Optimizing Testimonials for Behavior Change in a Digital Intervention for Binge Eating: Human-Centered Design Study
Notice bibliographique
Résumé
Background: Testimonials from credible sources are an evidence-based strategy for behavior change. Behavioral health interventions have used testimonials to promote health behaviors (eg, physical activity and healthy eating). Integrating testimonials into eating disorder (ED) interventions poses a nuanced challenge because ED testimonials can promote ED behaviors. Testimonials in ED interventions must therefore be designed carefully. Some optimal design elements of testimonials are known, but questions remain about testimonial speakers, messaging, and delivery, especially for ED interventions. Objective: We sought to learn how to design and deliver testimonials focused on positive behavior change strategies within our multisession digital binge eating intervention. Methods: We applied human-centered design methods to learn users' preferences for testimonial speakers, messaging, and delivery (modalities, over time, and as "nudges" for selecting positive behavior change strategies they could practice). We recruited target users of our multisession intervention to complete design sessions. Adults (N=22, 64% self-identified as female; 32% as non-Hispanic Black, 41% as non-Hispanic White, and 27% as Hispanic) with recurrent binge eating and obesity completed individual interviews. Data were analyzed using methods from thematic analysis. Results: Most participants preferred designs with testimonials (vs without) for their motivation and validation of the intervention's efficacy. A few distrusted testimonials for appearing too "commercial" or personally irrelevant. For speakers, participants preferred sociodemographically tailored testimonials and were willing to report personal data in the intervention to facilitate tailoring. For messaging, some preferred testimonials with "how-to" advice, whereas others preferred "big picture" success stories. For delivery interface, participants were interested in text, video, and multimedia testimonials. For delivery over time, participants preferred testimonials from new speakers to promote engagement. When the intervention allowed users to choose between actions (eg, behavioral strategies), participants preferred testimonials to be available across all actions but said that selectively delivering a testimonial with one action could "nudge" them to select it. Conclusions: Results indicated that intervention users were interested in testimonials. While participants preferred sociodemographically tailored testimonials, they said different characteristics mattered to them, indicating that interventions should assess users' most pertinent identities and tailor testimonials accordingly. Likewise, users' divided preferences for testimonial messaging (ie, "big picture" vs "how-to") suggest that optimal messaging may differ by user. To improve the credibility of testimonials, which some participants distrusted, interventions could invite current users to submit testimonials for future integration in the intervention. Aligned with nudge theory, our findings indicate testimonials could be used as "nudges" within interventions-a ripe area for further inquiry-though future work should test if delivering a testimonial only with the nudged choice improves its uptake. Further research is needed to validate these design ideas in practice, including evaluating their impact on behavior change toward improving ED behaviors.
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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 | 0,045 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».