—Highlights From The Clinical Trials on Alzheimer’s Disease conference (CTAD). J. Prev. Alzheimer’s Dis. 2022, 9, 236–240. [Google Scholar] [CrossRef] [PubMed]
  40. Sposaro, F.; Danielson, J.; Tyson, G. iWander: An Android application for dementia patients. In Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina, 31 August–4 September 2010; pp. 3875–3878. [Google Scholar]
  41. Sakamoto, M.; Guo, Y.P.; Wong, K.L.Y.; Mann, J.; Berndt, A.; Boger, J.; Currie, L.; Raber, C.; Egeberg, E.; Burke, C. Co-design of a digital app “WhatMatters” to support person-centred care: A critical reflection. Int. J. Geriatr. Psychiatry 2023, 38, e6014. [Google Scholar] [CrossRef]
  42. Anthony Berauk, V.L.; Murugiah, M.K.; Soh, Y.C.; Chuan Sheng, Y.; Wong, T.W.; Ming, L.C. Mobile health applications for caring of older people: Review and comparison. Ther. Innov. Regul. Sci. 2018, 52, 374–382. [Google Scholar] [CrossRef]
  43. Tak, S.H. In Quest of Tablet Apps for Elders With Alzheimer’s Disease: A Descriptive Review. Comput. Inform. Nurs. 2021, 39, 347–354. [Google Scholar] [CrossRef]
  44. Kuo, H.L.; Chang, C.H.; Ma, W.F. A Survey of Mobile Apps for the Care Management of Patients with Dementia. Healthcare 2022, 10, 1173. [Google Scholar] [CrossRef] [PubMed]
  45. Clay, F.; Howett, D.; FitzGerald, J.; Fletcher, P.; Chan, D.; Price, A. Use of Immersive Virtual Reality in the Assessment and Treatment of Alzheimer’s Disease: A Systematic Review. J. Alzheimer’s Dis. 2020, 75, 23–43. [Google Scholar] [CrossRef] [PubMed]
  46. Ambegaonkar, A.; Ritchie, C.; de la Fuente Garcia, S. The Use of Mobile Applications as Communication Aids for People with Dementia: Opportunities and Limitations. J. Alzheimer’s Dis. Rep. 2021, 5, 681–692. [Google Scholar] [CrossRef] [PubMed]
  47. Cammisuli, D.M.; Cipriani, G.; Castelnuovo, G. Technological Solutions for Diagnosis, Management and Treatment of Alzheimer’s Disease-Related Symptoms: A Structured Review of the Recent Scientific Literature. Int. J. Environ. Res. Public Health 2022, 19, 3122. [Google Scholar] [CrossRef] [PubMed]
  48. Oliveira, J.; Gamito, P.; Souto, T.; Conde, R.; Ferreira, M.; Corotnean, T.; Fernandes, A.; Silva, H.; Neto, T. Virtual Reality-Based Cognitive Stimulation on People with Mild to Moderate Dementia due to Alzheimer’s Disease: A Pilot Randomized Controlled Trial. Int. J. Environ. Res. Public Health 2021, 18, 5290. [Google Scholar] [CrossRef] [PubMed]
  49. Son, C.; Park, J.H. Ecological Effects of VR-Based Cognitive Training on ADL and IADL in MCI and AD patients: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2022, 19, 5875. [Google Scholar] [CrossRef]
  50. Marin, A.; DeCaro, R.; Schiloski, K.; Elshaar, A.; Dwyer, B.; Vives-Rodriguez, A.; Palumbo, R.; Turk, K.; Budson, A. Home-Based Electronic Cognitive Therapy in Patients With Alzheimer Disease: Feasibility Randomized Controlled Trial. JMIR Form. Res. 2022, 6, e34450. [Google Scholar] [CrossRef]
  51. Rai, H.K.; Kernaghan, D.; Schoonmade, L.; Egan, K.J.; Pot, A.M. Digital Technologies to Prevent Social Isolation and Loneliness in Dementia: A Systematic Review. J. Alzheimer’s Dis. 2022, 90, 513–528. [Google Scholar] [CrossRef]
  52. Iso-Markku, P.; Kujala, U.M.; Knittle, K.; Polet, J.; Vuoksimaa, E.; Waller, K. Physical activity as a protective factor for dementia and Alzheimer’s disease: Systematic review, meta-analysis and quality assessment of cohort and case-control studies. Br. J. Sports Med. 2022, 56, 701–709. [Google Scholar] [CrossRef]
  53. Papatsimpas, V.; Vrouva, S.; Papathanasiou, G.; Papadopoulou, M.; Bouzineki, C.; Kanellopoulou, S.; Moutafi, D.; Bakalidou, D. Does Therapeutic Exercise Support Improvement in Cognitive Function and Instrumental Activities of Daily Living in Patients with Mild Alzheimer’s Disease? A Randomized Controlled Trial. Brain Sci. 2023, 13, 1112. [Google Scholar] [CrossRef]
  54. Bleibel, M.; El Cheikh, A.; Sadier, N.S.; Abou-Abbas, L. The effect of music therapy on cognitive functions in patients with Alzheimer’s disease: A systematic review of randomized controlled trials. Alzheimer’s Res. Ther. 2023, 15, 65. [Google Scholar] [CrossRef]
  55. Du, Z.; Li, Y.; Li, J.; Zhou, C.; Li, F.; Yang, X. Physical activity can improve cognition in patients with Alzheimer’s disease: A systematic review and meta-analysis of randomized controlled trials. Clin. Interv. Aging 2018, 13, 1593–1603. [Google Scholar] [CrossRef] [PubMed]
  56. Li, K.; Cui, C.; Zhang, H.; Jia, L.; Li, R.; Hu, H.Y. Exploration of combined physical activity and music for patients with Alzheimer’s disease: A systematic review. Front. Aging Neurosci. 2022, 14, 962475. [Google Scholar] [CrossRef] [PubMed]
  57. Prinz, A.; Schumacher, A.; Witte, K. Changes in Selected Cognitive and Motor Skills as Well as the Quality of Life After a 24-Week Multidimensional Music-Based Exercise Program in People With Dementia. Am. J. Alzheimer’s Dis. Other Demen 2023, 38, 15333175231191022. [Google Scholar] [CrossRef]
  58. Jung, Y.H.; Park, S.C.; Lee, J.H.; Kim, M.J.; Lee, S.; Chung, S.J.; Moon, J.Y.; Choi, Y.H.; Ju, J.; Han, H.J.; et al. Effect of internet-based vs. in-person multimodal interventions on patients with mild to moderate Alzheimer’s disease: A randomized, cross-over, open-label trial. Front. Public Health 2023, 11, 1203201. [Google Scholar] [CrossRef]
  59. Shepherd, A.; Zhang, T.D.; Zeleznikow-Johnston, A.M.; Hannan, A.J.; Burrows, E.L. Transgenic Mouse Models as Tools for Understanding How Increased Cognitive and Physical Stimulation Can Improve Cognition in Alzheimer’s Disease. Brain Plast. 2018, 4, 127–150. [Google Scholar] [CrossRef] [PubMed]
  60. Stazi, M.; Wirths, O. Physical activity and cognitive stimulation ameliorate learning and motor deficits in a transgenic mouse model of Alzheimer’s disease. Behav. Brain Res. 2021, 397, 112951. [Google Scholar] [CrossRef]
  61. Jin, X.; Li, T.; Zhang, L.; Ma, J.; Yu, L.; Li, C.; Niu, L. Environmental Enrichment Improves Spatial Learning and Memory in Vascular Dementia Rats with Activation of Wnt/β-Catenin Signal Pathway. Med. Sci. Monit. 2017, 23, 207–215. [Google Scholar] [CrossRef]
  62. Zhou, T.; Lin, L.; Hao, C.; Liao, W. Environmental enrichment rescues cognitive impairment with suppression of TLR4-p38MAPK signaling pathway in vascular dementia rats. Neurosci. Lett. 2020, 737, 135318. [Google Scholar] [CrossRef]
  63. Cao, M.; Hu, P.P.; Zhang, Y.L.; Yan, Y.X.; Shields, C.B.; Zhang, Y.P.; Hu, G.; Xiao, M. Enriched physical environment reverses spatial cognitive impairment of socially isolated APPswe/PS1dE9 transgenic mice before amyloidosis onset. CNS Neurosci. Ther. 2018, 24, 202–211. [Google Scholar] [CrossRef]
  64. Zhang, S.S.; Zhu, L.; Peng, Y.; Zhang, L.; Chao, F.L.; Jiang, L.; Xiao, Q.; Liang, X.; Tang, J.; Yang, H.; et al. Long-term running exercise improves cognitive function and promotes microglial glucose metabolism and morphological plasticity in the hippocampus of APP/PS1 mice. J. Neuroinflammation 2022, 19, 34. [Google Scholar] [CrossRef]
  65. Gholami, J.S.N.S.; Rajabian, A.; Saburi, E.; Hajali, V. The effect of combination pretreatment of donepezil and environmental enrichment on memory deficits in amyloid-beta-induced Alzheimer-like rat model. Biochem. Biophys. Rep. 2022, 32, 101392. [Google Scholar] [CrossRef]
  66. Nakai, T.; Yamada, K.; Mizoguchi, H. Alzheimer’s Disease Animal Models: Elucidation of Biomarkers and Therapeutic Approaches for Cognitive Impairment. Int. J. Mol. Sci. 2021, 22, 5549. [Google Scholar] [CrossRef] [PubMed]
  67. Gelfo, F. Does Experience Enhance Cognitive Flexibility? An Overview of the Evidence Provided by the Environmental Enrichment Studies. Front. Behav. Neurosci. 2019, 13, 150. [Google Scholar] [CrossRef] [PubMed]
  68. Liew, A.K.Y.; Teo, C.H.; Soga, T. The Molecular Effects of Environmental Enrichment on Alzheimer’s Disease. Mol. Neurobiol. 2022, 59, 7095–7118. [Google Scholar] [CrossRef]
  69. Aletaha, D.; Smolen, J.S. Diagnosis and Management of Rheumatoid Arthritis: A Review. JAMA 2018, 320, 1360–1372. [Google Scholar] [CrossRef] [PubMed]
  70. Bullock, J.; Rizvi, S.A.A.; Saleh, A.M.; Ahmed, S.S.; Do, D.P.; Ansari, R.A.; Ahmed, J. Rheumatoid Arthritis: A Brief Overview of the Treatment. Med. Princ. Pract. 2018, 27, 501–507. [Google Scholar] [CrossRef]
  71. Sánchez-Flórez, J.C.; Seija-Butnaru, D.; Valero, E.G.; Acosta, C.; Amaya, S. Pain Management Strategies in Rheumatoid Arthritis: A Narrative Review. J. Pain Palliat. Care Pharmacother. 2021, 35, 291–299. [Google Scholar] [CrossRef]
  72. Albert, D.A. Are All Biologics the Same? Optimal Treatment Strategies for Patients With Early Rheumatoid Arthritis: Systematic Review and Indirect Pairwise Meta-Analysis. J. Clin. Rheumatol. 2015, 21, 398–404. [Google Scholar] [CrossRef]
  73. Findeisen, K.E.; Sewell, J.; Ostor, A.J.K. Biological Therapies for Rheumatoid Arthritis: An Overview for the Clinician. Biologics 2021, 15, 343–352. [Google Scholar] [CrossRef]
  74. Adas, M.A.; Allen, V.B.; Yates, M.; Bechman, K.; Clarke, B.D.; Russell, M.D.; Rutherford, A.I.; Cope, A.P.; Norton, S.; Galloway, J.B. A systematic review and network meta-analysis of the safety of early interventional treatments in rheumatoid arthritis. Rheumatology 2021, 60, 4450–4462. [Google Scholar] [CrossRef]
  75. Lorig, K.; Ritter, P.L.; Plant, K. A disease-specific self-help program compared with a generalized chronic disease self-help program for arthritis patients. Arthritis Rheum. 2005, 53, 950–957. [Google Scholar] [CrossRef] [PubMed]
  76. Mollard, E.; Michaud, K. Mobile Apps for Rheumatoid Arthritis: Opportunities and Challenges. Rheum. Dis. Clin. N. Am. 2019, 45, 197–209. [Google Scholar] [CrossRef] [PubMed]
  77. Mollard, E.; Michaud, K. Self-Management of Rheumatoid Arthritis: Mobile Applications. Curr. Rheumatol. Rep. 2020, 23, 2. [Google Scholar] [CrossRef] [PubMed]
  78. Najm, A.; Gossec, L.; Weill, C.; Benoist, D.; Berenbaum, F.; Nikiphorou, E. Mobile Health Apps for Self-Management of Rheumatic and Musculoskeletal Diseases: Systematic Literature Review. JMIR mHealth uHealth 2019, 7, e14730. [Google Scholar] [CrossRef] [PubMed]
  79. Fedkov, D.; Berghofen, A.; Weiss, C.; Peine, C.; Lang, F.; Knitza, J.; Kuhn, S.; Krämer, B.K.; Leipe, J. Efficacy and safety of a mobile app intervention in patients with inflammatory arthritis: A prospective pilot study. Rheumatol. Int. 2022, 42, 2177–2190. [Google Scholar] [CrossRef] [PubMed]
  80. Rodriguez Sanchez-Laulhe, P.; Luque-Romero, L.G.; Barrero-Garcia, F.J.; Biscarri-Carbonero, A.; Blanquero, J.; Suero-Pineda, A.; Heredia-Rizo, A.M. An Exercise and Educational and Self-management Program Delivered With a Smartphone App (CareHand) in Adults With Rheumatoid Arthritis of the Hands: Randomized Controlled Trial. JMIR mHealth uHealth 2022, 10, e35462. [Google Scholar] [CrossRef]
  81. Elnady, B.; Abd-Elmaksoud, S.; El Miedany, O.; El Miedany, Y. AB0325 Development of a Mobile App for Disease-Specific Cognitive Behavior Therapy Management in Individuals with Rheumatoid Arthritis. Ann. Rheum. Dis. 2023, 82, 1346. [Google Scholar]
  82. Luo, D.; Wang, P.; Lu, F.; Elias, J.; Sparks, J.A.; Lee, Y.C. Mobile Apps for Individuals With Rheumatoid Arthritis: A Systematic Review. J. Clin. Rheumatol. 2019, 25, 133–141. [Google Scholar] [CrossRef]
  83. Grainger, R.; Townsley, H.; White, B.; Langlotz, T.; Taylor, W.J. Apps for People With Rheumatoid Arthritis to Monitor Their Disease Activity: A Review of Apps for Best Practice and Quality. JMIR mHealth uHealth 2017, 5, e7. [Google Scholar] [CrossRef]
  84. Bearne, L.M.; Sekhon, M.; Grainger, R.; La, A.; Shamali, M.; Amirova, A.; Godfrey, E.L.; White, C.M. Smartphone Apps Targeting Physical Activity in People With Rheumatoid Arthritis: Systematic Quality Appraisal and Content Analysis. JMIR mHealth uHealth 2020, 8, e18495. [Google Scholar] [CrossRef]
