Purpose: Some types of sedentary behaviors tend to cluster in individuals or groups of older adults. Insight into how these different types of sedentary behavior cluster is needed, as recent research suggests that not all types of sedentary behavior may have the same negative effects on physical and mental health. Therefore, the aim of this study was to identify sex-specific typologies of older adults’ sedentary behavior, and to examine their associations with health-related and socio-demographic factors.

Methods: Cross-sectional data were collected as part of the BEPAS Seniors, and the Busschaert study among 696 Flemish older adults (60+). Typologies of self-reported sedentary behavior were identified using latent profile analysis, and associations with health-related and sociodemographic factors were examined using analyses of variances.

Results: Five distinct typologies were identified from seven sedentary behaviors (television time, computer time, transport-related sitting time, sitting for reading, sitting for hobbies, sitting for socializing and sitting for meals) in men, and three typologies were identified from six sedentary behaviors (television time, transport-related sitting time, sitting for reading, sitting for hobbies, sitting for socializing and sitting for meals) in women. Typologies that are characterized by high television time seem to be related to more negative health outcomes, like a higher BMI, less grip strength, and a lower physical and mental health-related quality-of-life. Typologies that are represented by high computer time and motorized transport seem to be related to more positive health outcomes, such as a lower body mass index, more grip strength and a higher physical and mental health-related quality-of-life.

Conclusions: Although causal direction between identified typologies and health outcomes remains uncertain, our results suggests that future interventions should better focus on specific types of sedentary behavior (e.g. television time), or patterns of sedentary behavior, rather than on total sedentary behavior.

Background: Evidence on whether leisure-time physical activity (LTPA) facilitates individuals’ adoption of multiple healthy behaviors remains scarce. This study investigated the associations of longitudinal LTPA trajectories from childhood to adulthood with diet, screen time, smoking, binge drinking, sleep difficulties, and sleep duration in adulthood.

Methods: Data were drawn from the Cardiovascular Risk in Young Finns Study. Participants were aged 9-18 years (N=3553; 51% females) in 1980 and 33-49 years at the latest follow-up in 2011. The LTPA trajectories were identified using a latent profile analysis. Differences in self-reported health-related behaviors across the LTPA trajectories were studied separately for women and men. Models were adjusted for age, BMI, education level, marital status, total energy intake and previous corresponding behaviors.

Results: Persistently active, persistently low-active, decreasingly and increasingly active trajectories were identified in both genders and an additional inactive trajectory for women. After adjusting the models, the inactive women had an unhealthier diet than the women in the other trajectories (p<0.01; effect size(ES)>0.50). The low-active men followed an unhealthier diet than the persistently and increasingly active men (p<0.01; ES>0.50). Compared to their inactive and low-active peers, smoking frequency was lower in the increasingly active women and men (p<0.01; ES>0.20) and persistently active men (p<0.05; ES>0.20). The increasingly active men reported lower screen time than the low-active (p<0.001; ES>0.50) and persistently active (p<0.05; ES>0.20) men. The increasingly and persistently active women reported fewer sleep difficulties than the inactive (p<0.001; ES>0.80) and low-active (p<0.05; ES>0.50 and >0.80, respectively) women. Sleep duration and binge drinking were not associated with the LTPA trajectories in either gender, nor were sleep difficulties in men and screen time in women.

Conclusions: Not only persistently higher LTPA but an increasing tendency to engage in LTPA after childhood/adolescence were associated with healthier diet and lower smoking frequency in both genders, having less sleep difficulties in women and lower screen time in increasingly active men. Inactivity and low activity were associated with the accumulation of several unhealthy behaviors in adulthood. Associations were stronger in women.

Purpose

The effects of concurrent changes in adiposity and physical activity over time with mortality are not well understood. In this study we examined the joint-association of changes in adiposity and physical activity with cardiovascular disease (CVD), cancer, and all-cause mortality. We also examined the dose-response associations of changes in abdominal adiposity with mortality across changes in physical activity categories

Methods

A repeated measures analysis of 146,062 men and women (age = 37.0 ± 12.4; female = 50.5%) from the MJ prospective cohort. Linear regression against elapsed time was used to calculate overall physical activity and adiposity change. Participants were categorized into sex-specific tertiles of physical activity and waist to hip ratio. Hazard ratio (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression models for all-cause mortality. To estimate joint-associations for cause-specific mortality, we used the Fine-Gray subdistribution method to reduce bias from competing risks. Dose-response relationships for changes in adiposity across physical activity levels were modelled with restricted cubic spline functions.

