Digital Interventions for the Rehabilitation of First-Episode Psychosis: An Integrated Perspective
Abstract
:1. Introduction
1.1. The Impact of Digital Technologies on the Management of First-Episode Psychosis (FEP)
1.2. Digital Rehabilitation for First-Episode Psychosis: Objectives and Scope of the Review
1.3. Challenges and Opportunities in Implementing Digital Technologies for FEP Rehabilitation
2. Methods
2.1. Search Strategy and Study Eligibility Criteria
2.2. Study Selection
2.3. Quality Assessment
2.4. Data Extraction and Data Synthesis
2.5. Risk of Bias Assessment
3. Results
3.1. Clinician Perspectives on Telepsychiatry
3.2. Digital Mental Health Interventions
3.3. Study Protocols
4. Discussion
5. Conclusions
6. Future Directions
Author Contributions
Funding
Conflicts of Interest
Appendix A
- Machine Learning (ML) Algorithms:
- Predictive Models: Supervised learning algorithms (e.g., logistic regression, random forests, support vector machines) are widely used to predict the onset or relapse of psychosis by analyzing patterns in clinical, genetic, or imaging data.
- Symptom Tracking and Progression Analysis: ML models analyze longitudinal patient data, such as sleep patterns, activity levels, and symptom changes, to predict clinical outcomes and inform treatment adjustments.
- Natural Language Processing (NLP):
- Clinical Note Analysis: NLP tools analyze electronic health records (EHRs) to identify early signs of psychosis or track symptom trajectories.
- Speech Analysis: Algorithms process speech patterns to detect disorganized speech, a hallmark symptom of psychosis. Features like semantic coherence and prosody are evaluated to assess cognitive and emotional states.
- Deep Learning:
- Neuroimaging: Convolutional neural networks (CNNs) analyze MRI and fMRI scans to detect structural or functional abnormalities associated with psychosis.
- EEG Signal Analysis: Recurrent neural networks (RNNs) or CNNs are applied to EEG data to identify neural activity patterns indicative of psychosis.
- Behavioral and Digital Biomarker Analysis:
- Passive Data Collection: Smartphones and wearables collect data on movement, sleep, and social interaction. AI models process these passive data streams to detect early warning signs of psychosis relapse.
- Digital Phenotyping: AI analyzes behavioral data captured through smartphones or apps, such as screen usage, typing patterns, or GPS location, to infer mental health status.
- Reinforcement Learning:
- Personalized Interventions: Algorithms dynamically optimize therapeutic recommendations based on real-time feedback from patients. For example, reinforcement learning can personalize Cognitive Behavioral Therapy (CBT) modules based on user interactions.
- Generative Models:
- Simulating Patient Scenarios: Generative models like Variational Autoencoders (VAEs) simulate potential patient trajectories, aiding clinicians in decision-making and risk assessment.
- Synthetic Data Generation: AI generates synthetic datasets for training and validating predictive models in psychosis research, addressing privacy concerns with real patient data.
- Network Analysis and Graph Theory:
- Connectome Studies: AI applies graph-theoretical approaches to study brain connectivity and identify network-level disruptions associated with psychosis.
- Social Network Analysis: Evaluates patterns in patient social interactions, which can inform recovery trajectories and peer support effectiveness.
- Integration with Predictive Analytics:
- Risk Assessment Tools: AI models integrate clinical, genetic, and environmental data to calculate individualized risk scores for developing psychosis.
- Treatment Outcome Prediction: Predictive tools assess the likelihood of treatment adherence or response to antipsychotic medications.
- Examples of Practical Applications:
- Horyzons Platform: Uses AI for peer-moderated social networks, tracking patient engagement and suggesting interventions.
- ReMindCare: AI predicts relapse risk using passive smartphone data.
- Actissist: Incorporates AI-driven symptom monitoring to guide therapeutic interactions.
- Challenges and Future Directions:
- While these techniques offer immense potential, some challenges remain, including data privacy concerns, the need for large, diverse datasets, and ensuring AI models are interpretable and clinically actionable. Future efforts should focus on integrating AI with clinical workflows, validating models in diverse populations, and enhancing collaboration between clinicians and AI developers.