  85. Cozad, M.J.; Crum, M.; Tyson, H.; Fleming, P.R.; Stratton, J.; Kennedy, A.B.; Lindley, L.C.; Horner, R.D. Mobile Health Apps for Patient-Centered Care: Review of United States Rheumatoid Arthritis Apps for Engagement and Activation. JMIR mHealth uHealth 2022, 10, e39881. [Google Scholar] [CrossRef] [PubMed]
  86. Mollard, E.; Michaud, K. A Mobile App With Optical Imaging for the Self-Management of Hand Rheumatoid Arthritis: Pilot Study. JMIR mHealth uHealth 2018, 6, e12221. [Google Scholar] [CrossRef] [PubMed]
  87. Labinsky, H.; Gupta, L.; Raimondo, M.G.; Schett, G.; Knitza, J. Real-world usage of digital health applications (DiGA) in rheumatology: Results from a German patient survey. Rheumatol. Int. 2023, 43, 713–719. [Google Scholar] [CrossRef] [PubMed]
  88. Richter, J.G.; Chehab, G.; Stachwitz, P.; Hagen, J.; Larsen, D.; Knitza, J.; Schneider, M.; Voormann, A.; Specker, C. One year of digital health applications (DiGA) in Germany—Rheumatologists’ perspectives. Front. Med. 2022, 9, 1000668. [Google Scholar] [CrossRef]
  89. Roodenrijs, N.M.T.; Hamar, A.; Kedves, M.; Nagy, G.; van Laar, J.M.; van der Heijde, D.; Welsing, P.M.J. Pharmacological and non-pharmacological therapeutic strategies in difficult-to-treat rheumatoid arthritis: A systematic literature review informing the EULAR recommendations for the management of difficult-to-treat rheumatoid arthritis. RMD Open 2021, 7, e001512. [Google Scholar] [CrossRef]
  90. Taylor, P.C.; Van de Laar, M.; Laster, A.; Fakhouri, W.; Quebe, A.; de la Torre, I.; Jain, S. Call for action: Incorporating wellness practices into a holistic management plan for rheumatoid arthritis-going beyond treat to target. RMD Open 2021, 7, e001959. [Google Scholar] [CrossRef]
  91. Majnik, J.; Császár-Nagy, N.; Böcskei, G.; Bender, T.; Nagy, G. Non-pharmacological treatment in difficult-to-treat rheumatoid arthritis. Front. Med. 2022, 9, 991677. [Google Scholar] [CrossRef]
  92. Thorlund, J.B.; Simic, M.; Pihl, K.; Berthelsen, D.B.; Day, R.; Koes, B.; Juhl, C.B. Similar Effects of Exercise Therapy, Nonsteroidal Anti-inflammatory Drugs, and Opioids for Knee Osteoarthritis Pain: A Systematic Review with Network Meta-analysis. J. Orthop. Sports Phys. Ther. 2022, 52, 207–216. [Google Scholar] [CrossRef]
  93. Derue, H.; Ribeiro-da-Silva, A. Therapeutic exercise interventions in rat models of arthritis. Neurobiol. Pain 2023, 13, 100130. [Google Scholar] [CrossRef]
  94. Kito, T.; Teranishi, T.; Nishii, K.; Sakai, K.; Matsubara, M.; Yamada, K. Effectiveness of exercise-induced cytokines in alleviating arthritis symptoms in arthritis model mice. Okajimas Folia Anat. Jpn. 2016, 93, 81–88. [Google Scholar] [CrossRef]
  95. González-Chávez, S.A.; López-Loeza, S.M.; Acosta-Jiménez, S.; Cuevas-Martínez, R.; Pacheco-Silva, C.; Chaparro-Barrera, E.; Pacheco-Tena, C. Low-Intensity Physical Exercise Decreases Inflammation and Joint Damage in the Preclinical Phase of a Rheumatoid Arthritis Murine Model. Biomolecules 2023, 13, 488. [Google Scholar] [CrossRef] [PubMed]
  96. Huffman, K.M.; Andonian, B.J.; Abraham, D.M.; Bareja, A.; Lee, D.E.; Katz, L.H.; Huebner, J.L.; Kraus, W.E.; White, J.P. Exercise protects against cardiac and skeletal muscle dysfunction in a mouse model of inflammatory arthritis. J. Appl. Physiol. 2021, 130, 853–864. [Google Scholar] [CrossRef] [PubMed]
  97. Estrázulas, M.; Freitas, R.D.S.; Käfer, E.T.; Dagino, A.P.A.; Campos, M.M. Central and peripheral effects of environmental enrichment in a mouse model of arthritis. Int. Immunopharmacol. 2022, 102, 108386. [Google Scholar] [CrossRef] [PubMed]
  98. Bruggers, C.S.; Altizer, R.A.; Kessler, R.R.; Caldwell, C.B.; Coppersmith, K.; Warner, L.; Davies, B.; Paterson, W.; Wilcken, J.; D’Ambrosio, T.A.; et al. Patient-empowerment interactive technologies. Sci. Transl. Med. 2012, 4, 152ps116. [Google Scholar] [CrossRef] [PubMed]
  99. Govender, M.; Bowen, R.C.; German, M.L.; Bulaj, G.; Bruggers, C.S. Clinical and Neurobiological Perspectives of Empowering Pediatric Cancer Patients Using Videogames. Games Health J. 2015, 4, 362–374. [Google Scholar] [CrossRef] [PubMed]
  100. Thomas, T.H.; Go, K.; Go, K.; McKinley, N.J.; Dougherty, K.R.; You, K.L.; Lee, Y.J. Empowerment through technology: A systematic evaluation of the content and quality of mobile applications to empower individuals with cancer. Int. J. Med. Inform. 2022, 163, 104782. [Google Scholar] [CrossRef] [PubMed]
  101. Graetz, I.; Hu, X.; Curry, A.N.; Robles, A.; Vidal, G.A.; Schwartzberg, L.S. Mobile application to support oncology patients during treatment on patient outcomes: Evidence from a randomized controlled trial. Cancer Med. 2023, 12, 6190–6199. [Google Scholar] [CrossRef] [PubMed]
  102. Lee, K.; Kim, S.; Kim, S.H.; Yoo, S.H.; Sung, J.H.; Oh, E.G.; Kim, N.; Lee, J. Digital Health Interventions for Adult Patients With Cancer Evaluated in Randomized Controlled Trials: Scoping Review. J. Med. Internet Res. 2023, 25, e38333. [Google Scholar] [CrossRef]
  103. Wasserman, S.; Ould Brahim, L.; Attiya, A.; Belzile, E.; Lambert, S.D. An Evaluation of Interactive mHealth Applications for Adults Living with Cancer. Curr. Oncol. 2023, 30, 7151–7166. [Google Scholar] [CrossRef]
  104. Katsaros, D.; Hawthorne, J.; Patel, J.; Pothier, K.; Aungst, T.; Franzese, C. Optimizing Social Support in Oncology with Digital Platforms. JMIR Cancer 2022, 8, e36258. [Google Scholar] [CrossRef]
  105. Beale, I.L.; Kato, P.M.; Marin-Bowling, V.M.; Guthrie, N.; Cole, S.W. Improvement in cancer-related knowledge following use of a psychoeducational video game for adolescents and young adults with cancer. J. Adolesc. Health 2007, 41, 263–270. [Google Scholar] [CrossRef]
  106. Kato, P.M.; Cole, S.W.; Bradlyn, A.S.; Pollock, B.H. A video game improves behavioral outcomes in adolescents and young adults with cancer: A randomized trial. Pediatrics 2008, 122, e305–e317. [Google Scholar] [CrossRef]
  107. Bruggers, C.S.; Baranowski, S.; Beseris, M.; Leonard, R.; Long, D.; Schulte, E.; Shorter, A.; Stigner, R.; Mason, C.C.; Bedrov, A.; et al. A Prototype Exercise-Empowerment Mobile Video Game for Children With Cancer, and Its Usability Assessment: Developing Digital Empowerment Interventions for Pediatric Diseases. Front. Pediatr. 2018, 6, 69. [Google Scholar] [CrossRef] [PubMed]
  108. Iivanainen, S.; Alanko, T.; Vihinen, P.; Konkola, T.; Ekstrom, J.; Virtanen, H.; Koivunen, J. Follow-Up of Cancer Patients Receiving Anti-PD-(L)1 Therapy Using an Electronic Patient-Reported Outcomes Tool (KISS): Prospective Feasibility Cohort Study. JMIR Form. Res. 2020, 4, e17898. [Google Scholar] [CrossRef] [PubMed]
  109. Lijnsvelt, J.; Addeo, A.; Vitale, M.; Mohr, P.; Queirolo, P.; Ekström, J.; Vainio, J.; Kataja, V.; Calado, F.; Fagan, A. 840P Electronic patient-reported outcomes (ePROs) of adults with BRAF V600–mutant stage III-IV melanoma treated with dabrafenib+ trametinib (D+ T) collected using the Kaiku Health digital patient (pt) monitoring platform. Ann. Oncol. 2022, 33, S933–S934. [Google Scholar] [CrossRef]
  110. Schmalz, O.; Jacob, C.; Ammann, J.; Liss, B.; Iivanainen, S.; Kammermann, M.; Koivunen, J.; Klein, A.; Popescu, R.A. Digital Monitoring and Management of Patients With Advanced or Metastatic Non-Small Cell Lung Cancer Treated With Cancer Immunotherapy and Its Impact on Quality of Clinical Care: Interview and Survey Study Among Health Care Professionals and Patients. J. Med. Internet Res. 2020, 22, e18655. [Google Scholar] [CrossRef] [PubMed]
  111. Denis, F.; Yossi, S.; Septans, A.L.; Charron, A.; Voog, E.; Dupuis, O.; Ganem, G.; Pointreau, Y.; Letellier, C. Improving Survival in Patients Treated for a Lung Cancer Using Self-Evaluated Symptoms Reported Through a Web Application. Am. J. Clin. Oncol. 2017, 40, 464–469. [Google Scholar] [CrossRef]
  112. Gussoni, G.; Ravot, E.; Zecchina, M.; Recchia, G.; Santoro, E.; Ascione, R.; Perrone, F. Digital therapeutics in oncology: Findings, barriers and prospects. A narrative review. Ann. Res. Oncol. 2022, 2, 55–69. [Google Scholar] [CrossRef]
  113. Shaffer, K.M.; Turner, K.L.; Siwik, C.; Gonzalez, B.D.; Upasani, R.; Glazer, J.V.; Ferguson, R.J.; Joshua, C.; Low, C.A. Digital health and telehealth in cancer care: A scoping review of reviews. Lancet Digit. Health 2023, 5, e316–e327. [Google Scholar] [CrossRef]
  114. Graetz, I.; McKillop, C.N.; Stepanski, E.; Vidal, G.A.; Anderson, J.N.; Schwartzberg, L.S. Use of a web-based app to improve breast cancer symptom management and adherence for aromatase inhibitors: A randomized controlled feasibility trial. J. Cancer Surviv. 2018, 12, 431–440. [Google Scholar] [CrossRef]
  115. Zheng, C.; Chen, X.; Weng, L.; Guo, L.; Xu, H.; Lin, M.; Xue, Y.; Lin, X.; Yang, A.; Yu, L.; et al. Benefits of Mobile Apps for Cancer Pain Management: Systematic Review. JMIR mHealth uHealth 2020, 8, e17055. [Google Scholar] [CrossRef] [PubMed]
  116. Sun, Y.; Jiang, F.; Gu, J.J.; Wang, Y.K.; Hua, H.; Li, J.; Cheng, Z.; Liao, Z.; Huang, Q.; Hu, W.; et al. Development and Testing of an Intelligent Pain Management System (IPMS) on Mobile Phones Through a Randomized Trial Among Chinese Cancer Patients: A New Approach in Cancer Pain Management. JMIR mHealth uHealth 2017, 5, e108. [Google Scholar] [CrossRef] [PubMed]
  117. Yang, J.; Weng, L.; Chen, Z.; Cai, H.; Lin, X.; Hu, Z.; Li, N.; Lin, B.; Zheng, B.; Zhuang, Q.; et al. Development and Testing of a Mobile App for Pain Management Among Cancer Patients Discharged From Hospital Treatment: Randomized Controlled Trial. JMIR mHealth uHealth 2019, 7, e12542. [Google Scholar] [CrossRef] [PubMed]
  118. Zeng, Y.; Zhang, J.E.; Cheng, A.S.K.; Cheng, H.; Wefel, J.S. Meta-Analysis of the Efficacy of Virtual Reality-Based Interventions in Cancer-Related Symptom Management. Integr. Cancer Ther. 2019, 18, 1534735419871108. [Google Scholar] [CrossRef] [PubMed]
  119. Zhang, H.; Xu, H.; Zhang, Z.X.; Zhang, Q. Efficacy of virtual reality-based interventions for patients with breast cancer symptom and rehabilitation management: A systematic review and meta-analysis. BMJ Open 2022, 12, e051808. [Google Scholar] [CrossRef] [PubMed]
  120. Gautama, M.S.N.; Huang, T.-W.; Haryani, H. A systematic review and meta-analysis of randomized controlled trials on the effectiveness of immersive virtual reality in cancer patients receiving chemotherapy. Eur. J. Oncol. Nurs. 2023, 67, 102424. [Google Scholar] [CrossRef]
  121. Ester, M.; Eisele, M.; Wurz, A.; McDonough, M.H.; McNeely, M.; Culos-Reed, S.N. Current Evidence and Directions for Future Research in eHealth Physical Activity Interventions for Adults Affected by Cancer: Systematic Review. JMIR Cancer 2021, 7, e28852. [Google Scholar] [CrossRef]
  122. Purdy, G.M.; Venner, C.P.; Tandon, P.; McNeely, M.L. Feasibility of a tailored and virtually supported home exercise program for people with multiple myeloma using a novel eHealth application. Digit. Health 2022, 8, 20552076221129066. [Google Scholar] [CrossRef]
  123. Purdy, G.M.; Sobierajski, F.M.; Al Onazi, M.M.; Effa, C.J.; Venner, C.P.; Tandon, P.; McNeely, M.L. Exploring participant perceptions of a virtually supported home exercise program for people with multiple myeloma using a novel eHealth application: A qualitative study. Support. Care Cancer 2023, 31, 298. [Google Scholar] [CrossRef]
  124. Robertson, M.C.; Tsai, E.; Lyons, E.J.; Srinivasan, S.; Swartz, M.C.; Baum, M.L.; Basen-Engquist, K.M. Mobile Health Physical Activity Intervention Preferences in Cancer Survivors: A Qualitative Study. JMIR mHealth uHealth 2017, 5, e3. [Google Scholar] [CrossRef]
  125. Hong, Y.; Dahlke, D.V.; Ory, M.; Hochhalter, A.; Reynolds, J.; Purcell, N.P.; Talwar, D.; Eugene, N. Designing iCanFit: A Mobile-Enabled Web Application to Promote Physical Activity for Older Cancer Survivors. JMIR Res. Protoc. 2013, 2, e12. [Google Scholar] [CrossRef] [PubMed]
  126. Hong, Y.A.; Goldberg, D.; Ory, M.G.; Towne, S.D., Jr.; Forjuoh, S.N.; Kellstedt, D.; Wang, S. Efficacy of a Mobile-Enabled Web App (iCanFit) in Promoting Physical Activity Among Older Cancer Survivors: A Pilot Study. JMIR Cancer 2015, 1, e7. [Google Scholar] [CrossRef] [PubMed]
