A Digital Behavior Change Intervention for Health Promotion for Adults in Midlife: Protocol for a Multidimensional Assessment Study
Notice bibliographique
Résumé
BACKGROUND: To support lifelong health promotion and disease prevention, Santé publique France studied the methodology for building a social marketing scheme with a digital intervention targeting middle-aged adults, specifically socioeconomically disadvantaged groups. The digital intervention aims to encourage people aged 40-55 years to look after their health in the short and medium terms by adopting small actions relating to 8 health determinants: nutrition, physical activity, smoking, alcohol, stress, cognitive health, sleep, and environmental health. In the long term, the intervention intends to prevent frailty and reduce the burden of multimorbidities in older age, particularly for lower socioeconomic groups. OBJECTIVE: This study aims to measure behavior changes among registered users of the future website. The protocol assesses the impact of the website based on users' implementation of small actions relating to the 8 health determinants. Specifically, it intends to evaluate the website's performance in terms of engaging a specific population, triggering behavior change, raising awareness about a multifactorial approach to health, and encouraging user interaction with the website's resources. METHODS: The methodology is based on clinical assessments developed alongside the website according to the functionalities offered to registered users in their personalized space. The assessment tool design draws on logic models for digital interventions, and their consistency for digital applications is verified. The target audience is clearly defined from the outset. The protocol sets out a 3-step assessment: upon registration, after 3 weeks of use, and after 10 weeks of use (end of assessment). Users are divided into 2 groups (socioeconomically disadvantaged users and others) to characterize differences and make corrections. The protocol uses a mixed assessment approach based on website traffic and user login data. Specific and identifiable behavior changes are documented by monitoring the same individuals from T0 to T2, using verbatim comments to classify them into profiles and conducting semistructured individual interviews with a sample of users. RESULTS: The protocol creates a multidimensional assessment of digital intervention, showing that during a given timeline, interactions with users can reveal their capabilities, opportunities, and motivations to adopt healthy lifestyles. The protocol's principles were integrated into the development of a personal account to assess users' behavior changes. Given the delayed launch of the website, no recruitment or effects analysis of the protocol took place. CONCLUSIONS: As no multidimensional assessment protocol is currently available for digital behavior change interventions, our methods reveal that the different framework stages can strengthen the effect measurement, consolidate the choice of assumptions used within the logic model and steer the digital intervention toward action while reducing the burden of information. The suitability of the assessment protocol remains to be evaluated given the delayed launch of the website. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/60559.
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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 | 0,034 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,004 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,045 | 0,011 |
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 ».