Effect of a Gender-Tailored eHealth Weight Loss Program on the Depressive Symptoms of Overweight and Obese Men: Pre-Post Study
Bibliographic record
Abstract
BACKGROUND: Obesity and depression are of two of the largest contributors to the global burden of disease in men. Although lifestyle behavior change programs can improve participants' weight and depressive symptoms, the evidence is limited by a lack of male participants and a reliance on face-to-face treatment approaches, which are not accessible or appealing for many men. OBJECTIVE: This study examined the effect of a gender-tailored electronic health (eHealth) program on the depressive symptoms of a community sample of overweight and obese men with or without depression. A secondary aim was to determine whether the eHealth, self-directed format of the program was a feasible and acceptable treatment approach for the subgroup of men with depression at baseline. METHODS: In total, 209 overweight/obese men from the Hunter Region of Australia were assessed before and after completing a self-administered eHealth weight loss program over 3 months. To increase engagement, most program elements were socio-culturally targeted to appeal specifically to men and included printed materials, a DVD, motivational text messages, online- or app-based self-monitoring, and other weight loss tools (eg, pedometer). Depressive symptoms were measured with the validated 8-item Patient Health Questionnaire (PHQ-8). Program feasibility and acceptability were assessed with a process questionnaire plus recruitment and retention rates. Changes in depressive symptoms and weight were examined using intention-to-treat linear mixed models, adjusted for the centered baseline score and other covariates. Effect sizes were estimated with Cohen's d. RESULTS: At baseline, the mean weight and age of the sample was 105.7 kg (standard deviation [SD] 14.0) and 46.6 years (SD 11.3), respectively. Overall, 36 men (36/209, 17.2%) were experiencing depression (PHQ-8 score ≥10). Retention rates were comparable between men with and without depression (32/36, 88.9% vs 145/173, 83.8%; P=.44). At posttest, depressive symptoms had reduced by 1.8 units (95% CI 1.3 to 2.3; P<.001; d=0.5) for the whole sample. These improvements were particularly notable in the subgroup of men with depression (-5.5 units; P<.001; d=1.0) and 72.2% (26/36) of this subgroup no longer met the criterion for depression at posttest. A corresponding, albeit smaller, intervention effect on depressive symptoms was also observed in men without depression (-1.0 units; P<.001; d=0.4). The overall intervention effect on weight was -4.7 kg (d=1.3), which did not vary significantly by depression status. Program acceptability, feasibility, and online engagement metrics were also comparable between men with and without depression. CONCLUSIONS: A gender-tailored eHealth lifestyle program generated short-term improvements in the mental health of overweight and obese men, particularly for men with depression at baseline. Despite receiving no personalized support, men with depression reported high levels of satisfaction and engagement with the program. As such, a longer-term controlled trial testing an adapted version of the program for this subgroup is warranted. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12612000749808; https://www.anzctr.org.au/ Trial/Registration/TrialReview.aspx?id=362575 (Archived by WebCite at http://www.webcitation.org/6wJvbRsNW).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".