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Mobile health (mHealth) development has advanced rapidly, indicating promise as an effective patient intervention. mHealth has many potential benefits that could help the treatment of patients, and the development of rehabilitation in low- and middle-income countries (LMICs). mHealth is a low-cost option that does not need rapid access to healthcare clinics or employees. It increases the feasibility and rationality of clinical treatment expectations in comparison to the conventional clinical model of management by promoting patient adherence to the treatment plan. mHealth can also serve as a basis for formulating treatment plans and partially compensate for the shortcomings of the traditional model. In addition, mHealth can help achieve universal rehabilitation service coverage by overcoming geographical barriers, thereby increasing the number of ways patients can benefit from the rehabilitation service, and by providing rehabilitation to individuals in remote areas and communities with insufficient healthcare services. However, despite these positive potential aspects, there is currently only a very limited number of studies performed in LMICs using mHealth. In this study, we first reviewed the current evidence supporting the use of mHealth in rehabilitation to identify the countries where studies have been carried out. Then, we identify the current limitations of the implementation of such mHealth solutions and propose a 10-point action plan, focusing on the macro (e.g., policymakers), meso (e.g., technology and healthcare institutions), and micro (e.g., patients and relatives) levels to ease the use, validation, and implementation in LMICs and thus participate in the development and recognition of public health and rehabilitation in these countries.

Changes in lifestyle, finances and work status during COVID-19 lockdownsmay have led to biopsychosocial changes in people with pre-existing vulnerabilities such as MajorDepressive Disorders (MDDs) and Multiple Sclerosis (MS). Methods: Data were collected as a part the RADAR-CNS (Remote Assessment of Disease and Relapse-Central Nervous System) program.We analyzed the following data from long-term participants in a decentralized multinational study:J. Clin. Med. 2022, 11, 7163. https://doi.org/10.3390/jcm11237163 https://www.mdpi.com/journal/jcm J. Clin. Med. 2022, 11, 7163 2 of 20 symptoms of depression, heart rate (HR) during the day and night; social activity; sedentary state,steps and physical activity of varying intensity. Linear mixed-effects regression analyses with repeated measures were fitted to assess the changes among three time periods (pre, during and post-lockdown) across the groups, adjusting for depression severity before the pandemic and gender. Results: Participants with MDDs (N = 255) and MS (N = 214) were included in the analyses. Overall,depressive symptoms remained stable across the three periods in both groups. A lower mean HR and HR variation were observed between pre and during lockdown during the day for MDDs and during the night for MS. HR variation during rest periods also decreased between pre- and post-lockdown in both clinical conditions. We observed a reduction in physical activity for MDDs and MS upon the introduction of lockdowns. The group with MDDs exhibited a net increase in social interaction via social network apps over the three periods. Conclusions: Behavioral responses to the lockdown measured by social activity, physical activity and HR may reflect changes in stress in people with MDDs and MS. Remote technology monitoring might promptly activate an early warning of physical and social alterations in these stressful situations. Future studies must explore how stress does or does not impact depression severity

Background Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored. Objective The aim of this study was to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. Methods We used two ambulatory data sets (N=71 and N=215) with acceleration signals collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effects models were used to explore the associations between daily-life gait features and depression symptom severity measured by the 15-item Geriatric Depression Scale (GDS-15) and 8-item Patient Health Questionnaire (PHQ-8) self-reported questionnaires. The likelihood-ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features. Results Higher depression symptom severity was significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both data sets. The linear regression model with long-term daily-life gait features (R2=0.30) fitted depression scores significantly better (LR test P=.001) than the model with only laboratory gait features (R2=0.06). Conclusions This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings.

COVID-19 has abruptly disrupted healthcare services; however, the continuity of rehabilitation could be guaranteed using mobile technologies. This review aims to analyze the feasibility and effectiveness of telehealth solutions proposed to guarantee the continuity of rehabilitation during the COVID-19 pandemic. The PubMed, Cochrane Library, Web of Science and PEDro databases were searched; the search was limited to randomized controlled trials, observational and explorative studies published up to 31 May 2022, assessing the feasibility and effectiveness of telerehabilitation during the COVID-19 pandemic. Twenty studies were included, for a total of 224,806 subjects: 93.1% with orthopedic complaints and 6.9% with non-orthopedic ones. The main strategies used were video and audio calls via commonly available technologies and free videoconferencing tools. Based on the current evidence, it is suggested that telerehabilitation is a feasible and effective solution, allowing the continuity of rehabilitation while reducing the risk of infection and the burden of travel. However, it is not widely used in clinical settings, and definitive conclusions cannot be currently drawn. Telerehabilitation seems a feasible and safe option to remotely deliver rehabilitation using commonly available mobile technologies, guaranteeing the continuity of care while respecting social distancing. Further research is, however, needed to strengthen and confirm these findings. This research received no external funding. The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph191610325/s1

Purpose To evaluate the effectiveness of mobile health applications (mHealth apps) containing a physical training component on physical function and physical activity in stroke rehabilitation. Materials and methods A systematic literature search was conducted in three databases for studies published from inception to 12 July 2022. Clinical trials including mHealth apps with a physical training component were included using outcomes of physical function and physical activity. Quality of life was extracted as a secondary outcome. Results Five RCTs, two non-RCTs, and four uncontrolled clinical trials were included with a total of 264 stroke survivors. Eleven apps were identified with a physical training component using features of gamification (six apps), exercise prescription (three apps), and physical activity (two apps). Six out of seven studies reported statistically significant improvements in physical function in favor of the experimental group, with the most robust findings for upper extremity function. For physical activity, statistically significant improvements were seen in the experimental groups. Only one study showed significant improvement in quality of life. Overall study quality was fair. Conclusions mHealth apps containing a physical training component are promising for physical function and physical activity in stroke rehabilitation. Further research is warranted to confirm these conclusions. The author(s) reported there is no funding associated with the work featured in this article. No potential conflict of interest was reported by the author(s).

Background The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored. Objective We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. Methods Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse–Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants’ location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility. Results This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=−0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=−0.07, P Conclusions Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings.

Additional file 5. Between-site stratification.

Additional file 1. Schedule of introduction of different RADAR-MDD assessments.

Additional file 4. Reasons for withdrawal.

Challenges exist for the analysis of mobile health data. These include: (i) maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; (ii) distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening participants at high risks; (iii) understanding the heterogeneity with which depression manifests itself in behavioural patterns quantified by the passive features. We extracted 21 features reflecting mobility, sleep, and phone use from 479 participants with MDD. We investigated the impact of the number of days of available data on feature quality using intraclass correlation coefficients and Bland-Altman analysis. We then investigated the nature of the correlation between the 8-item Patient Health Questionnaire depression scale (PHQ-8; measured every 14 days) and the features using the pooled correlation coefficient, repeated measures correlation coefficient, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioural difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. We demonstrated that 8 (range: 2-12) days were needed for reliable calculation of most features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, while features such as awake duration after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioural difference between periods of depression and no depression.

Additional file 3. Operationalisation of depression definitions.

Background: Overweight and obesity are major problems worldwide, and they lead to an increased risk for several diseases. The use of technology in the treatment of obesity is promising, but in the existing literature, there is considerable uncertainty regarding its efficacy. In this review, we included web- and mobile-based weight loss interventions that were implemented remotely in rehabilitation settings. Objective: The aim of this systematic review is to study the effectiveness of physical activity-promoting web- and mobile-based distance weight loss interventions in rehabilitation settings on body composition in comparison with control groups that did not use technology. Methods: Studies were searched from 9 databases. The inclusion criteria were as follows: population: age 18-65 years; intervention: physical activity-promoting web- and mobile-based distance weight loss interventions; comparison: control groups without the use of technology; outcome: changes in BMI, waist circumference, or body fat percentage; study design: randomized controlled trial. The quality of the studies was assessed by 2 researchers. Meta-analysis was performed, and we also conducted a meta-regression analysis to evaluate the factors associated with the changes in body composition outcomes if statistical heterogeneity was observed. Results: The meta-analysis included 30 studies. The mean quality of the studies was 7 of 13 (SD 1.9; range 3-10). A statistically significant difference was observed in BMI (mean difference [MD] 0.83, 95% CI 0.51-1.15 kg/m2; P

Additional file 2. The North Wind and the Sun.

The development of mobile technology and mobile Internet offers new possibilities in rehabilitation and clinical assessment in a longitudinal perspective for multiple sclerosis management. However, because the mobile health applications (mHealth) have only been developed recently, the level of evidence supporting the use of mHealth in people with multiple sclerosis (pwMS) is currently unclear. Therefore, this review aims to list and describe the different mHealth available for rehabilitation and self-assessment of pwMS and to define the level of evidence supporting these interventions for functioning problems categorized within the International Classification of Functioning, Disability and Health (ICF). In total, 36 studies, performed with 22 different mHealth, were included in this review, 30 about rehabilitation and six for self-assessment, representing 3091 patients. For rehabilitation, most of the studies were focusing on cognitive function and fatigue. Concerning the efficacy, we found a small but significant effect of the use of mHealth for cognitive training (Standardized Mean Difference (SMD) = 0.28 [0.12; 0.45]) and moderate effect for fatigue (SMD = 0.61 [0.47; 0.76]). mHealth is a promising tool in pwMS but more studies are needed to validate these solutions in the other ICF categories. More replications studies are also needed as most of the mHealth have only been assessed in one single study.

Momentary assessment techniques, such as the Experience Sampling Method (ESM), are structured diary techniques to frequently assess momentary experiences in the context of daily life. Momentary assessment techniques have several advantages over traditional retrospective assessments, such as minimized recall bias. However, ESM might be a demanding assessment tool as subjects are asked to provide self-assessments several times a day. Additionally, the method may impact on the natural environment and induce reactivity to the method. The purpose of this PhD project is to investigate the data quality in ESM research in individuals with mental health and central nervous system disorders. We will investigate compliance and data quality in ESM and develop methodological guidelines and analysis schemes when using ESM as a remote assessment tool. The PhD project is a part of an international project called "Remote Assessment of Disease and Relapse in Central Nervous System Disorders (RADAR-CNS)" that explores the potential of using wearable devices and smartphone technology to monitor and help prevent and treat various central nervous system disorders. ESM will be initiated in the project as a remote assessment tool to investigate self-reported mood, physical activity, stress, and social interactions in individuals with multiple sclerosis, epilepsy, and major depressive disorder. status: published