Abstract
Introduction: The Dietary Approaches to Stop Hypertension dietary pattern is a proven way to manage hypertension, but adherence remains low. Dietary tracking applications offer a highly disseminable way to self-monitor intake on the pathway to reaching dietary goals but require consistent engagement to support behavior change. Few studies use longitudinal dietary self-monitoring data to assess trajectories and predictors of engagement. We used dietary self-monitoring data from participants in Dietary Approaches to Stop Hypertension Cloud (N=59), a feasibility trial to improve diet quality among women with hypertension, to identify trajectories of engagement and explore associations between participant characteristics. Methods: We used latent class growth modeling to identify trajectories of engagement with a publicly available diet tracking application and used bivariate and regression analyses to assess the associations of classifications of engagement with participant characteristics. Results: We identified 2 latent classes of engagement: consistent engagers and disengagers. Consistent engagers were more likely to be older, more educated, and married or living with a partner. Although consistent engagers exhibited slightly greater changes in Dietary Approaches to Stop Hypertension score, the difference was not significant. Conclusions: This study highlights an important yet underutilized methodologic approach for uncovering dietary self-monitoring engagement patterns. Understanding how certain individuals engage with digital technologies is an important step toward designing cost-effective behavior change interventions. Trial registration: This study is registered at www.clinicaltrials.gov NCT03215472.
| Original language | English (US) |
|---|---|
| Article number | 100037 |
| Journal | AJPM Focus |
| Volume | 1 |
| Issue number | 2 |
| DOIs | |
| State | Published - Dec 2022 |
Keywords
- DASH
- Latent class analysis
- diet quality
- engagement
- hypertension
- mhealth
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health
- Health Informatics
- Epidemiology
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