Understanding Physical Activity Determinants in an HIV Self-Management Intervention: Qualitative Analysis Guided by the Theory of Planned Behavior
Bibliographic record
Abstract
BACKGROUND: People living with HIV have long life expectancy and are experiencing more comorbid conditions, being at an increased risk for developing cardiovascular disease (CVD) and diabetes, further exacerbated due to the HIV or inflammatory process. One effective intervention shown to decrease mortality and improve health outcomes related to CVD and diabetes in people living with HIV is increased regular physical activity. However, people living with HIV often fall short of the daily recommended physical activity levels. While studies show that mobile health (mHealth) can potentially help improve people's daily activity levels and reduce mortality rates due to comorbid conditions, these studies do not specifically focus on people living with HIV. As such, it is essential to understand how mHealth interventions, such as wearables, can improve the physical activity of people living with HIV. OBJECTIVE: This study aimed to understand participants' experiences wearing a fitness tracker and an app to improve their physical activity. METHODS: In total, 6 focus groups were conducted with participants who completed the control arm of a 6-month randomized controlled trial (ClinicalTrials.gov NCT03205982). The control arm received daily walk step reminders to walk at least 5000 steps per day and focused on the overall wellness of the individual. The analysis of the qualitative focus groups used inductive content analysis using the theory of planned behavior as a framework to guide and organize the analysis. RESULTS: In total, 41 people living with HIV participated in the focus groups. The majority (n=26, 63%) of participants reported their race as Black or African American, and 32% (n=13) of them identified their ethnicity as Hispanic or Latino. In total, 9 major themes were identified and organized following the theory of planned behavior constructs. Overall, 2 major themes (positive attitude toward tracking steps and tracking steps is motivating) related to attitudes toward the behavior, 2 major themes (social support or motivation from the fitness tracker and app and encouragement from family and friends) related to participant's subjective norms, 1 theme (you can adjust your daily habits with time) related to perceived behavioral control, 2 themes (reach their step goal and have a healthier lifestyle) related to participant's intention, and 2 themes (continuing to walk actively and regularly wearing the fitness tracker) related to participant's changed behavior. Participants highlighted how the mHealth interface with the avatar and daily step tracking motivated them to both begin and continue to engage in physical activity by adjusting their daily routines. CONCLUSIONS: Findings from this study illustrate how features of mHealth apps may motivate people living with HIV to start and continue sustained engagement in physical activities. This sustained increase in physical activity is crucial for reducing the risk of comorbid conditions such as diabetes or CVD. TRIAL REGISTRATION: ClinicalTrials.gov NCT03205982; https://classic.clinicaltrials.gov/ct2/show/NCT03205982.
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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 | 0.028 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".