Acceptability of Intervention Design Factors in mHealth Intervention Research: Experimental Factorial Study
Notice bibliographique
Résumé
BACKGROUND: With the growing interest in mobile health (mHealth), behavioral medicine researchers are increasingly conducting intervention studies that use mobile technology (eg, to support healthy behavior change). Such studies' scientific premises are often sound, yet there is a dearth of implementational data on which to base mHealth research methodologies. Notably, mHealth approaches must be designed to be acceptable to research participants to support meaningful engagement, but little empirical data about design factors influencing acceptability in such studies exist. OBJECTIVE: This study aims to evaluate the impact of two common design factors in mHealth intervention research-requiring multiple devices (eg, a study smartphone and wrist sensor) relative to requiring a single device and providing individually tailored feedback as opposed to generic content-on reported participant acceptability. METHODS: A diverse US adult convenience sample (female: 104/255, 40.8%; White: 208/255, 81.6%; aged 18-74 years) was recruited to complete a web-based experiment. A 2×2 factorial design (number of devices×nature of feedback) was used. A learning module explaining the necessary concepts (eg, behavior change interventions, acceptability, and tailored content) was presented, followed by four vignettes (representing each factorial cell) that were presented to participants in a random order. The vignettes each described a hypothetical mHealth intervention study featuring different combinations of the two design factors (requiring a single device vs multiple devices and providing tailored vs generic content). Participants rated acceptability dimensions (interest, benefit, enjoyment, utility, confidence, difficulty, and overall likelihood of participating) for each study presented. RESULTS: Reported interest, benefit, enjoyment, confidence in completing study requirements, and perceived utility were each significantly higher for studies featuring tailored (vs generic) content, and the overall estimate of the likelihood of participation was significantly higher. Ratings of interest, benefit, and perceived utility were significantly higher for studies requiring multiple devices (vs a single device); however, multiple device studies also had significantly lower ratings of confidence in completing study requirements, and participation was seen as more difficult and was associated with a lower estimated likelihood of participation. The two factors did not exhibit any evidence of statistical interactions in any of the outcomes tested. CONCLUSIONS: The results suggest that potential research participants are sensitive to mHealth design factors. These mHealth intervention design factors may be important for initial perceptions of acceptability (in research or clinical settings). This, in turn, may be associated with participant (eg, self) selection processes, differential compliance with study or treatment processes, or retention over time.
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 | 0,059 | 0,126 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
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 ».