Mobile health: Is your next rehabilitation's specialist in your pocket?

The past few decades have witnessed an unprecedented surge in health-related mobile applications. However, most of these applications primarily focus on lifestyle domains such as sleep, fitness, and nutrition. A notable stride in this landscape involves the emergence of applications catering specifically to rehabilitation needs. This expert review aims to provide an encompassing overview of the wide spectrum of apps available for both assessment and rehabilitation. It delves into the existing constraints associated with these tools and deliberates on the potential avenues for future advancements and integration for future advancements and integration. The transformative potential of this mobile, affordable, and user-friendly technology in reshaping the field of rehabilitation sciences will be highlighted. This article underscores how harnessing these innovations can elevate accessibility and effectiveness in the rehabilitation processes, leading to improved overall outcomes and wellbeing.

Accurate Prediction Equations for Ventilatory Thresholds in Cardiometabolic Disease When Gas Exchange Analysis is Unavailable: Development and Validation.

AIMS: To develop and validate equations predicting heart rate (HR) at the first and second ventilatory thresholds (VTs) and an optimized range-adjusted prescription for patients with cardiometabolic disease (CMD). To compare their performance against guideline-based exercise intensity domains. METHODS: Cross-sectional study involving 2,868 CMD patients from nine countries. HR predictive equations for first and second VTs (VT1, VT2) were developed using multivariate linear regression with 975 cycle-ergometer cardiopulmonary exercise tests (CPET). 'Adjusted' percentages of peak HR (%HRpeak) and HR reserve (%HRR) were derived from this group. External validation with 1,893 CPET (cycle-ergometer or treadmill) assessed accuracy, agreement, and reliability against guideline-based %HRpeak and %HRR prescriptions using mean absolute percentage error (MAPE), Bland-Altman analyses, intraclass correlation coefficients (ICC). RESULTS: HR predictive equations (R²: 0.77 VT1, 0.88 VT2) and adjusted %HRR (VT1: 42%, VT2: 77%) were developed. External validation demonstrated superiority over widely used guideline-directed intensity domains for %HRpeak and %HRR. The new methods showed consistent performance across both VTs with lower MAPE (VT1: 7.1%, VT2: 5.0%), 'good' ICC for VT1 (0.81, 0.82) and 'excellent' for VT2 (0.93). Guideline-based exercise intensity domains had higher MAPE (VT1: 6.8%-21.3%, VT2: 5.1%-16.7%), 'poor' to 'good' ICC for VT1, and 'poor' to 'excellent' for VT2, indicating inconsistencies related to specific VTs across guidelines. CONCLUSION: Developed and validated HR predictive equations and the optimized %HRR for CMD patients for determining VT1 and VT2 outperformed the guideline-based exercise intensity domains and showed ergometer interchangeability. They offer a superior alternative for prescribing moderate intensity exercise when CPET is unavailable.

Equations to predict heart rate at ventilatory thresholds were developed and externally validated, offering a new perspective when a cardiopulmonary exercise test is unavailable to accurately determine the aerobic exercise intensity domains. Additionally, an adjusted range for exercise intensity prescription based on the percentage of heart rate reserve (%HRR) was provided, utilizing a large sample from eight countries. The proposed equations and the range-adjusted %HRR significantly outperformed the guideline-directed methods for determining exercise intensity, exhibiting higher accuracy, agreement, and reliability. Exercise intensity prescription based on the percentage of heart rate peak showed higher errors, raising concerns about its clinical applicability. Our study may enhance the efficacy of exercise training and physical activity advice when gas exchange analysis is unavailable, potentially leading to improved clinical outcomes, even in low-resource settings. Employing these approaches in research could facilitate more tailored and consistent interventions, introducing a contemporary perspective for studies comparing exercise intensity prescriptions.

Incorporating Wearable Technology for Enhanced Rehabilitation Monitoring after Hip and Knee Replacement.

Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients' dynamic activity profiles.

Exploring the Use of Mobile Health for the Rehabilitation of Long COVID Patients: A Scoping Review.