References
- Torous, J.; Jän Myrick, K. Digital Mental Health and COVID-19: Using Technology Today to Accelerate the Curve on Access and Quality Tomorrow. JMIR Ment. Health 2020, 7, e18848. [Google Scholar] [CrossRef] [PubMed]
- Clarke, J.; Proudfoot, J. Mobile Phone and Web-based Cognitive Behavior Therapy for Depressive Symptoms and Mental Health Comorbidities in People Living With Diabetes: Results of a Feasibility Study. JMIR Ment. Health 2016, 3, e23. [Google Scholar] [CrossRef] [PubMed]
- Naslund, J.A.; Aschbrenner, K.A. The future of mental health care: Peer-to-peer support and social media. Epidemiol. Psychiatr. Sci. 2016, 25, 113–122. [Google Scholar] [CrossRef]
- Johnson, K.B.; Wei, W.Q. Precision Medicine, AI, and the Future of Personalized Health Care. Clin. Transl. Sci. 2021, 14, 86–93. [Google Scholar] [CrossRef]
- Drake, R.J.; Husain, N. Effect of delaying treatment of first-episode psychosis on symptoms and social outcomes: A longitudinal analysis and modelling study. Lancet Psychiatry 2020, 7, 602–610. [Google Scholar] [CrossRef]
- Penttilä, M.; Jääskeläinen, E. Duration of untreated psychosis as predictor of long-term outcome in schizophrenia: Systematic review and meta-analysis. Br. J. Psychiatry 2014, 205, 88–94. [Google Scholar] [CrossRef]
- Colizzi, M.; Lasalvia, A. Prevention and early intervention in youth mental health: Is it time for a multidisciplinary and trans-diagnostic model for care? Int. J. Ment. Health Syst. 2020, 24, 14–23. [Google Scholar] [CrossRef]
- Salazar de Pablo, G.; Aymerich, C. What is the duration of untreated psychosis worldwide?—A meta-analysis of pooled mean and median time and regional trends and other correlates across 369 studies. Psychol. Med. 2024, 54, 652–662. [Google Scholar] [CrossRef]
- Haleem, A.; Javaid, M. Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sens. Int. 2021, 2, 100117. [Google Scholar] [CrossRef] [PubMed]
- Vaessen, T.; Steinhart, H. ACT in daily life in early psychosis: An ecological momentary intervention approach. Psychosis 2019, 11, 93–104. [Google Scholar] [CrossRef]
- Balaskas, A.; Schueller, S.M. Understanding users’ perspectives on mobile apps for anxiety management. Front. Digit. Health 2022, 4, 854263. [Google Scholar] [CrossRef] [PubMed]
- Maechling, C.; Yrondi, A. Mobile health in the specific management of first-episode psychosis: A systematic literature review. Front. Psychiatry 2023, 14, 1137644. [Google Scholar] [CrossRef] [PubMed]
- Kane, J.M.; Perlis, R.H. First experience with a wireless system incorporating physiologic assessments and direct confirmation of digital tablet ingestions in ambulatory patients with schizophrenia or bipolar disorder. J. Clin. Psychiatry 2013, 74, e533–e540. [Google Scholar] [CrossRef]
- Patel, M.X.; Bishara, D. Quality of prescribing for schizophrenia: Evidence from a national audit in England and Wales. Eur. Neuropsychopharmacol. 2014, 24, 499–509. [Google Scholar] [CrossRef] [PubMed]
- Morales-Pillado, C.; Fernández-Castilla, B. Efficacy of technology-based interventions in psychosis: A systematic review and network meta-analysis. Psychol. Med. 2023, 53, 6304–6315. [Google Scholar] [CrossRef] [PubMed]
- Arnold, C.; Farhall, J. Engagement with online psychosocial interventions for psychosis: A review and synthesis of relevant factors. Internet Interv. 2021, 25, 100411. [Google Scholar] [CrossRef]
- Malla, A.; Joober, R. COVID-19 and the Future with Digital Mental Health: Need for Attention to Complexities. Can. J. Psychiatry 2021, 66, 14–16. [Google Scholar] [CrossRef]
- Beattie, L.; Robb, F. Exploring digital cognitive behavioural therapy for insomnia in an early intervention in psychosis service-A study protocol for an initial feasibility study with process evaluation. Early Interv. Psychiatry 2023, 17, 519–526. [Google Scholar] [CrossRef]
- Bonet, L.; Torous, J. ReMindCare App for Early Psychosis: Pragmatic Real World Intervention and Usability Study. JMIR Mhealth Uhealth 2020, 8, e22997. [Google Scholar] [CrossRef]
