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Article

Effects of a Multimodal Immersive Virtual Reality Intervention on Heart Rate Variability in Adults with Post-COVID-19 Syndrome

by
Neus Cano
1,2,
Oscar Casas
3,
Mar Ariza
1,4,
Olga Gelonch
1,
Yemila Plana
1,2,
Bruno Porras-Garcia
1,2,* and
Maite Garolera
1,2,5,*
1
Brain, Cognition and Behavior Research Group, Consorci Sanitari de Terrassa (CST), 08227 Terrassa, Spain
2
BrainXRLab, Department of Psychology, Universitat Internacional de Catalunya, 08195 Sant Cugat, Spain
3
Instrumentation, Sensors and Interfaces Group, Department of Electronics, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
4
Unit of Medical Psychology, Department of Medicine, Universitat de Barcelona, 08036 Barcelona, Spain
5
Neuropsychology Unit, Consorci Sanitari de Terrassa (CST), 08227 Terrassa, Spain
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2025, 15(8), 4111; https://doi.org/10.3390/app15084111
Submission received: 17 December 2024 / Revised: 14 March 2025 / Accepted: 27 March 2025 / Published: 8 April 2025
(This article belongs to the Special Issue Virtual Reality (VR) in Healthcare)
Versions Notes

Abstract

:
Background: Post-COVID-19 syndrome (PCC) is characterized by autonomic nervous system (ANS) dysregulation. Reduced heart rate variability (HRV) serves as a biomarker for ANS function. Few studies have assessed HRV modulations over treatment in PCC patients. This study evaluates the effects of a multimodal immersive virtual reality intervention—integrating cognitive training, physical exercise, and mindfulness practices—on HRV parameters. Methods: Eighteen PCC adults were assigned to reduced (16 sessions) and extended (24 sessions) training. HRV was assessed using an electrocardiogram weight scale at baseline, in the mid-term, and at the end of the intervention. Time-domain and frequency-domain HRV measures were extracted. Results: No significant group-by-time interactions were found. However, certain time-domain HRV parameters showed significant changes over time. Unexpectedly, HRV decreased from baseline to mid-intervention in both groups, with recovery by the end of the intervention. No significant changes were observed in frequency-domain measures. Conclusions: The temporary reduction in HRV suggested that the initial cognitive and physical demands may have temporarily induced physiological stress. The subsequent restoration of HRV suggested adaptation and increased resilience. The absence of enhanced HRV with extended training suggests that session intensity may be more influential than the number of sessions in modulating HRV among PCC patients.

1. Introduction

Post-COVID-19 syndrome (PCC) occurs as a sequela of acute SARS-CoV-2 infection and is characterized by symptoms persisting beyond 12 weeks and/or the appearance of new symptoms within this period [1]. PCC manifests as a multisystem disorder with common symptoms including chronic fatigue, cognitive impairment (such as memory dysfunction and brain fog), reduced physical performance, muscular weakness, pain, dyspnea, and psychological distress, resembling post-traumatic stress [2,3,4,5]. PCC can occur following mild, moderate, or severe infection, though individual risk factors remain subject to debate [6,7]. Incidence estimates vary, being influenced by population characteristics, symptom severity, and virus variants [7]. While many patients gradually recover without treatment, effective rehabilitation is crucial for those with persistent PCC. COVID-19 patients requiring ICU admission constitute a minority of cases, but their progression can be challenging, marked by a pathological inflammatory response known as a “cytokine storm”, which coincides with a steep rise in inflammatory markers like C-reactive protein [8]. One non-invasive tool that can provide early warning of a cytokine storm is heart rate variability (HRV) [9], a physiological metric regulated by the autonomic nervous system (ANS) that has been used for decades to assess general well-being in clinical settings. HRV reflects the modulation of heart rhythm by the sympathetic and parasympathetic branches of the ANS [9].
HRV has been used to assess ANS dysregulation in acute COVID-19 patients and has been investigated for changes in HRV in PCC patients with short to medium symptom durations [10,11,12,13]. Understanding the long-term cardiovascular impact of COVID-19 is crucial due to its potential impact on morbidity and mortality. Survivors face risks of heart attack, stroke, and heart failure due to damage to the heart and vasculature [14]. ANS dysfunction related to COVID-19 may occur through direct viral invasion or autoimmunity and systemic inflammation [15,16]. Studies have shown reduced HRV in COVID-19 patients, with it being associated with poorer health and worse clinical outcomes [17,18,19,20]. Reduced HRV has also been predictive of ICU admission and mortality [21]. ANS dysfunction is not resolved in some COVID-19 survivors, with even young and middle-aged patients suffering lingering and persistent symptoms [17,22].
While there is strong support for patients with PCC showing reduced HRV, to date very few studies have attempted to modify HRV in patients with PCC through interventions focusing on physical, physiological, or even psychological mechanisms that can either directly or indirectly influence HRV. Physical activity positively influences HRV [23,24,25], with studies showing the beneficial effects of regular exercise across various clinical populations [26,27,28], including patients with PCC [28]. Mindfulness practices may also improve HRV, potentially reducing the impact of sources of emotional stress related to PCC, such as anxiety and stress symptoms. Mindfulness can enhance dispositional awareness, moderating stress responses and physiological reactions [29,30]. Recent reviews have provided tentative evidence of mindfulness-based training and other mind-body therapies [31], including Tai Chi, Yoga, Qi Gong, and meditation, modifying inflammatory biomarkers and HRV [32,33].
The role of ANS in emotional regulation and HRV is well known, but its relationship with cognitive functions is less clear. The neurovisceral integration model proposes that sympathetic hyperactivation, resulting in prefrontal hypoactivation, facilitates disinhibition of the amygdala, leading to a decrease in HRV and an increase in heart rate [34]. This hypervigilant response could be associated with reduced cognitive flexibility. Conversely, parasympathetic activity, with its lack of prefrontal hypoactivation, could lead to an increase in HRV and improved cognitive functions [34]. Several studies have confirmed this link between higher resting HRV and active inhibitory prefrontal-subcortical circuits [35,36]. To be more specific, higher resting-state HRV was linked to increased activity in executive brain regions [35], while lower resting HRV was associated with hypoactive prefrontal regulation [37,38]. Consequently, vagal control of the heart has been associated with effective self-regulatory neural circuits, which putatively enable the organism to respond more quickly and flexibly to environmental demands [34,35].
Considering the previous evidence, a multimodal therapeutic approach combining physical activity, mindfulness-based techniques, and cognitive training may offer significant benefits for COVID-19 survivors experiencing reduced HRV. Immersive virtual reality (IVR) is a promising technology that has the potential to enhance this multimodal program and offers varying levels of immersion. IVR typically produces a computer-generated 360° virtual world through an immersive display device [36]. Several studies have investigated the efficacy of using IVR cognitive training interventions to treat vulnerable populations. Recent systematic reviews and meta-analyses have found that VR exergames, which combine gaming and physical movement, can lead to significant improvements in cognitive function, memory, and mood in older adults [37,38]. Such interventions are perceived as less intensive and more enjoyable than traditional exercises, thus encouraging longer and more frequent sessions and healthier changes in lifestyle [39].
Recent research underscores the transformative impact of VR in post-COVID-19 rehabilitation, offering significant benefits for patient recovery and quality of life. Recent studies have shown that digital interventions can enhance quality of life and hasten a return to independence [40], while home-based VR exercises have been well received, demonstrating notable improvements in participant health and well-being [41]. A systematic review further suggested that VR applications could mitigate functional and cognitive impairments associated with COVID-19, leading to high levels of patient satisfaction and empowering individuals to manage their own healthcare more effectively [42]. Other reviews of VR used in cardiac rehabilitation have revealed that such interactive tools make therapy more enjoyable and engaging, thereby increasing motivation [43]. Although these studies suggest that interactive VR can improve engagement in cognitive and/or physical activities, further research in hospital environments is needed to explore the full potential of VR in this field.
Based on the previously reviewed evidence, there is growing support for the use of VR multimodal training paradigms. It has been demonstrated that they offer significant potential to transform rehabilitation for patients with PCC, including the modulation of HRV parameters. Targeted interventions focused on improving the physical, cognitive, and emotional impairments commonly reported by patients with PCC, which can directly influence HRV parameters, may serve as important indicators of ANS health and the potential for overall recovery.
The primary objective of this study was to evaluate the effects of a multimodal IVR intervention, including cognitive training, physical exercise, and mindfulness interventions, on HRV parameters in adults with PCC. We hypothesized that participants receiving the multimodal IVR intervention would show significant improvements in both time- and frequency-domain measures of HRV. As reduced HRV parameters commonly reflect HRV impairment [21], we expected to observe progressively increasing HRV levels over the course of the multimodal intervention, from the baseline to the mid-term and end of the intervention. As a secondary objective, we sought to explore how intervention intensity affected HRV outcomes. To be more specific, this study investigated whether increasing the number of multimodal sessions across two groups (reduced multimodal training; 16 sessions vs. extended multimodal training; 24 sessions per group) would lead to greater improvements in HRV levels during the intervention.