The COVID-19 pandemic has led to a substantial revolution in the incorporation of digital solutions in healthcare. This systematic review investigates the enduring physical and psychological consequences individuals experience up to two years post-recovery. Additionally, it focuses on examining the influence of mHealth interventions on these effects. Significantly, 41.7% of survivors experience lingering symptoms that have not been addressed, while 14.1% encounter difficulties in returning to work. The presence of anxiety, compromised respiratory functioning, and persistent symptoms highlight the immediate requirement for specific therapies. Telehealth, particularly telerehabilitation, presents itself as a possible way to address these difficulties. The study thoroughly examines 10 studies encompassing 749 COVID-19 patients, investigating the efficacy of telerehabilitation therapies in addressing various health markers. Telerehabilitation-based breathing exercises yield substantial enhancements in functional performance, dyspnea, and overall well-being. The results emphasize the potential of telerehabilitation to have a favorable effect on patient outcomes; however, more research is needed to strengthen the existing evidence base, as one of the most important limitations is the limited number of trials and the evaluation of varied therapies. This analysis highlights the significance of digital solutions in post-COVID care and calls for ongoing research to improve the comprehension and implementation of telehealth interventions in a swiftly changing healthcare environment.

Unlocking the Black Box? A Comprehensive Exploration of Large Language Models in Rehabilitation.

ABSTRACT: Rehabilitation is a vital component of healthcare, aiming to restore function and improve the well-being of individuals with disabilities or injuries. Nevertheless, the rehabilitation process is often likened to a 'black box', with complexities that pose challenges for comprehensive analysis and optimization. The emergence of Large Language Models (LLMs) offers promising solutions to better understand this 'black box'. LLMs excel at comprehending and generating human-like text, making them valuable in the healthcare sector. In rehabilitation, healthcare professionals must integrate a wide range of data to create effective treatment plans, akin to selecting the best ingredients for the 'black box'. LLMs enhance data integration, communication, assessment, and prediction.This paper delves into the ground-breaking use of LLMs as a tool to further understand the rehabilitation process. LLMs address current rehabilitation issues, including data bias, contextual comprehension, and ethical concerns. Collaboration with healthcare experts and rigorous validation is crucial when deploying LLMs. Integrating LLMs into rehabilitation yields insights into this intricate process, enhancing data-driven decision-making, refining clinical practices, and predicting rehabilitation outcomes. Although challenges persist, LLMs represent a significant stride in rehabilitation, underscoring the importance of ethical use and collaboration.

STASISM: A Versatile Serious Gaming Multi-Sensor Platform for Personalized Telerehabilitation and Telemonitoring.

Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a sensor-based telerehabilitation and telemonitoring system, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for the remote monitoring of a patient's progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients' quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings.

Unveiling mortality risk factors in paediatric sickle cell disease patients during acute crises in the Democratic Republic of the Congo.

Sickle cell disease (SCD) is a significant health burden in the Democratic Republic of the Congo (DRC). This study aims to identify predictive factors of mortality in SCD children admitted to emergency care in Lubumbashi, DRC. We performed a non-interventional cohort follow-up on SCD patients aged 0 to 16 admitted for a "true emergency". Demographic, clinical, and biological data were collected. Univariate and multivariate logistic regression analyses were performed to identify significant risk factors associated with mortality. Among the 121 patients included, 24 died during the follow-up period. Univariate regression revealed age, Mikobi score, referral origin, stroke, and severe infection as significant risk factors. Multivariate analyses identified Hb, WBC, SR, and LDH as predictive factors of mortality. Notably, patients aged 12 to 16 years faced a higher risk, shifting the age of mortality from early to late childhood and adolescence. This study provides valuable insights into mortality risk factors for paediatric SCD patients during acute crises. Early diagnosis, regular follow-up, and therapeutic education are essential to improve patient outcomes and survival rates. These findings contribute to better disease management and targeted interventions, aiming to reduce mortality associated with SCD.

Relationships between cardiorespiratory fitness, physical activity practices, and functional outcomes one-year post-stroke in northern Benin: A case-control study.

