Mobile Phone–Based Telemedicine Practice in Older Chinese Patients with Type 2 Diabetes Mellitus: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Previous studies on telemedicine interventions have shown that older diabetic patients experience difficulty in using computers, which is a barrier to remote communication between medical teams and older diabetic patients. However, older people in China tend to find it easy to use mobile phones and personal messaging apps that have a user-friendly interface. Therefore, we designed a mobile health (mHealth) system for older people with diabetes that is based on mobile phones, has a streamlined operation interface, and incorporates maximum automation. OBJECTIVE: The goal of the research was to investigate the use of mobile phone-based telemedicine apps for management of older Chinese patients with type 2 diabetes mellitus (T2DM). Variables of interest included efficacy and safety. METHODS: A total of 91 older (aged over 65 years) patients with T2DM who presented to our department were randomly assigned to one of two groups. Patients in the intervention group (n=44) were provided glucometers capable of data transmission and received advice pertaining to medication, diet, and exercise via the mHealth telemedicine system. Patients assigned to the control group (n=47) received routine outpatient care with no additional intervention. Patients in both groups were followed up at regular 3-month intervals. RESULTS: After 3 months, patients in the intervention group showed significant (P<.05) improvement in postprandial plasma glucose level. After 6 months, patients in the intervention group exhibited a decreasing trend in postprandial plasma glucose and glycated hemoglobin levels compared with the baseline and those in the control group (P<.05). CONCLUSIONS: Mobile phone-based telemedicine apps help improve glycemic control in older Chinese patients with T2DM. TRIAL REGISTRATION: China Clinical Trial Registration Center ChiCTR 1800015214; http://www.chictr.org.cn/showprojen.aspx?proj=25949 (Archived by WebCite at http://www.webcitation.org/73wKj1GMq).
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".