2. Materials and Methods

2.1. Participants

In this study, 18 adults with PCC were enrolled from eight public primary care centers belonging to the Hospital Univesitari de Terrassa-Consorci Sanitari de Terrasa, Spain.
Potential participants, all of whom had been diagnosed with PCC, were given a detailed explanation of the study. A clinical neuropsychologist then carried out initial screening to verify their eligibility based on the study’s inclusion and exclusion criteria. This process involved a clinical interview to evaluate the progression of their PCC symptoms.
Participants were eligible if they were over 18 years of age and met the criteria for PCC. Symptoms included fatigue, difficulty thinking or concentrating (brain fog), palpitations, muscle and/or joint pain, respiratory problems, tingling feelings, gastrointestinal dysfunction, insomnia, loss of smell and/or taste, hair loss, and/or rash. In addition, participants were required to report symptoms of anxiety or depression using the Patient Health Questionnaire-9 (PHQ-9) [44] and Generalized Anxiety Disorder-7 (GAD-7) [45] scales. Only those who scored ≥6 on the PHQ-9 and/or ≥10 on the GAD-7 were eligible. All participants had to understand Spanish or Catalan and give informed consent to participate. Exclusion criteria included pre-existing psychiatric, neurological, neurodevelopmental, or systemic disorders resulting in cognitive impairment, as well as motor or sensory deficits that could interfere with completing the program (e.g., severe dysarthria, paresis, visual, or auditory field problems). Participants received no financial compensation but were covered by research insurance throughout the study.

2.2. Measurements

HRV was assessed using an Electrocardiogram (ECG) Weight Scale [46,47], which is a prototype device combining a standard electronic weight scale with four dry electrodes positioned so as to record signals from the limbs.
For the design of the ECG-Weight Scale, two sensors are located on the surface of the scale for foot contact, while the remaining two are handheld during weight measurement. These four electrodes are connected to an electronic circuit with a bandwidth of 0.05 Hz to 100 Hz, a 16-bit resolution, and a 1 kHz sampling rate, capturing signals at 333 Hz per lead. A laptop provides a 5 V power supply via USB 3.0 and manages signal visualization and storage. Signal processing was performed using MATLAB 24.1. For electrocardiogram data, R-wave detection was achieved through the Pan-Tompkins algorithm [48], incorporating filtering, derivation, squaring, integration, and adaptive thresholding. To validate the accuracy of the designed system, a 60 s ECG was recorded in 10 volunteers in parallel with the designed scale and the commercial Biopac system (Biopac MP36, ECG module SS2LB). Analogue and digital filters used by both systems were also adjusted to be similar in both devices. A Bland–Altman study was carried out for each pair of signals, obtaining values of r > 0.95 and SD < 0.08 for the ECG measurements, and in the HR estimations, an r > 0.98 was obtained confirming the possibility of using the system in the application for which it was designed.
The assessment involved monitoring participants over three 5 min periods at the beginning of the first session (week 1), mid-term (after the 8th session for group 1 and 12th session for group 2), and the last session (the 16th session for group 1 and the 24th session for group 2). Each recording lasted five minutes and was conducted at the beginning of the session (0th minute), before engaging in any intervention activities. HRV analysis was conducted using both time-domain and frequency-domain metrics [9].

2.2.1. Time-Domain

The root mean square of successive differences (RMSSD) was calculated as the square root of the mean squared differences between adjacent R-R intervals and provided insight into parasympathetic activity and vagal tone. The standard deviation of successive differences (SDSD) was also assessed, which reflected short-term variability and parasympathetic activity similar to RMSSD. pNN50 was assessed by counting the percentage of pairs of consecutive R-R intervals that differed by more than 50 milliseconds. Finally, the standard deviation of NN intervals (SDNN) was measured to assess the overall HRV. This captures cyclic components that contribute to variability and includes influences from both the sympathetic and parasympathetic nervous systems.

2.2.2. Frequency-Domain

Frequency-domain measures were analyzed using three primary frequency bands: very low-frequency (VLF: 0.0033–0.04 Hz), low-frequency (LF: 0.04–0.15 Hz), and high-frequency (HF: 0.15–0.40 Hz). The LF band represented both sympathetic and parasympathetic activity, while the HF band predominantly indicated parasympathetic activity. The LF/HF ratio was calculated to assess the balance between sympathetic and parasympathetic activity. This ratio provides a valuable measure of the sympathovagal balance, with higher values suggesting sympathetic dominance and lower values indicating parasympathetic dominance.

2.3. Procedure

The Ethical Review Board of the Terrassa Health Consortium (Consorci Sanitari de Terrassa) provided approval for all the procedures related to this study (reference number: 02-22-107-029). Informed consent was obtained from all participants, ensuring that any questions or concerns they had regarding the assessments or intervention were fully addressed prior to signing the consent form. Participants were informed about data confidentiality, and each participant was assigned a distinct identification code to protect their privacy. The study was conducted between January 2023 and September 2025.
To complete the study, participants were required to attend at least 80% of the scheduled sessions. Those who failed to meet this attendance requirement were excluded. HRV measurements were taken at three stages: in the first session, mid-way through the intervention, and in the final session.