- Bucci, S.; Berry, N. Effects of Actissist, a digital health intervention for early psychosis: A randomized clinical trial. Psychiatry Res. 2024, 339, 116025. [Google Scholar] [CrossRef] [PubMed]
- Cella, M.; He, Z. Blending active and passive digital technology methods to improve symptom monitoring in early psychosis. Early Interv. Psychiatry 2019, 13, 1271–1275. [Google Scholar] [CrossRef]
- Fowler, J.C.; Cope, N. Hummingbird Study: A study protocol for a multicentre exploratory trial to assess the acceptance and performance of a digital medicine system in adults with schizophrenia, schizoaffective disorder or first-episode psychosis. BMJ Open 2019, 9, e025952. [Google Scholar] [CrossRef] [PubMed]
- Engel, L.; Alvarez-Jimenez, M. The Cost-Effectiveness of a Novel Online Social Therapy to Maintain Treatment Effects From First-Episode Psychosis Services: Results From the Horyzons Randomized Controlled Trial. Schizophr. Bull. 2024, 50, 427–436. [Google Scholar] [CrossRef]
- Lal, S.; Abdel-Baki, A. Clinician perspectives on providing telepsychiatry services to young adults with first-episode psychosis during COVID-19. Early Interv. Psychiatry 2023, 17, 1189–1198. [Google Scholar] [CrossRef]
- Steare, T.; O’Hanlon, P. Smartphone-delivered self-management for first-episode psychosis: The ARIES feasibility randomised controlled trial. BMJ Open 2020, 10, e034927. [Google Scholar] [CrossRef]
- Lal, S.; Tobin, R. Experiences of a Digital Mental Health Intervention from the Perspectives of Young People Recovering from First-Episode Psychosis: A Focus Group Study. Int. J. Environ. Res. Public Health 2023, 20, 5745. [Google Scholar] [CrossRef] [PubMed]
- Vitger, T.; Austin, S.F. The Momentum trial: The efficacy of using a smartphone application to promote patient activation and support shared decision making in people with a diagnosis of schizophrenia in outpatient treatment settings: A randomized controlled single-blind trial. BMC Psychiatry 2019, 19, 185. [Google Scholar] [CrossRef] [PubMed]
- Guyatt, G.H.; Oxman, A.D. GRADE guidelines: A new series of articles in the Journal of Clinical Epidemiology. J. Clin. Epidemiol. 2011, 64, 380–382. [Google Scholar] [CrossRef]
- Valentine, L.; McEnery, C. Blended Digital and Face-to-Face Care for First-Episode Psychosis Treatment in Young People: Qualitative Study. JMIR Ment. Health 2020, 7, e18990. [Google Scholar] [CrossRef]
- Borghouts, J.; Eikey, E. Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions: Systematic Review. J. Med. Internet Res. 2021, 23, e24387. [Google Scholar] [CrossRef]
- Boucher, E.M.; Raiker, J.S. Engagement and retention in digital mental health interventions: A narrative review. BMC Digit. Health 2024, 2, 52. [Google Scholar] [CrossRef]
- Borges do Nascimento, I.J.; Abdulazeem, H. Barriers and facilitators to utilizing digital health technologies by healthcare professionals. NPJ Digit. Med. 2023, 6, 161. [Google Scholar] [CrossRef] [PubMed]
- Krukowski, R.; Ross, K.M. Digital health interventions for all? Examining inclusivity across all stages of the digital health intervention research process. Trials 2024, 25, 98. [Google Scholar] [CrossRef]
- Smith, K.A.; Hardy, A.; Vinnikova, A.; Blease, C.; Milligan, L.; Hidalgo-Mazzei, D.; Lambe, S.; Marzano, L.; Uhlhaas, P.J.; Ostinelli, E.G.; et al. Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions. JMIR Ment. Health 2024, 11, e57155. [Google Scholar] [CrossRef]
Author | Year and Country | Period | Study Design | Study Sample | Treatment | Comparator Group | Measures | Outcome |
---|---|---|---|---|---|---|---|---|
