Long‐Term Weight Loss in Adults With Overweight or Obesity Using a Breath Biofeedback mHealth App: A One‐Year Follow‐Up of a Randomized Trial
Bibliographic record
Abstract
ABSTRACT Background Long‐term weight loss success with dietary interventions is notoriously limited. Mobile health (mHealth) interventions offering personalized dietary guidance combined with real‐time biofeedback may enhance long‐term adherence and provide a sustainable solution for weight management. Objectives This study reports the prespecified secondary outcome of weight loss at 48 weeks from a parallel‐arm randomized clinical trial (ClinicalTrials.gov: NCT04165707) that aimed to evaluate the long‐term effectiveness and sustainability of a Mediterranean‐style low‐carbohydrate diet delivered via an mHealth application paired with breath biofeedback compared with a calorie‐restricted low‐fat diet application. Methods Adults with overweight or obesity ( N = 155; mean ± SD age, 41 ± 11 years; 71% female; BMI, 33.5 ± 4.7 kg/m 2 ) were randomized to either an intervention promoting a Mediterranean‐style low‐carbohydrate diet combined with biofeedback from a handheld breath acetone device or an evidence‐based comparator promoting a calorie‐restricted, low‐fat diet. Participants recorded their daily weights using Bluetooth scales. Weight loss over 48 weeks was analyzed using a linear mixed‐effects model, incorporating all available daily weight measurements from participants who provided at least one follow‐up measurement. Results At 48 weeks, participants using the breath biofeedback mHealth app achieved clinically meaningful weight loss (−9.54 kg, 95% CI: −12.27 to −6.81 kg). In contrast, participants using the low‐fat diet app did not achieve statistically significant weight loss (−2.68 kg, 95% CI: −5.49 to 0.14 kg), resulting in a statistically significant between‐group difference (−6.9 kg, 95% CI: −10.8 to −2.9, p < 0.001). No adverse effects were reported in either group. Conclusions This study demonstrates that a Mediterranean‐style diet promoting carbohydrate restriction coupled with biofeedback support delivered via an mHealth app results in clinically meaningful sustained weight loss at 48 weeks. Given its practicality and demonstrated effectiveness, this approach presents a promising non‐pharmacological alternative or complement for longer‐term weight management.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".