  127. Gao, Z.; Ryu, S.; Zhou, W.; Adams, K.; Hassan, M.; Zhang, R.; Blaes, A.; Wolfson, J.; Sun, J. Effects of personalized exercise prescriptions and social media delivered through mobile health on cancer survivors’ physical activity and quality of life. J. Sport. Health Sci. 2023, 12, 705–714. [Google Scholar] [CrossRef] [PubMed]
  128. Gao, J.; Chen, S.; Lin, S.; Han, H. Effect of music therapy on pain behaviors in rats with bone cancer pain. J. BUON 2016, 21, 466–472. [Google Scholar] [PubMed]
  129. Bice, B.D.; Stephens, M.R.; Georges, S.J.; Venancio, A.R.; Bermant, P.C.; Warncke, A.V.; Affolter, K.E.; Hidalgo, J.R.; Angus-Hill, M.L. Environmental Enrichment Induces Pericyte and IgA-Dependent Wound Repair and Lifespan Extension in a Colon Tumor Model. Cell Rep. 2017, 19, 760–773. [Google Scholar] [CrossRef]
  130. Foglesong, G.D.; Queen, N.J.; Huang, W.; Widstrom, K.J.; Cao, L. Enriched environment inhibits breast cancer progression in obese models with intact leptin signaling. Endocr. Relat. Cancer 2019, 26, 483–495. [Google Scholar] [CrossRef]
  131. Takai, T.; Abe, A.; Miura, H.; Tanaka, S.; Komura, J. Minimum environmental enrichment is effective in activating antitumor immunity to transplanted tumor cells in mice. Exp. Anim. 2019, 68, 569–576. [Google Scholar] [CrossRef]
  132. Li, G.; Gan, Y.; Fan, Y.; Wu, Y.; Lin, H.; Song, Y.; Cai, X.; Yu, X.; Pan, W.; Yao, M.; et al. Enriched environment inhibits mouse pancreatic cancer growth and down-regulates the expression of mitochondria-related genes in cancer cells. Sci. Rep. 2015, 5, 7856. [Google Scholar] [CrossRef]
  133. Yang, L.; Morielli, A.R.; Heer, E.; Kirkham, A.A.; Cheung, W.Y.; Usmani, N.; Friedenreich, C.M.; Courneya, K.S. Effects of Exercise on Cancer Treatment Efficacy: A Systematic Review of Preclinical and Clinical Studies. Cancer Res. 2021, 81, 4889–4895. [Google Scholar] [CrossRef]
  134. Eschke, R.K.; Lampit, A.; Schenk, A.; Javelle, F.; Steindorf, K.; Diel, P.; Bloch, W.; Zimmer, P. Impact of Physical Exercise on Growth and Progression of Cancer in Rodents-A Systematic Review and Meta-Analysis. Front. Oncol. 2019, 9, 35. [Google Scholar] [CrossRef]
  135. Martín-Ruiz, A.; Fiuza-Luces, C.; Rincón-Castanedo, C.; Fernández-Moreno, D.; Gálvez, B.G.; Martínez-Martínez, E.; Martín-Acosta, P.; Coronado, M.J.; Franco-Luzón, L.; González-Murillo, Á. Benefits of exercise and immunotherapy in a murine model of human non-small-cell lung carcinoma. Exerc. Immunol. Rev. 2020, 26, 100–115. [Google Scholar]
  136. Kurz, E.; Hirsch, C.A.; Dalton, T.; Shadaloey, S.A.; Khodadadi-Jamayran, A.; Miller, G.; Pareek, S.; Rajaei, H.; Mohindroo, C.; Baydogan, S. Exercise-induced engagement of the IL-15/IL-15Rα axis promotes anti-tumor immunity in pancreatic cancer. Cancer Cell 2022, 40, 720–737.e725. [Google Scholar] [CrossRef] [PubMed]
  137. Brummer, C.; Pukrop, T.; Wiskemann, J.; Bruss, C.; Ugele, I.; Renner, K. Can Exercise Enhance the Efficacy of Checkpoint Inhibition by Modulating Anti-Tumor Immunity? Cancers 2023, 15, 4668. [Google Scholar] [CrossRef] [PubMed]
  138. Jeyabal, P.; Bhagat, A.; Wang, F.; Roth, M.; Livingston, J.A.; Gilchrist, S.C.; Banchs, J.; Hildebrandt, M.A.T.; Chandra, J.; Deswal, A.; et al. Circulating microRNAs and cytokines as prognostic biomarkers for doxorubicin-induced cardiac injury and for evaluating the effectiveness of an exercise intervention. Clin. Cancer Res. 2023, 29, 4430–4440. [Google Scholar] [CrossRef] [PubMed]
  139. Berrueta, L.; Bergholz, J.; Munoz, D.; Muskaj, I.; Badger, G.J.; Shukla, A.; Kim, H.J.; Zhao, J.J.; Langevin, H.M. Stretching Reduces Tumor Growth in a Mouse Breast Cancer Model. Sci. Rep. 2018, 8, 7864. [Google Scholar] [CrossRef]
  140. Canali, M.M.; Guyot, M.; Simon, T.; Daoudlarian, D.; Chabry, J.; Panzolini, C.; Petit-Paitel, A.; Hypolite, N.; Nicolas, S.; Bourdely, P.; et al. Environmental signals perceived by the brain abate pro-metastatic monocytes by dampening glucocorticoids receptor signaling. Cancer Cell Int. 2023, 23, 15. [Google Scholar] [CrossRef]
  141. Nunez, M.J.; Mana, P.; Linares, D.; Riveiro, M.P.; Balboa, J.; Suarez-Quintanilla, J.; Maracchi, M.; Mendez, M.R.; Lopez, J.M.; Freire-Garabal, M. Music, immunity and cancer. Life Sci. 2002, 71, 1047–1057. [Google Scholar] [CrossRef]
  142. Treede, R.D.; Rief, W.; Barke, A.; Aziz, Q.; Bennett, M.I.; Benoliel, R.; Cohen, M.; Evers, S.; Finnerup, N.B.; First, M.B.; et al. Chronic pain as a symptom or a disease: The IASP Classification of Chronic Pain for the International Classification of Diseases (ICD-11). Pain 2019, 160, 19–27. [Google Scholar] [CrossRef]
  143. Cohen, S.P.; Vase, L.; Hooten, W.M. Chronic pain: An update on burden, best practices, and new advances. Lancet 2021, 397, 2082–2097. [Google Scholar] [CrossRef]
  144. Zimmer, Z.; Fraser, K.; Grol-Prokopczyk, H.; Zajacova, A. A global study of pain prevalence across 52 countries: Examining the role of country-level contextual factors. Pain 2022, 163, 1740–1750. [Google Scholar] [CrossRef]
  145. Lerman, S.F.; Rudich, Z.; Brill, S.; Shalev, H.; Shahar, G. Longitudinal associations between depression, anxiety, pain, and pain-related disability in chronic pain patients. Psychosom. Med. 2015, 77, 333–341. [Google Scholar] [CrossRef] [PubMed]
  146. Vowles, K.E.; McEntee, M.L.; Julnes, P.S.; Frohe, T.; Ney, J.P.; Van Der Goes, D.N. Rates of opioid misuse, abuse, and addiction in chronic pain: A systematic review and data synthesis. Pain 2015, 156, 569–576. [Google Scholar] [CrossRef] [PubMed]
  147. Jones, M.R.; Viswanath, O.; Peck, J.; Kaye, A.D.; Gill, J.S.; Simopoulos, T.T. A Brief History of the Opioid Epidemic and Strategies for Pain Medicine. Pain Ther. 2018, 7, 13–21. [Google Scholar] [CrossRef] [PubMed]
  148. Shi, Y.; Wu, W. Multimodal non-invasive non-pharmacological therapies for chronic pain: Mechanisms and progress. BMC Med. 2023, 21, 372. [Google Scholar] [CrossRef] [PubMed]
  149. Garza-Villarreal, E.A.; Pando, V.; Vuust, P.; Parsons, C. Music-Induced Analgesia in Chronic Pain Conditions: A Systematic Review and Meta-Analysis. Pain Physician 2017, 20, 597–610. [Google Scholar] [CrossRef]
  150. Sihvonen, A.J.; Pitkäniemi, A.; Särkämö, T.; Soinila, S. Isn’t There Room for Music in Chronic Pain Management? J. Pain Off. J. Am. Pain Soc. 2022, 23, 1143–1150. [Google Scholar] [CrossRef]
  151. Hayden, J.A.; Ellis, J.; Ogilvie, R.; Stewart, S.A.; Bagg, M.K.; Stanojevic, S.; Yamato, T.P.; Saragiotto, B.T. Some types of exercise are more effective than others in people with chronic low back pain: A network meta-analysis. J. Physiother. 2021, 67, 252–262. [Google Scholar] [CrossRef] [PubMed]
  152. Flynn, D.M. Chronic Musculoskeletal Pain: Nonpharmacologic, Noninvasive Treatments. Am. Fam. Physician 2020, 102, 465–477. [Google Scholar]
  153. Hochheim, M.; Ramm, P.; Amelung, V. The effectiveness of low-dosed outpatient biopsychosocial interventions compared to active physical interventions on pain and disability in adults with nonspecific chronic low back pain: A systematic review with meta-analysis. Pain Pract. 2023, 23, 409–436. [Google Scholar] [CrossRef]
  154. Bilika, P.; Karampatsou, N.; Stavrakakis, G.; Paliouras, A.; Theodorakis, Y.; Strimpakos, N.; Kapreli, E. Virtual Reality-Based Exercise Therapy for Patients with Chronic Musculoskeletal Pain: A Scoping Review. Healthcare 2023, 11, 2412. [Google Scholar] [CrossRef]
  155. Bulaj, G.; Ahern, M.M.; Kuhn, A.; Judkins, Z.S.; Bowen, R.C.; Chen, Y. Incorporating Natural Products, Pharmaceutical Drugs, Self-care and Digital/Mobile Health Technologies into Molecular-Behavioral Combination Therapies for Chronic Diseases. Curr. Clin. Pharmacol. 2016, 11, 128–145. [Google Scholar] [CrossRef] [PubMed]
  156. Hoffman, H.G.; Doctor, J.N.; Patterson, D.R.; Carrougher, G.J.; Furness, T.A., 3rd. Virtual reality as an adjunctive pain control during burn wound care in adolescent patients. Pain 2000, 85, 305–309. [Google Scholar] [CrossRef] [PubMed]
  157. Hoffman, H.G.; Seibel, E.J.; Richards, T.L.; Furness, T.A.; Patterson, D.R.; Sharar, S.R. Virtual reality helmet display quality influences the magnitude of virtual reality analgesia. J. Pain 2006, 7, 843–850. [Google Scholar] [CrossRef] [PubMed]
  158. Hoffman, H.G.; Patterson, D.R.; Seibel, E.; Soltani, M.; Jewett-Leahy, L.; Sharar, S.R. Virtual reality pain control during burn wound debridement in the hydrotank. Clin. J. Pain 2008, 24, 299–304. [Google Scholar] [CrossRef]
  159. Priebe, J.A.; Haas, K.K.; Moreno Sanchez, L.F.; Schoefmann, K.; Utpadel-Fischler, D.A.; Stockert, P.; Thoma, R.; Schiessl, C.; Kerkemeyer, L.; Amelung, V.; et al. Digital Treatment of Back Pain versus Standard of Care: The Cluster-Randomized Controlled Trial, Rise-uP. J. Pain Res. 2020, 13, 1823–1838. [Google Scholar] [CrossRef] [PubMed]
  160. Garcia, L.M.; Birckhead, B.J.; Krishnamurthy, P.; Sackman, J.; Mackey, I.G.; Louis, R.G.; Salmasi, V.; Maddox, T.; Darnall, B.D. An 8-Week Self-Administered At-Home Behavioral Skills-Based Virtual Reality Program for Chronic Low Back Pain: Double-Blind, Randomized, Placebo-Controlled Trial Conducted During COVID-19. J. Med. Internet Res. 2021, 23, e26292. [Google Scholar] [CrossRef]
  161. Maddox, T.; Oldstone, L.; Sparks, C.Y.; Sackman, J.; Oyao, A.; Garcia, L.; Maddox, R.U.; Ffrench, K.; Garcia, H.; Adair, T.; et al. In-Home Virtual Reality Program for Chronic Lower Back Pain: A Randomized Sham-Controlled Effectiveness Trial in a Clinically Severe and Diverse Sample. Mayo Clin. Proc. Digit. Health 2023, 1, 563–573. [Google Scholar] [CrossRef]
  162. Toelle, T.R.; Utpadel-Fischler, D.A.; Haas, K.K.; Priebe, J.A. App-based multidisciplinary back pain treatment versus combined physiotherapy plus online education: A randomized controlled trial. npj Digit. Med. 2019, 2, 34. [Google Scholar] [CrossRef]
  163. Priebe, J.A.; Utpadel-Fischler, D.; Toelle, T.R. Less pain, better sleep? The effect of a multidisciplinary back pain app on sleep quality in individuals suffering from back pain–a secondary analysis of app user data. J. Pain Res. 2020, 13, 1121–1128. [Google Scholar] [CrossRef]
  164. Chuan, A.; Zhou, J.J.; Hou, R.M.; Stevens, C.J.; Bogdanovych, A. Virtual reality for acute and chronic pain management in adult patients: A narrative review. Anaesthesia 2021, 76, 695–704. [Google Scholar] [CrossRef]
  165. Nagpal, A.S.; Raghunandan, A.; Tata, F.; Kibler, D.; McGeary, D. Virtual Reality in the Management of Chronic Low Back Pain: A Scoping Review. Front. Pain Res. 2022, 3, 856935. [Google Scholar] [CrossRef] [PubMed]
  166. O’Connor, S.; Mayne, A.; Hood, B. Virtual Reality-Based Mindfulness for Chronic Pain Management: A Scoping Review. Pain Manag. Nurs. 2022, 23, 359–369. [Google Scholar] [CrossRef] [PubMed]
  167. Won, A.S.; Bailey, J.; Bailenson, J.; Tataru, C.; Yoon, I.A.; Golianu, B. Immersive Virtual Reality for Pediatric Pain. Children 2017, 4, 52. [Google Scholar] [CrossRef]
  168. Pfeifer, A.C.; Uddin, R.; Schröder-Pfeifer, P.; Holl, F.; Swoboda, W.; Schiltenwolf, M. Mobile Application-Based Interventions for Chronic Pain Patients: A Systematic Review and Meta-Analysis of Effectiveness. J. Clin. Med. 2020, 9, 3557. [Google Scholar] [CrossRef]
  169. Shetty, A.; Delanerolle, G.; Zeng, Y.; Shi, J.Q.; Ebrahim, R.; Pang, J.; Hapangama, D.; Sillem, M.; Shetty, S.; Shetty, B.; et al. A systematic review and meta-analysis of digital application use in clinical research in pain medicine. Front. Digit. Health 2022, 4, 850601. [Google Scholar] [CrossRef]
  170. Darnall, B.D.; Edwards, K.A.; Courtney, R.E.; Ziadni, M.S.; Simons, L.E.; Harrison, L.E. Innovative treatment formats, technologies, and clinician trainings that improve access to behavioral pain treatment for youth and adults. Front. Pain Res. 2023, 4, 1223172. [Google Scholar] [CrossRef]
