Effectiveness of Mobile Phone and Web-Based Interventions for Diabetes and Obesity Among African American and Hispanic Adults in the United States: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) and web-based technological advances allow for new approaches to deliver behavioral interventions for chronic diseases such as obesity and diabetes. African American and Hispanic adults experience a disproportionate burden of major chronic diseases. OBJECTIVE: This paper reviews the evidence for mHealth and web-based interventions for diabetes and obesity in African American and Hispanic adults. METHODS: Literature searches of PubMed/Medline, The Cochrane Library, EMBASE, CINAHL Plus, Global Health, Scopus, and Library & Information Science Source were conducted for relevant English-language articles. Articles identified through searches were reviewed by 2 investigators and, if they met the inclusion criteria, were extracted and assessed for risk of bias. Findings were summarized in tabular and narrative format. The overall strength of the evidence was assessed as high, moderate, low, or insufficient on the basis of risk of bias, consistency of findings, directness, precision, and other limitations. RESULTS: Searches yielded 2358 electronic publications, 196 reports were found to be eligible for inclusion, and 7 studies met the eligibility criteria. All 7 included studies were randomized control trials. Five studies evaluated the effectiveness of an mHealth intervention for weight loss, including one that evaluated the effectiveness for diabetes and two studies focused on diabetes. Of all the studies that focused on weight loss, 3 reported significant differences in weight loss in participants in the intervention group compared with those in the usual care group. Although all studies on diabetes control showed greater improvement in glycemic control for the intervention group compared to that in the control group, only one study showed a significant difference between the 2 groups. CONCLUSIONS: This analysis indicates that there are few published studies that assessed mHealth interventions among minority populations and focused on weight or diabetes. Although the overall strength of evidence was low for diabetes control, it was moderate for weight loss, and our findings suggest that mHealth and web-based interventions may provide a promising approach for interventions among African American and Hispanic adults who have obesity or diabetes.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".