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An Adaptive, Algorithm-based Text Message Intervention to Promote Health Behavior Adherence in Type 2 Diabetes: Treatment Development and Proof-of-Concept Trial
- Source :
- J Diabetes Sci Technol
- Publication Year :
- 2021
- Publisher :
- SAGE Publications, 2021.
-
Abstract
- Background: Most individuals with type 2 diabetes (T2D) struggle to adhere to one or more health behaviors. Text message interventions (TMIs) have the potential to improve adherence but have had mixed effects on diet and activity in T2D. We developed an eight-week, adaptive, algorithm-driven TMI to promote physical activity, diet, self-care, and well-being. Then, in a single-arm trial, we assessed its feasibility, acceptability, and preliminary efficacy in 15 individuals with T2D and suboptimal adherence. Methods: Participants received daily text messages and were asked to rate the utility of each message (0=not helpful, 10=very helpful). These ratings were used by an algorithm to select subsequent messages based on each participant’s prior ratings. We assessed intervention feasibility by rates of message transmission/response and acceptability through ratings of message utility and burden. Finally, we examined pre-post changes in diabetes self-care, diet, physical activity, and psychological outcomes and calculated effect sizes (Cohen’s d). Results: All text messages were delivered, and participants provided ratings for 79% of messages, above our a priori thresholds for feasibility. Participants rated the individual messages and overall TMI as subjectively useful (utility: 8.1 [SD=2.1] and 7.8 [SD=2.0], respectively) and not burdensome (burden: 0.8 [SD=1.8]). The intervention led to significant, medium- to large-sized improvements in self-care ( d=0.77), diet ( d=0.99), and activity ( d=0.61) but minimal change in psychological outcomes. Conclusions: The TMI was feasible and well-accepted, and it led to promising improvements in adherence-related outcomes. These findings should be confirmed in a larger randomized controlled trial.
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- J Diabetes Sci Technol
- Accession number :
- edsair.doi.dedup.....003b2f2945195107cdc60bc2ab048fbe