Acceptability of a Self-Regulation Theory-Based mHealth Behavior Intervention for Older Adults with Type 2 Diabetes and Obesity
Bibliographic record
Abstract
Background: The successful use of mobile health (mHealth) in lifestyle changes for older adults with type 2 diabetes is unknown. We report here acceptability of a mHealth intervention for older adults. Method: We used a one-group pre-posttest design. Participants received an 8-week theory-based mHealth intervention, using the Lose It! App for daily self-monitoring of food intake, a Fitbit, and Bluetooth-enabled glucometers and weighing scales. Linear mixed models were used for analysis. Results: The sample (N=9) was white (88.9%), female (44.4%), with a mean age of 76.4±6.0 years (range: 69-89), 15.7±2.0 years of education, BMI of 33.3±3.1 kg/m2 and HbA1c 7.4%±0.8. The Montreal Cognitive Assessment score was 24.6±2.7, indicating no severe cognitive impairment. Over 56 days, the % days of using the Lose It!, Fitbit, glucometer, and scales were 92.7±7.9, 93.7±10.6, 76.4±23.5, 52.4±37.8, respectively. The data showed a significant % weight loss (b=-0.04, p<.001), decreased calorie intake (b=-4.6, p=.0004) and increased steps (b1=34.0, b2=-1.0, p=.02) over time. The mean % weight loss from baseline was 4.44%±3.19. The dose of oral hypoglycemic agents or insulin was reduced among 4 participants. Conclusion: Older adults are able to use mHealth to improve outcomes. The additional 3-month follow-up is ongoing to provide insight into long-term feasibility and acceptability. Disclosure Y. Zheng: None. K. Weinger: None. M.C. Gregas: None. J. Greenberg: None. Z. Li: None. L.E. Burke: None. C. Qi: None. C. Slyne: None. T. Greaves: None. M. Munshi: Consultant; Self; Sanofi.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".