  171. Jain, D.; Norman, K.; Werner, Z.; Makovoz, B.; Baker, T.; Huber, S. Using postmarket surveillance to assess safety-related events in a digital rehabilitation app (Kaia App): Observational study. JMIR Hum. Factors 2021, 8, e25453. [Google Scholar] [CrossRef]
  172. Ma, K.P.K.; Stephens, K.A.; Geyer, R.E.; Prado, M.G.; Mollis, B.L.; Zbikowski, S.M.; Waters, D.; Masterson, J.; Zhang, Y. Developing Digital Therapeutics for Chronic Pain in Primary Care: A Qualitative Human-Centered Design Study of Providers’ Motivations and Challenges. JMIR Form. Res. 2023, 7, e41788. [Google Scholar] [CrossRef] [PubMed]
  173. Magee, M.; Gholamrezaei, A.; McNeilage, A.G.; Dwyer, L.; Sim, A.; Ferreira, M.; Darnall, B.; Glare, P.; Ashton-James, C. Evaluating acceptability and feasibility of a mobile health intervention to improve self-efficacy in prescription opioid tapering in patients with chronic pain: Protocol for a pilot randomised, single-blind, controlled trial. BMJ Open 2022, 12, e057174. [Google Scholar] [CrossRef]
  174. Magee, M.R.; McNeilage, A.G.; Avery, N.; Glare, P.; Ashton-James, C.E. mHealth Interventions to Support Prescription Opioid Tapering in Patients With Chronic Pain: Qualitative Study of Patients’ Perspectives. JMIR Form. Res. 2021, 5, e25969. [Google Scholar] [CrossRef]
  175. White, R.; Bruggink, L.; Hayes, C.; Boyes, A.; Paul, C. Feasibility of patient-focused behavioral interventions to support adults experiencing chronic noncancer pain during opioid tapering: A systematic literature review. Transl. Behav. Med. 2021, 11, 1481–1494. [Google Scholar] [CrossRef] [PubMed]
  176. Brown, L.; DiCenso-Fleming, T.; Ensign, T.; Boyd, A.J.; Monaghan, G.; Binder, D.S. Chronic pain education delivered with a virtual reality headset in outpatient physical therapy clinics: A multi-site exploratory trial. Am. J. Transl. Res. 2023, 15, 3500–3510. [Google Scholar]
  177. Azizoddin, D.R.; Adam, R.; Kessler, D.; Wright, A.A.; Kematick, B.; Sullivan, C.; Zhang, H.; Hassett, M.J.; Cooley, M.E.; Ehrlich, O. Leveraging mobile health technology and research methodology to optimize patient education and self-management support for advanced cancer pain. Support. Care Cancer 2021, 29, 5741–5751. [Google Scholar] [CrossRef] [PubMed]
  178. Howlin, C.; Rooney, B. The Cognitive Mechanisms in Music Listening Interventions for Pain: A Scoping Review. J. Music. Ther. 2020, 57, 127–167. [Google Scholar] [CrossRef] [PubMed]
  179. Hsu, H.F.; Chen, K.M.; Belcastro, F. The effect of music interventions on chronic pain experienced by older adults: A systematic review. J. Nurs. Scholarsh. 2022, 54, 64–71. [Google Scholar] [CrossRef]
  180. Lee, J.H. The Effects of Music on Pain: A Meta-Analysis. J. Music. Ther. 2016, 53, 430–477. [Google Scholar] [CrossRef]
  181. Lin, C.L.; Hwang, S.L.; Jiang, P.; Hsiung, N.H. Effect of Music Therapy on Pain After Orthopedic Surgery-A Systematic Review and Meta-Analysis. Pain Pract. 2020, 20, 422–436. [Google Scholar] [CrossRef]
  182. Lunde, S.J.; Vuust, P.; Garza-Villarreal, E.A.; Vase, L. Music-induced analgesia: How does music relieve pain? Pain 2018, 160, 989–993. [Google Scholar] [CrossRef]
  183. Chai, P.R.; Carreiro, S.; Ranney, M.L.; Karanam, K.; Ahtisaari, M.; Edwards, R.; Schreiber, K.L.; Ben-Ghaly, L.; Erickson, T.B.; Boyer, E.W. Music as an Adjunct to Opioid-Based Analgesia. J. Med. Toxicol. 2017, 13, 249–254. [Google Scholar] [CrossRef]
  184. Chai, P.R.; Schreiber, K.L.; Taylor, S.W.; Jambaulikar, G.D.; Kikut, A.; Hasdianda, M.A.; Boyer, E.W. The Feasibility and Acceptability of a Smartphone-Based Music Intervention for Acute Pain. Proc. Annu. Hawaii Int. Conf. Syst. Sci. 2019, 2019, 3917–3925. [Google Scholar]
  185. Chen, Q.Y.; Wan, J.; Wang, M.; Hong, S.; Zhuo, M. Sound-induced analgesia cannot always be observed in adult mice. Mol. Pain 2023, 19, 17448069231197158. [Google Scholar] [CrossRef]
  186. Zhou, W.; Ye, C.; Wang, H.; Mao, Y.; Zhang, W.; Liu, A.; Yang, C.L.; Li, T.; Hayashi, L.; Zhao, W.; et al. Sound induces analgesia through corticothalamic circuits. Science 2022, 377, 198–204. [Google Scholar] [CrossRef] [PubMed]
  187. Mao, X.; Cai, D.; Lou, W. Music alleviates pain perception in depression mouse models by promoting the release of glutamate in the hippocampus of mice to act on GRIK5. Nucleosides Nucleotides Nucleic Acids 2022, 41, 463–473. [Google Scholar] [CrossRef] [PubMed]
  188. Senba, E.; Kami, K. A new aspect of chronic pain as lifestyle-related disease. Neurobiol. Pain 2017, 1, 6–15. [Google Scholar] [CrossRef]
  189. Ibrahim, M.M.; Patwardhan, A.; Gilbraith, K.B.; Moutal, A.; Yang, X.; Chew, L.A.; Largent-Milnes, T.; Malan, T.P.; Vanderah, T.W.; Porreca, F.; et al. Long-lasting antinociceptive effects of green light in acute and chronic pain in rats. Pain 2017, 158, 347–360. [Google Scholar] [CrossRef] [PubMed]
  190. Cheng, K.; Martin, L.F.; Slepian, M.J.; Patwardhan, A.M.; Ibrahim, M.M. Mechanisms and Pathways of Pain Photobiomodulation: A Narrative Review. J. Pain Off. J. Am. Pain Soc. 2021, 22, 763–777. [Google Scholar] [CrossRef]
  191. Martin, L.F.; Cheng, K.; Washington, S.M.; Denton, M.; Goel, V.; Khandekar, M.; Largent-Milnes, T.M.; Patwardhan, A.; Ibrahim, M.M. Green Light Exposure Elicits Anti-inflammation, Endogenous Opioid Release and Dampens Synaptic Potentiation to Relieve Post-surgical Pain. J. Pain Off. J. Am. Pain Soc. 2023, 24, 509–529. [Google Scholar] [CrossRef]
  192. Martin, L.F.; Moutal, A.; Cheng, K.; Washington, S.M.; Calligaro, H.; Goel, V.; Kranz, T.; Largent-Milnes, T.M.; Khanna, R.; Patwardhan, A.; et al. Green Light Antinociceptive and Reversal of Thermal and Mechanical Hypersensitivity Effects Rely on Endogenous Opioid System Stimulation. J. Pain Off. J. Am. Pain Soc. 2021, 22, 1646–1656. [Google Scholar] [CrossRef]
  193. Tai, L.W.; Yeung, S.C.; Cheung, C.W. Enriched Environment and Effects on Neuropathic Pain: Experimental Findings and Mechanisms. Pain Pract. 2018, 18, 1068–1082. [Google Scholar] [CrossRef]
  194. Wang, X.; Zhang, G.; Jia, M.; Xie, Z.; Yang, J.; Shen, J.; Zhou, Z. Environmental enrichment improves pain sensitivity, depression-like phenotype, and memory deficit in mice with neuropathic pain: Role of NPAS4. Psychopharmacology 2019, 236, 1999–2014. [Google Scholar] [CrossRef]
  195. Tai, W.L.; Sun, L.; Li, H.; Gu, P.; Joosten, E.A.; Cheung, C.W. Additive Effects of Environmental Enrichment and Ketamine on Neuropathic Pain Relief by Reducing Glutamatergic Activation in Spinal Cord Injury in Rats. Front. Neurosci. 2021, 15, 635187. [Google Scholar] [CrossRef] [PubMed]
  196. Sadegzadeh, F.; Sakhaie, N.; Isazadehfar, K.; Saadati, H. Effects of exposure to enriched environment during adolescence on passive avoidance memory, nociception, and prefrontal BDNF level in adult male and female rats. Neurosci. Lett. 2020, 732, 135133. [Google Scholar] [CrossRef] [PubMed]
  197. Kimura, L.F.; Sant’Anna, M.B.; Zambelli, V.O.; Giardini, A.C.; Jared, S.G.S.; Antoniazzi, M.M.; de Moura Mattaraia, V.G.; Pagano, R.L.; Picolo, G. Early exposure to environmental enrichment protects male rats against neuropathic pain development after nerve injury. Exp. Neurol. 2020, 332, 113390. [Google Scholar] [CrossRef]
  198. Bushnell, M.C.; Case, L.K.; Ceko, M.; Cotton, V.A.; Gracely, J.L.; Low, L.A.; Pitcher, M.H.; Villemure, C. Effect of environment on the long-term consequences of chronic pain. Pain 2015, 156 (Suppl. S1), S42–S49. [Google Scholar] [CrossRef] [PubMed]
  199. Ji, N.N.; Xia, M. Enriched environment alleviates adolescent visceral pain, anxiety- and depression-like behaviors induced by neonatal maternal separation. Transl. Pediatr. 2022, 11, 1398–1407. [Google Scholar] [CrossRef]
  200. Cobos, E.J.; Ghasemlou, N.; Araldi, D.; Segal, D.; Duong, K.; Woolf, C.J. Inflammation-induced decrease in voluntary wheel running in mice: A nonreflexive test for evaluating inflammatory pain and analgesia. Pain 2012, 153, 876–884. [Google Scholar] [CrossRef] [PubMed]
  201. Kandasamy, R.; Morgan, M.M. ‘Reinventing the wheel’ to advance the development of pain therapeutics. Behav. Pharmacol. 2021, 32, 142–152. [Google Scholar] [CrossRef]
  202. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry 2022, 9, 137–150. [CrossRef]
  203. Baxter, A.J.; Scott, K.M.; Vos, T.; Whiteford, H.A. Global prevalence of anxiety disorders: A systematic review and meta-regression. Psychol. Med. 2013, 43, 897–910. [Google Scholar] [CrossRef]
  204. Read, J.; Williams, J. Adverse effects of antidepressants reported by a large international cohort: Emotional blunting, suicidality, and withdrawal effects. Curr. Drug Saf. 2018, 13, 176–186. [Google Scholar] [CrossRef]
  205. Guina, J.; Merrill, B. Benzodiazepines I: Upping the care on downers: The evidence of risks, benefits and alternatives. J. Clin. Med. 2018, 7, 17. [Google Scholar] [CrossRef] [PubMed]
  206. Ho, S.C.; Chong, H.Y.; Chaiyakunapruk, N.; Tangiisuran, B.; Jacob, S.A. Clinical and economic impact of non-adherence to antidepressants in major depressive disorder: A systematic review. J. Affect. Disord. 2016, 193, 1–10. [Google Scholar] [CrossRef] [PubMed]
  207. McIntyre, R.S.; Filteau, M.J.; Martin, L.; Patry, S.; Carvalho, A.; Cha, D.S.; Barakat, M.; Miguelez, M. Treatment-resistant depression: Definitions, review of the evidence, and algorithmic approach. J. Affect. Disord. 2014, 156, 1–7. [Google Scholar] [CrossRef] [PubMed]
  208. Cuijpers, P.; Quero, S.; Noma, H.; Ciharova, M.; Miguel, C.; Karyotaki, E.; Cipriani, A.; Cristea, I.A.; Furukawa, T.A. Psychotherapies for depression: A network meta-analysis covering efficacy, acceptability and long-term outcomes of all main treatment types. World Psychiatry 2021, 20, 283–293. [Google Scholar] [CrossRef]
  209. Wei, W.; Sambamoorthi, U.; Olfson, M.; Walkup, J.T.; Crystal, S. Use of psychotherapy for depression in older adults. Am. J. Psychiatry 2005, 162, 711–717. [Google Scholar] [CrossRef] [PubMed]
  210. Bartram, M.; Stewart, J.M. Income-based inequities in access to psychotherapy and other mental health services in Canada and Australia. Health Policy 2019, 123, 45–50. [Google Scholar] [CrossRef] [PubMed]
  211. McIntyre, R.S.; Greenleaf, W.; Bulaj, G.; Taylor, S.T.; Mitsi, G.; Saliu, D.; Czysz, A.; Silvesti, G.; Garcia, M.; Jain, R. Digital health technologies and major depressive disorder. CNS Spectr. 2023, 28, 662–673. [Google Scholar] [CrossRef]
  212. Wang, K.; Varma, D.S.; Prosperi, M. A systematic review of the effectiveness of mobile apps for monitoring and management of mental health symptoms or disorders. J. Psychiatr. Res. 2018, 107, 73–78. [Google Scholar] [CrossRef]
  213. Roepke, A.M.; Jaffee, S.R.; Riffle, O.M.; McGonigal, J.; Broome, R.; Maxwell, B. Randomized Controlled Trial of SuperBetter, a Smartphone-Based/Internet-Based Self-Help Tool to Reduce Depressive Symptoms. Games Health J. 2015, 4, 235–246. [Google Scholar] [CrossRef]
  214. Merry, S.N.; Stasiak, K.; Shepherd, M.; Frampton, C.; Fleming, T.; Lucassen, M.F. The effectiveness of SPARX, a computerised self help intervention for adolescents seeking help for depression: Randomised controlled non-inferiority trial. BMJ 2012, 344, e2598. [Google Scholar] [CrossRef]
  215. Lecomte, T.; Potvin, S.; Corbière, M.; Guay, S.; Samson, C.; Cloutier, B.; Francoeur, A.; Pennou, A.; Khazaal, Y. Mobile apps for mental health issues: Meta-review of meta-analyses. JMIR mHealth uHealth 2020, 8, e17458. [Google Scholar] [CrossRef] [PubMed]
  216. Litke, S.G.; Resnikoff, A.; Anil, A.; Montgomery, M.; Matta, R.; Huh-Yoo, J.; Daly, B.P. Mobile Technologies for Supporting Mental Health in Youths: Scoping Review of Effectiveness, Limitations, and Inclusivity. JMIR Ment. Health 2023, 10, e46949. [Google Scholar] [CrossRef] [PubMed]
  217. Berger, T.; Krieger, T.; Sude, K.; Meyer, B.; Maercker, A. Evaluating an e-mental health program (“deprexis”) as adjunctive treatment tool in psychotherapy for depression: Results of a pragmatic randomized controlled trial. J. Affect. Disord. 2018, 227, 455–462. [Google Scholar] [CrossRef] [PubMed]