 

 Results/findings

A total of 7,405 deaths (1,451 due to CVD and 3,163 due to cancer) occurred during 2,011,063 person-years and an average follow-up of 13.8 years (4.8) years. There was a statistically significant interaction between changes in adiposity and physical activity (p< 0.01). The dose-response curve of changes in adiposity across physical activity level was U-shaped. Adiposity stability over time was associated with significant reductions in mortality risk, regardless of changes in physical activity. The largest reductions for all-cause mortality risk were among participants who maintained adiposity levels and concurrently maintained (HR [95%CI]: 0.36 [0.31-0.41]) or increased (0.38 [0.34-0.44]) physical activity. Among participants with stable adiposity, similar patterns were observed for CVD and cancer mortality risk among physical activity maintainers and increasers (e.g. the CVD mortality risk was 0.37 [0.27-0.50]) for maintainers and (0.34 [0.25-0.46]) for increasers). We observed similar results for BMI

Conclusion

Fluctuations in adiposity over time were associated with increased CVD, cancer, and all-cause mortality. Our results suggest that adiposity stability while preventing reductions in physical activity over time may mitigate mortality risk.

Purpose: Electrically-assisted bicycles (EAB) may offer an opportunity to maintain or increase older adults’ cycling levels and consequently their health, functionality and life space area. However, there is a dearth of research on the longitudinal effects of EAB, particularly in a non-experimental setting and among older adults. This observational longitudinal study aimed to examine the effects of EAB use on older adults’ cycling frequencies, self-rated health, functionality and life space area.

Methods: Baseline survey data were collected among 887 Flemish and Dutch older cyclists (≥ 65 years) who were recruited through several channels (e.g., a research panel, senior organizations). One year later, 225 older adults (response rate= 25.4%) completed follow-up measurements. Participants self-reported socio-demographics, frequencies of conventional and electrically-assisted cycling, self-rated health, functionality and life space area. Participants were classified into four EAB groups; not using EAB at both time points (n=113), using EAB at both time points (n= 86), started using EAB (n= 17) and stopped using EAB (n= 9). Non-parametric repeated measures ANOVAs and Wilcoxon signed rank tests were applied.

Results: A significant interaction effect between time and EAB group was observed for total cycling frequency (F= 11.4, df= 3, p<0.001). Total cycling frequency significantly decreased in those not using EAB at both time points (z= -3.1, p<0.01, effect size r= -0.3), those using EAB at both time points (z= -2.6, p= 0.01, r= -0.3) and those who stopped using EAB (z= -2.7, p<0.01, r= -0.9). Total cycling frequency significantly increased among those who started using EAB (z= -3.2, p= 0.001, r= -0.8). A significant interaction effect between time and EAB group was observed for functionality (F= 5.5, df= 3, p= 0.001). Functionality non-significantly decreased in all EAB groups, except among those who stopped using EAB for whom functionality remained stable. No significant interaction effects were observed for self-rated health and life space area.

Conclusions: Policies stimulating EAB use may increase cycling levels among older adults. Future studies with longer follow-up periods should examine effects of EAB use on (objectively-assessed) total physical activity levels and health outcomes and potential harmful effects (i.e. crash risk).

Purpose: Just-in-time-adaptive-interventions (JITAIs) provide real-time ‘in the moment’ behaviour change support to people when they need it most. JITAIs can be delivered via smartphones, at scale, to target populations, but none so far have targeted older adults. This study describes the feasibility of delivering a novel, personalised, JITAI designed to support older adults to increase their physical activity (PA) level.

Methods: Using the Behaviour Change Wheel and COM-B framework, we developed a mobile app (JitaBug) that delivered just-in-time prompts tailored using real-time PA data (Fitbit Charge 4) and weather conditions, to encourage achievement of personalised PA goals. We tested the feasibility of the JITAI with older adults in a 6-week trial using a mixed-methods approach. The intervention was delivered entirely remotely. Physical activity was measured using a wrist-worn accelerometer during a baseline and follow-up period, and using intra-day Fitbit data throughout the intervention. Contextual details on PA were gathered using a voice-based ecological momentary assessment approach (snippets). Mental wellbeing was assessed weekly and mood was assessed twice weekly using short forms of the Warwick-Edinburgh Mental Wellbeing Scale and the Multidimensional Mood State Questionnaire, respectively. Feasibility outcomes included: (1) recruitment and retention, (2) intervention fidelity, (3) data collection processes and identification of missing data. 

Results: Initially, 46 participants consented to participate but 5 withdrew prior to, and 10 withdrew during, the intervention. In total, 31 older adults (mean ± SD; 65.5 ± 5.4 years) completed the intervention. The fidelity of the intervention was high; 27 participants were successfully onboarded and set activity goals, 94% of PA message prompts were successfully delivered, and 99% of Fitbit and 100% of weather data calls were successful. Accelerometer data were obtained from 96% at baseline and 96% at follow up. Of a possible 496 snippets, 212 (43%) valid recordings were obtained. On average, participants recorded 8/16 (50%) snippets, 3/8 (38%) mood assessments, and 2/4 (50%) wellbeing assessments via the app.

Conclusions: Smartphone-delivered JITAIs are a feasible way to reach older adults and provide them with remote support to increase their PA level. While low-burden self-report assessments are possible via smartphones, passive sensing is more successful.