Beattie & Robb [18] | 2023, UK | Not specified | Feasibility study protocol Grade: ** | N = 40; mean age not specified; first-episode psychosis with insomnia; capacity to consent and access to technology required. | Sleepio digital CBT program; 6 sessions (15–20 min each); cognitive, behavioral, and educational techniques delivered via animated avatar; accessible via web and iOS applications. | None | Changes in SCI-2, ISI, DASS-21, R-GPTS, SPEQ, and Fear of COVID-19 scores. | Digital CBT interventions, like Sleepio, show promise in improving insomnia and mental health in first-episode psychosis. Feasibility metrics highlight potential for integration despite barriers like digital poverty, with qualitative insights informing future scalability. |
Bonet & Torous [19] | 2020, Spain and US (Massachusetts) | 50 weeks, with a minimum of 18 days and a maximum of 594 days | Observational Study Grade: ** | N = 57 (gender distribution not specified); aged between 17–65 years old; DSM-5 diagnosis of psychotic disorder; first-episode psychosis patients enrolled in a specialized program. | ReMindCare app; 3 daily assessment of anxiety, sadness, and irritability; weekly questionnaires on medication adherence, side effects, and prodromal symptoms; integrated with the hospital electronic medical record; available for a maximum period of 5 years. | None | Changes in CGI, GAF, PANSS, PAS, DAI-10, BCIS, sociodemographic information, clinical information and SMAQ. Number of relapses, number of visits to urgent care units at the hospital, number of hospital admissions | The ReMindCare app aims to improve treatment delivery, enhance patient-clinician communication, increase adherence to medication, and facilitate early relapse detection. Anticipated outcomes include reduced hospital admissions, fewer urgent care visits, and improved insight and alliance between patients and clinicians. Early findings suggest high retention and compliance, supporting its integration into routine psychiatric care. |
Bucci & Berry [20] | 2024, UK | 12 weeks | Randomized controlled trial (RTC) Grade: **** | N = 172 (108 M, 64 F); mean age 29.1 ± 9.2; ICD-10 diagnosis of schizophrenia-spectrum psychosis; within 5 years of first-episode onset, with moderate symptom severity. | Actissist app based on CBT principles; 12-week treatment window; daily question-answer exchanges addressing relapse risk factors; integrated multimedia tools for coping and behavior strategies; the control group received ClinTouch symptom monitoring app. | Comparison group used a monitoring app called “ClinTouch” | Changes in PANSS, PSYRATS, CDSS, PSP, PCPW, QPR, WEMWEBS, ISMI, ERS, AUDIT | No significant difference in PANSS total score between Actissist and ClinTouch groups; both groups showed symptom improvement over time; Actissist was found safe and acceptable but not superior to symptom monitoring; secondary measures also showed no significant differences. |
Cella & He [21] | 2019, UK | 1 week and 2 days | Pilot Study Grade: *** | N = 15 (12 M, 3 F); mean age 28.1 ± 3.8; first-episode psychosis; onset within the last 12 months. | ClinTouch mobile app for active symptom monitoring; Empatica E4 wearable device for passive monitoring (EDA and HRV); 10-day simultaneous use; daily app prompts assessing psychotic symptoms and distress levels. | None | Changes in PANSS, GAF, EDA, HRV, Motion and Skin Temperature | EDA levels significantly higher during distressing hallucinations and delusions; no significant changes in HRV during distressing symptoms; high acceptability and compliance with app and wearable device; study supports the feasibility of combining active and passive monitoring for early psychosis. |