  218. Twomey, C.; O’Reilly, G.; Bültmann, O.; Meyer, B. Effectiveness of a tailored, integrative Internet intervention (deprexis) for depression: Updated meta-analysis. PLoS ONE 2020, 15, e0228100. [Google Scholar] [CrossRef] [PubMed]
  219. Meyer, B.; Berger, T.; Caspar, F.; Beevers, C.; Andersson, G.; Weiss, M. Effectiveness of a novel integrative online treatment for depression (Deprexis): Randomized controlled trial. J. Med. Internet Res. 2009, 11, e1151. [Google Scholar] [CrossRef] [PubMed]
  220. Kulikov, V.N.; Crosthwaite, P.C.; Hall, S.A.; Flannery, J.E.; Strauss, G.S.; Vierra, E.M.; Koepsell, X.L.; Lake, J.I.; Padmanabhan, A. A CBT-based mobile intervention as an adjunct treatment for adolescents with symptoms of depression: A virtual randomized controlled feasibility trial. Front. Digit. Health 2023, 5, 1062471. [Google Scholar] [CrossRef] [PubMed]
  221. Keller, O.C.; Budney, A.J.; Struble, C.A.; Teepe, G.W. Blending digital therapeutics within the healthcare system. In Digital Therapeutics for Mental Health and Addiction; Elsevier: Amsterdam, The Netherlands, 2023; pp. 45–64. [Google Scholar]
  222. Kraemer, L.V.; Gruenzig, S.-D.; Baumeister, H.; Ebert, D.D.; Bengel, J. Effectiveness of a guided web-based intervention to reduce depressive symptoms before outpatient psychotherapy: A pragmatic randomized controlled trial. Psychother. Psychosom. 2021, 90, 233–242. [Google Scholar] [CrossRef]
  223. Carl, J.R.; Miller, C.B.; Henry, A.L.; Davis, M.L.; Stott, R.; Smits, J.A.J.; Emsley, R.; Gu, J.; Shin, O.; Otto, M.W.; et al. Efficacy of digital cognitive behavioral therapy for moderate-to-severe symptoms of generalized anxiety disorder: A randomized controlled trial. Depress. Anxiety 2020, 37, 1168–1178. [Google Scholar] [CrossRef]
  224. O’Daffer, A.; Colt, S.F.; Wasil, A.R.; Lau, N. Efficacy and conflicts of interest in randomized controlled trials evaluating Headspace and calm apps: Systematic review. JMIR Ment. Health 2022, 9, e40924. [Google Scholar] [CrossRef]
  225. Birney, A.J.; Gunn, R.; Russell, J.K.; Ary, D.V. MoodHacker Mobile Web App With Email for Adults to Self-Manage Mild-to-Moderate Depression: Randomized Controlled Trial. JMIR mHealth uHealth 2016, 4, e8. [Google Scholar] [CrossRef]
  226. Twomey, C.; O’Reilly, G. Effectiveness of a freely available computerised cognitive behavioural therapy programme (MoodGYM) for depression: Meta-analysis. Aust. N. Z. J. Psychiatry 2017, 51, 260–269. [Google Scholar] [CrossRef] [PubMed]
  227. Twomey, C.; O’Reilly, G.; Byrne, M.; Bury, M.; White, A.; Kissane, S.; McMahon, A.; Clancy, N. A randomized controlled trial of the computerized CBT programme, MoodGYM, for public mental health service users waiting for interventions. Br. J. Clin. Psychol. 2014, 53, 433–450. [Google Scholar] [CrossRef] [PubMed]
  228. Baghaei, N.; Chitale, V.; Hlasnik, A.; Stemmet, L.; Liang, H.-N.; Porter, R. Virtual reality for supporting the treatment of depression and anxiety: Scoping review. JMIR Ment. Health 2021, 8, e29681. [Google Scholar] [CrossRef] [PubMed]
  229. Ioannou, A.; Papastavrou, E.; Avraamides, M.N.; Charalambous, A. Virtual reality and symptoms management of anxiety, depression, fatigue, and pain: A systematic review. SAGE Open Nurs. 2020, 6, 2377960820936163. [Google Scholar] [CrossRef] [PubMed]
  230. Riadi, I.; Kervin, L.; Dhillon, S.; Teo, K.; Churchill, R.; Card, K.G.; Sixsmith, A.; Moreno, S.; Fortuna, K.L.; Torous, J. Digital interventions for depression and anxiety in older adults: A systematic review of randomised controlled trials. Lancet Healthy Longev. 2022, 3, e558–e571. [Google Scholar] [CrossRef]
  231. Wang, M.; Chen, H.; Yang, F.; Li, J. Effects of digital psychotherapy for depression and anxiety: A systematic review and bayesian network meta-analysis. J. Affect. Disord. 2023, 338, 569–580. [Google Scholar] [CrossRef]
  232. Mamukashvili-Delau, M.; Koburger, N.; Dietrich, S.; Rummel-Kluge, C. Long-Term Efficacy of Internet-Based Cognitive Behavioral Therapy Self-Help Programs for Adults With Depression: Systematic Review and Meta-Analysis of Randomized Controlled Trials. JMIR Ment. Health 2023, 10, e46925. [Google Scholar] [CrossRef]
  233. Mamukashvili-Delau, M.; Koburger, N.; Dietrich, S.; Rummel-Kluge, C. Efficacy of computer- and/or internet-based cognitive-behavioral guided self-management for depression in adults: A systematic review and meta-analysis of randomized controlled trials. BMC Psychiatry 2022, 22, 730. [Google Scholar] [CrossRef]
  234. Lin, Z.; Cheng, L.; Han, X.; Wang, H.; Liao, Y.; Guo, L.; Shi, J.; Fan, B.; Teopiz, K.M.; Jawad, M.Y.; et al. The Effect of Internet-Based Cognitive Behavioral Therapy on Major Depressive Disorder: Randomized Controlled Trial. J. Med. Internet Res. 2023, 25, e42786. [Google Scholar] [CrossRef]
  235. Fundoiano-Hershcovitz, Y.; Breuer Asher, I.; Ritholz, M.D.; Feniger, E.; Manejwala, O.; Goldstein, P. Specifying the Efficacy of Digital Therapeutic Tools for Depression and Anxiety: Retrospective, 2-Cohort, Real-World Analysis. J. Med. Internet Res. 2023, 25, e47350. [Google Scholar] [CrossRef]
  236. Wu, M.S.; Wickham, R.E.; Chen, S.-Y.; Chen, C.; Lungu, A. Examining the impact of digital components across different phases of treatment in a blended care cognitive behavioral therapy intervention for depression and anxiety: Pragmatic retrospective study. JMIR Form. Res. 2021, 5, e33452. [Google Scholar] [CrossRef] [PubMed]
  237. Mantani, A.; Kato, T.; Furukawa, T.A.; Horikoshi, M.; Imai, H.; Hiroe, T.; Chino, B.; Funayama, T.; Yonemoto, N.; Zhou, Q.; et al. Smartphone Cognitive Behavioral Therapy as an Adjunct to Pharmacotherapy for Refractory Depression: Randomized Controlled Trial. J. Med. Internet Res. 2017, 19, e373. [Google Scholar] [CrossRef] [PubMed]
  238. Imai, H.; Yamada, M.; Inagaki, M.; Watanabe, N.; Chino, B.; Mantani, A.; Furukawa, T.A. Behavioral Activation Contributed to the Total Reduction of Depression Symptoms in the Smartphone-based Cognitive Behavioral Therapy: A Secondary Analysis of a Randomized, Controlled Trial. Innov. Clin. Neurosci. 2020, 17, 21–25. [Google Scholar]
  239. McKennon, S.; Levitt, S.E.; Bulaj, G. Commentary: A Breathing-Based Meditation Intervention for Patients with Major Depressive Disorder Following Inadequate Response to Antidepressants: A Randomized Pilot Study. Front. Med. 2017, 4, 37. [Google Scholar] [CrossRef] [PubMed]
  240. Schriewer, K.; Bulaj, G. Music Streaming Services as Adjunct Therapies for Depression, Anxiety, and Bipolar Symptoms: Convergence of Digital Technologies, Mobile Apps, Emotions, and Global Mental Health. Front. Public Health 2016, 4, 217. [Google Scholar] [CrossRef]
  241. Gadd, S.; Tak, C.; Bulaj, G. Developing music streaming as an adjunct digital therapy for depression: A survey study to assess support from key stakeholders. J. Affect. Disord. Rep. 2020, 2, 100048. [Google Scholar] [CrossRef]
  242. Branchi, I.; Santarelli, S.; Capoccia, S.; Poggini, S.; D’Andrea, I.; Cirulli, F.; Alleva, E. Antidepressant Treatment Outcome Depends on the Quality of the Living Environment: A Pre-Clinical Investigation in Mice. PLoS ONE 2013, 8, e62226. [Google Scholar] [CrossRef]
  243. Poggini, S.; Matte Bon, G.; Golia, M.T.; Ciano Albanese, N.; Viglione, A.; Poleggi, A.; Limatola, C.; Maggi, L.; Branchi, I. Selecting antidepressants according to a drug-by-environment interaction: A comparison of fluoxetine and minocycline effects in mice living either in enriched or stressful conditions. Behav. Brain Res. 2021, 408, 113256. [Google Scholar] [CrossRef]
  244. Alboni, S.; van Dijk, R.M.; Poggini, S.; Milior, G.; Perrotta, M.; Drenth, T.; Brunello, N.; Wolfer, D.P.; Limatola, C.; Amrein, I.; et al. Fluoxetine effects on molecular, cellular and behavioral endophenotypes of depression are driven by the living environment. Mol. Psychiatry 2017, 22, 552–561. [Google Scholar] [CrossRef]
  245. Tricklebank, M.D.; Robbins, T.W.; Simmons, C.; Wong, E.H.F. Time to re-engage psychiatric drug discovery by strengthening confidence in preclinical psychopharmacology. Psychopharmacology 2021, 238, 1417–1436. [Google Scholar] [CrossRef]
  246. Cordner, Z.A.; Marshall-Thomas, I.; Boersma, G.J.; Lee, R.S.; Potash, J.B.; Tamashiro, K.L. Fluoxetine and environmental enrichment similarly reverse chronic social stress-related depression-and anxiety-like behavior, but have differential effects on amygdala gene expression. Neurobiol. Stress. 2021, 15, 100392. [Google Scholar] [CrossRef] [PubMed]
  247. Coutens, B.; Lejards, C.; Bouisset, G.; Verret, L.; Rampon, C.; Guiard, B.P. Enriched environmental exposure reduces the onset of action of the serotonin norepinephrin reuptake inhibitor venlafaxine through its effect on parvalbumin interneurons plasticity in mice. Transl. Psychiatry 2023, 13, 227. [Google Scholar] [CrossRef] [PubMed]
  248. Sparling, J.E.; Barbeau, K.; Boileau, K.; Konkle, A.T.M. Environmental enrichment and its influence on rodent offspring and maternal behaviours, a scoping style review of indices of depression and anxiety. Pharmacol. Biochem. Behav. 2020, 197, 172997. [Google Scholar] [CrossRef] [PubMed]
  249. Goes, T.C.; Antunes, F.D.; Teixeira-Silva, F. Environmental enrichment for adult rats: Effects on trait and state anxiety. Neurosci. Lett. 2015, 584, 93–96. [Google Scholar] [CrossRef] [PubMed]
  250. Fox, C.; Merali, Z.; Harrison, C. Therapeutic and protective effect of environmental enrichment against psychogenic and neurogenic stress. Behav. Brain Res. 2006, 175, 1–8. [Google Scholar] [CrossRef]
  251. Gong, X.; Chen, Y.; Chang, J.; Huang, Y.; Cai, M.; Zhang, M. Environmental enrichment reduces adolescent anxiety-and depression-like behaviors of rats subjected to infant nerve injury. J. Neuroinflammation 2018, 15, 262. [Google Scholar] [CrossRef] [PubMed]
  252. Fu, Q.; Qiu, R.; Chen, L.; Chen, Y.; Qi, W.; Cheng, Y. Music prevents stress-induced depression and anxiety-like behavior in mice. Transl. Psychiatry 2023, 13, 317. [Google Scholar] [CrossRef]
  253. Mahati, K.; Bhagya, V.; Christofer, T.; Sneha, A.; Rao, B.S.S. Enriched environment ameliorates depression-induced cognitive deficits and restores abnormal hippocampal synaptic plasticity. Neurobiol. Learn. Mem. 2016, 134, 379–391. [Google Scholar] [CrossRef]
  254. Papadakakis, A.; Sidiropoulou, K.; Panagis, G. Music exposure attenuates anxiety- and depression-like behaviors and increases hippocampal spine density in male rats. Behav. Brain Res. 2019, 372, 112023. [Google Scholar] [CrossRef]
  255. Li, W.J.; Yu, H.; Yang, J.M.; Gao, J.; Jiang, H.; Feng, M.; Zhao, Y.X.; Chen, Z.Y. Anxiolytic effect of music exposure on BDNFMet/Met transgenic mice. Brain Res. 2010, 1347, 71–79. [Google Scholar] [CrossRef]
  256. Huang, G.J.; Ben-David, E.; Tort Piella, A.; Edwards, A.; Flint, J.; Shifman, S. Neurogenomic evidence for a shared mechanism of the antidepressant effects of exercise and chronic fluoxetine in mice. PLoS ONE 2012, 7, e35901. [Google Scholar] [CrossRef] [PubMed]
  257. Tang, J.; Liang, X.; Dou, X.; Qi, Y.; Yang, C.; Luo, Y.; Chao, F.; Zhang, L.; Xiao, Q.; Jiang, L.; et al. Exercise rather than fluoxetine promotes oligodendrocyte differentiation and myelination in the hippocampus in a male mouse model of depression. Transl. Psychiatry 2021, 11, 622. [Google Scholar] [CrossRef] [PubMed]
  258. Fisher, R.S.; Acevedo, C.; Arzimanoglou, A.; Bogacz, A.; Cross, J.H.; Elger, C.E.; Engel, J., Jr.; Forsgren, L.; French, J.A.; Glynn, M.; et al. ILAE official report: A practical clinical definition of epilepsy. Epilepsia 2014, 55, 475–482. [Google Scholar] [CrossRef] [PubMed]
  259. Keezer, M.R.; Sisodiya, S.M.; Sander, J.W. Comorbidities of epilepsy: Current concepts and future perspectives. Lancet Neurol. 2016, 15, 106–115. [Google Scholar] [CrossRef] [PubMed]
