Personalized Coaching via Texting for Behavior Change to Understand a Healthy Lifestyle Intervention in a Naturalistic Setting: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Digital health interventions, such as personalized SMS coaching, are considered affordable and scalable methods to support healthy lifestyle changes. SMS, or texting, is a readily available service to most people in Sweden, and personalized SMS coaching has shown great promise in supporting behavior changes. OBJECTIVE: This study aims to explore the effectiveness of highly personalized SMS coaching for behavior change according to the Capability, Opportunity, Motivation-Behavior (COM-B) model on a sample of physically inactive adults in a nonprofit fitness organization in Sweden. METHODS: The study used a mixed methods design in which clients acted as their own controls. The participants were clients (n=28) and fitness consultants (n=12). Three types of data were collected: (1) quantitative data at baseline and after the SMS intervention and the waitlist from the clients, (2) qualitative data from semistructured interviews with the fitness consultants, and (3) pseudonymized texting conversations between the fitness consultants and clients. RESULTS: Overall, the results showed that personalized SMS coaching was effective in supporting the clients' behavior changes. The quantitative analysis showed how the clients' capabilities (Cohen d=0.50), opportunities (Cohen d=0.43), and relationship with the fitness consultants (Cohen d=0.51) improved during the SMS intervention in comparison with baseline. Furthermore, the qualitative analysis revealed how personalized texts added value to existing work methods (eg, increasing continuity and flexibility) and how the relationship between the clients and fitness consultants changed during the intervention, which helped motivate the clients. CONCLUSIONS: Personalized SMS coaching is an effective method for supporting healthy behavior changes. The human connection that emerged in this study needs to be further explored to fully understand the effectiveness of a digital health intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".