Exploring the Use of Personal Technology in Type 2 Diabetes Management Among Ethnic Minority Patients: Cross-Sectional Analysis of Survey Data from the Lifestyle Intervention for the Treatment of Diabetes Study (LIFT Diabetes)
Bibliographic record
Abstract
BACKGROUND: Minority populations have higher morbidity from chronic diseases and typically experience worse health outcomes. Internet technology may afford a low-cost method of ongoing chronic disease management to promote improved health outcomes among minority populations. OBJECTIVE: The objective of our study was to assess the feasibility of capitalizing on the pervasive use of technology as a secondary means of delivering diabetic counseling though an investigation of correlates to technology use within the context of an ongoing diabetes intervention study. METHODS: The Lifestyle Intervention for the Treatment of Diabetes study (LIFT Diabetes) randomly assigned 260 overweight and obese adults with type 2 diabetes mellitus to 2 intervention arms. At baseline, we administered a survey evaluating access to and use of various technologies and analyzed the responses using descriptive statistics and logistic regression. RESULTS: levels (P=.003). Minority participants were less likely to have a home computer (106/142, 74.7% vs 110/118, 93.2%; P<.001) and less likely to have email access at home (P=.03). Ownership of a home computer was correlated to higher income (P<.001), higher educational attainment (P<.001), full-time employment (P=.01), and ownership of a smartphone (P=.001). Willingness to complete questionnaires online was correlated to higher income (P=.001), higher education (P<.001), full-time employment (P=.01), and home access to a computer, internet, and smartphone (P≤.05). Racial disparities in having a home computer persisted after controlling for demographic variables and owning a smartphone (adjusted OR 0.26, 95% CI 0.10-0.67; P=.01). Willingness to complete questionnaires online was driven by ownership of a home computer (adjusted OR 3.87, 95% CI 1.14-13.2; P=.03). CONCLUSIONS: Adults who self-identified as being part of a minority group were more likely to report limited access to technology than were white adults. As ownership of a home computer is central to a willingness to use online tools, racial disparities in access may limit the potential of Web-based interventions to reach this population. TRIAL REGISTRATION: ClinicalTrials.gov NCT01806727; https://clinicaltrials.gov/ct2/show/NCT01806727 (Archived by WebCite at http://www.webcitation.org/6xOq2b7Tv).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".