Prescribing Physical Activity for Healthy Aging: Longitudinal Follow-Up and Mixed Method Analysis of a Primary Care Intervention
Bibliographic record
Abstract
BACKGROUND: There is a shortage of literature describing the experience of individuals who have participated in a physical activity and mobile health (mHealth) intervention. Many physical activity interventions are of short duration and do not report long-term changes in clinical measures or adoption of prescribed health behaviors. Previously, we have reported the clinical and behavioral outcomes from the first phase of a physical activity prescription and mHealth intervention delivered through the primary care setting. The purpose of this next phase is to perform a longitudinal follow-up 6-months postintervention. METHODS: Mixed methods analysis including repeated measures ANOVA of functional aerobic capacity (VO2max) at preintervention, postintervention, and follow-up clinic visits, and whole text analysis of semistructured interviews discussing the participant experience in a health behavior intervention. RESULTS: Twenty participants, mean age 63 ± 5 years, participated. Gains made in VO2max were maintained at 6 months (P < 0.05). Participants reported engaging in sustained and routine physical activity, yet some identified a need for additional support to adopt the prescribed health behaviors. Emergent themes included the desire for short-term mHealth intervention to educate individuals about prescribed health behaviors without need for ongoing management by clinicians, leveraging mHealth to build social networks around prescribed health behaviors and to connect individuals to build a sense of community, and participant views of physical activity as medicine. CONCLUSIONS: The present study investigated both the long-term adoption of physical activity behaviors as well as the participant experience in a physical activity and mHealth intervention. Findings from the current study may be used to inform the development of user-centered lifestyle interventions.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".