Stroke is the second most common cause of death and the third leading cause of disability worldwide,1,2 with the burden expected to increase during the next 20 years.1 Almost 40% of the people with stroke have a recurrent stroke within 10 years,3 making secondary prevention vital.3,4 High amounts of sedentary time have been found to increase the risk of cardiovascular disease,5–11 particularly when the sedentary time is accumulated in prolonged bouts.12–15 Sedentary behavior, is defined as “any waking behavior characterized by an energy expenditure ≤1.5 Metabolic Equivalent of Task (METs) while in a sitting, reclining or lying posture”.16,17 Studies in healthy people, as well as people with diabetes and obesity, have shown that reducing the total amount of sedentary time and/or breaking up long periods of uninterrupted sedentary time, reduces metabolic risk factors associated with cardiovascular disease.6,9,10,12–15 Recent studies have shown that people living in the community after stroke spend more time each day sedentary, and more time in uninterrupted bouts of sedentary time compared to age-matched healthy peers.18–20 Reducing sedentary time and breaking up long sedentary bouts with short bursts of activity may be a promising intervention to reduce the risk of recurrent stroke and other cardiovascular diseases in people with stroke. To develop effective interventions, it is important to understand the factors associated with sedentary time in people with stroke. Previous studies have found associations between self-reported physical function after stroke and total sedentary time, but inconsistent results with regards to the relationship of age, stroke severity, and walking speed with sedentary time.20,21 These results are from secondary analyses of single-site observational studies, not powered to address associations, and inconsistent in the methods used to determine waking hours; thus making direct comparisons between studies difficult.20,21 Individual participant data pooling, with consistent processing of wake time data, allows novel exploratory analyses of larger datasets with greater power. By pooling all available individual participant data internationally, this study aimed to comprehensively explore the factors associated with sedentary time in community-dwelling people with stroke. Specifically, our research questions were: (1) What factors are associated with total sedentary time during waking hours after stroke? (2) What factors are associated with time spent in prolonged sedentary bouts during waking hours?
Objective: Despite the increasing availability of eRehabilitation, its use remains limited. The aim of this study was to assess factors associated with willingness to use eRehabilitation. Design: Cross-sectional survey. Subjects: Stroke patients, informal caregivers, health-care professionals. Methods: The survey included personal characteristics, willingness to use eRehabilitation (yes/no) and barri-ers/facilitators influencing this willingness (4-point scale). Barriers/facilitators were merged into factors. The association between these factors and willingness to use eRehabilitation was assessed using logistic regression analyses. Results: Overall, 125 patients, 43 informal caregivers and 105 healthcare professionals participated in the study. Willingness to use eRehabilitation was positively influenced by perceived patient benefits (e.g. reduced travel time, increased motivation, better outcomes), among patients (odds ratio (OR) 2.68; 95% confidence interval (95% CI) 1.34–5.33), informal caregivers (OR 8.98; 95% CI 1.70–47.33) and healthcare professionals (OR 6.25; 95% CI 1.17–10.48). Insufficient knowledge decreased willingness to use eRehabilitation among pa-tients (OR 0.36, 95% CI 0.17–0.74). Limitations of the study include low response rates and possible response bias. Conclusion: Differences were found between patients/informal caregivers and healthcare professionals. Ho-wever, for both groups, perceived benefits of the use of eRehabilitation facilitated willingness to use eRehabili-tation. Further research is needed to determine the benefits of such programs, and inform all users about the potential benefits, and how to use eRehabilitation. Lay Abstract The use of digital eRehabilitation after stroke (e.g. in serious games, e-consultation and education) is increasing. However, the use of eRehabilitation in daily practice is limited. As a first step in increasing the use of eRehabilitation in stroke care, this study examined which factors influence the willingness of stroke patients, informal caregivers and healthcare professionals to use eRehabilitation. Beliefs about the benefits of eRehabilitation were found to have the largest positive impact on willingness to use eRehabilitation. These benefits included reduced travel time, increased adherence to therapy or motivation, and better health outcomes. The willingness to use eRehabilitation is limited by a lack of knowledge about how to use eRehabilitation.
MULTIFILE
Alongside the growing number of older persons, the prevalence of chronic diseases is increasing, leading to higher pressure on health care services. eHealth is considered a solution for better and more efficient health care. However, not every patient is able to use eHealth, for several reasons. This study aims to provide an overview of: (1) sociodemographic factors that influence the use of eHealth; and (2) suggest directions for interventions that will improve the use of eHealth in patients with chronic disease. A structured literature review of PubMed, ScienceDirect, Association for Computing Machinery Digital Library (ACMDL), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) was conducted using four sets of keywords: “chronic disease”, “eHealth”, “factors”, and “suggested interventions”. Qualitative, quantitative, and mixed-method studies were included. Four researchers each assessed quality and extracted data. Twenty-two out of 1639 articles were included. Higher age and lower income, lower education, living alone, and living in rural areas were found to be associated with lower eHealth use. Ethnicity revealed mixed outcomes. Suggested solutions were personalized support, social support, use of different types of Internet devices to deliver eHealth, and involvement of patients in the development of eHealth interventions. It is concluded that eHealth is least used by persons who need it most. Tailored delivery of eHealth is recommended
