Applying Meaning and Self-Determination Theory to the Development of a Web-Based mHealth Physical Activity Intervention: Proof-of-Concept Pilot Study
Notice bibliographique
Résumé
BACKGROUND: Meaning in life is positively associated with health, well-being, and longevity, which may be partially explained by engagement in healthier behaviors, including physical activity (PA). However, promoting awareness of meaning is a behavior change strategy that has not been tested in previous PA interventions. OBJECTIVE: This study aims to develop, refine, and pilot-test the Meaningful Activity Program (MAP; MAP to Health), a web-based mobile health PA intervention, theoretically grounded in meaning and self-determination theory, for insufficiently active middle-aged adults. METHODS: Following an iterative user-testing and refinement phase, we used a single-arm double baseline proof-of-concept pilot trial design. Participants included 35 insufficiently active adults in midlife (aged 40-64 years) interested in increasing their PA. After a 4-week baseline period, participants engaged in MAP to Health for 8 weeks. MAP to Health used a web-based assessment and just-in-time SMS text messaging to individualize the intervention; promote meaning salience; support the basic psychological needs of autonomy, competence, and relatedness; and increase PA. Participants completed measures of the hypothesized mechanisms of behavior change, including meaning salience, needs satisfaction, and autonomous motivation at pretest (-4 weeks), baseline (0 weeks), midpoint (4 weeks), and posttest (8 weeks) time points, and wore accelerometers for the study duration. At the end of the intervention, participants completed a qualitative interview. Mixed models compared changes in behavioral mechanisms during the intervention to changes before the intervention. Framework matrix analyses were used to analyze qualitative data. RESULTS: Participants were aged 50.8 (SD 8.2) years on average; predominantly female (27/35, 77%); and 20% (7/35) Asian, 9% (3/35) Black or African American, 66% (23/35) White, and 6% (2/35) other race. Most (32/35, 91%) used MAP to Health for ≥5 of 8 weeks. Participants rated the intervention as easy to use (mean 4.3, SD 0.8 [out of 5.0]) and useful (mean 4.3, SD 0.6). None of the hypothesized mechanisms changed significantly during the preintervention phase (Cohen d values <0.15). However, autonomy (P<.001; Cohen d=0.76), competence (P<.001; Cohen d=0.65), relatedness (P=.004; Cohen d=0.46), autonomous motivation (P<.001; Cohen d=0.37), and meaning salience (P<.001; Cohen d=0.40) increased significantly during the intervention. Comparison of slopes before the intervention versus during the intervention revealed that increases during the intervention were significantly greater for autonomy (P=.002), competence (P<.001), and meaning salience (P=.001); however, slopes were not significantly different for relatedness (P=.10) and autonomous motivation (P=.17). Qualitative themes offered suggestions for improvement. CONCLUSIONS: MAP to Health was acceptable to participants, feasible to deliver, and associated with increases in the target mechanisms of behavior change. This is the first intervention to use meaning as a behavior change strategy in a PA intervention. Future research will test the efficacy of the intervention in increasing PA compared to a control condition.
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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 | 0,022 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».