  260. Harden, C.; Tomson, T.; Gloss, D.; Buchhalter, J.; Cross, J.H.; Donner, E.; French, J.A.; Gil-Nagel, A.; Hesdorffer, D.C.; Smithson, W.H. Practice guideline summary: Sudden unexpected death in epilepsy incidence rates and risk factors: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology and the American Epilepsy Society. Epilepsy Curr. 2017, 17, 180–187. [Google Scholar] [CrossRef]
  261. Kanner, A.M.; Bicchi, M.M. Antiseizure Medications for Adults With Epilepsy: A Review. JAMA 2022, 327, 1269–1281. [Google Scholar] [CrossRef]
  262. Ryvlin, P.; Rheims, S.; Hirsch, L.J.; Sokolov, A.; Jehi, L. Neuromodulation in epilepsy: State-of-the-art approved therapies. Lancet Neurol. 2021, 20, 1038–1047. [Google Scholar] [CrossRef]
  263. Engel, J., Jr. The current place of epilepsy surgery. Curr. Opin. Neurol. 2018, 31, 192–197. [Google Scholar] [CrossRef]
  264. Löscher, W.; Potschka, H.; Sisodiya, S.M.; Vezzani, A. Drug Resistance in Epilepsy: Clinical Impact, Potential Mechanisms, and New Innovative Treatment Options. Pharmacol. Rev. 2020, 72, 606–638. [Google Scholar] [CrossRef]
  265. Chen, B.; Choi, H.; Hirsch, L.J.; Katz, A.; Legge, A.; Buchsbaum, R.; Detyniecki, K. Psychiatric and behavioral side effects of antiepileptic drugs in adults with epilepsy. Epilepsy Behav. 2017, 76, 24–31. [Google Scholar] [CrossRef]
  266. Mendorf, S.; Prell, T.; Schönenberg, A. Detecting Reasons for Nonadherence to Medication in Adults with Epilepsy: A Review of Self-Report Measures and Key Predictors. J. Clin. Med. 2022, 11, 4308. [Google Scholar] [CrossRef]
  267. Alexander, H.B.; Broshek, D.K.; Quigg, M. Quality of life in adults with epilepsy is associated with anticonvulsant polypharmacy independent of seizure status. Epilepsy Behav. 2018, 78, 96–99. [Google Scholar] [CrossRef] [PubMed]
  268. Asadi-Pooya, A.A.; Patel, A.A.; Trinka, E.; Mazurkiewicz-Beldzinska, M.; Cross, J.H.; Welty, T.E.; Force, I.E.T. Recommendations for treatment strategies in people with epilepsy during times of shortage of antiseizure medications. Epileptic Disord. 2022, 24, 751–764. [Google Scholar] [CrossRef] [PubMed]
  269. Chen, Z.; Brodie, M.J.; Liew, D.; Kwan, P. Treatment outcomes in patients with newly diagnosed epilepsy treated with established and new antiepileptic drugs: A 30-year longitudinal cohort study. JAMA Neurol. 2018, 75, 279–286. [Google Scholar] [CrossRef] [PubMed]
  270. Esmaeili, B.; Vieluf, S.; Dworetzky, B.A.; Reinsberger, C. The Potential of Wearable Devices and Mobile Health Applications in the Evaluation and Treatment of Epilepsy. Neurol. Clin. 2022, 40, 729–739. [Google Scholar] [CrossRef]
  271. Alzamanan, M.Z.; Lim, K.S.; Akmar Ismail, M.; Abdul Ghani, N. Self-Management Apps for People With Epilepsy: Systematic Analysis. JMIR mHealth uHealth 2021, 9, e22489. [Google Scholar] [CrossRef]
  272. Escoffery, C.; McGee, R.; Bidwell, J.; Sims, C.; Thropp, E.K.; Frazier, C.; Mynatt, E.D. A review of mobile apps for epilepsy self-management. Epilepsy Behav. 2018, 81, 62–69. [Google Scholar] [CrossRef]
  273. DiIorio, C.; Escoffery, C.; Yeager, K.A.; McCarty, F.; Henry, T.R.; Koganti, A.; Reisinger, E.; Robinson, E.; Kobau, R.; Price, P. WebEase: Development of a Web-based epilepsy self-management intervention. Prev. Chronic Dis. 2009, 6, A28. [Google Scholar]
  274. DiIorio, C.; Escoffery, C.; McCarty, F.; Yeager, K.A.; Henry, T.R.; Koganti, A.; Reisinger, E.L.; Wexler, B. Evaluation of WebEase: An epilepsy self-management Web site. Health Educ. Res. 2009, 24, 185–197. [Google Scholar] [CrossRef]
  275. DiIorio, C.; Bamps, Y.; Walker, E.R.; Escoffery, C. Results of a research study evaluating WebEase, an online epilepsy self-management program. Epilepsy Behav. 2011, 22, 469–474. [Google Scholar] [CrossRef]
  276. Shegog, R.; Bamps, Y.A.; Patel, A.; Kakacek, J.; Escoffery, C.; Johnson, E.K.; Ilozumba, U.O. Managing Epilepsy Well: Emerging e-Tools for epilepsy self-management. Epilepsy Behav. 2013, 29, 133–140. [Google Scholar] [CrossRef]
  277. Mirpuri, P.; Chandra, P.P.; Samala, R.; Agarwal, M.; Doddamani, R.; Kaur, K.; Ramanujan, B.; Chandra, P.S.; Tripathi, M. The development and efficacy of a mobile phone application to improve medication adherence for persons with epilepsy in limited resource settings: A preliminary study. Epilepsy Behav. 2021, 116, 107794. [Google Scholar] [CrossRef]
  278. Le Marne, F.A.; Butler, S.; Beavis, E.; Gill, D.; Bye, A.M.E. EpApp: Development and evaluation of a smartphone/tablet app for adolescents with epilepsy. J. Clin. Neurosci. 2018, 50, 214–220. [Google Scholar] [CrossRef] [PubMed]
  279. Si, Y.; Xiao, X.; Xia, C.; Guo, J.; Hao, Q.; Mo, Q.; Niu, Y.; Sun, H. Optimising epilepsy management with a smartphone application: A randomised controlled trial. Med. J. Aust. 2020, 212, 258–262. [Google Scholar] [CrossRef]
  280. Dastgheib, S.S.; Layegh, P.; Sadeghi, R.; Foroughipur, M.; Shoeibi, A.; Gorji, A. The effects of Mozart’s music on interictal activity in epileptic patients: Systematic review and meta-analysis of the literature. Curr. Neurol. Neurosci. Rep. 2014, 14, 420. [Google Scholar] [CrossRef]
  281. Sesso, G.; Sicca, F. Safe and sound: Meta-analyzing the Mozart effect on epilepsy. Clin. Neurophysiol. 2020, 131, 1610–1620. [Google Scholar] [CrossRef] [PubMed]
  282. Grigg-Damberger, M.; Foldvary-Schaefer, N. Bidirectional relationships of sleep and epilepsy in adults with epilepsy. Epilepsy Behav. 2021, 116, 107735. [Google Scholar] [CrossRef] [PubMed]
  283. Haut, S.R.; Lipton, R.B.; Cornes, S.; Dwivedi, A.K.; Wasson, R.; Cotton, S.; Strawn, J.R.; Privitera, M. Behavioral interventions as a treatment for epilepsy: A multicenter randomized controlled trial. Neurology 2018, 90, e963–e970. [Google Scholar] [CrossRef]
  284. Maguire, J.; Salpekar, J.A. Stress, seizures, and hypothalamic-pituitary-adrenal axis targets for the treatment of epilepsy. Epilepsy Behav. 2013, 26, 352–362. [Google Scholar] [CrossRef]
  285. May, T.W.; Pfafflin, M. The efficacy of an educational treatment program for patients with epilepsy (MOSES): Results of a controlled, randomized study. Modular Service Package Epilepsy. Epilepsia 2002, 43, 539–549. [Google Scholar] [CrossRef]
  286. Akyuz, E.; Eroglu, E. Envisioning the crosstalk between environmental enrichment and epilepsy: A novel perspective. Epilepsy Behav. 2021, 115, 107660. [Google Scholar] [CrossRef] [PubMed]
  287. Kotloski, R.J.; Sutula, T.P. Environmental enrichment: Evidence for an unexpected therapeutic influence. Exp. Neurol. 2015, 264, 121–126. [Google Scholar] [CrossRef] [PubMed]
  288. Dezsi, G.; Ozturk, E.; Salzberg, M.R.; Morris, M.; O’Brien, T.J.; Jones, N.C. Environmental enrichment imparts disease-modifying and transgenerational effects on genetically-determined epilepsy and anxiety. Neurobiol. Dis. 2016, 93, 129–136. [Google Scholar] [CrossRef] [PubMed]
  289. Manno, I.; Macchi, F.; Caleo, M.; Bozzi, Y. Environmental enrichment reduces spontaneous seizures in the Q54 transgenic mouse model of temporal lobe epilepsy. Epilepsia 2011, 52, e113–e117. [Google Scholar] [CrossRef] [PubMed]
  290. Yang, M.; Ozturk, E.; Salzberg, M.R.; Rees, S.; Morris, M.; O’Brien, T.J.; Jones, N.C. Environmental enrichment delays limbic epileptogenesis and restricts pathologic synaptic plasticity. Epilepsia 2016, 57, 484–494. [Google Scholar] [CrossRef]
  291. Zhang, X.; Liu, T.; Zhou, Z.; Mu, X.; Song, C.; Xiao, T.; Zhao, M.; Zhao, C. Enriched Environment Altered Aberrant Hippocampal Neurogenesis and Improved Long-Term Consequences After Temporal Lobe Epilepsy in Adult Rats. J. Mol. Neurosci. 2015, 56, 409–421. [Google Scholar] [CrossRef] [PubMed]
  292. Vrinda, M.; Sasidharan, A.; Aparna, S.; Srikumar, B.N.; Kutty, B.M.; Shankaranarayana Rao, B.S. Enriched environment attenuates behavioral seizures and depression in chronic temporal lobe epilepsy. Epilepsia 2017, 58, 1148–1158. [Google Scholar] [CrossRef]
  293. Nair, K.P.; Salaka, R.J.; Srikumar, B.N.; Kutty, B.M.; Shankaranarayana Rao, B.S. Enriched Environment Rescues Impaired Sleep-Wake Architecture and Abnormal Neural Dynamics in Chronic Epileptic Rats. Neuroscience 2022, 495, 97–114. [Google Scholar] [CrossRef]
  294. Suemaru, K.; Yoshikawa, M.; Aso, H.; Watanabe, M. Environmental enrichment alleviates cognitive and behavioral impairments in EL mice. Epilepsy Behav. 2018, 85, 227–233. [Google Scholar] [CrossRef]
  295. Xing, Y.; Qin, Y.; Jing, W.; Zhang, Y.; Wang, Y.; Guo, D.; Xia, Y.; Yao, D. Exposure to Mozart music reduces cognitive impairment in pilocarpine-induced status epilepticus rats. Cogn. Neurodyn 2016, 10, 23–30. [Google Scholar] [CrossRef]
  296. Bodner, M.; Turner, R.P.; Schwacke, J.; Bowers, C.; Norment, C. Reduction of seizure occurrence from exposure to auditory stimulation in individuals with neurological handicaps: A randomized controlled trial. PLoS ONE 2012, 7, e45303. [Google Scholar] [CrossRef] [PubMed]
  297. Rafiee, M.; Patel, K.; Groppe, D.M.; Andrade, D.M.; Bercovici, E.; Bui, E.; Carlen, P.L.; Reid, A.; Tai, P.; Weaver, D.; et al. Daily listening to Mozart reduces seizures in individuals with epilepsy: A randomized control study. Epilepsia Open 2020, 5, 285–294. [Google Scholar] [CrossRef]
  298. Paprad, T.; Veeravigrom, M.; Desudchit, T. Effect of Mozart K.448 on interictal epileptiform discharges in children with epilepsy: A randomized controlled pilot study. Epilepsy Behav. 2021, 114, 107177. [Google Scholar] [CrossRef] [PubMed]
  299. Ding, R.; Tang, H.; Liu, Y.; Yin, Y.; Yan, B.; Jiang, Y.; Toussaint, P.J.; Xia, Y.; Evans, A.C.; Zhou, D.; et al. Therapeutic effect of tempo in Mozart’s “Sonata for two pianos” (K. 448) in patients with epilepsy: An electroencephalographic study. Epilepsy Behav. 2023, 145, 109323. [Google Scholar] [CrossRef] [PubMed]
  300. Quon, R.J.; Casey, M.A.; Camp, E.J.; Meisenhelter, S.; Steimel, S.A.; Song, Y.; Testorf, M.E.; Leslie, G.A.; Bujarski, K.A.; Ettinger, A.B.; et al. Musical components important for the Mozart K448 effect in epilepsy. Sci. Rep. 2021, 11, 16490. [Google Scholar] [CrossRef] [PubMed]
  301. Hughes, J.R. The Mozart Effect. Epilepsy Behav. 2001, 2, 396–417. [Google Scholar] [CrossRef]
  302. Lin, L.C.; Juan, C.T.; Chang, H.W.; Chiang, C.T.; Wei, R.C.; Lee, M.W.; Mok, H.K.; Yang, R.C. Mozart K.448 attenuates spontaneous absence seizure and related high-voltage rhythmic spike discharges in Long Evans rats. Epilepsy Res. 2013, 104, 234–240. [Google Scholar] [CrossRef]
  303. Xu, C.L.; Nao, J.Z.; Shen, Y.J.; Gong, Y.W.; Tan, B.; Zhang, S.; Shen, K.X.; Sun, C.R.; Wang, Y.; Chen, Z. Long-term music adjuvant therapy enhances the efficacy of sub-dose antiepileptic drugs in temporal lobe epilepsy. CNS Neurosci. Ther. 2022, 28, 206–217. [Google Scholar] [CrossRef]
  304. Johnson, E.C.; Helen Cross, J.; Reilly, C. Physical activity in people with epilepsy: A systematic review. Epilepsia 2020, 61, 1062–1081. [Google Scholar] [CrossRef]
  305. Arida, R.M.; Cavalheiro, E.A.; Da Silva, A.C.; Scorza, F.A. Physical activity and epilepsy: Proven and predicted benefits. Sports Med. 2008, 38, 607–615. [Google Scholar] [CrossRef]
  306. Häfele, C.A.; Freitas, M.P.; da Silva, M.C.; Rombaldi, A.J. Are physical activity levels associated with better health outcomes in people with epilepsy? Epilepsy Behav. 2017, 72, 28–34. [Google Scholar] [CrossRef]
  307. Häfele, C.A.; Rombaldi, A.J.; Feter, N.; Häfele, V.; Gervini, B.L.; Domingues, M.R.; da Silva, M.C. Effects of an exercise program on health of people with epilepsy: A randomized clinical trial. Epilepsy Behav. 2021, 117, 107904. [Google Scholar] [CrossRef] [PubMed]
  308. Lin, X.-Y.; Cui, Y.; Wang, L.; Chen, W. Chronic exercise buffers the cognitive dysfunction and decreases the susceptibility to seizures in PTZ-treated rats. Epilepsy Behav. 2019, 98, 173–187. [Google Scholar] [CrossRef] [PubMed]
  309. Arida, R.M.; Scorza, F.A.; dos Santos, N.F.; Peres, C.A.; Cavalheiro, E.A. Effect of physical exercise on seizure occurrence in a model of temporal lobe epilepsy in rats. Epilepsy Res. 1999, 37, 45–52. [Google Scholar] [CrossRef]
