Objective: To determine the effectiveness of health-care interventions promoting physical activity, ...

Physical activity monitoring with wearable technology has the potential to support stroke rehabilitation. Little is known about how physical therapists use and value the use of wearable activity monitors. This cross-sectional study explores the use, perspectives, and barriers to wearable activity monitoring in day-to-day stroke care routines amongst physical therapists. Over 300 physical therapists in primary and geriatric care and rehabilitation centers in the Netherlands were invited to fill in an online survey that was developed based on previous studies and interviews with experts. In total, 103 complete surveys were analyzed. Out of the 103 surveys, 27% of the respondents were already using activity monitoring. Of the suggested treatment purposes of activity monitoring, 86% were perceived as useful by more than 55% of the therapists. The most recognized barriers to clinical implementation were lack of skills and knowledge of patients (65%) and not knowing what brand and type of monitor to choose (54%). Of the non-users, 79% were willing to use it in the future. In conclusion, although the concept of remote activity monitoring was perceived as useful, it was not widely adopted by physical therapists involved in stroke care. To date, skills, beliefs, and attitudes of individual therapists determine the current use of wearable technology.

The purpose of this study was to investigate the relationship between body mass index (BMI) class and physical activity and sedentary behavior in patients with acute coronary syndrome (ACS) during cardiac rehabilitation (CR). This study was a secondary analysis of the OPTICARE trial. Physical activity and sedentary behavior were measured in participants with ACS (n = 359) using actigraphy at baseline, directly after completion of a multidisciplinary 12-week exercise-based CR program and 9 months thereafter. Outcome measures were step count and duration of time (percentage of wear time) spent in light physical activity, moderate-to-vigorous physical activity, and sedentary behavior. Participants were classified as normal weight (BMI = 18.5–24.99 kg/m²; n = 82), overweight (BMI = 25.0–29.99 kg/m²; n = 182), or obese (BMI ≥ 30.0 kg/m²; n = 95). Linear mixed-effects models were applied to study the relationship between BMI class and physical activity and sedentary behavior. At the start of CR, compared with participants with normal weight, participants with obesity made on average 1.11 steps fewer per minute (952 steps/d), spent 2.9% (25 min/d) less time in light physical activity, and spent 3.31% (28 min/d) more time in sedentary behavior. Participants of all BMI classes improved their physical activity and sedentary behavior levels similarly during CR, and these improvements were maintained after completion of CR. Participants with ACS who had obesity started CR with a less favorable physical activity and sedentary behavior profile than that of participants with normal weight. Because all BMI classes showed similar improvement during CR, this deficit was preserved. This study indicates that reconsideration of the CR program in the Netherlands for patients with ACS and obesity is warranted, and development of more inclusive interventions for specific populations is needed. A new program for people with obesity should include added counseling on increasing physical activity and preventing sedentary behavior to facilitate weight loss and reduce mortality risk. People with ACS who have obesity are less active and sit more than individuals with normal weight, both during and after CR. This study suggests that CR needs to be changed to help individuals increase their physical activity to help them lose weight and reduce their risk of death.

Background: Stroke survivors show deteriorated physical functioning and physical activity levels. Physical activity levels of stroke survivors are generally low. It is increasingly recognized that physical activity is a multidimensional construct that cannot be captured in a single outcome. In-depth insight into multidimensional physical activity patterns may guide the development and timing of targeted rehabilitation interventions. This longitudinal cohort study explored how multidimensional physical activity outcomes develop during recovery in the subacute phase after stroke and if changes in physical activity were correlated to recovery of lower limb motor function.Methods: Patients were recruited during inpatient rehabilitation. At 3, 12, and 26 weeks post-onset, motor function was measured by the Fugl-Meyer Lower Extremity Assessment (FMA-LE). Physical activity was measured with the Activ8 accelerometer in multiple outcomes: counts per minute during walking (CPM_walking; a measure of Intensity), number of active bouts (Frequency), mean length of active bouts (Distribution) and % of waking time in upright positions (Duration). Generalized estimating equations (GEE) were used to study changes in physical activity over time and the relation with the change in lower limb motor recovery.Results: Thirty-nine patients (age 56 ± 9, 77% male, 89% ischemic stroke) were included. GEE models showed a significant main effect of time for PA Intensity (+ 13%, p = 0.007) and Duration (+ 64%, p = 0.012) between 3 and 12 weeks. Motor function did not show a significant effect in all PA models across the 3 timepoints (p > 0.020). A significant interaction effect of time × motor function was observed (p < 0.001).Conclusions: Patterns of PA recovery depend on the PA dimensions: PA Intensity and Duration increased mostly between 3 and 12 weeks post-stroke, whereas Frequency and Distribution did not show substantial changes. Further, no strong associations with motor recovery and high inter-individual variability were documented, which underlies the need to consider factors specific to the disease, the individual patient and the context.

Multiple sclerosis often leads to fatigue and changes in physical behavior (PB). Changes in PB are often assumed as a consequence of fatigue, but effects of interventions that aim to reduce fatigue by improving PB are not sufficient. Since the heterogeneous nature of MS related symptoms, levels of PB of fatigued patients at the start of interventions might vary substantially. Better understanding of the variability by identification of PB subtypes in fatigued patients may help to develop more effective personalized rehabilitation programs in the future. This study aimed to identify PB subtypes in fatigued patients with multiple sclerosis based on multidimensional PB outcome measures. Baseline accelerometer (Actigraph) data, demographics and clinical characteristics of the TREFAMS-ACE participants (n = 212) were used for secondary analysis. All patients were ambulatory and diagnosed with severe fatigue based on a score of ≥35 on the fatigue subscale of the Checklist Individual Strength (CIS20r). Fifteen PB measures were used derived from 7 day measurements with an accelerometer. Principal component analysis was performed to define key outcome measures for PB and two-step cluster analysis was used to identify PB types. Analysis revealed five key outcome measures: percentage sedentary behavior, total time in prolonged moderate-to-vigorous physical activity, number of sedentary bouts, and two types of change scores between day parts (morning, afternoon and evening). Based on these outcomes three valid PB clusters were derived. Patients with severe MS-related fatigue show three distinct and homogeneous PB subtypes. These PB subtypes, based on a unique set of PB outcome measures, may offer an opportunity to design more individually-tailored interventions in rehabilitation. Clinical trial registration no ISRCTN 82353628 , ISRCTN 69520623 and ISRCTN 58583714 .