Fowler & Cope [22] | 2019, US (New York) | 8 weeks | Study Protocol Grade: ** | N = 60 (gender distribution not specified); aged 18–65 years; diagnosis of schizophrenia, schizoaffective disorder, or first-episode psychosis (ICD-10 codes F20/F25); prescribed oral atypical antipsychotics (aripiprazole, olanzapine, quetiapine, or risperidone). | Digital Medicine System (DMS); composed of a sensor-embedded oral tablet, wearable patch, mobile application, and web-based dashboard; 8-week intervention with continuous patch wear and ingestion monitoring; data accessible to patients, caregivers (with consent), and HCPs via secure web portals. | None | Changes in PANSS, subjects usability and satisfaction scale, physician utility survey, caregiver/support person involvement scale, personal and social performance scale, patient activation measure-mental health scale. | High acceptance and engagement reported among patients and HCPs; preliminary findings suggest feasibility of DMS for monitoring adherence and supporting clinical decisions; no formal power calculations as this was an exploratory feasibility study. |
Engel & Alvarez-Jimenez [23] | 2024, Australia | 18 months (approx. 78 weeks) | Randomized Controlled Trial (RCT) Grade: **** | N = 170 (90 M, 80 F); mean age 20.91 ± 2.88; young adults diagnosed with first-episode psychosis; in remission and nearing discharge from specialized services. | Horyzons online intervention; 18-month duration; based on the Moderated Online Social Therapy model (MOST); which integrates: online therapy, peer-to-peer social networking, and clinician support; targeting social functioning, vocational recovery, and relapse prevention. | Treatment as Usual (TAU) | Changes in PSP, AQoL-8D, QALYs | Horyzons group demonstrated significantly lower costs compared to treatment as usual (TAU); small improvements in social functioning (PSP) over TAU, though not statistically significant; greater engagement with therapeutic components linked to improved outcomes; Horyzons shown to be cost-effective, offering potential budget savings and supporting long-term recovery. |
Lal & Abdel-Baki [24] | 2023, Canada | Not specified | Qualitative Study Grade: * | N = 26 (22 F, 4 M); mental health service providers working in SEI for psychosis program; professional backgrounds included social workers, nurses, physicians, occupational therapists, and peer support workers; conducted during COVID-19 using telepsychiatry services (REACTS platform). | Telepsychiatry using REACTS platform; video/audio communication, file sharing, and messaging capabilities; used for patient engagement, therapy sessions, follow-ups, and family meetings; clinician training provided pre-pandemic, with additional booster training during the pandemic. | None | Primary: Frequency and duration of telepsychiatry sessions using the REACTS platform. Secondary: Clinician-reported safety, ease of use, and satisfaction with telepsychiatry; technical issues encountered (e.g., sound, image quality, session initiation); perceptions of client engagement, continuity of care, and technical support needs; qualitative analysis of barriers and facilitators to telepsychiatry adoption over time. | Positive perceptions of telepsychiatry maintained over time; improvements in perceived ease of use and session efficiency; benefits included enhanced client engagement and continuity of care; barriers included initial usability challenges and technical issues; clinicians supported continued use of telepsychiatry post-pandemic. |
Steare & O’Hanlon [25] | 2020, UK | Median of 38.1 weeks | Randomized Controlled Trial (RCT) Grade: **** | N = 40 (28 M, 12 F); mean age 29.7 ± 9.78; ICD-10 diagnosis of psychotic disorders (F20–F29) and mood disorders (F30–F39); users of Early Intervention in Psychosis (EIP) services owning Android smartphones. | My Journey 3 app; features include symptom tracking, relapse prevention, medication reminders, and psychoeducation; delivered with clinician support for set-up and training; median access duration 38.1 weeks; the control group received treatment as usual (TAU). | Treatment as Usual (TAU) | Changes in QPR, WEMWBS, DIALOG, PANSS, SES, SIX, qualitative survey | Feasibility confirmed with 75% 12-month retention; My Journey 3 use was low (median use 16.5 times per participant); no significant differences in primary or secondary outcomes between groups; qualitative feedback indicated acceptability but barriers included technical issues and limited clinician support; relapse rates were low across both groups. |
