Designing Patient-Centered Text Messaging Interventions for Increasing Physical Activity Among Participants With Type 2 Diabetes: Qualitative Results From the Text to Move Intervention
Notice bibliographique
Résumé
BACKGROUND: Type 2 diabetes mellitus (T2DM) is a disease affecting approximately 29.1 million people in the United States, and an additional 86 million adults have prediabetes. Diabetes self-management education, a complex health intervention composed of 7 behaviors, is effective at improving self-care behaviors and glycemic control. Studies have employed text messages for education, reminders, and motivational messaging that can serve as "cues to action," aiming to improve glucose monitoring, self-care behaviors, appointment attendance, and medication adherence. OBJECTIVES: The Text to Move (TTM) study was a 6-month 2-parallel group randomized controlled trial of individuals with T2DM to increase physical activity, measured by a pedometer. The intervention arm received text messages twice daily for 6 months that were tailored to the participant's stage of behavior change as defined by the transtheoretical model of behavior change. METHODS: We assessed participants' attitudes regarding their experience with text messaging, focusing on perceived barriers and facilitators, through two focus groups and telephone interviews. All interviews were audiorecorded, transcribed verbatim, coded, and analyzed using a grounded theory approach. RESULTS: The response rate was 67% (31/46 participants). The average age was 51.4 years and 61% (19/31 participants) were male. The majority of individuals were English speakers and married, had completed at least 12th grade and approximately half of the participants were employed full-time. Overall, participants were satisfied with the TTM program and recalled the text messages as educational, informational, and motivational. Program involvement increased the sense of connection with their health care center. The wearing of pedometers and daily step count information served as motivational reminders and created a sense of accountability through the sentinel effect. However, there was frustration concerning the automation of the text message program, including the repetitiveness, predictability of text time delivery, and lack of customization and interactivity of text message content. Participants recommended personalization of texting frequency as well as more contact time with personnel for a stronger sense of support, including greater surveillance and feedback based on their own results and comparison to other participants. CONCLUSIONS: Participants in a theory-based text messaging intervention identified key facilitators and barriers to program efficacy that should be incorporated into future texting interventions to optimize participant satisfaction and outcomes. TRIAL REGISTRATION: Clinicaltrials.gov NCT01569243; http://clinicaltrials.gov/ct2/show/NCT01569243 (Archived by Webcite at http://www.webcitation.org/6pfH6yXag).
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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 | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,007 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».