Trajectories of Physical Activity During a 6-Month Mobile App-Based Lifestyle Modification Intervention in Physically Inactive Adults With Cardiovascular Risk Factors

Abstract

Mobile app-based health promotion allows frequent and longitudinal monitoring of lifestyle modifications for cardiovascular prevention. We explored the trajectories of physical activity (PA) during mobile app-based disease management programs in adults with cardiovascular risk using group-based trajectory modeling (GBTM). Male participants who had cardiovascular risk factors, a step count < 8000 steps/day, and completed a 6-month lifestyle intervention using a mobile app were included. Daily step counts were recorded using a wrist-worn activity tracker. GBTM was used to identify distinct patterns of longitudinal step-count changes during lifestyle interventions. We included 1369 participants in the analysis (median age, 55 years). GBTM identified three step-count trajectory groups: Group 1, 32.5% (mean step count: baseline, 4823 steps/day; 3-month, 4837 step/day; 6-month, 5221 steps/day); Group 2, 50.5% (mean step count: baseline, 6676 steps/day; 3-month, 7453 step/day; 6-month, 7656 steps/day); Group 3, 17.0% (mean step count: baseline, 7506 steps/day; 3-month, 10 960 step/day; 6-month, 11 178 steps/day). In the univariate analysis, older age, lower body mass index, lower level of triglyceride, and longer mobile app usage time were associated with trajectories with greater PA. Multivariate logistic regression analysis showed that older age (odds ratio 1.03 per 1 year; 95% confidence interval 1.01-1.06; p = 0.018) and longer app usage time for the first two weeks (1.02 per 1 min/day; 1.01-1.04; p = 0.021) were significantly associated with Group 3. GBTM identified a distinct trajectory pattern of increasing PA within the first three months of lifestyle intervention that was associated with age and mobile app use.

Keywords: cardiovascular risk; cluster analysis; physical activity; remote intervention.

FIGURE 1

Participant selection flowchart.

FIGURE 2

Physical activity trajectories identified in group‐based trajectory modeling.

FIGURE 3

Spaghetti plots with data from the individuals comprising each trajectory group.

FIGURE 4

Application usage time of each trajectory group. Kruskal‐Wallis test showed the significant difference in app usage time during the 6‐month program among the groups (p = 0.005). The Group 2 showed shorter app usage time compared to Group 1 (p = 0.013) and Group 3 (p = 0.006).