Despite evidence that inactivity is a major factor causing ill health in people with intellectual disabilities (pwID) there are gaps in our knowledge of their physical activity (PA). To date, there is no published systematic review of their PA levels. Therefore, we performed a systematic review from January-October 2015, comprising studies from across the globe to establish PA levels, determine how they were measured, and what factors influenced PA in adults with intellectual disabilities (awID). Five databases were searched. Studies were included if written in English, peer-reviewed, had primary research data, and measured PA levels of awID. Quality was assessed using a 19-item checklist. Meta-summary of the findings was performed and a meta-analysis of factors influencing PA using multiple regression. Fifteen studies were included consisting of 3159 awID, aged 16-81 years, 54% male and 46% female. Only 9% of participants achieved minimum PA guidelines. PA levels were measured using objective and subjective methods. ID severity, living in care, gender, and age were independently significantly correlated with the number of participants achieving PA guidelines with the strongest predictor being ID severity (Beta 0.631, p < 0.001). Findings should be in the context that most of the participants were in the mild/moderate range of ID severity and none of the studies objectively measured PA in people with profound ID. To inform measurement and intervention design for improved PA, we recommend that there is an urgent need for future PA studies in awID population to include all disability severity levels. PROSPERO registration number CRD42015016675.

Background: The most effective exercise dose has yet to be established for multiple sclerosis (MS).

Integrating rehabilitation services through wearable systems has the potential to accurately assess the type, intensity, duration, and quality of movement necessary for procuring key outcome measures. This review aims to explore wearable accelerometry-based technology (ABT) capable of assessing mobility-related functional activities intended for rehabilitation purposes in community settings for neurological populations. In this review, we focus on the accuracy of ABT-based methods, types of outcome measures, and the implementation of ABT in non-clinical settings for rehabilitation purposes. Cochrane, PubMed, Web of Knowledge, EMBASE, and IEEE Xplore. The search strategy covered three main areas, namely wearable technology, rehabilitation, and setting. Potentially relevant studies were categorized as systems either evaluating methods or outcome parameters. Methodological qualities of studies were assessed by two customized checklists, depending on their categorization and rated independently by three blinded reviewers. Twelve studies involving ABT met the eligibility criteria, of which three studies were identified as having implemented ABT for rehabilitation purposes in non-clinical settings. From the twelve studies, seven studies achieved high methodological quality scores. These studies were not only capable of assessing the type, quantity, and quality measures of functional activities, but could also distinguish healthy from non-healthy subjects and/or address disease severity levels. While many studies support ABT's potential for telerehabilitation, few actually utilized it to assess mobility-related functional activities outside laboratory settings. To generate more appropriate outcome measures, there is a clear need to translate research findings and novel methods into practice.

Diagnosis of people with mild Parkinson's symptoms is difficult. Nevertheless, variations in gait pattern can be utilised to this purpose, when measured via Inertial Measurement Units (IMUs). Human gait, however, possesses a high degree of variability across individuals, and is subject to numerous nuisance factors. Therefore, off-the-shelf Machine Learning techniques may fail to classify it with the accuracy required in clinical trials. In this paper we propose a novel framework in which IMU gait measurement sequences sampled during a 10m walk are first encoded as hidden Markov models (HMMs) to extract their dynamics and provide a fixed-length representation. Given sufficient training samples, the distance between HMMs which optimises classification performance is learned and employed in a classical Nearest Neighbour classifier. Our tests demonstrate how this technique achieves accuracy of 85.51% over a 156 people with Parkinson's with a representative range of severity and 424 typically developed adults, which is the top performance achieved so far over a cohort of such size, based on single measurement outcomes. The method displays the potential for further improvement and a wider application to distinguish other conditions.

Objective: Physical activity in people with stroke remains low despite considerable research. This overview aimed to provide high-level synthesis and aid clinical decision-making. The Capability, Opportunity, Motivation-Behaviour (COM-B) model was used to classify interventions to understand which components improve physical activity behaviour in people with stroke. Data Sources: CINAHL, Cochrane Database, MEDLINE, PEDro, PsychINFO, SPORTDiscus Review Methods: A systematic search was conducted (November 2023) to identify reviews of interventions to improve physical activity in people with stroke. Results were screened and assessed for eligibility. Participant characteristics, intervention classification using COM-B, and effect of intervention were extracted. Quality was assessed using AMSTAR2, and Corrected Cover Analysis for study overlap. Narrative synthesis was used to understand components of interventions to improve physical activity behaviour. Results: 1801 references were screened and 29 full-text references assessed for eligibility. Twenty reviews were included. Quality ranged from critically low ( n = 3) to high ( n = 10). Study overlap calculated using corrected cover area indicated slight overlap (0.028) and minimal reporting bias. The majority of participants were mobile with mild stroke and community dwelling. Twenty-three interventions were classified using COM-B. Three of twelve interventions classified to one aspect of the COM-B were effective. Fourteen of sixteen effective interventions combined at least two COM-B elements, ten of these combined capability and motivation. Conclusion: Interventions including at least two elements of the COM-B are most likely to improve physical activity in mobile stroke survivors. Further research is needed to understand physical activity behaviour in those with moderate to severe stroke.