Multimodal IVR Intervention

The multimodal IVR program was conducted in two different time frames: 8 weeks for group 1 and 12 weeks for group 2. Participants attended two weekly 60 min sessions, resulting in 16 sessions for group 1 and 24 for group 2.
The intervention was delivered using MK360 IVR hardware developed by Broomx Technologies, a lightweight, cost-effective solution designed for individual and small group sessions in small or medium-sized spaces, accommodating 1 to 10 participants. This headset-free IVR system employs a multiple-patented projection technology that displays 360° media and interactive virtual environments across three walls and the ceiling, creating an immersive group experience. The MK360 device integrates a projection module, CPU, GPU, speaker system, WiFi hotspot, and multiple connectivity options, ensuring seamless operation and adaptability to various settings.
The study was conducted in a spacious clinical room at the hospital, optimized for group interaction, with all windows covered to prevent external light interference and furniture removed to eliminate obstructions, ensuring an unobstructed and fully immersive IVR experience. The sessions were conducted in groups of five and led by a neuropsychologist.
Each session began with a 10 min introduction given by the neuropsychologist, who outlined the goals of the session and explained their importance. The briefing covered how PCC affects cognitive and emotional functions, and the neuropsychologist encouraged participants to incorporate new skills into their everyday routines. Participants were also encouraged to evaluate their learning strategies and recognize their cognitive patterns. The 60 min sessions included three main components: mindfulness, cognitive training, and physical exercises. Although the specifics of each session varied, the structure and timing of the activities remained constant.
Mindfulness (10 min): An adapted version of Jon Kabat-Zinn’s Mindfulness-Based Stress Reduction program [49] was followed, including body scanning, seated meditation, and gentle Hatha yoga to increase body awareness. The neuropsychologist highlighted that the objective of mindfulness was to help participants maintain functional stability under difficult conditions and to thereby enhance their ability to remain active and independent.
Cognitive Training (30 min): Cognitive exercises were carried out in immersive virtual environments designed to replicate real-life settings (e.g., parks, tourist destinations) to strengthen cognitive abilities. The tasks were sequential and focused on different cognitive areas, including attention, memory, and executive function, with the degree of difficulty being adjusted to participant performance. The “Parc Güell” simulation involved recalling stimuli and details to boost attention, social cognition, and memory. The “Crazy Lines” activity promoted working memory and processing speed by asking participants to follow color-coded numerical sequences. The “Emoticons” task improved visual tracking, attention, processing speed, and mental arithmetic through the identification of specific emoticons in a virtual environment.
Physical Exercise (20 min): This section included a mix of multimodal limb exercises and therapeutic activities aimed at improving balance, flexibility, and muscle strength. Repetitive movements included chair squats, pedaling, and step-up exercises.

2.4. Data Analysis

Statistical analyses were made with IBM SPSS Statistics 27.0 (IBM Corporation, Armonk, NY, USA). Descriptive statistics (mean and standard deviations; or percentages) were calculated for all the study variables. Initially, independent samples t-tests for continuous variables, along with their nonparametric equivalents, were used to determine any significant differences between groups about demographic (i.e., age, years of education) and clinical variables (i.e., global cognition, levels of anxiety and depression, body mass index, and physical exercise).
Mixed ANOVAs with Bonferroni corrections were performed to analyze HRV measurements across both between-group (group) and within-group (first-middle-last assessments) factors. Tukey-corrected post hoc analyses were then performed to examine differences between the multimodal IVR intervention and the assessment time points. The threshold for statistical significance was set at α = 0.05. Partial eta squared (partial η2) was used to measure effect size.
Levene’s test confirmed homogeneity of variance for most variables, indicating that the assumption of equal variances between groups was largely met. However, normality was not met for some variables based on the Shapiro–Wilk test. Despite this, ANOVA remains a robust method against violations of normality [50]. Additionally, some measures violated the assumption of sphericity, as indicated by Mauchly’s test. To account for this, the Greenhouse–Geisser correction was applied to adjust the degrees of freedom, ensuring a more accurate estimation of significance levels in the presence of correlated repeated measures. This approach is widely recommended when sphericity is not met, as it provides a more conservative and reliable interpretation of within-subject effects.

3. Results

Of 91 individuals contacted, 44 met the eligibility criteria. However, HRV data were collected from only 18 participants who were available to engage in the intervention and complete the HRV assessments. The mean age of the participants was 47.74 years (SD = 8.65 years), with 42.1% women and 57.9% men. Fisher’s exact test, the chi-square test of homogeneity, and the independent-samples t-test showed that there were no significant differences between the groups at baseline in any of the demographic and clinical measures (Table 1).
Mean and standard deviations for all time-and-frequency domain HRV measurements can be reviewed in Table 2. Independent sample t-tests and Mann–Whitney U tests did not reveal any significant group differences at baseline for any of the HRV measurements (p > 0.05).

3.1. Time-Domain HRV Measures

Mixed ANOVA analyses did not show any statistically significant interactions between group and assessment time in any of the measures (p > 0.05). However, there were main effects of time in SDSD (F(2, 32) = 3.674, p = 0.037, partial η2 = 0.187), SDNN (F(2, 32) = 4.237, p = 0.023, partial η2 = 0.209), RMSSD (F(2, 32) = 4.303, p = 0.022, partial η2 = 0.212), and a marginally important effect of time in pNN50 (F(2, 32) = 3.019, p = 0.063, partial η2 = 0.159). Pairwise comparison results, with mean differences (MD), showed that, regardless of the group, there were significant reductions, measurable in milliseconds (ms), from the baseline to the mid-term intervention in SDSD (MD = 0.059 ms, p = 0.033), SDNN (MD = 0.066 ms, p = 0.037), and RMSSD (MD = 0.079 ms, p = 0.044). No other statistically significant differences were found between the mid-term and the end of the intervention assessments, or between the baseline and the end of the intervention assessments for those measures. In pNN50, pairwise comparison results did not reveal any statistically significant differences between time conditions (p > 0.05). Overall, these tendencies (i.e., a significant reduction in scores between the baseline and mid-term) were particularly marked among participants who underwent the extended multimodal training, while participants who did the reduced multimodal training showed a more balanced tendency over the different assessment periods.

3.2. Frequency-Domain HRV Measurements

Mixed ANOVA analyses did not show any statistically significant interactions between group and assessment time (p > 0.05), or for the main effect of time (p > 0.05), in any of the measurements, including the LF, HF, VLF, and HF/LF ratio measurements.