BACKGROUND: Currently, little is known on the relationships between cardiorespiratory fitness (CF), physical activity (PA), and functional outcomes after stroke, especially in low- and middle-income countries. OBJECTIVES: We examine the relationships between CF, PA, and functional outcomes in one-year poststroke in Benin, a lower middle-income country. METHODS: A case-control study was carried out in northern Benin. Twenty-one participants with chronic strokes were matched to 42 controls according to sex and age. PA patterns and associated energy expenditure (EE) were assessed with a BodyMedia's senseWear armband. CF was evaluated with the Physical Working Capacity at 75% of the predicted maximal heart rate index. The functional outcomes were evaluated using the modified Rankin scale (mRS) and the ACTIVLIM-Stroke scale. RESULTS: Both people with stroke and the healthy pairs spent much time in sedentary behavior (median [P25; P75]: 672 [460; 793] min vs 515 [287; 666] min, p = 0.006). Although people with chronic stroke performed fewer steps compared to healthy controls (median: 2767 vs 5524, p = 0.005), results showed that total EE was not statistically significant in either group (median: 7166 Kcal vs 8245 Kcal, p = 0.07). In addition, the mRS score (r = 0.47, p = 0.033) and the ACTIVLIM-Stroke measure (r = 0.52, p = 0.016) were moderately associated with the CF index of people with chronic stroke. CONCLUSION: The study showed clear trends for lower levels of PA in both people with chronic stroke and health controls. A correlation exists between CF, disability, and functional outcomes among stroke patients.

Promoting physical activity and a healthy active lifestyle in community-dwelling older adults: a design thinking approach for the development of a mobile health application.

Background: Physical activity (PA) has wide-ranging, and well documented benefits for older adults, encompassing physical, cognitive, and mental well-being. The World Health Organization advocates for a minimum of 150-300 min of moderate intensity PA per week, supplemented by muscle-strengthening exercises. However, the rates of PA among older adults remain a concern. While portable technologies hold promises in promoting PA, sustaining long-term engagement continues to be a challenge. Objective: The aims of this study are to identify barriers and facilitators to PA in older adults, to develop an mHealth app promoting PA and an active healthy lifestyle in collaboration with community-dwelling older adults guided by the design thinking process, and to test it. Methods: A co-creative process was used, employing design thinking. Interviews were conducted to understand the needs of the target population and identify the problem of insufficient PA. Two cocreation sessions involving older adults and experts were conducted to generate innovative ideas. Participants were selected based on age (≥65 years), no severe illness, Dutch language proficiency, and active participation ability. Results were qualitatively analyzed and coded. Finally a prototype was developed and tested. Results: Interviews with older adults highlighted diverse perceptions of PA but unanimous agreement on its importance. They recognized health benefits such as improved mobility, balance, and reduced fall risk, while emphasizing the social and mental aspects. Barriers included poor health, time constraints, weather conditions and fear of falling. Cocreation sessions identified key topics: perception of a healthy lifestyle, coping strategies, mHealth App features, screen visualization, and tailored notifications, which led to the development of a mobile app promoting PA and an active lifestyle. The app was stepwise prototyped. Conclusion: This study emphasizes the importance of promoting PA among older adults through a collaborative design thinking approach. However, the implementation of mHealth apps faces obstacles due to the digital divide, necessitating personalized solutions to bridge the gap. Moreover, it calls for further research to investigate the long-term impact of such interventions and explore behavior change patterns in this population.

Mobile technologies for rehabilitation in non-specific spinal disorders: a systematic review of the efficacy and potential for implementation in low- and middle-income countries.

PURPOSE: The aim of this systematic review was primarily to identify the types of mHealth technologies for the rehabilitation of non-specific spinal disorders, second to evaluate their efficacy, and finally to determine their applicability in LMICs. METHODS: Three databases (Scopus, PubMed, and Web of Science) were searched for randomized controlled trials and clinical trials from January 2012 until December 2022. Studies were found eligible when using mHealth technologies for the rehabilitation of non-specific spinal disorders. To evaluate efficacy, the primary outcome was pain intensity, and the secondary outcomes were disability and quality of life. To evaluate the applicability in LMICs, information about financial and geographical accessibility, offline usability, and languages was extracted. RESULTS: Fifteen studies were included comprising 1828 participants who suffer from non-specific low back pain (86.05%) and non-specific neck pain (13.95%). Fourteen distinct smartphone-based interventions and two sensor system interventions were found, with a duration ranging from four weeks to six months. All mHealth interventions demonstrated efficacy for the improvement of pain, disability and quality of life in non-specific spinal disorders, particularly low back pain. Five of the evaluated smartphone applications were free of charge accessible and had language features that could be adapted for use in LMICs. CONCLUSION: mHealth interventions can be used and integrated into the conventional treatment of non-specific spinal disorders in rehabilitation. They have demonstrated efficacy and could be implemented in LMICs with minor adaptations to overcome language barriers and the absolute necessity of the internet.

