Methodological Challenges in Randomized Controlled Trials of mHealth Interventions: Cross-Sectional Survey Study and Consensus-Based Recommendations
Notice bibliographique
Résumé
BACKGROUND: Mobile health (mHealth) refers to using mobile communication devices such as smartphones to support health, health care, and public health. mHealth interventions have their own nature and characteristics that distinguish them from traditional health care interventions, including drug interventions. Thus, randomized controlled trials (RCTs) of mHealth interventions present specific methodological challenges. Identifying and overcoming those challenges is essential to determine whether mHealth interventions improve health outcomes. OBJECTIVE: We aimed to identify specific methodological challenges in RCTs testing mHealth interventions' effects and develop consensus-based recommendations to address selected challenges. METHODS: A 2-phase participatory research project was conducted. First, we sent a web-based survey to authors of mHealth RCTs. Survey respondents rated on a 5-point scale how challenging they found 21 methodological aspects in mHealth RCTs compared to non-mHealth RCTs. Nonsystematic searches until June 2022 informed the selection of the methodological challenges listed in the survey. Second, a subset of survey respondents participated in an online workshop to discuss recommendations to address selected methodological aspects identified in the survey. Finally, consensus-based recommendations were developed based on the workshop discussion and email interaction. RESULTS: We contacted 1535 authors of mHealth intervention RCTs, of whom 80 (5.21%) completed the survey. Most respondents (74/80, 92%) identified at least one methodological aspect as more or much more challenging in mHealth RCTs. The aspects most frequently reported as more or much more challenging were those related to mHealth intervention integrity, that is, the degree to which the study intervention was implemented as intended, in particular managing low adherence to the mHealth intervention (43/77, 56%), defining adherence (39/79, 49%), measuring adherence (33/78, 42%), and determining which mHealth intervention components are used or received by the participant (31/75, 41%). Other challenges were also frequent, such as analyzing passive data (eg, data collected from smartphone sensors; 24/58, 41%) and verifying the participants' identity during recruitment (28/68, 41%). In total, 11 survey respondents participated in the subsequent workshop (n=8, 73% had been involved in at least 2 mHealth RCTs). We developed 17 consensus-based recommendations related to the following four categories: (1) how to measure adherence to the mHealth intervention (7 recommendations), (2) defining adequate adherence (2 recommendations), (3) dealing with low adherence rates (3 recommendations), and (4) addressing mHealth intervention components (5 recommendations). CONCLUSIONS: RCTs of mHealth interventions have specific methodological challenges compared to those of non-mHealth interventions, particularly those related to intervention integrity. Following our recommendations for addressing these challenges can lead to more reliable assessments of the effects of mHealth interventions on health outcomes.
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 | 0,890 | 0,933 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,007 |
| Méta-épidémiologie (sens large) | 0,010 | 0,012 |
| Bibliométrie | 0,019 | 0,014 |
| Études des sciences et des technologies | 0,008 | 0,015 |
| Communication savante | 0,017 | 0,024 |
| Science ouverte | 0,013 | 0,015 |
| Intégrité de la recherche | 0,017 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».