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BackgroundThere is a limited understanding of components that should be included in digital interventions for 24-hour movement behaviors (physical activity [PA], sleep, and sedentary behavior [SB]). For intervention effectiveness, user engagement is important. This can be enhanced by a user-centered design to, for example, explore and integrate user preferences for intervention techniques and features. ObjectiveThis study aimed to examine adult users’ preferences for techniques and features in mobile apps for 24-hour movement behaviors. MethodsA total of 86 participants (mean age 37.4 years [SD 9.2]; 49/86, 57% female) completed a Web-based survey. Behavior change techniques (BCTs) were based on a validated taxonomy v2 by Abraham and Michie, and engagement features were based on a list extracted from the literature. Behavioral data were collected using Fitbit trackers. Correlations, (repeated measures) analysis of variance, and independent sample t tests were used to examine associations and differences between and within users by the type of health domain and users’ behavioral intention and adoption. ResultsPreferences were generally the highest for information on the health consequences of movement behavior self-monitoring, behavioral feedback, insight into healthy lifestyles, and tips and instructions. Although the same ranking was found for techniques across behaviors, preferences were stronger for all but one BCT for PA in comparison to the other two health behaviors. Although techniques fit user preferences for addressing PA well, supplemental techniques may be able to address preferences for sleep and SB in a better manner. In addition to what is commonly included in apps, sleep apps should consider providing tips for sleep. SB apps may wish to include more self-regulation and goal-setting techniques. Few differences were found by users’ intentions or adoption to change a particular behavior. Apps should provide more self-monitoring (P=.03), information on behavior health outcome (P=.048), and feedback (P=.04) and incorporate social support (P=.048) to help those who are further removed from healthy sleep. A virtual coach (P

Background: Investigating the effects of breaking up sedentary behavior with short bouts of Yoga and Tai-Chi on glycemic control, concentration, and well-being in healthy individuals. Methods: In this randomized balanced incomplete block study, 15 adults (age = 26 [2.50] y, 8 females) completed 2 of 3 protocols: uninterrupted sitting (Control), sitting interrupted with 3 minutes of Yoga every 30 minutes, or with 3 minutes of Tai-Chi every 30 minutes. Protocols lasted 7.5 hours and included a standardized diet. Glucose was measured every 30 minutes with a glucometer (Abbott FreeStyle Libre). Concentration and well-being were recorded with self-reported ecological momentary assessment. Area under the curve was calculated for glucose data. Statistical analyses were performed as a hierarchical repeated-measures model. Results: Glucose area under the curve for the Yoga intervention (34.55 [3.12] mmol/L) was significantly lower than the Control (38.14 [3.18] mmol/L; P <.05). There was a trend toward lower glucose in the Tai-Chi group compared with the Control, but no significant differences were found (AUCTai-Chi = 36.64 [3.11] mmol/L; P =.57). Mean concentration in all groups decreased throughout the day, with the largest decrease in the Control. Well-being for the Yoga and Control groups decreased but increased with Tai-Chi. Concentration and well-being responses were not statistically significant between intervention groups. Conclusions: Breaking up sedentary behavior using 3-minute bouts of Yoga significantly lowers blood glucose in healthy individuals without compromising concentration or well-being. Tai-Chi did not provide the same significant effect on glucose levels but allowed better maintenance of concentration and well-being. These interventions provide effective ways to combat the deleterious effects of prolonged sedentary time while maintaining concentration and well-being. [ABSTRACT FROM AUTHOR]

AimThe aim of the study was to identify sociodemographic, disease-related, physical and mental health-related determinants of fatigue at 2-year follow-up in individuals with symptomatic knee osteoarthritis (OA).MethodsA longitudinal analysis of participants with symptomatic knee OA from the Multicenter Osteoarthritis Study (MOST) was conducted to identify predictors of fatigue at 2-year follow-up. Participants self-reported fatigue at baseline for the first time in the MOST cohort and at follow-up using a 0-10 visual analog scale. At baseline, questionnaires on sociodemographics, disease-related symptoms, physical and mental health factors were completed. Data were analyzed using linear regressions with a backwards elimination approach.ResultsOf the 2330 individuals in the MOST cohort at baseline, 576 had symptomatic knee OA and of these, 449 with complete fatigue values at baseline and follow-up were included in this analysis. Minimally important fatigue change (ie, worsening [≥1.13], no change [

