Feasibility Randomized Controlled Trial of ImpulsePal: Smartphone App–Based Weight Management Intervention to Reduce Impulsive Eating in Overweight Adults
Bibliographic record
Abstract
BACKGROUND: ImpulsePal is a theory-driven (dual-process), evidence-informed, and person-centered smartphone app intervention designed to help people manage impulsive processes that prompt unhealthy eating to facilitate dietary change and weight loss. OBJECTIVE: The aims of this study were to (1) assess the feasibility of trial procedures for evaluation of the ImpulsePal intervention, (2) estimate standard deviations of outcomes, and (3) assess usability of, and satisfaction with, ImpulsePal. METHODS: , and having access to an Android-based device. Weight was measured (as the proposed primary outcome for a full-scale trial) at baseline, 1 month, and 3 months of follow-up. Participants were randomized in a 2:1 allocation ratio to the ImpulsePal intervention or a waiting list control group. A nested action-research study allowed for data-driven refinement of the intervention across 2 cycles of feedback. RESULTS: We screened 179 participants for eligibility, and 58 were randomized to the intervention group and 30 to the control group. Data were available for 74 (84%, 74/88) participants at 1 month and 67 (76%, 67/88) participants at 3 months. The intervention group (n=43) lost 1.03 kg (95% CI 0.33 to 1.74) more than controls (n=26) at 1 month and 1.01 kg (95% CI -0.45 to 2.47) more than controls (n=43 and n=24, respectively) at 3 months. Feedback suggested changes to intervention design were required to (1) improve receipt and understanding of instructions and (2) facilitate further engagement with the app and its strategies. CONCLUSIONS: The evaluation methods and delivery of the ImpulsePal app intervention are feasible, and the trial procedures, measures, and intervention are acceptable and satisfactory to the participants. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN): 14886370; http://www.isrctn.com/ISRCTN14886370 (Archived by WebCite at http://www.webcitation.org/76WcEpZ51).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".