4. Discussion

The primary aim of this study was to investigate the impact of a multimodal IVR intervention, which integrated cognitive training, physical exercise, and mindfulness practices, on HRV parameters in adults with PCC. Our main hypothesis was that participants undergoing the multimodal IVR intervention would exhibit significant improvements in both time- and frequency-domain HRV measures throughout the intervention period. More specifically, we anticipated a progressive increase in HRV from the baseline, mid-term, and end of the intervention, demonstrating a trend toward recovery and enhanced ANS function. We also sought to determine whether an extended intervention duration (24 sessions) would provide greater improvements in HRV than a shorter one (16 sessions).
Contrary to our primary hypothesis, the study revealed an unexpectedly significant reduction in the time-domain HRV parameters from the baseline to the mid-term intervention. Several factors may account for this unexpected result. Firstly, the observed reduction in HRV could have reflected an overload response during the early phases of the intervention: a phenomenon that has been observed in other studies [51,52]. For instance, reductions in HRV have been previously observed during the initial weeks of training and/or in the early stages of a rehabilitation program, followed by recovery when the training load was reduced. The cognitive demands imposed by the intervention probably also influenced this outcome. Cognitive training tasks, and particularly those requiring executive control, have been shown to significantly reduce HRV in comparison with tasks that do not require such high levels of executive performance [53,54]. In our study, the cognitive training component was designed to engage participants in certain executive function tasks that demanded considerable mental effort and concentration. This may have led to a temporary reduction in HRV, particularly in the early sessions. Participants also engaged in physical exercise and mindfulness practices, both of which placed additional demands on cognitive and physical resources.
Finally, mental fatigue, which is often associated with prolonged cognitive overload, is another factor that could have influenced HRV in the early stages of the intervention. The relationship between mental fatigue and HRV is complex and not entirely understood: reported study findings have been mixed. Some studies have suggested that mental fatigue may produce an increase in HRV [55,56], while others found that prolonged dedication to cognitive tasks resulted in reductions in HRV [57,58]. This inconsistency reflects the complex nature of mental fatigue and its impact on autonomic function. In addition, participants’ lack of familiarity with the IVR technology used in the intervention could have triggered certain stress responses in the early sessions. The novelty of the IVR system, combined with the immersive nature of the intervention, may have caused some participants to experience heightened stress, which is known to be associated with reduced HRV [59]. According to the generalized unsafety theory [60], when an individual encounters a threat or stressor—such as an unfamiliar environment or task—the body initiates a default stress response. This induces physiological changes, such as an increase in heart rate and/or a reduction in HRV. From the technological side, exposure to the novel, multisensory stimuli inherent in IVR systems typically elevates attentional demands, promoting increased vigilance and autonomic arousal, which can transiently suppress HRV. Over time, as participants became more familiar with the IVR system and their perceptions of threat diminished, it is likely that their HRV levels began to recover, reflecting a reduction in their stress response.
A secondary aim of this study was to determine whether increasing the number of multimodal IVR sessions (from 16 to 24 sessions) would produce greater improvements in HRV. Our findings showed no statistically significant differences in HRV outcomes between the reduced and extended session groups; this suggested that simply increasing the number of sessions did not offer any additional HRV benefits. It is possible that factors such as the intensity and content of the sessions could play a greater role in influencing HRV than the total number of sessions. In line with this hypothesis, previous research has demonstrated that intensity of exercise has a more pronounced effect on HRV than session duration or frequency [61]. For example, a previous study reported that HRV decreased with the intensity of exercise, with the resulting reductions in HRV being more substantial than those solely associated with the number of sessions [62].
While no significant improvements in HRV metrics were observed during the intervention, this study offers a novel approach for assessing HRV in a group of patients with PCC following an IVR rehabilitation program. A key innovation was the use of the Electrocardiogram Weight Scale [47,48] for HRV measurement, which meets important healthcare criteria. The device is wearable, comfortable, and non-intrusive, making it ideal for patient compliance in rehabilitation, and more specifically for PCC. It is adaptable, with various settings, portable, and can be easily attached to a wheelchair, providing flexibility for different patient needs. Additionally, it is cost-effective, which enhances the potential for its widespread use. The wireless communication feature streamlines data collection and improves usability by eliminating the need for cables, thereby allowing real-time monitoring while ensuring patient mobility and comfort during sessions. One of the objectives of this study was to minimize the preparation and measurement time required to obtain HRV data. Reducing these times could mitigate the impact of waiting periods between measurement and the start or end of rehabilitation, thereby providing more accurate estimates related to the rehabilitation process. Additionally, shorter measurement times would facilitate integration into existing medical routines. While many portable ECG-based solutions offer this capability, the use of the scale presents two key advantages. Firstly, users are already accustomed to minimizing movement on the scale due to routine weight measurements. As a result, this platform has demonstrated a reduced influence of motion artifacts, even without direct medical supervision. Secondly, beyond ECG signal acquisition, the platform can simultaneously capture additional signals, such as impedance plethysmography, which provides insights into respiratory rhythm. Although this aspect was not the primary focus of the study, it holds significant potential for future research. Numerous studies have associated PCC with cardiovascular deterioration, suggesting that this platform could be valuable for monitoring patients outside of a hospital setting.
Secondly, this study presents a number of multimodal IVR interventions, which may combine cognitive, physical, and emotional elements. It has also been shown to offer a cost-effective and efficient way to enhance traditional rehabilitation approaches for individuals with PCC [40,41,42,63,64]. IVR systems offer immersive environments that engage patients in ways that traditional rehabilitation techniques may not, making them a promising tool for improving the cognitive and psychological well-being of vulnerable populations [65]. However, despite the potential benefits of IVR, current systems often rely on individual headsets, which may be unsuitable or inaccessible for certain populations due to their technological, physical, and/or cognitive limitations [66]. Our study addresses this challenge by employing a novel IVR setup: a CAVE-based MK360 IVR system, which eliminates the need for individual headsets and creates an immersive, room-scaled, multisensory environment. This system is designed to be more inclusive and user-friendly, particularly for patients with physical, cognitive, and/or emotional constraints [67]. By removing the barrier often presented by individual headsets, the CAVE-based system allows for broader accessibility and more effective engagement with the intervention.
In addition, delivering the intervention in a group-based format offers significant advantages over individual IVR sessions. Group-based IVR interventions foster social interaction, enhance motivation, and encourage constructive feedback among participants, all of which are key elements in promoting successful rehabilitation outcomes [68]. Engaging in shared virtual experiences allows participants to support one another, exchange insights, and collectively navigate the challenges of the rehabilitation process. Moreover, the presence of peers in a virtual group setting can serve as a strong motivational driver, reinforcing accountability and adherence to the intervention. Witnessing the progress of fellow participants can inspire individuals to stay engaged and committed. Lastly, and from a practical standpoint, group-based IVR interventions also offer significant advantages in healthcare settings with high patient volumes, such as those managing PCC rehabilitation. The scalability of group-based interventions presents a potentially efficient way to deliver advanced therapeutic technologies in a cost-effective and resource-efficient manner [69].
In summary, by integrating the benefits of social interaction, enhanced motivation, improved performance, and increased scalability, group-based IVR interventions represent an innovative and resource-efficient approach to rehabilitation.
Despite the promising results presented, several limitations of this study should be acknowledged. First, the small sample size of 18 participants, with only nine individuals in each group, limited the statistical power of our findings and restricted the ability to generalize the results obtained. Larger-scale studies are needed to confirm the findings presented here and to explore whether similar outcomes could be achieved in a more diverse population. Additionally, the absence of a control group that did not receive the intervention, or which received standard care, made it difficult to isolate the impact of the multimodal IVR intervention on HRV. Similarly, the multimodal nature of the intervention, which combined cognitive training, physical exercise, and mindfulness, made it challenging to discern which specific components contributed to the changes in HRV that were observed. Future studies, with larger samples, may address this challenge by using a factorial design to isolate the effects of cognitive training, physical exercise, and mindfulness on HRV. For instance, separate experimental groups could help determine individual and combined contributions of the IRV multimodal training.
Furthermore, HRV measurements were taken at only three points in time: after the first session, at mid-intervention, and after the final session. More frequent HRV assessments, conducted throughout the intervention, would provide a clearer picture of how HRV changes over time and how participants respond at different stages of the intervention. Finally, the lack of follow-up HRV measurements after concluding the intervention left it unclear as to whether the observed changes in HRV were sustained over time. Future studies should take a longitudinal approach, which could provide insights into the sustained HRV changes of the intervention.
In conclusion, this study provides additional insights about how controlling HRV parameters within IVR interventions offers promising tools for monitoring both the physiological and psychological recovery of PCC patients. Our primary hypothesis of observing significant improvements in HRV over the course of the intervention was not confirmed, as we observed reductions in HRV during the early stages of the intervention. These reductions may have been attributable to overload training, high cognitive demands, and/or mental fatigue, as well as to stress during the first stage of the intervention. Our secondary hypothesis, that extending the number of sessions would lead to greater improvements in HRV, was similarly not supported, as no significant differences were found between the reduced and extended session groups. Our findings highlight the complex interplay of factors that influence HRV and underscore the need for further research to optimize multimodal interventions for enhancing ANS function and recovery in PCC patients.

