Associations Between Rural or Urban Status, Health Outcomes and Behaviors, and COVID-19 Perceptions Among Meditation App Users: Longitudinal Survey Study
Bibliographic record
Abstract
BACKGROUND: Rural and urban differences in health outcomes and behaviors have been well-documented, with significant rural health disparities frequently highlighted. Mobile health (mHealth) apps, such as meditation apps, are a novel method for improving health and behaviors. These apps may be a critical health promotion strategy during the COVID-19 pandemic and could potentially be used to address rural health disparities. However, limited research has assessed whether meditation app health outcomes are associated with rural and urban residence, and it is unclear whether disparities in health and behaviors between rural and urban populations would persist among meditation app users. OBJECTIVE: We aimed to explore associations between rural or urban status, psychological outcomes, and physical activity among users of a mobile meditation app. We further aimed to explore associations between rural or urban status and perceived effects of COVID-19 on stress, mental health, and physical activity, and to explore changes in these outcomes in rural versus urban app users over time. METHODS: This study was a secondary analysis of a national survey conducted among subscribers to the meditation app Calm. Eligible participants completed online baseline surveys from April to June 2020, and follow-up surveys from June to September 2020, assessing demographics, psychological outcomes, physical activity, and perceived effects of COVID-19 on stress, mental health, and physical activity. RESULTS: Participants (N=8392) were mostly female (7041/8392, 83.9%), non-Hispanic (7855/8392, 93.6%), and White (7704/8392, 91.8%); had high socioeconomic status (income ≥US $100,000: 4389/8392, 52.3%; bachelor's degree or higher: 7251/8392, 86.4%); and resided in a metropolitan area core (rural-urban commuting area code 1: 7192/8392, 85.7%). Rural or urban status was not associated with baseline stress, depression, anxiety, pre-COVID-19 and current physical activity, or perceived effects of COVID-19 on stress, mental health, and physical activity. Repeated-measures models showed overall decreases in depression, anxiety, and perceived effects of COVID-19 on physical activity from baseline to follow-up, and no significant changes in stress or perceived effects of COVID-19 on stress and mental health over time. Models also showed no significant main effects of rural or urban status, COVID-19 statewide prevalence at baseline, or change in COVID-19 statewide prevalence. CONCLUSIONS: We did not find associations between rural or urban status and psychological outcomes (ie, stress, depression, and anxiety), physical activity, or perceived effects of COVID-19 on stress, mental health, and physical activity. Rural or urban status does not appear to drive differences in outcomes among meditation app users, and the use of mHealth apps should continue to be explored as a health promotion strategy in both rural and urban populations. Furthermore, our results did not show negative cumulative effects of COVID-19 on psychological outcomes and physical activity among app users in our sample, the majority of whom were urban, White, female, and of high socioeconomic status. Further research is needed to investigate meditation app use as a health promotion strategy in rural and urban populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".