Lal &Tobin [26] | 2023, Canada | 8 weeks | Qualitative focus group study Grade: * | N = 23 (6 F, 2 M, 1 non-binary, 14 gender not specified); aged 18–34 years (mean age 26.9 ± 6.2); diagnosed with psychotic disorders within the first three years of treatment; recruited from a Canadian first-episode psychosis program. | Horyzons-Canada (HoryzonsCa) online intervention; platform included peer networking, therapeutic content, and clinical moderation; focused on stress management, self-improvement, and recovery strategies; pilot study duration was 8 weeks. | None | Participant feedback on recovery strategies and platform usability; focus group data exploring core themes: ease of use, peer networking, therapeutic content, and moderation; engagement metrics from platform use (logins and activity completion); qualitative thematic analysis. | Participants found HoryzonsCa helpful for recovery and stress management; key strengths included ease of navigation, connection with peers, and access to therapeutic content; challenges included limited awareness of platform features and need for deeper, personalized content; positive feedback on moderator support and desire for expanded platform scale and functionality. |
Vitger & Austin [27] | 2019, Denmark | 6 months, with data collection at baseline, 3 months and 6 months | Randomized Controlled Trial (RCT) Grade: **** | N = 260 (gender distribution not specified); aged 18+; diagnosed with schizophrenia, schizotypal, or delusional disorders (ICD-10 codes F20–F29); receiving treatment in OPUS specialized early intervention program; participants must own a smartphone and have daily access. | Momentum smartphone app with TAU; features include daily self-evaluation (stress, sleep, well-being), recovery goal setting, and action plans; web portal for clinicians to view patient input before consultations; control group received TAU only; 6-month intervention. | Treatment as Usual (TAU) | Changes in CHAI-MH, CDMS, WAI (short form), ASH, GSE, PEPPI-5, PrepDM, CSQ-8, ARQ, sociodemographic measures, SES, PSP, GAF, SAPS, SANS | Expected: Improved patient activation and preparedness for SDM; potential secondary benefits include enhanced alliance, functioning, self-efficacy, and satisfaction; data on app engagement correlated with improvements; study aims to validate digital tools for supporting SDM in outpatient mental health care. |
References | Name of Digital Health Interventions | Overall Risk | Randomization | Intervention | Missing Data | Outcome Measurement | Grade |
---|---|---|---|---|---|---|---|
Bucci & Berry (2024) [20] | Actissist | +/− | - | +/− | - | +/− | +/− |
Steare & O’Hanlon (2020) [25] | ARIES | +/− | - | +/− | +/− | +/− | +/− |
Engel & Alvarez-Jimenez (2024) [23] | Horyzons | - | - | - | - | - | + |
Vitger & Austin (2019) [27] | Momentum | +/− | - | +/− | - | +/− | +/− |
Author/Year | Intervention | Key Outcomes | Gaps/Barriers | Study Duration |
---|---|---|---|---|
Lal et al., (2023) [24] | Horyzons Platform | Improved social functioning; cost-effective | Need for tailored content and better onboarding | 8 weeks |
Steare & O’Hanlon (2020) [25] | My Journey 3 App | Feasibility confirmed; barriers to daily use | Low engagement and interface issues | 38.1 weeks (median) |
Engel & Alvarez-Jimenez (2024) [23] | Horyzons Online Intervention | Cost-effective; moderate engagement improvement | Limited social functioning improvement | 18 months |
Bucci & Berry (2024) [20] | Actissist App | Safe but not superior to symptom monitoring | Requires further refinement for efficacy | 12 weeks |
Cella et al. (2019) [21] | ClinTouch & Wearable Device | High acceptability; early warning feasibility | Technical issues; need larger sample size | 10 days |
Beattie & Robb (2023) [18] | Digital CBT for Insomnia (Sleepio) | Improved insomnia; highlights scalability challenges | Digital poverty and onboarding challenges | 6 sessions over 6 weeks |