Author Contributions

Conceptualization, M.A., B.P.-G. and M.G.; methodology, B.P.-G.; software, O.C.; validation, N.C., M.A., O.G. and Y.P.; formal analysis, O.C. and B.P.-G.; investigation, N.C., O.C., M.A., Y.P. and M.G.; resources, O.C. and M.G.; data curation, N.C., O.C., and B.P.-G.; writing—original draft preparation, N.C. and B.P.-G.; writing—review and editing, O.C., B.P.-G. and M.G.; visualization, B.P.-G.; supervision, B.P.-G. and M.G.; project administration, N.C.; funding acquisition, M.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Agencia Estatal de Investigación (AEI) through the Spanish Ministry of Industry, Commerce and Tourism (Ministerio de Industria, Comercio y Turismo) under grant AEI-010500-2021b-196, and the Spanish Ministry of Science, Innovation and Universities (Ministerio de Ciencia, Innovación y Universidades) under grants TED2021-130409B-C5, JDC2022-048939-I (MCIU/AEI/10.13039/501100011033; European Union “NextGenerationEU”/PRTR) and PID2020-116011RB-C21 (MCIN/AEI/10.13039/501100011033).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the Consorci Sanitari de Terrassa (02-22-107-029; approval date: 7 March 2022 and 01-21-107-111; approval date: 28 February 2022).

Informed Consent Statement

Informed consent was obtained from all the subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

