Web-Based and Mobile Delivery of an Episodic Future Thinking Intervention for Overweight and Obese Families: A Feasibility Study
Bibliographic record
Abstract
BACKGROUND: The bias toward immediate gratification is associated with maladaptive eating behaviors and has been cross-sectionally and prospectively related to obesity. Engaging in episodic future thinking, which involves mental self-projection to pre-experience future events, reduces this bias and energy intake in overweight/obese adults and children. To examine how episodic future thinking can be incorporated into clinical interventions, a Web-based system was created to provide training for adults and children in their everyday lives. OBJECTIVE: Our study examined the technical feasibility, usability, and acceptability of a Web-based system that is accessible by mobile devices and adapts episodic future thinking for delivery in family-based obesity interventions. METHODS: We recruited 20 parent-child dyads (N=40) from the surrounding community and randomized to episodic future thinking versus a nutritional information thinking control to test the feasibility of a 4-week Web-based intervention. Parents were 44.1 (SD 7.8) years of age with BMI of 34.2 (SD 6.8) kg/m(2). Children were 11.0 (SD 1.3) years of age with BMI percentile of 96.0 (SD 1.8). Families met weekly with a case manager for 4 weeks and used the system daily. Adherence was collected through the Web-based system, and perceived acceptance of the Web-based system was assessed postintervention. Measurements of body composition and dietary intake were collected at baseline and after the 4 weeks of intervention. RESULTS: All 20 families completed the intervention and attended all sessions. Results showed parents and children had high adherence to the Web-based system and perceived it to be easy to use, useful, and helpful. No differences between conditions were found in adherence for parents (P=.65) or children (P=.27). In addition, results suggest that basic nutrition information along with episodic future thinking delivered through our Web-based system may reduce energy intake and weight. CONCLUSIONS: We showed that our Web-based system is an accepted technology and a feasible utility. Furthermore, results provide initial evidence that our system can be incorporated into family-based treatments targeting behaviors related to weight control. These results show promising utility in using our Web-based system in interventions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".