Gait Stride Length Estimation Using Embedded Machine Learning.

INTRODUCTION: Spatiotemporal gait parameters, e.g., gait stride length, are measurements that are classically derived from instrumented gait analysis. Today, different solutions are available for gait assessment outside the laboratory, specifically for spatiotemporal gait parameters. Such solutions are wearable devices that comprise an inertial measurement unit (IMU) sensor and a microcontroller (MCU). However, these existing wearable devices are resource-constrained. They contain a processing unit with limited processing and memory capabilities which limit the use of machine learning to estimate spatiotemporal gait parameters directly on the device. The solution for this limitation is embedded machine learning or tiny machine learning (tinyML). This study aims to create a machine-learning model for gait stride length estimation deployable on a microcontroller. MATERIALS AND METHOD: Starting from a dataset consisting of 4467 gait strides from 15 healthy people, measured by IMU sensor, and using state-of-the-art machine learning frameworks and machine learning operations (MLOps) tools, a multilayer 1D convolutional float32 and int8 model for gait stride length estimation was developed. RESULTS: The developed float32 model demonstrated a mean accuracy and precision of 0.23 ± 4.3 cm, and the int8 model demonstrated a mean accuracy and precision of 0.07 ± 4.3 cm. The memory usage for the float32 model was 284.5 kB flash and 31.9 kB RAM. The int8 model memory usage was 91.6 kB flash and 13.6 kB RAM. Both models were able to be deployed on a Cortex-M4F 64 MHz microcontroller with 1 MB flash memory and 256 kB RAM. CONCLUSIONS: This study shows that estimating gait stride length directly on a microcontroller is feasible and demonstrates the potential of embedded machine learning, or tinyML, in designing wearable sensor devices for gait analysis.

Does ethnicity influence dementia, stroke and mortality risk? Evidence from the UK Biobank.

Introduction: The number of people with dementia and stroke is increasing worldwide. There is increasing evidence that there are clinically relevant genetic differences across ethnicities. This study aims to quantify risk factors of dementia, stroke, and mortality in Asian and black participants compared to whites. Methods: 272,660 participants from the UK Biobank were included in the final analysis, among whom the vast majority are white (n = 266,671, 97.80%), followed by Asian (n = 3,790, 1.35%), and black (n = 2,358, 0.84%) participants. Cumulative incidence risk was calculated based on all incident cases occurring during the follow-up of the individuals without dementia and stroke at baseline. We compared the allele frequency of variants in Asian and black participants with the referent ethnicity, whites, by chi-square test. Hierarchical cluster analysis was used in the clustering analysis. Significance level corrected for the false discovery rate was considered. Results: After adjusting for risk factors, black participants have an increased risk of dementia and stroke compared to white participants, while Asians has similar odds to the white. The risk of mortality is not different in blacks and white participants but Asians have a decreased risk. Discussion: The study provides important insights into the potential differences in the risk of dementia and stroke among different ethnic groups. Specifically, the study found that black individuals had a higher incidence of dementia and stroke compared to white individuals living in the UK. These findings are particularly significant as they suggest that there may be underlying factors that contribute to these differences, including genetic, environmental, and social factors. By identifying these differences, the study helps to inform interventions and policies aimed at reducing the risk of dementia and stroke, particularly among high-risk populations.

Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals.