We extend our heartfelt gratitude to Ignasi Capella Ballbé, Chief Officer of Marketing & Business Development at Broomx, for his invaluable technical support and guidance throughout our clinical study. His expertise and dedication, together with the outstanding contributions of the Broomx team, played a key role in addressing complex technical challenges and meticulously shaping the study environment. Their unwavering commitment and collaboration were instrumental in ensuring the seamless execution and ultimate success of our research. We are grateful to Josep Gómez Hernández and Silvia Morón González for their valuable contributions to this work. We extend our sincere appreciation to the participants, whose generous participation made this study possible.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Koczulla, A.R.; Ankermann, T.; Behrends, U.; Berlit, P.; Böing, S.; Brinkmann, F.; Franke, C.; Glöckl, R.; Gogoll, C.; Hummel, T.; et al. S1-leitlinie post-COVID/long-COVID [S1 guideline post-COVID/long-COVID]. Pneumologie 2021, 75, 869–900. [Google Scholar] [CrossRef] [PubMed]
  2. Oronsky, B.; Larson, C.; Hammond, T.C.; Oronsky, A.; Kesari, S.; Lybeck, M.; Reid, T.R. A Review of Persistent Post-COVID Syndrome (PPCS). Clin. Rev. Allergy Immunol. 2023, 64, 66–74. [Google Scholar] [CrossRef] [PubMed]
  3. Nalbandian, A.; Sehgal, K.; Gupta, A.; Madhavan, M.V.; McGroder, C.; Stevens, J.S.; Cook, J.R.; Nordvig, A.S.; Shalev, D.; Sehrawat, T.S.; et al. Post-acute COVID-19 syndrome. Nat. Med. 2021, 27, 601–615. [Google Scholar] [CrossRef] [PubMed]
  4. Rabady, S.; Altenberger, J.; Brose, M.; Denk-Linnert, D.M.; Fertl, E.; Götzinger, F.; de la Cruz Gomez Pellin, M.; Hofbaur, B.; Hoffmann, K.; Hoffmann-Dorninger, R.; et al. Leitlinie S1: Long COVID: Differenzialdiagnostik und Behandlungsstrategien [Guideline S1: Long COVID: Diagnostics and treatment strategies]. Wien. Klin. Wochenschr. 2021, 133, 237–278. [Google Scholar] [CrossRef]
  5. Soriano, J.B.; Murthy, S.; Marshall, J.C.; Relan, P.; Diaz, J.V. WHO Clinical Case Definition Working Group on Post-COVID-19 Condition. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infect. Dis. 2022, 22, e102–e107. [Google Scholar] [CrossRef]
  6. Antonelli, M.; Pujol, J.C.; Spector, T.D.; Ourselin, S.; Steves, C.J. Risk of long COVID associated with delta versus omicron variants of SARS-CoV-2. Lancet 2022, 399, 2263–2264. [Google Scholar] [CrossRef]
  7. Sudre, C.H.; Murray, B.; Varsavsky, T.; Graham, M.S.; Penfold, R.S.; Bowyer, R.C.; Capdevila, P.J.; Klaser, K.; Antonelli, M.; Canas, L.S.; et al. Attributes and predictors of long COVID. Nat. Med. 2021, 27, 626–631. [Google Scholar] [CrossRef]
  8. Mehta, P.; McAuley, D.F.; Brown, M.; Sanchez, E.; Tattersall, R.S.; Manson, J.J. COVID-19: Consider cytokine storm syndrome and immunosuppression. Lancet 2020, 395, 1033–1034. [Google Scholar] [CrossRef]
  9. Shaffer, F.; Ginsberg, J.P. An overview of heart rate variability metrics and norms. Front. Public Health 2017, 5, 258. [Google Scholar] [CrossRef]
  10. Dani, M.; Dirksen, A.; Taraborrelli, P.; Torocastro, M.; Panagopoulos, D.; Sutton, R.; Lim, P.B. Autonomic dysfunction in “long COVID”: Rationale, physiology and management strategies. Clin. Med. 2021, 21, e63–e67. [Google Scholar] [CrossRef]
  11. Al-Kuraishy, H.M.; Al-Gareeb, A.I.; Qusti, S.; Alshammari, E.M.; Atanu, F.O.; Batiha, G.E. COVID-19-induced dysautonomia: A menace of sympathetic storm. ASN Neuro. 2021, 13, 17590914211057635. [Google Scholar] [CrossRef] [PubMed]
  12. Jamal, S.M.; Landers, D.B.; Hollenberg, S.M.; Turi, Z.G.; Glotzer, T.V.; Tancredi, J.; Parrillo, J.E. Prospective evaluation of autonomic dysfunction in post-acute sequela of COVID-19. J. Am. Coll. Cardiol. 2022, 79, 2325–2330. [Google Scholar] [CrossRef] [PubMed]
  13. Scala, I.; Rizzo, P.A.; Bellavia, S.; Brunetti, V.; Colò, F.; Broccolini, A.; Della Marca, G.; Calabresi, P.; Luigetti, M.; Frisullo, G. Autonomic dysfunction during acute SARS-CoV-2 infection: A systematic review. J. Clin. Med. 2022, 11, 3883. [Google Scholar] [CrossRef]
  14. Xie, J.; Prats-Uribe, A.; Feng, Q.; Wang, Y.; Gill, D.; Paredes, R.; Prieto-Alhambra, D. Clinical and genetic risk factors for acute incident venous thromboembolism in ambulatory patients with COVID-19. JAMA Intern. Med. 2022, 182, 1063–1070. [Google Scholar] [CrossRef]
  15. Meinhardt, J.; Radke, J.; Dittmayer, C.; Franz, J.; Thomas, C.; Mothes, R.; Laue, M.; Schneider, J.; Brünink, S.; Greuel, S.; et al. Olfactory transmucosal SARS-CoV-2 invasion as a port of central nervous system entry in individuals with COVID-19. Nat. Neurosci. 2021, 24, 168–175. [Google Scholar] [CrossRef]
  16. Raman, B.; Bluemke, D.A.; Lüscher, T.F.; Neubauer, S. Long COVID: Post-acute sequelae of COVID-19 with a cardiovascular focus. Eur. Heart J. 2022, 43, 1157–1172. [Google Scholar] [CrossRef]
  17. Asarcikli, L.D.; Hayiroglu, M.I.; Osken, A.; Keskin, K.; Bakan, N.; Kocayigit, I. Heart rate variability and cardiac autonomic functions in post-COVID period. J. Interv. Card. Electrophysiol. 2022, 63, 715–721. [Google Scholar] [CrossRef]
  18. Barizien, N.; le Guen, M.; Russel, S.; Touche, P.; Huang, F.; Vallée, A. Clinical characterization of dysautonomia in long COVID-19 patients. Sci. Rep. 2021, 11, 14042. [Google Scholar] [CrossRef]
  19. Hasty, F.; García, G.; Dávila, H.; Wittels, S.H.; Hendricks, S.; Chong, S. Heart rate variability as a possible predictive marker for acute inflammatory response in COVID-19 patients. Mil. Med. 2021, 186, e34–e38. [Google Scholar] [CrossRef]
  20. Kaliyaperumal, D.; Rk, K.; Alagesan, M.; Ramalingam, S. Characterization of cardiac autonomic function in COVID-19 using heart rate variability: A hospital based preliminary observational study. J. Basic Clin. Physiol. Pharmacol. 2021, 32, 247–253. [Google Scholar] [CrossRef]
  21. Mol, M.B.A.; Strous, M.T.A.; van Osch, F.H.M.; Vogelaar, F.J.; Barten, D.G.; Farchi, M.; Foudraine, N.A.; Gidron, Y. Heart-rate-variability (HRV), predicts outcomes in COVID-19. PLoS ONE 2021, 16, e0258841. [Google Scholar] [CrossRef] [PubMed]
  22. Soliński, M.; Mamcarz, A.; Mamcarz, M.; Głowińska, A.; Kaliszewski, P. Heart rate variability comparison between young males after 4–6 weeks from the end of SARS-CoV-2 infection and controls. Sci. Rep. 2022, 12, 8832. [Google Scholar] [CrossRef] [PubMed]
  23. Henje, B.E.; Olsson, E.M.; Serlachius, E.; Ericson, M.; Ingvar, M. Heart rate variability is related to self-reported physical activity in a healthy adolescent population. Eur. J. Appl. Physiol. 2009, 106, 877–883. [Google Scholar] [CrossRef]
  24. Kluttig, A.; Schumann, B.; Swenne, C.A.; Kors, J.A.; Kuss, O.; Schmidt, H.; Werdan, K.; Haerting, J.; Greiser, K. Association of health behaviour with heart rate variability: A population-based study. BMC Cardiovasc. Disord. 2010, 10, 58. [Google Scholar] [CrossRef]
  25. Manser, P.; Thalmann, M.; Adcock, M.; Knols, R.H.; De Bruin, E.D. Can reactivity of heart rate variability be a potential biomarker and monitoring tool to promote healthy aging? A systematic review with meta-analyses. Front. Physiol. 2021, 12, 686129. [Google Scholar] [CrossRef]