  310. Peixinho-Pena, L.F.; Fernandes, J.; de Almeida, A.A.; Gomes, F.G.N.; Cassilhas, R.; Venancio, D.P.; de Mello, M.T.; Scorza, F.A.; Cavalheiro, E.A.; Arida, R.M. A strength exercise program in rats with epilepsy is protective against seizures. Epilepsy Behav. 2012, 25, 323–328. [Google Scholar] [CrossRef] [PubMed]
  311. Barzroodi Pour, M.; Bayat, M.; Navazesh, A.; Soleimani, M.; Karimzadeh, F. Exercise improved the anti-epileptic effect of carbamazepine through gaba enhancement in epileptic rats. Neurochem. Res. 2021, 46, 2112–2130. [Google Scholar] [CrossRef]
  312. Acar, S.; Kapucu, A.; Akgün-Dar, K. The effects of regular swimming exercise during sodium valproate treatment on seizure behaviors and EEG recordings in pentylenetetrazole-kindled rats. Epilepsy Res. 2022, 179, 106830. [Google Scholar] [CrossRef]
  313. Arida, R.M.; de Almeida, A.-C.G.; Cavalheiro, E.A.; Scorza, F.A. Experimental and clinical findings from physical exercise as complementary therapy for epilepsy. Epilepsy Behav. 2013, 26, 273–278. [Google Scholar] [CrossRef]
  314. Cavalcante, B.R.R.; Improta-Caria, A.C.; de Melo, V.H.; De Sousa, R.A.L. Exercise-linked consequences on epilepsy. Epilepsy Behav. 2021, 121, 108079. [Google Scholar] [CrossRef]
  315. Agarwal, P.; Mukerji, G.; Desveaux, L.; Ivers, N.M.; Bhattacharyya, O.; Hensel, J.M.; Shaw, J.; Bouck, Z.; Jamieson, T.; Onabajo, N. Mobile app for improved self-management of type 2 diabetes: Multicenter pragmatic randomized controlled trial. JMIR mHealth uHealth 2019, 7, e10321. [Google Scholar] [CrossRef]
  316. Quinn, C.C.; Clough, S.S.; Minor, J.M.; Lender, D.; Okafor, M.C.; Gruber-Baldini, A. WellDoc mobile diabetes management randomized controlled trial: Change in clinical and behavioral outcomes and patient and physician satisfaction. Diabetes Technol. Ther. 2008, 10, 160–168. [Google Scholar] [CrossRef]
  317. Wu, Y.; Yao, X.; Vespasiani, G.; Nicolucci, A.; Dong, Y.; Kwong, J.; Li, L.; Sun, X.; Tian, H.; Li, S. Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy. JMIR mHealth uHealth 2017, 5, e35. [Google Scholar] [CrossRef] [PubMed]
  318. Hou, C.; Carter, B.; Hewitt, J.; Francisa, T.; Mayor, S. Do Mobile Phone Applications Improve Glycemic Control (HbA1c) in the Self-management of Diabetes? A Systematic Review, Meta-analysis, and GRADE of 14 Randomized Trials. Diabetes Care 2016, 39, 2089–2095. [Google Scholar] [CrossRef]
  319. Fleming, G.A.; Petrie, J.R.; Bergenstal, R.M.; Holl, R.W.; Peters, A.L.; Heinemann, L. Diabetes Digital App Technology: Benefits, Challenges, and Recommendations. A Consensus Report by the European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) Diabetes Technology Working Group. Diabetes Care 2020, 43, 250–260. [Google Scholar] [CrossRef] [PubMed]
  320. Doyle-Delgado, K.; Chamberlain, J.J. Use of diabetes-related applications and digital health tools by people with diabetes and their health care providers. Clin. Diabetes 2020, 38, 449–461. [Google Scholar] [CrossRef]
  321. Daly, A.B.; Boughton, C.K.; Nwokolo, M.; Hartnell, S.; Wilinska, M.E.; Cezar, A.; Evans, M.L.; Hovorka, R. Fully automated closed-loop insulin delivery in adults with type 2 diabetes: An open-label, single-center, randomized crossover trial. Nat. Med. 2023, 29, 203–208. [Google Scholar] [CrossRef]
  322. Kario, K.; Harada, N.; Okura, A. Digital therapeutics in hypertension: Evidence and perspectives. Hypertension 2022, 79, 2148–2158. [Google Scholar] [CrossRef] [PubMed]
  323. Bozorgi, A.; Hosseini, H.; Eftekhar, H.; Majdzadeh, R.; Yoonessi, A.; Ramezankhani, A.; Mansouri, M.; Ashoorkhani, M. The effect of the mobile “blood pressure management application” on hypertension self-management enhancement: A randomized controlled trial. Trials 2021, 22, 413. [Google Scholar] [CrossRef]
  324. Moravcová, K.; Karbanová, M.; Bretschneider, M.P.; Sovová, M.; Ožana, J.; Sovová, E. Comparing digital therapeutic intervention with an intensive obesity management program: Randomized controlled trial. Nutrients 2022, 14, 2005. [Google Scholar] [CrossRef]
  325. Schaaf, J.; Weber, T.; von Wagner, M.; Stephan, C.; Carney, J.; Köhler, S.M.; Voigt, A.; Noll, R.; Storf, H.; Müller, A. Interviews with HIV Experts for Development of a Mobile Health Application in HIV Care-A Qualitative Study. Healthcare 2023, 11, 2180. [Google Scholar] [CrossRef]
  326. Crowley, T.; Petinger, C.; Nchendia, A.I.; van Wyk, B. Effectiveness, Acceptability and Feasibility of Technology-Enabled Health Interventions for Adolescents Living with HIV in Low- and Middle-Income Countries: A Systematic Review. Int. J. Environ. Res. Public Health 2023, 20, 2464. [Google Scholar] [CrossRef] [PubMed]
  327. Laurenzi, C.A.; du Toit, S.; Ameyan, W.; Melendez-Torres, G.J.; Kara, T.; Brand, A.; Chideya, Y.; Abrahams, N.; Bradshaw, M.; Page, D.T.; et al. Psychosocial interventions for improving engagement in care and health and behavioural outcomes for adolescents and young people living with HIV: A systematic review and meta-analysis. J. Int. AIDS Soc. 2021, 24, e25741. [Google Scholar] [CrossRef] [PubMed]
  328. de Sousa Fernandes, M.S.; Santos, G.C.J.; Filgueira, T.O.; Gomes, D.A.; Barbosa, E.A.S.; Dos Santos, T.M.; Câmara, N.O.S.; Castoldi, A.; Souto, F.O. Cytokines and immune cells profile in different tissues of rodents induced by environmental enrichment: Systematic review. Int. J. Mol. Sci. 2022, 23, 11986. [Google Scholar] [CrossRef]
  329. Lee, M.-H.; Wang, T.; Jang, M.-H.; Steiner, J.; Haughey, N.; Ming, G.-L.; Song, H.; Nath, A.; Venkatesan, A. Rescue of adult hippocampal neurogenesis in a mouse model of HIV neurologic disease. Neurobiol. Dis. 2011, 41, 678–687. [Google Scholar] [CrossRef] [PubMed]
  330. Lee, M.-H.; Amin, N.D.; Venkatesan, A.; Wang, T.; Tyagi, R.; Pant, H.C.; Nath, A. Impaired neurogenesis and neurite outgrowth in an HIV-gp120 transgenic model is reversed by exercise via BDNF production and Cdk5 regulation. J. Neurovirology 2013, 19, 418–431. [Google Scholar] [CrossRef] [PubMed]
  331. McArthur, J.C.; Johnson, T.P. Chronic inflammation mediates brain injury in HIV infection: Relevance for cure strategies. Curr. Opin. Neurol. 2020, 33, 397–404. [Google Scholar] [CrossRef] [PubMed]
  332. van Roon-Mom, W.; Ferguson, C.; Aartsma-Rus, A. From Failure to Meet the Clinical Endpoint to US Food and Drug Administration Approval: 15th Antisense Oligonucleotide Therapy Approved Qalsody (Tofersen) for Treatment of SOD1 Mutated Amyotrophic Lateral Sclerosis. Nucleic Acid Ther. 2023, 33, 234–237. [Google Scholar] [CrossRef]
  333. Stevens, D.; Claborn, M.K.; Gildon, B.L.; Kessler, T.L.; Walker, C. Onasemnogene abeparvovec-xioi: Gene therapy for spinal muscular atrophy. Ann. Pharmacother. 2020, 54, 1001–1009. [Google Scholar] [CrossRef]
  334. Johnson, S.A.; Karas, M.; Burke, K.M.; Straczkiewicz, M.; Scheier, Z.A.; Clark, A.P.; Iwasaki, S.; Lahav, A.; Iyer, A.S.; Onnela, J.-P. Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures. npj Digit. Med. 2023, 6, 34. [Google Scholar] [CrossRef]
  335. Ortega-Hombrados, L.; Molina-Torres, G.; Galán-Mercant, A.; Sánchez-Guerrero, E.; González-Sánchez, M.; Ruiz-Muñoz, M. Systematic Review of Therapeutic Physical Exercise in Patients with Amyotrophic Lateral Sclerosis over Time. Int. J. Environ. Res. Public Health 2021, 18, 1074. [Google Scholar] [CrossRef]
  336. Gerhalter, T.; Müller, C.; Maron, E.; Thielen, M.; Schätzl, T.; Mähler, A.; Schütte, T.; Boschmann, M.; Herzer, R.; Spuler, S.; et al. “suMus”, a novel digital system for arm movement metrics and muscle energy expenditure. Front. Physiol. 2023, 14, 1057592. [Google Scholar] [CrossRef] [PubMed]
  337. Mercuri, E.; Finkel, R.S.; Muntoni, F.; Wirth, B.; Montes, J.; Main, M.; Mazzone, E.S.; Vitale, M.; Snyder, B.; Quijano-Roy, S. Diagnosis and management of spinal muscular atrophy: Part 1: Recommendations for diagnosis, rehabilitation, orthopedic and nutritional care. Neuromuscul. Disord. 2018, 28, 103–115. [Google Scholar] [CrossRef] [PubMed]
  338. Ma, C.-C.; Wang, Z.-L.; Xu, T.; He, Z.-Y.; Wei, Y.-Q. The approved gene therapy drugs worldwide: From 1998 to 2019. Biotechnol. Adv. 2020, 40, 107502. [Google Scholar] [CrossRef] [PubMed]
  339. Shapiro, M.; Renly, S.; Maiorano, A.; Young, J.; Medina, E.; Neinstein, A.; Odisho, A.Y. Digital Health at Enterprise Scale: Evaluation Framework for Selecting Patient-Facing Software in a Digital-First Health System. JMIR Form. Res. 2023, 7, e43009. [Google Scholar] [CrossRef]
  340. Cafazzo, J.A. A digital-first model of diabetes care. Diabetes Technol. Ther. 2019, 21, S2-25–S2-58. [Google Scholar] [CrossRef] [PubMed]
  341. Ekman, B.; Nero, H.; Lohmander, L.; Dahlberg, L. Costing analysis of a digital first-line treatment platform for patients with knee and hip osteoarthritis in Sweden. PLoS ONE 2020, 15, e0236342. [Google Scholar] [CrossRef]
  342. Dell’Isola, A.; Nero, H.; Dahlberg, L.E.; Ignjatovic, M.M.; Lohmander, L.S.; Cronström, A.; Kiadaliri, A. Within-person change in patient-reported outcomes and their association with the wish to undergo joint surgery during a digital first-line intervention for osteoarthritis. Osteoarthr. Cartil. 2023, 31, 1257–1264. [Google Scholar] [CrossRef]
  343. Kerschbaumer, A.; Sepriano, A.; Smolen, J.S.; van der Heijde, D.; Dougados, M.; van Vollenhoven, R.; McInnes, I.B.; Bijlsma, J.W.J.; Burmester, G.R.; de Wit, M.; et al. Efficacy of pharmacological treatment in rheumatoid arthritis: A systematic literature research informing the 2019 update of the EULAR recommendations for management of rheumatoid arthritis. Ann. Rheum. Dis. 2020, 79, 744–759. [Google Scholar] [CrossRef]
  344. Pushpakom, S.; Iorio, F.; Eyers, P.A.; Escott, K.J.; Hopper, S.; Wells, A.; Doig, A.; Guilliams, T.; Latimer, J.; McNamee, C.; et al. Drug repurposing: Progress, challenges and recommendations. Nat. Rev. Drug Discov. 2019, 18, 41–58. [Google Scholar] [CrossRef]
  345. Cha, Y.; Erez, T.; Reynolds, I.J.; Kumar, D.; Ross, J.; Koytiger, G.; Kusko, R.; Zeskind, B.; Risso, S.; Kagan, E.; et al. Drug repurposing from the perspective of pharmaceutical companies. Br. J. Pharmacol. 2018, 175, 168–180. [Google Scholar] [CrossRef]
  346. Oprea, T.I.; Bauman, J.E.; Bologa, C.G.; Buranda, T.; Chigaev, A.; Edwards, B.S.; Jarvik, J.W.; Gresham, H.D.; Haynes, M.K.; Hjelle, B.; et al. Drug Repurposing from an Academic Perspective. Drug Discov. Today Ther. Strateg. 2011, 8, 61–69. [Google Scholar] [CrossRef] [PubMed]
  347. Chaffey, L.; Roberti, A.; Greaves, D.R. Drug repurposing in cardiovascular inflammation: Successes, failures, and future opportunities. Front. Pharmacol. 2022, 13, 1046406. [Google Scholar] [CrossRef] [PubMed]
  348. Hong, J.; Bang, M. Anti-inflammatory strategies for schizophrenia: A review of evidence for therapeutic applications and drug repurposing. Clin. Psychopharmacol. Neurosci. 2020, 18, 10. [Google Scholar] [CrossRef]
  349. Sisignano, M.; Gribbon, P.; Geisslinger, G. Drug repurposing to target neuroinflammation and sensory neuron-dependent pain. Drugs 2022, 82, 357–373. [Google Scholar] [CrossRef] [PubMed]
  350. Petitdemange, A.; Blaess, J.; Sibilia, J.; Felten, R.; Arnaud, L. Shared development of targeted therapies among autoimmune and inflammatory diseases: A systematic repurposing analysis. Ther. Adv. Musculoskelet. Dis. 2020, 12, 1759720X20969261. [Google Scholar] [CrossRef] [PubMed]
  351. Gibbons, J.; Laber, M.; Bennett, C. Humira: The First $20 Billion Drug. Am. J. Manag. Care 2023, 29, 78–80. [Google Scholar] [CrossRef]
  352. Cisek, S.; Choi, D.; Stubbings, J.; Bhat, S. Preparing for the market entry of adalimumab biosimilars in the US in 2023: A primer for specialty pharmacists. Am. J. Health Syst. Pharm. 2023, 80, 1223–1233. [Google Scholar] [CrossRef] [PubMed]
  353. Simpson, J.; Kelly, J.P. The impact of environmental enrichment in laboratory rats—Behavioural and neurochemical aspects. Behav. Brain Res. 2011, 222, 246–264. [Google Scholar] [CrossRef]