Importance: Metabolomics reflect the net effect of genetic and environmental influences and thus provide a comprehensive approach to evaluating the pathogenesis of complex diseases, such as depression. Objective: To identify the metabolic signatures of major depressive disorder (MDD), elucidate the direction of associations using mendelian randomization, and evaluate the interplay of the human gut microbiome and metabolome in the development of MDD. Design, Setting and Participants: This cohort study used data from participants in the UK Biobank cohort (n = 500 000; aged 37 to 73 years; recruited from 2006 to 2010) whose blood was profiled for metabolomics. Replication was sought in the PREDICT and BBMRI-NL studies. Publicly available summary statistics from a 2019 genome-wide association study of depression were used for the mendelian randomization (individuals with MDD = 59 851; control individuals = 113 154). Summary statistics for the metabolites were obtained from OpenGWAS in MRbase (n = 118 000). To evaluate the interplay of the metabolome and the gut microbiome in the pathogenesis of depression, metabolic signatures of the gut microbiome were obtained from a 2019 study performed in Dutch cohorts. Data were analyzed from March to December 2021. Main Outcomes and Measures: Outcomes were lifetime and recurrent MDD, with 249 metabolites profiled with nuclear magnetic resonance spectroscopy with the Nightingale platform. Results: The study included 6811 individuals with lifetime MDD compared with 51 446 control individuals and 4370 individuals with recurrent MDD compared with 62 508 control individuals. Individuals with lifetime MDD were younger (median [IQR] age, 56 [49-62] years vs 58 [51-64] years) and more often female (4447 [65%] vs 2364 [35%]) than control individuals. Metabolic signatures of MDD consisted of 124 metabolites spanning the energy and lipid metabolism pathways. Novel findings included 49 metabolites, including those involved in the tricarboxylic acid cycle (ie, citrate and pyruvate). Citrate was significantly decreased (ß [SE], -0.07 [0.02]; FDR = 4 × 10-04) and pyruvate was significantly increased (ß [SE], 0.04 [0.02]; FDR = 0.02) in individuals with MDD. Changes observed in these metabolites, particularly lipoproteins, were consistent with the differential composition of gut microbiota belonging to the order Clostridiales and the phyla Proteobacteria/Pseudomonadota and Bacteroidetes/Bacteroidota. Mendelian randomization suggested that fatty acids and intermediate and very large density lipoproteins changed in association with the disease process but high-density lipoproteins and the metabolites in the tricarboxylic acid cycle did not. Conclusions and Relevance: The study findings showed that energy metabolism was disturbed in individuals with MDD and that the interplay of the gut microbiome and blood metabolome may play a role in lipid metabolism in individuals with MDD.

Cognitive Computerized Training for Older Adults and Patients with Neurological Disorders: Do the Amount and Training Modality Count? An Umbrella Meta-Regression Analysis.

Numerous applications have been created to train cognition and challenge the brain, a process known as computerized cognitive training (CCT). Despite potential positive results, important questions remain unresolved: the appropriate training duration, the efficacy of CCT depending on its type (commercial or developed in-house for the rehabilitation of specific patients) and delivery mode (at-home or on-site), and the patients most likely to benefit such intervention. This study aims to perform an umbrella meta-analysis and meta-regression to determine if the type of CCT, the delivery mode, the amount of training, and participants' age at inclusion influence the improvement of the cognitive function. To do so, we performed a umbrella meta-analysis. One hundred studies were included in this analysis representing 6407 participants. Statistical improvements were found for the different conditions after the training. We do not find statistical difference between the type of intervention or the delivery mode. No dose-response relationship between the total amount of training and the improvement of cognitive functions was found. CCT is effective in improving cognitive function in patients suffering from neurological conditions and in healthy aging. There is therefore an urgent need for health care systems to recognize its therapeutic potential and to evaluate at a larger scale their integration into the clinical pipeline as preventive and rehabilitation tool.

Clinical phenotypes and prognostic factors in persons with hip osteoarthritis undergoing total hip arthroplasty: protocol for a longitudinal prospective cohort study (HIPPROCLIPS).

BACKGROUND: Large heterogeneity exists in the clinical manifestation of hip osteoarthritis (OA). It is therefore not surprising that pain and disability in individuals with hip OA and after total hip arthroplasty (THA) cannot be explained by biomedical variables alone. Indeed, also maladaptive pain-related cognitions and emotions can contribute to pain and disability, and can lead to poor treatment outcomes. Traumatic experiences, mental disorders, self-efficacy and social support can influence stress appraisal and strategies to cope with pain, but their influence on pain and disability has not yet been established in individuals with hip OA undergoing THA. This study aims (1) to determine the influence of traumatic experiences and mental disorders on pain processing before and shortly after THA (2) to identify preoperative clinical phenotypes in individuals with hip OA eligible for THA, (3) to identify pre- and early postoperative prognostic factors for outcomes in pain and disability after THA, and (4) to identify postoperative clinical phenotypes in individuals after THA. METHODS: This prospective longitudinal cohort study will investigate 200 individuals undergoing THA for hip OA. Phenotyping variables and candidate prognostic factors include pain-related fear-avoidance behaviour, perceived injustice, mental disorders, traumatic experiences, self-efficacy, and social support. Peripheral and central pain mechanisms will be assessed with thermal quantitative sensory testing. The primary outcome measure is the hip disability and osteoarthritis outcome score. Other outcome measures include performance-based measures, hip muscle strength, the patient-specific functional scale, pain intensity, global perceived effect, and outcome satisfaction. All these measurements will be performed before surgery, as well as 6 weeks, 3 months, and 12 months after surgery. Pain-related cognitions and emotions will additionally be assessed in the early postoperative phase, on the first, third, fifth, and seventh day after THA. Main statistical methods that will be used to answer the respective research questions include: LASSO regression, decision tree learning, gradient boosting algorithms, and recurrent neural networks. DISCUSSION: The identification of clinical phenotypes and prognostic factors for outcomes in pain and disability will be a first step towards pre- and postoperative precision medicine for individuals with hip OA undergoing THA. TRIAL REGISTRATION: ClinicalTrials.gov: NCT05265858. Registered on 04/03/2022.