  26. Guiraud, T.; Labrunee, M.; Gaucher-Cazalis, K.; Despas, F.; Meyer, P.; Bosquet, L.; Gales, C.; Vaccaro, A.; Bousquet, M.; Galinier, M.; et al. High-intensity interval exercise improves vagal tone and decreases arrhythmias in chronic heart failure. Med. Sci. Sports Exerc. 2013, 45, 1861–1867. [Google Scholar] [CrossRef]
  27. Ramírez-Vélez, R.; Tordecilla-Sanders, A.; Téllez-T, L.A.; Camelo-Prieto, D.; Hernández-Quiñonez, P.A.; Correa-Bautista, J.E.; Garcia-Hermoso, A.; Ramírez-Campillo, R.; Izquierdo, M. Effect of moderate- versus high-intensity interval exercise training on heart rate variability parameters in inactive Latin-American adults: A randomized clinical trial. J. Strength Cond. Res. 2020, 34, 3403–3415. [Google Scholar] [CrossRef]
  28. Del Valle, M.F.; Valenzuela, J.; Marzuca-Nassr, G.N.; Cabrera-Inostroza, C.; Del Sol, M.; Lizana, P.A.; Escobar-Cabello, M.; Muñoz-Cofre, R. Eight weeks of supervised pulmonary rehabilitation are effective in improving resting heart rate and heart rate recovery in severe COVID-19 patient survivors of mechanical ventilation. Medicina 2022, 58, 514. [Google Scholar] [CrossRef]
  29. Allen, T.D.; Henderson, T.G.; Mancini, V.S.; French, K.A. Mindfulness and meditation practice as moderators of the relationship between age and subjective wellbeing among working adults. Mindfulness 2017, 8, 1055–1063. [Google Scholar] [CrossRef]
  30. Brown, K.W.; Weinstein, N.; Creswell, D.J. Trait mindfulness modulates neuroendocrine and affective responses to social evaluative threat. Psychoneuroendocrinology 2012, 37, 2037–2041. [Google Scholar] [CrossRef]
  31. Rådmark, L.; Sidorchuk, A.; Osika, W.; Niemi, M. A systematic review and meta-analysis of the impact of mindfulness-based interventions on heart rate variability and inflammatory markers. J. Clin. Med. 2019, 8, 1638. [Google Scholar] [CrossRef] [PubMed]
  32. Morgan, N.; Irwin, M.R.; Chung, M.; Wang, C. The effects of mind-body therapies on the immune system: Meta-analysis. PLoS ONE 2014, 9, e100903. [Google Scholar] [CrossRef] [PubMed]
  33. Zou, L.; Sasaki, J.E.; Wei, G.X.; Huang, T.; Yeung, A.S.; Neto, O.B.; Chen, K.W.; Hui, S.S.-C. Effects of mind-body exercises (Tai Chi/Yoga) on heart rate variability parameters and perceived stress: A systematic review with meta-analysis of randomized controlled trials. J. Clin. Med. 2018, 7, 404. [Google Scholar] [CrossRef] [PubMed]
  34. Thayer, J.F.; Hansen, A.L.; Saus-Rose, E.; Johnsen, B.H. Heart rate variability, prefrontal neural function, and cognitive performance: The neurovisceral integration perspective on self-regulation, adaptation, and health. Ann. Behav. Med. 2009, 37, 141–153. [Google Scholar] [CrossRef]
  35. Thayer, J.F.; Ahs, F.; Fredrikson, M.; Sollers, J.J., 3rd; Wager, T.D. A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neurosci. Biobehav. Rev. 2012, 36, 747–756. [Google Scholar] [CrossRef]
  36. Yen, H.Y.; Chiu, H.L. Virtual reality exergames for improving older adults' cognition and depression: A systematic review and meta-analysis of randomized controlled trials. J. Am. Med. Dir. Assoc. 2021, 22, 995–1002. [Google Scholar] [CrossRef]
  37. Li, J.; Theng, Y.L.; Foo, S. Effect of exergames on depression: A systematic review and meta-analysis. Cyberpsychol. Behav. Soc. Netw. 2016, 19, 34–42. [Google Scholar] [CrossRef]
  38. Riva, G.; Mancuso, V.; Cavedoni, S.; Stramba-Badiale, C. Virtual reality in neurorehabilitation: A review of its effects on multiple cognitive domains. Expert. Rev. Med. Devices. 2020, 17, 1035–1061. [Google Scholar] [CrossRef]
  39. Matamala-Gomez, M.; Stasolla, F.; Seinfeld, S.; Caffò, A.O.; Banakou, D.; Bottiroli, S. Neuropsychological and cognitive-behavioral assessment of neurodegenerative disease and rehabilitation using new technologies and virtual reality. Front. Psychol. 2021, 12, 691909. [Google Scholar] [CrossRef]
  40. Ostrowska, P.M.; Śliwiński, M.; Studnicki, R.; Hansdorfer-Korzon, R. Telerehabilitation of post-stroke patients as a therapeutic solution in the era of the COVID-19 pandemic. Healthcare 2021, 9, 654. [Google Scholar] [CrossRef]
  41. Groenveld, T.; Achttien, R.; Smits, M.; de Vries, M.; van Heerde, R.; Staal, B.; van Goor, H.; COVID Rehab Group. Feasibility of virtual reality exercises at home for post–COVID-19 condition: Cohort study. JMIR Rehabil. Assist. Technol. 2022, 9, e36836. [Google Scholar] [CrossRef] [PubMed]
  42. Ahmadi Marzaleh, M.; Peyravi, M.; Azhdari, N.; Bahaadinbeigy, K.; Sharifian, R.; Samad-Soltani, T.; Sarpourian, F. Virtual reality applications for rehabilitation of COVID-19 patients: A systematic review. Health Sci. Rep. 2022, 5, e853. [Google Scholar] [CrossRef] [PubMed]
  43. García-Bravo, S.; Cuesta-Gómez, A.; Campuzano-Ruiz, R.; López-Navas, M.J.; Domínguez-Paniagua, J.; Araújo-Narváez, A.; Barreñada-Copete, E.; García-Bravo, C.; Flórez-García, M.T.; Botas-Rodríguez, J.; et al. Virtual reality and video games in cardiac rehabilitation programs: A systematic review. Disabil Rehabil. 2021, 43, 448–457. [Google Scholar] [CrossRef]
  44. Diez-Quevedo, C.; Rangil, T.; Sanchez-Planell, L.; Kroenke, K.; Spitzer, R.L. Validation and utility of the patient health questionnaire in diagnosing mental disorders in 1003 general hospital Spanish inpatients. Psychosom. Med. 2001, 63, 679–686. [Google Scholar] [CrossRef]
  45. García-Campayo, J.; Zamorano, E.; Ruiz, M.A.; Pardo, A.; Pérez-Páramo, M.; López-Gómez, V.; Freire, O.; Rejas, J. Cultural adaptation into Spanish of the generalized anxiety disorder-7 (GAD-7) scale as a screening tool. Health Qual. Life Outcomes 2010, 8, 8. [Google Scholar] [CrossRef]
  46. Couderc, J.-P.; Beshaw, C.; Niu, X.; Serrano-Finetti, E.; Casas, O.; Pallas-Areny, R.; Rosero, S.; Zareba, W. The QT scale: A weight scale measuring the QTc interval. Ann. Noninvasive Electrocardiol. 2017, 22, 12378. [Google Scholar] [CrossRef]
  47. Hernández-Urrea, M.; Casanella, R.; Javierre, C.; Casas, O. An easy-to-use hand-to-hand impedance-based sensor to obtain carotid pulse arrival time. IEEE Sens. J. 2023, 23, 5362–5369. [Google Scholar] [CrossRef]
  48. Pan, J.; Tompkins, W.J. A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 1985, 32, 230–236. [Google Scholar] [CrossRef]
  49. Kabat-Zinn, J. The Effect of Mindfulness-Based Stress Reduction, Mindfulness and Stress Level. In Holland, J.C., Ed.; Oxford University Press: New York, NY, USA, 1998; pp. 767–779. [Google Scholar]
  50. Schmider, E.; Ziegler, M.; Danay, E.; Beyer, L.; Bühner, M. Is it really robust? Reinvestigating the robustness of ANOVA against violations of the normal distribution assumption. Methodology 2010, 6, 147–151. [Google Scholar] [CrossRef]
  51. Stanley, J.; Peake, J.M.; Buchheit, M. Cardiac parasympathetic reactivation following exercise: Implications for training prescription. Sports Med. 2013, 43, 1259–1277. [Google Scholar] [CrossRef]
  52. Pichot, V.; Roche, F.; Gaspoz, J.M.; Enjolras, F.; Antoniadis, A.; Minini, P.; Costes, F.; Busso, T.; Lacour, J.R.; Barthélémy, J.C. Relation between heart rate variability and training load in middle-distance runners. Med. Sci. Sports Exerc. 2000, 32, 1729–1736. [Google Scholar] [CrossRef] [PubMed]
  53. Luft, C.D.B.; Takase, E.; Darby, D. Heart rate variability and cognitive function: Effects of physical effort. Biol. Psychol. 2009, 82, 164–168. [Google Scholar] [CrossRef] [PubMed]