  354. Alarcón, T.A.; Presti-Silva, S.M.; Simões, A.P.T.; Ribeiro, F.M.; Pires, R.G.W. Molecular mechanisms underlying the neuroprotection of environmental enrichment in Parkinson’s disease. Neural Regen. Res. 2023, 18, 1450–1456. [Google Scholar]
  355. Balietti, M.; Conti, F. Environmental enrichment and the aging brain: Is it time for standardization? Neurosci. Biobehav. Rev. 2022, 139, 104728. [Google Scholar] [CrossRef]
  356. Ellis, T.D.; Earhart, G.M. Digital therapeutics in Parkinson’s disease: Practical applications and future potential. J. Park. Dis. 2021, 11, S95–S101. [Google Scholar] [CrossRef] [PubMed]
  357. Murray, G. What Would Digital Early Intervention for Bipolar Disorder Look Like? Theoretical and Translational Considerations for Future Therapies. Front. Psychiatry 2019, 10, 599. [Google Scholar] [CrossRef] [PubMed]
  358. Lagan, S.; Ramakrishnan, A.; Lamont, E.; Ramakrishnan, A.; Frye, M.; Torous, J. Digital health developments and drawbacks: A review and analysis of top-returned apps for bipolar disorder. Int. J. Bipolar Disord. 2020, 8, 1–8. [Google Scholar] [CrossRef]
  359. Sverdlov, O.; van Dam, J. Digital Therapeutics: Strategic, Scientific, Developmental, and Regulatory Aspects; CRC Press: Boca Raton, FL, USA, 2022. [Google Scholar]
  360. Velez, F.F.; Malone, D.C. Cost-Effectiveness Analysis of a Prescription Digital Therapeutic for the Treatment of Opioid Use Disorder. J. Mark. Access Health Policy 2021, 9, 1966187. [Google Scholar] [CrossRef]
  361. Lewkowicz, D.; Wohlbrandt, A.M.; Bottinger, E. Digital therapeutic care apps with decision-support interventions for people with low back pain in germany: Cost-effectiveness analysis. JMIR mHealth uHealth 2022, 10, e35042. [Google Scholar] [CrossRef] [PubMed]
  362. Nomura, A.; Tanigawa, T.; Kario, K.; Igarashi, A. Cost-effectiveness of digital therapeutics for essential hypertension. Hypertens. Res. 2022, 45, 1538–1548. [Google Scholar] [CrossRef]
  363. van Kessel, R.; Roman-Urrestarazu, A.; Anderson, M.; Kyriopoulos, I.; Field, S.; Monti, G.; Reed, S.D.; Pavlova, M.; Wharton, G.; Mossialos, E. Mapping factors that affect the uptake of digital therapeutics within health systems: Scoping review. J. Med. Internet Res. 2023, 25, e48000. [Google Scholar] [CrossRef]
  364. Dahlhausen, F.; Zinner, M.; Bieske, L.; Ehlers, J.P.; Boehme, P.; Fehring, L. There’s an app for that, but nobody’s using it: Insights on improving patient access and adherence to digital therapeutics in Germany. Digit. Health 2022, 8, 20552076221104672. [Google Scholar] [CrossRef]
Figure 1. Examples of drug + digital combination therapies for the treatment of chronic diseases. The reSET-O mobile medical app for opioid use disorder (OUD) was authorized by the FDA in combination with buprenorphine. The CimplyMe companion app was designed for rheumatoid arthritis or Crohn’s disease patients who take an anti-TNFα biologic, certolizumab pegol. DTx delivering epilepsy self-management and music-based intervention was proposed as a drug–device combination product together with an antiseizure drug (levetiracetam is shown as an example) [13].
Figure 1. Examples of drug + digital combination therapies for the treatment of chronic diseases. The reSET-O mobile medical app for opioid use disorder (OUD) was authorized by the FDA in combination with buprenorphine. The CimplyMe companion app was designed for rheumatoid arthritis or Crohn’s disease patients who take an anti-TNFα biologic, certolizumab pegol. DTx delivering epilepsy self-management and music-based intervention was proposed as a drug–device combination product together with an antiseizure drug (levetiracetam is shown as an example) [13].
Jcm 13 00403 g001
Figure 2. Examples of clinical and patient care benefits delivered by digital health technologies that can be combined with specific pharmacological agents targeting cognitive functions in AD and dementia patients. HCPs, healthcare professionals; NMDA N-methyl D-aspartate.
Figure 2. Examples of clinical and patient care benefits delivered by digital health technologies that can be combined with specific pharmacological agents targeting cognitive functions in AD and dementia patients. HCPs, healthcare professionals; NMDA N-methyl D-aspartate.
Jcm 13 00403 g002
Figure 3. Drug + digital combination therapies for AD and dementia can include daily digital interventions between intravenous infusions of amyloid-β targeting monoclonal antibodies. Currently, the drug-alone therapies using lecanemab and donanemab require IV infusions every 2–4 weeks, thus missing daily opportunities of receiving clinically beneficial and personalized digital interventions to further improve therapy outcomes.
Figure 3. Drug + digital combination therapies for AD and dementia can include daily digital interventions between intravenous infusions of amyloid-β targeting monoclonal antibodies. Currently, the drug-alone therapies using lecanemab and donanemab require IV infusions every 2–4 weeks, thus missing daily opportunities of receiving clinically beneficial and personalized digital interventions to further improve therapy outcomes.
Jcm 13 00403 g003
Figure 4. An overview of physiological effects of environmental enrichment and non-pharmacological interventions in animal models of human diseases. Upward arrows indicate an increase and an improvement. Downward arrows indicate a decrease.
Figure 4. An overview of physiological effects of environmental enrichment and non-pharmacological interventions in animal models of human diseases. Upward arrows indicate an increase and an improvement. Downward arrows indicate a decrease.
Jcm 13 00403 g004
Figure 5. Examples of “active ingredients” delivered by digital health technologies that can be combined with DMARDs and analgesics to improve RA therapy outcomes. DMARDs, disease-modifying antirheumatic drugs; TNF, tumor necrosis factor.
Figure 5. Examples of “active ingredients” delivered by digital health technologies that can be combined with DMARDs and analgesics to improve RA therapy outcomes. DMARDs, disease-modifying antirheumatic drugs; TNF, tumor necrosis factor.
Jcm 13 00403 g005
Figure 6. Examples of “active ingredients” delivered by digital health technologies that can be combined with antineoplastic drugs to improve cancer treatment outcomes. CAR T-cells, chimeric antigen receptor.
Figure 6. Examples of “active ingredients” delivered by digital health technologies that can be combined with antineoplastic drugs to improve cancer treatment outcomes. CAR T-cells, chimeric antigen receptor.
Jcm 13 00403 g006
Figure 7. Examples of “active ingredients” delivered by digital health technologies that can be combined with analgesics to improve pain relief and chronic pain management. NSAIDs, nonsteroidal anti-inflammatory drugs.
Figure 7. Examples of “active ingredients” delivered by digital health technologies that can be combined with analgesics to improve pain relief and chronic pain management. NSAIDs, nonsteroidal anti-inflammatory drugs.
Jcm 13 00403 g007
Figure 8. Examples of “active ingredients” delivered by digital health technologies that can be combined with antidepressants and anxiolytic drugs to improve therapy outcomes for mental disorders. Abbreviations: SSRIs, selective serotonin reuptake inhibitors; SNRIs, serotonin/norepinephrine reuptake inhibitors; TCAs, tricyclic antidepressants; MAOIs, monoamine oxidase inhibitors.
Figure 8. Examples of “active ingredients” delivered by digital health technologies that can be combined with antidepressants and anxiolytic drugs to improve therapy outcomes for mental disorders. Abbreviations: SSRIs, selective serotonin reuptake inhibitors; SNRIs, serotonin/norepinephrine reuptake inhibitors; TCAs, tricyclic antidepressants; MAOIs, monoamine oxidase inhibitors.
Jcm 13 00403 g008
Figure 9. Examples of “active ingredients” delivered by digital health technologies that can be combined with antiseizure medications to improve therapy outcomes for people with epilepsy, including those with refractory epilepsy.
Figure 9. Examples of “active ingredients” delivered by digital health technologies that can be combined with antiseizure medications to improve therapy outcomes for people with epilepsy, including those with refractory epilepsy.
Jcm 13 00403 g009
Figure 10. Examples of patient-centered scenarios for drug + digital combination therapies that offer personalized and just-in-time adaptive interventions (JITAIs) depending on a patient’s disease prognosis, including disease symptoms and biomarkers (both physiological and digital).
Figure 10. Examples of patient-centered scenarios for drug + digital combination therapies that offer personalized and just-in-time adaptive interventions (JITAIs) depending on a patient’s disease prognosis, including disease symptoms and biomarkers (both physiological and digital).
Jcm 13 00403 g010
Figure 11. Aspirin as an example of drug repurposing from pain to prevention of cardiovascular diseases. During discovery and validation of drug-repurposing candidates, preclinical studies in the presence of EE relevant to a new disease indication can improve therapeutic window (TD50/ED50, where TD50 is the median toxic dose, and ED50 is the median effective dose). Integration of a repurposed drug with disease-specific digital interventions via drug + digital combination therapy can further improve patient outcomes.
Figure 11. Aspirin as an example of drug repurposing from pain to prevention of cardiovascular diseases. During discovery and validation of drug-repurposing candidates, preclinical studies in the presence of EE relevant to a new disease indication can improve therapeutic window (TD50/ED50, where TD50 is the median toxic dose, and ED50 is the median effective dose). Integration of a repurposed drug with disease-specific digital interventions via drug + digital combination therapy can further improve patient outcomes.
Jcm 13 00403 g011
Figure 12. The role of DTx in innovating pharmacotherapies after brand-name drugs/biologics lose the market exclusivity due to patent protection. Selected examples of blockbuster drugs that face “patent cliff” by 2030.
Figure 12. The role of DTx in innovating pharmacotherapies after brand-name drugs/biologics lose the market exclusivity due to patent protection. Selected examples of blockbuster drugs that face “patent cliff” by 2030.
Jcm 13 00403 g012
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Biskupiak, Z.; Ha, V.V.; Rohaj, A.; Bulaj, G. Digital Therapeutics for Improving Effectiveness of Pharmaceutical Drugs and Biological Products: Preclinical and Clinical Studies Supporting Development of Drug + Digital Combination Therapies for Chronic Diseases. J. Clin. Med. 2024, 13, 403. https://doi.org/10.3390/jcm13020403

AMA Style

Biskupiak Z, Ha VV, Rohaj A, Bulaj G. Digital Therapeutics for Improving Effectiveness of Pharmaceutical Drugs and Biological Products: Preclinical and Clinical Studies Supporting Development of Drug + Digital Combination Therapies for Chronic Diseases. Journal of Clinical Medicine. 2024; 13(2):403. https://doi.org/10.3390/jcm13020403

Chicago/Turabian Style

Biskupiak, Zack, Victor Vinh Ha, Aarushi Rohaj, and Grzegorz Bulaj. 2024. "Digital Therapeutics for Improving Effectiveness of Pharmaceutical Drugs and Biological Products: Preclinical and Clinical Studies Supporting Development of Drug + Digital Combination Therapies for Chronic Diseases" Journal of Clinical Medicine 13, no. 2: 403. https://doi.org/10.3390/jcm13020403

APA Style

Biskupiak, Z., Ha, V. V., Rohaj, A., & Bulaj, G. (2024). Digital Therapeutics for Improving Effectiveness of Pharmaceutical Drugs and Biological Products: Preclinical and Clinical Studies Supporting Development of Drug + Digital Combination Therapies for Chronic Diseases. Journal of Clinical Medicine, 13(2), 403. https://doi.org/10.3390/jcm13020403

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop