BackgroundStroke, multiple sclerosis (MS), and Parkinson's disease (PD) cause balance and walking impairments, which increase the risk of falls and reduce quality of life. Technological advances have enabled novel reactive balance approaches.ObjectiveTo synthesize the current approaches of technology-based perturbation interventions during walking and their effects on physical function in stroke, MS, and PD.MethodsA literature search was conducted in PubMed, PEDro, and ScienceDirect for studies published from January 2014 to December 2025, supplemented by citation tracking and grey literature. Eligible studies included interventional studies utilizing technology-based perturbation during walking in stroke, MS, or PD.ResultsTen studies were included. Participants were diagnosed with stroke (n = 83, 42%), MS (n = 77, 39%), or PD (n = 38, 19%). Most interventions used treadmill-based perturbations to induce slips, trips, or lateral balance instability. Interventions comprised a median of nine sessions over three weeks (25 min per session). Outcomes were grouped into four categories: clinical assessments, patient-reported outcomes, technology-based assessments, and assessments during walking training. Between-group differences compared to similar treatments without perturbation were inconsistent, but within-group improvements in walking speed, balance, and functional mobility were commonly reported. Adverse events were rare, and safety protocols were followed. Considerable heterogeneity in intervention protocols and outcome measures limited synthesis.ConclusionTechnology-based perturbation training during walking may offer an alternative for improving physical function in stroke, MS, and PD. Evidence of superiority over non-perturbation interventions remains inconclusive due to heterogeneity and small samples. Within-group improvements suggest benefits, warranting standardized protocols and larger high-quality trials.

INTRODUCTION:Multiple sclerosis (MS) is a common central nervous system disease among young adults worldwide and Finland is one of the high-risk MS regions in Europe. Fatigue affects around 80% of individuals with MS, with prevalence rising to 95% as the disease progresses. Fatigue significantly limits daily activities and is associated with poorer employment outcomes and reduced quality of life. The objective of this qualitative study is to explore the lived experiences of individuals with MS and conceptions of MS-related fatigue among physiotherapists. METHODS AND ANALYSIS:This qualitative study is part of a larger research project entitled 'Building conceptualisation and understanding of momentary fatigue and activity-related fatigability in daily life for people with multiple sclerosis' (EMA-FAMS), consisting of several studies conducted from 2024 to 2028. For this qualitative study, 10 individuals with relapsing-remitting MS will be interviewed using a phenomenological approach to collect their lived experiences of fatigue. Additionally, 15 physiotherapists with experience in MS rehabilitation will be interviewed using a phenomenographic approach to explore their conceptions of MS-related fatigue. All interviews will be conducted remotely during 2025-2026. Interview data will be analysed in two phases using interpretative phenomenological analysis (IPA) and phenomenographic analysis methods. ETHICS AND DISSEMINATION:The EMA-FAMS study project has obtained ethical approval from the Regional Medical Research Ethics Committee of the Helsinki University Hospital District (HUS/10011/2024), and all the participants will provide written consent. Findings of this study will be shared through peer-reviewed articles, at academic conferences and with public healthcare and healthcare professionals.

BackgroundDaily activities shape individuals’ health and well-being, reflecting functioning and lived health. For people with neurological conditions, these activities are often disrupted, affecting autonomy and quality of life. Traditional assessments miss subtle, real-time fluctuations, whereas ecological momentary assessment (EMA) captures moment-to-moment activity within natural contexts, offering insight into person-environment-occupation interactions. Despite its growing use, it remains unclear how EMA protocols conceptualize daily activities and integrate person-environment-occupation dimensions in applications for neurological populations. ObjectiveThis scoping review aims to map the existing literature on the use of EMA to capture daily activities, ranging from basic self-care to more complex activities, in individuals with neurological disorders. MethodsA scoping review was conducted to map studies using EMA to capture daily activities in adults with neurological conditions, with a specific focus on content and practical application. A total of 341 articles were identified. ResultsA total of 20 studies using EMA to assess daily activities in neurological populations were included; most were observational, with 2 longitudinal studies and 2 randomized controlled trials. Daily activity questions and response formats varied, often using multiple-choice lists; only 1 study allowed open-ended responses. In addition to daily activity questions, EMA captured person (physical, affective, and cognitive), environment (physical and social), and occupation domains, as well as motivation and EMA disturbance. Protocols differed in setting, schedule, technology, and adherence, with most reporting completion rates above 70%. ConclusionsEMA captures daily activities in neurological populations and demonstrates generally high adherence despite variability in study designs, questions, and technologies. The findings suggest that the phrasing of EMA items, the predominance of closed-response formats, and the narrow focus on the verb “doing” limit the depth and nuance of the data collected, often overlooking important aspects of performance, engagement, or both in daily activities.