Physical Activity Level, Barriers, and Facilitators for Exercise Engagement for Chronic Community-Dwelling Stroke Survivors in Low-Income Settings: A Cross-Sectional Study in Benin.

After a stroke incident, physical inactivity is common. People with stroke may perceive several barriers to performing physical activity (PA). This study aimed to document the PA level and understand the barriers and facilitators to engaging in PA for community-dwelling stroke survivors in Benin, a lower middle-income country. A cross-sectional study was conducted in three hospitals in Benin. Levels of PA were recorded by means of the Benin version of the International Physical Activity Questionnaire long form (IPAQ-LF-Benin), which is validated for stroke survivors in Benin. The perceived exercise facilitators and barriers were assessed by the Stroke Exercise Preference Inventory-13 (SEPI-13). A descriptive analysis and associations were performed with a Confidence Interval of 95% and <0.05 level of significance. A total of 87 participants (52 men, mean age of 53 ± 10 years, mean time after a stroke of 11 (IQR: 15) months and an average of 264.5 ± 178.9 m as distance on the 6 min walking test (6MWT) were included. Overall, stroke survivors in Benin reached a total PA of 985.5 (IQR: 2520) metabolic equivalent (METs)-minutes per week and were least active at work, domestic, and leisure domains with 0 MET-minutes per week. The overview of PA level showed that 52.9% of participants performed low PA intensity. However, 41.4% performed moderate PA or walking per day for at least five days per week. Important perceived barriers were lack of information (45.3%), hard-to-start exercise (39.5%), and travelling to places to exercise (29.9%). The preference for exercise was with family or friends, outdoors, for relaxation or enjoyment (90.2%), and receiving feedback (78.3%). Several socio-demographic, clinical, and community factors were significantly associated with moderate or intense PA (p < 0.05) in stroke survivors in this study. Our findings show that the PA level among chronic stroke survivors in Benin is overall too low relative to their walking capacity. Cultural factors in terms of the overprotection of the patients by their entourage and/or the low health literacy of populations to understand the effect of PA on their health may play a role. There is a need for new approaches that consider the individual barriers and facilitators to exercise.

Current Technology Developments Can Improve the Quality of Research and Level of Evidence for Rehabilitation Interventions: A Narrative Review.

The current important limitations to the implementation of Evidence-Based Practice (EBP) in the rehabilitation field are related to the validation process of interventions. Indeed, most of the strict guidelines that have been developed for the validation of new drugs (i.e., double or triple blinded, strict control of the doses and intensity) cannot-or can only partially-be applied in rehabilitation. Well-powered, high-quality randomized controlled trials are more difficult to organize in rehabilitation (e.g., longer duration of the intervention in rehabilitation, more difficult to standardize the intervention compared to drug validation studies, limited funding since not sponsored by big pharma companies), which reduces the possibility of conducting systematic reviews and meta-analyses, as currently high levels of evidence are sparse. The current limitations of EBP in rehabilitation are presented in this narrative review, and innovative solutions are suggested, such as technology-supported rehabilitation systems, continuous assessment, pragmatic trials, rehabilitation treatment specification systems, and advanced statistical methods, to tackle the current limitations. The development and implementation of new technologies can increase the quality of research and the level of evidence supporting rehabilitation, provided some adaptations are made to our research methodology.