Patients with coronary artery disease (CAD) are more sedentary compared with the general population, but contemporary cardiac rehabilitation (CR) programmes do not specifically target sedentary behaviour (SB). We developed a 12-week, hybrid (centre-based+home-based) Sedentary behaviour IntervenTion as a personaLisEd Secondary prevention Strategy (SIT LESS). The SIT LESS programme is tailored to the needs of patients with CAD, using evidence-based behavioural change methods and an activity tracker connected to an online dashboard to enable self-monitoring and remote coaching. Following the intervention mapping principles, we first identified determinants of SB from literature to adapt theory-based methods and practical applications to target SB and then evaluated the intervention in advisory board meetings with patients and nurse specialists. This resulted in four core components of SIT LESS: (1) patient education, (2) goal setting, (3) motivational interviewing with coping planning, and (4) (tele)monitoring using a pocket-worn activity tracker connected to a smartphone application and providing vibrotactile feedback after prolonged sedentary bouts. We hypothesise that adding SIT LESS to contemporary CR will reduce SB in patients with CAD to a greater extent compared with usual care. Therefore, 212 patients with CAD will be recruited from two Dutch hospitals and randomised to CR (control) or CR+SIT LESS (intervention). Patients will be assessed prior to, immediately after and 3 months after CR. The primary comparison relates to the pre-CR versus post-CR difference in SB (objectively assessed in min/day) between the control and intervention groups. Secondary outcomes include between-group differences in SB characteristics (eg, number of sedentary bouts); change in SB 3 months after CR; changes in light-intensity and moderate-to-vigorous-intensity physical activity; quality of life; and patients’ competencies for self-management. Outcomes of the SIT LESS randomised clinical trial will provide novel insight into the effectiveness of a structured, hybrid and personalised behaviour change intervention to attenuate SB in patients with CAD participating in CR.
MULTIFILE
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: To investigate the level of agreement of the behavioural mapping method with an accelerometer to measure physical activity of hospitalized patients. DESIGN: A prospective single-centre observational study. SETTING: A university medical centre in the Netherlands. SUBJECTS: Patients admitted to the hospital. MAIN MEASURES: Physical activity of participants was measured for one day from 9 AM to 4 PM with the behavioural mapping method and an accelerometer simultaneously. The level of agreement between the percentages spent lying, sitting and moving from both measures was evaluated using the Bland-Altman method and by calculating Intraclass Correlation Coefficients. RESULTS: In total, 30 patients were included. Mean (±SD) age was 63.0 (16.8) years and the majority of patients were men (n = 18). The mean percentage of time (SD) spent lying was 47.2 (23.3) and 49.7 (29.8); sitting 42.6 (20.5) and 40.0 (26.2); and active 10.2 (6.1) and 10.3 (8.3) according to the accelerometer and observations, respectively. The Intraclass Correlation Coefficient and mean difference (SD) between the two measures were 0.852 and -2.56 (19.33) for lying; 0.836 and 2.60 (17.72) for sitting; and 0.782 and -0.065 (6.23) for moving. The mean difference between the two measures is small (⩽2.6%) for all three physical activity levels. On patient level, the variation between both measures is large with differences above and below the mean of ⩾20% being common. CONCLUSION: The overall level of agreement between the behavioural mapping method and an accelerometer to identify the physical activity levels 'lying', 'sitting' and 'moving' of hospitalized patients is reasonable.
Movebite aims to combat the issue of sedentary behavior prevalent among office workers. A recent report of the Nederlandse Sportraad reveal a concerning trend of increased sitting time among Dutch employees, leading to a myriad of musculoskeletal discomforts and significant health costs for employers due to increased sick leave. Recognizing the critical importance of addressing prolonged sitting in the workplace, Movebite has developed an innovative concept leveraging cutting-edge technology to provide a solution. The Movebite app seamlessly integrates into workplace platforms such as Microsoft Teams and Slack, offering a user-friendly interface to incorporate movement into their daily routines. Through scalable AI coaching and real-time movement feedback, Movebite assists individuals in scheduling and implementing active micro-breaks throughout the workday, thereby mitigating the adverse effects of sedentary behavior. In collaboration with the Avans research group Equal Chance on Healthy Choices, Movebite conducts user-centered testing to refine its offerings and ensure maximum efficacy. This includes testing initiatives at sports events, where the diverse crowd provides invaluable feedback to fine-tune the app's features and user experience. The testing process encompasses both quantitative and qualitative approaches based on the Health Belief Model. Through digital questionnaires, Movebite aims to gauge users' perceptions of sitting as a health threat and the potential benefits of using the app to alleviate associated risks. Additionally, semi-structured interviews delve deeper into user experiences, providing qualitative insights into the app's usability, look, and feel. By this, Movebite aims to not only understand the factors influencing adoption but also to tailor its interventions effectively. Ultimately, the goal is to create an environment encouraging individuals to embrace physical activity in small, manageable increments, thereby fostering long-term engagement promoting overall well-being.Through continuous innovation and collaboration with research partners, Movebite remains committed to empowering individuals to lead healthier, more active lifestyles, one micro-break at a time.
