Impact of a Health Coach–Led, Text-Based Digital Behavior Change Intervention on Weight Loss and Psychological Well-Being in Patients Receiving a Procedureless Intragastric Balloon Program: Prospective Single-Arm Study
Bibliographic record
Abstract
BACKGROUND: Digital health interventions show promise for weight management. However, few text-based behavior change interventions have been designed to support patients receiving intragastric balloons, and none have simultaneously evaluated weight loss, psychological well-being, and behavior change despite the crucial interplay of these factors in weight management. OBJECTIVE: This study aims to assess whether a health coach-led, asynchronous, text-based digital behavior change coaching intervention (DBCCI) delivered to participants receiving an intragastric balloon and its aftercare program was feasible and acceptable to participants and supported improved outcomes, including weight loss, psychological well-being, and lifestyle behavior change conducive to weight loss maintenance. METHODS: receiving a procedureless intragastric balloon (PIGB) at 5 bariatric clinics in the United Kingdom and the Netherlands. Participants received the DBCCI and the clinic-led PIGB aftercare program (remotely delivered) for 6 months after PIGB placement and then no intervention for an additional 6 months. The DBCCI was an evidence-based, personalized intervention wherein health coaches supported participants via exchanged asynchronous in-app text-based messages. Over the 12-month study, we assessed percentage of total body weight loss and psychological well-being via self-administered validated questionnaires (Warwick-Edinburgh Mental Wellbeing Scale, Generalized Anxiety Disorder Scale, Impact of Weight on Quality of Life-Lite-Clinical Trials Version, Loss of Control Over Eating Scale-Brief, Weight Efficacy Lifestyle Questionnaire-Short Form, and Barriers to Being Active Quiz). Participant engagement with and acceptability of the intervention were assessed via self-reported surveys. RESULTS: ) were included in the analysis. Mean total body weight loss was 13.5% (SEM 2.3%) at the end of the DBCCI and 11.22% (SEM 2.3%) at the 12-month follow-up (P<.001). Improvements were observed for all psychological well-being measures throughout the 12 months except for the Generalized Anxiety Disorder Scale (improvement at month 1) and Barriers to Being Active Quiz (improvements at months 3 and 6). Surveys showed high levels of engagement with and acceptability of the DBCCI. CONCLUSIONS: This study provides evidence that the health coach-led, asynchronous, text-based DBCCI was engaging and acceptable to participants with overweight and obesity. The DBCCI, delivered alongside the PIGB and its aftercare program, supported improved weight loss outcomes and psychological well-being versus baseline and was associated with lifestyle behavior changes known to help achieve and maintain long-term weight loss and improved health outcomes. Follow-up findings suggest a potential need for longer-term, more intense coaching to focus on weight loss maintenance and support ongoing self-coaching. This could be achieved by leveraging generative artificial intelligence to provide ongoing automated behavior change coaching support to augment human-led care. TRIAL REGISTRATION: ClinicalTrials.gov NCT05884606; https://clinicaltrials.gov/study/NCT05884606.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".