mHealth Intervention for Elevated Blood Pressure Among College Students: Single-Arm Intervention Study
Bibliographic record
Abstract
BACKGROUND: Current evidence reveals a growing pattern of hypertension among young adults, significantly increasing their risk for cardiovascular disease later in life. Young adults, particularly those of college age, often develop risk factors related to lifestyle choices in diet, exercise, and alcohol consumption. Developing useful interventions that can assist with screening and possible behavioral modifications that are suitable and appealing to college-aged young adults could help with early identification and intervention for hypertension. Recent studies indicate mobile health (mHealth) apps are acceptable and effective for communication and message delivery among this population. OBJECTIVE: The purpose of this study was to examine the feasibility of using a mobile smartphone delivery system that provides tailored messages based on participant self-measured blood pressure (BP) with college-aged young adults. METHODS: Using a single-arm intervention, pilot study design, the mHealth to Optimize BP Improvement (MOBILE) intervention was implemented with college students aged 18 years to 39 years who had systolic BP >120 mm Hg and diastolic BP ≥80 mm Hg. Participants were required to measure their BP daily for 28 days, submit the readings to the app, and receive preset educational text messages tailored to their BP value and related to encouraging healthy lifestyle modifications. Changes in a participant's BP was evaluated using a mixed regression model, and a postintervention survey evaluated their perspectives on the mHealth intervention. RESULTS: The participants' (N=9) mean age was 22.64 (SD 4.54) years; 56% (5/9) were overweight, and 11% (1/9) were obese. The average daily participation rate was 86%. Of the 9 participants, 8 completed the survey, and all indicated the intervention was easy to use, found it increased awareness of their individual BP levels, indicated the text messages were helpful, and reported making lifestyle changes based on the study intervention. They also provided suggestions for future implementation of the intervention and program. Overall, no significant changes were noted in BP over the 28 days. CONCLUSIONS: The mHealth-supported MOBILE intervention for BP monitoring and tailored text messaging was feasible to implement, as our study indicated high rates of participation and acceptability. These encouraging findings support further development and testing in a larger sample over a longer time frame and hold the potential for early identification and intervention among college-aged adults, filling a gap in current research.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".