Bonet & Torous (2020) [19] | ReMindCare App | Enhanced adherence and early relapse detection | Technical challenges; need for long-term data | 50 weeks |
Fowler & Cope (2019) [22] | Digital Medicine System (DMS) | High acceptance; feasible for adherence monitoring | Exploratory feasibility study; lacks power | 8 weeks |
Vitger & Austin (2019) [27] | Momentum App | Improved patient activation and preparedness | User adaptation and support system challenges | 6 months |
Lal & Tobin (2023) [26] | Horyzons-Canada (HoryzonsCa) | Helpful for recovery and stress management | Limited awareness of platform features | 8 weeks |
Category | Key Findings |
---|---|
Purpose of Digital Interventions | - Enhance patient engagement, reduce stigma, and improve access to mental health services. |
- Aid early diagnosis and intervention to prevent long-term negative outcomes. | |
Types of Interventions | - Telepsychiatry: Effective for continuity of care, especially during COVID-19. |
- Smartphone Apps: Tools like ReMindCare and Horyzons for symptom tracking, relapse prediction, and peer support. | |
Benefits | - Timely access to care and increased frequency of patient-provider interactions. |
- Cost-effectiveness and improved treatment adherence. | |
- Potential to integrate active/passive monitoring tools. | |
Challenges | - Low user engagement due to complex interfaces and lack of motivation. |
- Technical issues (e.g., connectivity, usability). | |
- Need for tailored, culturally relevant interventions. | |
Notable Applications | - Horyzons-Canada: Peer support for recovery; emphasizes engagement improvements. |
- ReMindCare: Tracks medication adherence and early relapse signs. | |
- CBT-I Programs: Digital therapy for insomnia co-occurring with FEP. | |
Implementation Strategies | - Importance of training for clinicians and patients. |
- Robust onboarding and user education. | |
- Incorporation of peer support and personalized features. | |
Research Insights | - Longitudinal studies and mixed-methods approaches are vital. |
- Predictive analytics and machine learning for early relapse detection. | |
- Integration with traditional care pathways enhances outcomes. | |
Future Directions | - Develop scalable, user-friendly platforms. |
- Focus on demographic diversity in intervention design. | |
- Collaborate among clinicians, researchers, and developers to optimize solutions. |
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. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Vignapiano, A.; Monaco, F.; Panarello, E.; Landi, S.; Di Gruttola, B.; Malvone, R.; Martiadis, V.; Raffone, F.; Marenna, A.; Pontillo, M.; et al. Digital Interventions for the Rehabilitation of First-Episode Psychosis: An Integrated Perspective. Brain Sci. 2025, 15, 80. https://doi.org/10.3390/brainsci15010080
Vignapiano A, Monaco F, Panarello E, Landi S, Di Gruttola B, Malvone R, Martiadis V, Raffone F, Marenna A, Pontillo M, et al. Digital Interventions for the Rehabilitation of First-Episode Psychosis: An Integrated Perspective. Brain Sciences. 2025; 15(1):80. https://doi.org/10.3390/brainsci15010080
Chicago/Turabian StyleVignapiano, Annarita, Francesco Monaco, Ernesta Panarello, Stefania Landi, Benedetta Di Gruttola, Raffaele Malvone, Vassilis Martiadis, Fabiola Raffone, Alessandra Marenna, Maria Pontillo, and et al. 2025. "Digital Interventions for the Rehabilitation of First-Episode Psychosis: An Integrated Perspective" Brain Sciences 15, no. 1: 80. https://doi.org/10.3390/brainsci15010080
APA StyleVignapiano, A., Monaco, F., Panarello, E., Landi, S., Di Gruttola, B., Malvone, R., Martiadis, V., Raffone, F., Marenna, A., Pontillo, M., Di Stefano, V., D’Angelo, M., Steardo, L., Jr., & Corrivetti, G. (2025). Digital Interventions for the Rehabilitation of First-Episode Psychosis: An Integrated Perspective. Brain Sciences, 15(1), 80. https://doi.org/10.3390/brainsci15010080