Characteristics of Tailored Text Messages that Maximize Physical Activity amongst Cardiac Rehabilitation Enrollees: Secondary Analysis of a Micro randomized Trial (Preprint)
Notice bibliographique
Résumé
BACKGROUND: Emerging data suggest that text message-based mobile health interventions may enhance physical activity levels in patients with cardiovascular disease enrolled in cardiac rehabilitation. The optimal characteristics of texts that lead to maximal patient engagement and drive meaningful behavioral change are not well understood. OBJECTIVE: This study aimed to understand how text- and participant-level characteristics impact physical activity levels after text delivery. METHODS: The VALENTINE (Virtual Application-Supported Environment to Increase Exercise) study was a randomized controlled trial designed to evaluate a mobile health intervention delivered to low- and moderate-risk adults enrolled in cardiac rehabilitation. Embedded within this study was a microrandomized trial focused on the effect of texts on physical activity levels among intervention participants. Participants in the intervention group received texts through a smartwatch (Apple Watch or Fitbit Versa) that were tailored to the time of day, day of the week (weekday vs weekend), weather, and time since enrollment in cardiac rehabilitation. Texts also differed in content type (walking vs antisedentary) and in the level of personalization (inclusion of the participant's name or not). Delivery was randomized at 4 user-selected time points daily, with participants having a 25% probability of receiving a text at any time point. The primary outcome was step count 60 minutes after a decision point. This analysis focuses on the text- and participant-level factors that moderated the intervention's effect on the primary outcome. Given potential measurement differences determined a priori, analyses were stratified by device type and phase of cardiac rehabilitation and adjusted for age, sex, and baseline activity status using a generalization of regression analysis. RESULTS: More than 70,552 randomizations occurred in 108 participants (mean age 59.5, SD 10.7 years; n=36, 33.3% female; n=19, 17.6% non-White; n=68, 63% Apple Watch users) over 6 months. Overall, no text characteristics (including personalization with the participant's name) or participant characteristics (including baseline physical activity) consistently impacted text responsiveness for either device type. Although the findings were not consistently significant between device types and across phases of the trial, there was a trend toward increased responsiveness to texts that promoted walking (compared to antisedentary texts) and that were delivered to younger (aged <65 years) and male participants. CONCLUSIONS: In this randomized clinical trial, we found that tailored texts improved physical activity levels among cardiac rehabilitation enrollees in the initiation phase, but this effect was not explained by text- or participant-level moderators. Additional work is needed to explore the impact of tailoring based on an extended set of personal and environmental factors to optimize the delivery and efficacy of text message-based interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT04587882; https://clinicaltrials.gov/study/NCT04587882. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.ahj.2022.02.012.
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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 | 0,003 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 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 ».