Evidence on the effects of robotic technology is required to develop rehabilitation services. This study aimed to evaluate the effects of robot-assisted walking training on walking and functional independence in everyday life in persons with spinal cord injury (SCI) and explore the covariates associated with these effects. We searched the MEDLINE (Ovid), CINAHL, PsycINFO, and ERIC databases until March 25, 2022. Two reviewers independently assessed the studies for inclusion. We included RCTs on people with SCI receiving robotic training. The Cochrane RoB2, meta-analysis, meta-regression, and Grading of Recommendations Assessment, Development, and Evaluation were performed. We included 23 RCTs focusing on SCI with outcomes of walking or functional independence, of which 14 were included in the meta-analysis and meta-regression analyses. Small improvements were observed in functional independence in favor of robot-assisted walking training compared to other physical exercises (Hedges’ g 0.31, 95

BackgroundWell-targeted balance, walking, and weight-shift training can improve balance capabilities in the chronic phase of stroke. There is an urgent need for a long-term approach to rehabilitation that extends beyond the acute and subacute phases, supporting participation without increasing the demand for health care staff. ObjectiveThis study aims to evaluate the effectiveness of therapeutic exercise interventions with virtual reality (VR) training on balance and walking at the activity and participation levels in individuals with chronic stroke, compared with control groups receiving no treatment, conventional physical therapy, specific training, similar treatment, or identical treatment without VR. MethodsStudies were searched across 6 databases. The inclusion criteria were as follows: Adults aged 18 years or older with a stroke diagnosis for at least 6 months (population). Therapeutic exercises within a VR environment, using VR glasses or interactive games (intervention). Control groups without the use of VR (including no treatment, conventional physical therapy, specific training, similar treatment without VR, or identical treatment without the additional use of VR; comparison). We evaluated the Berg Balance Scale score, Functional Reach Test performance, Activities-specific Balance Confidence Scale score, Six-minute Walk Test, Two-minute Walk Test, 10-meter Walk Test results, and cadence (outcome measures). We investigated randomized controlled trials (study design). A meta-analysis and a meta-regression analysis were conducted to evaluate whether the content of VR interventions or control groups, as well as the level of VR immersion used, was related to balance or walking outcomes. ResultsA total of 43 randomized controlled trials involving 1136 participants were included in this review. The use of VR training in therapeutic exercise interventions had a large effect on balance (standardized mean difference 0.51, 95% CI 0.29-0.72; P<.001) and a moderate effect on walking (standardized mean difference 0.31, 95% CI 0.09-0.53; P=.006) in individuals with chronic stroke, compared with pooled control groups (no treatment, conventional physical therapy, specific training, similar treatment, or identical treatment without the use of VR). According to the meta-regression findings, the content of VR interventions (P=.52), the type of control groups (P=.79), and the level of VR immersion (P=.82) were not significantly related to the pooled balance or walking outcomes. The GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) was moderate for balance and low for walking. ConclusionsTherapeutic exercise training with VR had a positive, albeit moderate, effect on balance and a low impact on walking at the level of activity (capacity), even in the chronic phase of stroke, without serious side effects. The results are applicable to working-aged stroke rehabilitees who are able to walk without assistance. Further research is needed with defined VR methods and outcomes that assess performance at the level of real-life participation.

The aim of this systematic review, meta-analysis, and meta-regression was to examine the effects of virtual reality-based training on global cognition and executive function compared with conventional training or information-based treatment in older adults, regardless of cognitive level. A systematic literature search was conducted using four databases. A total of 31 randomized controlled trials were identified. Pooled effect sizes were calculated, the risk of bias was assessed, and evidence was graded. The primary analyses showed a small but statistically significant effect of virtual reality-based training compared with control on global cognition (Hedges' g 0.42, 95% confidence interval [0.17, 0.68], I-2 = 70.1%, n = 876, 20 randomized controlled trials, low evidence) and executive function (Hedges' g 0.35, 95% confidence interval [0.06, 0.65], I-2 = 68.4%, n = 810, 16 randomized controlled trials, very low evidence). Meta-regression yielded inconclusive results. Virtual reality-based training may be more effective than control in improving cognition in older adults; however, more high-quality studies are needed.

The objective of this study was to evaluate the effectiveness of robot-assisted lower-limb rehabilitation on balance in stroke patients and to explore the covariates associated with these effects. A systematic literature search was carried out in four databases (MEDLINE (Ovid), CINAHL, PsycINFO, and ERIC) for studies published from inception to 25th of March 2022. Studies on robot-assisted lower-limb rehabilitation with a randomized controlled trial (RCT) design, participants with stroke, a comparison group with conventional training, and balance-related outcomes were included. Studies were assessed for Cochrane Risk of Bias 2 and quality of evidence. Meta-analysis and meta-regression were performed. A total of 48 (RCT) with 1472 participants were included. The overall risk of bias in the included studies was unclear (n = 32), high (n = 15) or low (n = 1). Compared to conventional rehabilitation, robot-assisted lower-limb rehabilitation interventions were more effective for balance improvement (Hedges’ g = 0.25, 95

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.

BACKGROUND:Remote measurement technologies (RMTs) have the potential to revolutionize major depressive disorder (MDD) disease management by offering the ability to assess, monitor, and predict symptom changes. However, the promise of RMT data depends heavily on sustained user engagement over extended periods. In this paper, we report a longitudinal qualitative study of the subjective experience of people with MDD engaging with RMTs to provide insight into system usability and user experience and to provide the basis for future promotion of RMT use in research and clinical practice.OBJECTIVE:We aimed to understand the subjective experience of long-term engagement with RMTs using qualitative data collected in a longitudinal study of RMTs for monitoring MDD. The objectives were to explore the key themes associated with long-term RMT use and to identify recommendations for future system engagement.METHODS:In this multisite, longitudinal qualitative research study, 124 semistructured interviews were conducted with 99 participants across the United Kingdom, Spain, and the Netherlands at 3-month, 12-month, and 24-month time points during a study exploring RMT use (the Remote Assessment of Disease and Relapse-Major Depressive Disorder study). Data were analyzed using thematic analysis, and interviews were audio recorded, transcribed, and coded in the native language, with the resulting quotes translated into English.RESULTS:There were 5 main themes regarding the subjective experience of long-term RMT use: research-related factors, the utility of RMTs for self-management, technology-related factors, clinical factors, and system amendments and additions.CONCLUSIONS:The subjective experience of long-term RMT use can be considered from 2 main perspectives: experiential factors (how participants construct their experience of engaging with RMTs) and system-related factors (direct engagement with the technologies). A set of recommendations based on these strands are proposed for both future research and the real-world implementation of RMTs into clinical practice. Future exploration of experiential engagement with RMTs will be key to the successful use of RMTs in clinical care.

BACKGROUND:Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity. METHODS:Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions. RESULTS:Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms. CONCLUSIONS:Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.

Intensive repeated momentary diary techniques, such as the experience sampling method (ESM), place considerable burden on study participants by repeatedly prompting them to fill in a questionnaire several times throughout the day. The objective of this study was to investigate the level of disturbance reported by participants and predictors thereof using a pooled dataset of seven ESM studies including 1319 healthy participants and individuals with different mental health conditions. All studies used a design of 10 prompts per day over the course of 4 to 6 days. Participants reported no disturbance at 37% of the measurement occasions and the overall level of disturbance was relatively low. Also, clinical samples did not report significantly different levels of disturbance compared to the healthy participants. However, higher levels of disturbance were found for later study days, in the morning, and on weekends. Also, younger participants reported feeling more disturbed by the prompts. In terms of momentary states, higher levels of activity, activity stress, and tiredness were associated with higher disturbance. Also, participants experienced more disturbance when in company compared to when alone, and the level of disturbance increased when in company with less familiar people. Our findings suggest that studies using high-frequency ESM protocols can be used in mental health research, but researchers should be aware of the possible disturbance caused by their research design.

A number of challenges exist for the analysis of mHealth data: maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. From 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral 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 at least 8 (range 2-12) days were needed for reliable calculation of most of the 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, whereas features such as wakefulness 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 behavioral difference between periods of depression and periods of no depression.

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.

Background: Changes in lifestyle, finances and work status during COVID-19 lockdowns may have led to biopsychosocial changes in people with pre-existing vulnerabilities such as Major Depressive Disorders (MDDs) and Multiple Sclerosis (MS). Methods: Data were collected as a part of 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: 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 Ambulatory monitoring is gaining popularity in mental and somatic health care to capture an individual's wellbeing or treatment course in daily-life. Experience sampling method collects subjective time-series data of patients' experiences, behavior, and context. At the same time, digital devices allow for less intrusive collection of more objective time-series data with higher sampling frequencies and for prolonged sampling periods. We refer to these data as parallel data. Combining these two data types holds the promise to revolutionize health care. However, existing ambulatory monitoring guidelines are too specific to each data type, and lack overall directions on how to effectively combine them. METHODS Literature and expert opinions were integrated to formulate relevant guiding principles. RESULTS Experience sampling and parallel data must be approached as one holistic time series right from the start, at the study design stage. The fluctuation pattern and volatility of the different variables of interest must be well understood to ensure that these data are compatible. Data have to be collected and operationalized in a manner that the minimal common denominator is able to answer the research question with regard to temporal and disease severity resolution. Furthermore, recommendations are provided for device selection, data management, and analysis. Open science practices are also highlighted throughout. Finally, we provide a practical checklist with the delineated considerations and an open-source example demonstrating how to apply it. CONCLUSIONS The provided considerations aim to structure and support researchers as they undertake the new challenges presented by this exciting multidisciplinary research field.

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.

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<.001) the subsequent periodicity of mobility. 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.

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.

BACKGROUND:The role of self-management in health promotion, as well as prevention and rehabilitation, is increasing through the use of mobile health (mHealth) apps. Such mHealth apps are also increasingly being used for self-management of low back pain (LBP), but their effectiveness has not been sufficiently explored. OBJECTIVE:The aim of this scoping review was to provide an overview of the literature on self-management mHealth apps and their effects on the levels of pain and disability in people with LBP. METHODS:We applied the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) methodology, including a priori research questions. A literature search was conducted in 2 databases (PubMed and PEDro) for studies published between January 1, 2015, and June 17, 2021. Interventional, cohort, or case series studies with an interventional period were included if the mHealth app included built-in self-management content, the app was used for self-management for people with LBP, and the study reported outcomes regarding pain and disability in people with LBP. RESULTS:In total, 7 studies were selected for the review with overall 2307 persons with LBP, of whom 1328 (57.56%) were women. Among the studies (5/7, 71%) that reported the type of pain, 85% (390/459) of the participants were experiencing chronic LBP. A total of 5 different mHealth apps were identified, of which 4 contributed to a statistically significant reduction in LBP and clinically meaningful changes. Of the 7 studies, 4 (57%) used 4 different assessments for disability, of which 3 (75%) showed statistically significant improvements in the level of functional ability of participants in the experimental groups using an mHealth app with built-in self-management content for LBP. CONCLUSIONS:This scoping review supports the conclusion that people with LBP may benefit from mHealth apps that provide self-management content. However, the generalizability of the findings is limited because of heterogeneity in the pain characterization of the included participants and the intervention durations. More high-quality studies with longer follow-up periods to investigate personalized mHealth approaches are recommended for LBP self-management.

Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute walk test (6MWT) is a widely used objective measure to evaluate the progression of MS. Yet, it has limitations such as the need for a clinical visit and a proper walkway. The widespread use of wearable devices capable of depicting patients activity profiles has the potential to assess the level of MS-induced disability in free-living conditions. In this work, we extracted 96 activity features in different temporal granularities (from minute-level to day-level) and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10-month duration. We combined these features with participant demographics using three regression models including elastic net, gradient boosted trees and random forest. In addition, we quantified the individual feature contribution using feature importance in these regression models, linear mixed-effects models, generalized estimating equations, and correlation-based feature selection (CFS). The results showed promising estimation performance with R2 of 0.30, which was derived using random forest after CFS. This model was able to distinguish the participants with low disability from those with high disability. Furthermore, we observed that the minute-level (no longer than 8 minutes) step count, particularly those capturing the upper end of the step count distribution, had a stronger association with 6MWT. The use of a walking aid was indicative of ambulatory function measured through 6MWT. This study provides a basis for future investigation into the clinical relevance and utility of wearables in assessing MS progression in free-living conditions.