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?
Background: Medically unexplained physical symptoms (MUPS) are a leading cause of reduced work functioning. It is not known which factors are associated with reduced work functioning in people with moderate MUPS. Insight in these factors can contribute to prevention of reduced work functioning, associated work-related costs and in MUPS becoming chronic. Therefore, the aim of this study was to identify which demographic and health-related factors are associated with reduced work functioning, operationalized as impaired work performance and absenteeism, in people with moderate MUPS. Methods: Data of 104 participants from an ongoing study on people with moderate MUPS were used in this cross-sectional study. Ten independent variables were measured at baseline to determine their association with reduced work functioning: severity of psychosocial symptoms (four domains, measured with the Four-Dimensional Symptom Questionnaire), physical health (RAND 36-Item Health Survey), moderate or vigorous physical activity (Activ8 activity monitor), age, sex, education level and duration of complaints. Two separate multivariable linear regression analyses were performed with backward stepwise selection, for both impaired work performance and absenteeism. Results: Absenteeism rate rose with 2.5 and 0.6% for every increased point on the Four-Dimensional Symptom Questionnaire for domain 'depression' (B = 0.025, SE = 0.009, p = .006) and domain 'somatization' (B = 0.006, SE = 0.003, p = .086), respectively. An R2 value of 0.118 was found. Impaired work performance rate rose with 0.2 and 0.5% for every increased point on the Four-Dimensional Symptom Questionnaire for domain 'distress' (B = 0.002, SE = 0.001, p = .084) and domain 'somatization' (B = 0.005, SE = 0.001, p < .001), respectively. An R2 value of 0.252 was found. Conclusions: Severity of distress, probability of a depressive disorder and probability of somatization are positively associated with higher rates of reduced work functioning in people with moderate MUPS. To prevent long-term absenteeism and highly impaired work performance severity of psychosocial symptoms seem to play a significant role. However, because of the low percentage of explained variance, additional research is necessary to gain insight in other factors that might explain the variance in reduced work functioning even better.
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
