Understanding Physical Activity Determinants in an HIV Self-Management Intervention: Qualitative Analysis Guided by the Theory of Planned Behavior
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,007 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».