  54. Botvinick, M.M.; Braver, T.S.; Barch, D.M.; Carter, C.S.; Cohen, J.D. Conflict monitoring and cognitive control. Psychol. Rev. 2001, 108, 624–652. [Google Scholar] [CrossRef]
  55. Matuz, A.; van der Linden, D.; Kisander, Z.; Hernádi, I.; Karádi, K.; Csathó, Á. Enhanced cardiac vagal tone in mental fatigue: Analysis of heart rate variability in Time-on-Task, recovery, and reactivity. PLoS ONE 2021, 16, e0238670. [Google Scholar] [CrossRef]
  56. Csathó, Á.; van der Linden, D.; Matuz, A. Change in heart rate variability with increasing time-on-task as a marker for mental fatigue: A systematic review. Biol. Psychol. 2024, 185, 108727. [Google Scholar] [CrossRef]
  57. Delliaux, S.; Delaforge, A.; Deharo, J.C.; Chaumet, G. Mental workload alters heart rate variability, lowering non-linear dynamics. Front. Physiol. 2019, 10, 565. [Google Scholar] [CrossRef]
  58. Melo, H.M.; Nascimento, L.M.; Takase, E. Mental fatigue and heart rate variability (HRV): The time-on-task effect. Psychol. Neurosci. 2017, 10, 428–436. [Google Scholar] [CrossRef]
  59. Kim, J.; Foo, J.C.; Murata, T.; Togo, F. Reduced heart rate variability is related to fluctuations in psychological stress levels in daily life. Stress Health 2024, 40, e3447. [Google Scholar] [CrossRef]
  60. Brosschot, J.F.; Verkuil, B.; Thayer, J.F. Exposed to events that never happen: Generalized unsafety, the default stress response, and prolonged autonomic activity. Neurosci. Biobehav. Rev. 2017, 74, 287–296. [Google Scholar] [CrossRef]
  61. Michael, S.; Graham, K.S.; Davis, G.M. Cardiac autonomic responses during exercise and post-exercise recovery using heart rate variability and systolic time intervals: A review. Front. Physiol. 2017, 8, 301. [Google Scholar] [CrossRef]
  62. Brockmann, L.; Hunt, K.J. Heart rate variability changes with respect to time and exercise intensity during heart-rate-controlled steady-state treadmill running. Sci. Rep. 2023, 13, 8515. [Google Scholar] [CrossRef] [PubMed]
  63. Rutkowski, S.; Bogacz, K.; Rutkowska, A.; Szczegielniak, J.; Casaburi, R. Inpatient post-COVID-19 rehabilitation program featuring virtual reality: Preliminary results of randomized controlled trial. Front. Public Health 2023, 11, 1121554. [Google Scholar] [CrossRef]
  64. Vibhuti; Kumar, N.; Kataria, C. Efficacy assessment of virtual reality therapy for neuromotor rehabilitation in home environment: A systematic review. Disabil. Rehabil. Assist. Technol. 2023, 18, 1200–1220. [Google Scholar] [CrossRef]
  65. Porras-Garcia, B.; Rojas-Rincón, J.; Adams, A.; Garolera, M.; Chang, R. Immersive virtual reality cognitive training for improving cognition and depressive symptoms among older adults. Cyberpsychol. Behav. Soc. Netw. 2024, 27, 692–703. [Google Scholar] [CrossRef]
  66. Czaja, S.J. Long-term care services and support systems for older adults: The role of technology. Am. Psychol. 2016, 71, 294–301. [Google Scholar] [CrossRef]
  67. Seifert, A.; Schlomannm, A. The use of virtual and augmented reality by older adults: Potentials and challenges. Front. Virtual Real. 2021, 2, 639718. [Google Scholar] [CrossRef]
  68. Borek, A.J.; Abraham, C. How do small groups promote behaviour change? An integrative conceptual review of explanatory mechanisms. Appl. Psychol. Health Well Being 2018, 10, 30–61. [Google Scholar] [CrossRef]
  69. Lattie, E.G.; Stiles-Shields, C.; Graham, A.K. An overview of and recommendations for more accessible digital mental health services. Nat. Rev. Psychol. 2022, 1, 87–100. [Google Scholar] [CrossRef]
Table 1. Descriptive results, including the demographic and clinical characteristics of both groups.
Table 1. Descriptive results, including the demographic and clinical characteristics of both groups.
VariableGroup 1 (n = 9)Group 2 (n = 9)t-Testp
M (SD)M (SD)
Age (years)48.20 (7.28)47.22 (10.40)2.390.814
Education (years)12.40 (2.46)14.33 (3.54)−1.3960.192
Body mass index (kg/m2)25.92 (3.45)26.25 (6.13)−0.1420.889
IPAQ—physical exercise (METS)887.15 (691.09)1791.87 (1109.32)−2.1230.069
GAD-7—anxiety symptoms 9.10 (5.63)11.75 (5.65)−0.9910.336
PHQ-9—depressive symptoms14.50 (5.21)15.38 (5.53)−0.3450.735
MoCA-Global cognition 25.80 (3.19)25.73 (2.15)0.0630.951
Note: Group 1 (reduced multimodal training), group 2 (extended multimodal training). Mean (M) and standard deviation (SD). International Physical Activity Questionnaire (IPAQ). METS = Metabolic equivalent, Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9) and Montreal Cognitive Assessment (MoCA).
Table 2. Mean and standard deviation of time and frequency-domain HRV measurements in the study.
Table 2. Mean and standard deviation of time and frequency-domain HRV measurements in the study.
HRV MeasuresGroup 1 (Reduced Multimodal Training)Group 2 (Extended Multimodal Training)
Baseline
M (SD)		Mid-Term (8th Session)
M (SD)		End of the Intervention
M (SD)		Baseline
M (SD)		Mid-Term (12th Session)
M (SD)		End of the Intervention
M (SD)
SDSD0.103 (0.213)0.094 (0.206)0.100 (0.179)0.137 (0.125)0.028 (0.028)0.054 (0.076)
SDNN0.075 (0.109)0.032(0.029)0.055 (0.070)0.132 (0.088)0.041 (0.024)0.059 (0.062)
RMSSD0.074 (0.130)0.027 (0.025)0.043 (0.078)0.137 (0.125)0.028 (0.028)0.054 (0.076)
pNN500.058 (0.097)0.027 (0.047)0.063 (0.133)0.166 (0.153)0.035 (0.039)0.114 (0.173)
VLF1.28 (1.54)1.15 (1.14)1.00 (0.999)0.343 (0.321)0.955 (0.646)0.432 (0.274)
LF0.974 (0.635)1.00 (0.701)1.21 (0.797)1.36 (0.942)1.27 (0.696)1.70 (0.985)
HF0.573 (0.537)0.751 (0.643)0.627 (0.323)0.698 (0.610)0.456 (0.463)0.604 (0.553)
LF/HF ratio5.56 (5.45)4.53 (6.83)7.15 (11.88)6.04 (9.71)6.04 (6.91)5.615 (5.96)
Note: Standard deviation of successive differences (SDSD); the standard deviation of NN intervals (SDNN); root mean square of successive differences (RMSSD); percentile of NN intervals (pNN50); very low-frequency (VLF); low-frequency (LF); high-frequency (HF); ratio of low-frequency power to high-frequency power (LF/HF ratio).
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Cano, N.; Casas, O.; Ariza, M.; Gelonch, O.; Plana, Y.; Porras-Garcia, B.; Garolera, M. Effects of a Multimodal Immersive Virtual Reality Intervention on Heart Rate Variability in Adults with Post-COVID-19 Syndrome. Appl. Sci. 2025, 15, 4111. https://doi.org/10.3390/app15084111

AMA Style

Cano N, Casas O, Ariza M, Gelonch O, Plana Y, Porras-Garcia B, Garolera M. Effects of a Multimodal Immersive Virtual Reality Intervention on Heart Rate Variability in Adults with Post-COVID-19 Syndrome. Applied Sciences. 2025; 15(8):4111. https://doi.org/10.3390/app15084111

Chicago/Turabian Style

Cano, Neus, Oscar Casas, Mar Ariza, Olga Gelonch, Yemila Plana, Bruno Porras-Garcia, and Maite Garolera. 2025. "Effects of a Multimodal Immersive Virtual Reality Intervention on Heart Rate Variability in Adults with Post-COVID-19 Syndrome" Applied Sciences 15, no. 8: 4111. https://doi.org/10.3390/app15084111

APA Style

Cano, N., Casas, O., Ariza, M., Gelonch, O., Plana, Y., Porras-Garcia, B., & Garolera, M. (2025). Effects of a Multimodal Immersive Virtual Reality Intervention on Heart Rate Variability in Adults with Post-COVID-19 Syndrome. Applied Sciences, 15(8), 4111. https://doi.org/10.3390/app15084111

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