Development of a Digital Behavioral Intervention to Reduce the Consumption of Sugar-Sweetened Beverages Among Rural Appalachian Adults: Multiphased, Human-Centered Design Approach
Notice bibliographique
Résumé
BACKGROUND: To avoid the low engagement and limited efficacy of digital behavioral health interventions, robust human-centered design (HCD) processes are needed. OBJECTIVE: The primary objective of this study was to describe a flexible, step-by-step HCD process to develop digital behavioral health interventions by illustrating iSIPsmarter as an example. iSIPsmarter is a digital intervention for reducing the consumption of sugar-sweetened beverages (SSBs) that comprises 6 internet-based cores metered out over time to deliver the program content, an integrated SMS text message strategy to engage users in reporting SSB behaviors, and an electronic cellular-enabled scale for in-home weighing. The secondary objective is to illustrate the key components and characteristics of iSIPsmarter that resulted from the HCD process. METHODS: The methods were guided by the Model for Internet Interventions and by best practices in HCD and instructional design processes (eg, rapid prototype development and think-aloud protocol). The 3-phased (ie, contextual, prototype testing, end user testing phases) process followed in this study included a series of 13 semistructured one-on-one interviews with 7 advisory team participants from the targeted Appalachian user group. The interviews were content coded by 2 researchers and then deductively coded to the suggested areas of digital behavioral health interventions. RESULTS: The participants provided rich perspectives pertaining to iSIPsmarter's appearance, behavioral prescriptions, burdens, content, delivery, message, participation, and assessment. These inputs included requests for built-in flexibility to account for varying internet and SMS text message accessibility among users; ideas to resolve the issues and problems encountered when using the prototypes, including those related to navigation and comprehension of content; ideas to enhance personalized feedback to support motivation and goal setting for SSB consumption and weight; and feedback to refine the development of realistic and relatable vignettes. The participants were able to interact with multiple prototype drafts, allowing researchers to capture and incorporate feedback related to the iSIPsmarter dashboard, daily SSB and weight diaries, action planning, core content, interactions, and vignettes. CONCLUSIONS: Using scientific models and established processes is critical for building robust and efficacious interventions. By applying an existing model and HCD and instructional design processes, we were able to identify assumptions and address the key areas of the iSIPsmarter intervention that were hypothesized to support users' engagement and promote behavior change. As evidenced by the rich feedback received from the advisory team members and the resulting iSIPsmarter product, the HCD methodology was instrumental in the development process. Although the final iSIPsmarter content is specific to improving SSB consumption behaviors among adults in rural areas, the intent is that this HCD process will have wide applications in the development of digital behavioral health interventions across multiple geographic and behavioral contexts.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».