Background and Aims Physical inactivity, sedentary behaviour (SB), and inadequate sleep are key behavioural risk factors of cardiometabolic diseases. Each behaviour is mainly considered in isolation, despite clear behavioural and biological interdependencies. The aim of this study was to investigate associations of five-part movement compositions with adiposity and cardiometabolic biomarkers. Methods Cross-sectional data from six studies (n = 15 253 participants; five countries) from the Prospective Physical Activity, Sitting and Sleep consortium were analysed. Device-measured time spent in sleep, SB, standing, light-intensity physical activity (LIPA), and moderate-vigorous physical activity (MVPA) made up the composition. Outcomes included body mass index (BMI), waist circumference, HDL cholesterol, total:HDL cholesterol ratio, triglycerides, and glycated haemoglobin (HbA1c). Compositional linear regression examined associations between compositions and outcomes, including modelling time reallocation between behaviours. Results The average daily composition of the sample (age: 53.7 ± 9.7 years; 54.7% female) was 7.7 h sleeping, 10.4 h sedentary, 3.1 h standing, 1.5 h LIPA, and 1.3 h MVPA. A greater MVPA proportion and smaller SB proportion were associated with better outcomes. Reallocating time from SB, standing, LIPA, or sleep into MVPA resulted in better scores across all outcomes. For example, replacing 30 min of SB, sleep, standing, or LIPA with MVPA was associated with −0.63 (95% confidence interval −0.48, −0.79), −0.43 (−0.25, −0.59), −0.40 (−0.25, −0.56), and −0.15 (0.05, −0.34) kg/m2 lower BMI, respectively. Greater relative standing time was beneficial, whereas sleep had a detrimental association when replacing LIPA/MVPA and positive association when replacing SB. The minimal displacement of any behaviour into MVPA for improved cardiometabolic health ranged from 3.8 (HbA1c) to 12.7 (triglycerides) min/day. Conclusions Compositional data analyses revealed a distinct hierarchy of behaviours. Moderate-vigorous physical activity demonstrated the strongest, most time-efficient protective associations with cardiometabolic outcomes. Theoretical benefits from reallocating SB into sleep, standing, or LIPA required substantial changes in daily activity. [ABSTRACT FROM AUTHOR]

Objective: The increasing health care costs associated with an ageing population and chronic disease burden are largely attributable to modifiable lifestyle factors that are complex and vary between individuals and settings. Traditional approaches to promoting healthy lifestyles have so far had limited success. Recently, co-creating public health interventions with end-users has been advocated to provide more effective and sustainable solutions. The aim of this study was to document and evaluate the co-creation of a public health intervention to reduce sedentary behaviour in older adults. Design: Community-dwelling older adults (N = 11, mean age = 74years) and academic researchers attended 10 interactive co-creation workshops together. Setting: Workshops took place on university campus and the co-creators completed fieldwork tasks outside the workshops. Method: Workshops were informed by the Participatory and Appreciative Action and Reflection methodology. Data were collected using field notes, video recording and worksheet tasks. Analysis was conducted using a qualitative content analysis approach. Results: The co-creators developed a tailored intervention delivered through a mode congruent with older adults' lives. Key elements of the intervention included (1) education on sedentary behaviour, (2) resources to interrupt sedentary behaviour, (3) self-monitoring, (4) action planning and (5) evaluating the benefits of interrupting sedentary behaviour. Conclusion: Co-creation is a feasible approach to develop public health interventions; however, it is limited by the lack of a systematic framework to guide the process. Future work should aim to develop principles and recommendations to ensure co-creation can be conducted in a more scientific and reproducible way. The effectiveness and scalability of the intervention should be assessed.