Incorporation of a Stress Reducing Mobile App in the Care of Patients With Type 2 Diabetes: A Prospective Study
Bibliographic record
Abstract
BACKGROUND: Severe and sustained emotional stress creates a physiological burden through increased sympathetic activity and higher energy demand. This may lead to increased oxidative stress and development of the metabolic syndrome. Emotional stress has been shown to contribute to the onset, progression, and control of type 2 diabetes (T2D). Stress management and biofeedback assisted relaxation have been shown to improve glycemic control. Use of a mobile app for stress management may enhance the scalability of such an approach. OBJECTIVE: The aim of this study was to assess the effect of using a mobile app of biofeedback-assisted relaxation on weight, blood pressure (BP), and glycemic measures of patients with T2D. METHODS: Adult patients with T2D and inadequate glycemic control (hemoglobin A1c [HbA1c]>7.5%) were recruited from the outpatient diabetes clinic. Baseline weight, BP, HbA1c, fasting plasma glucose (FPG), triglycerides (TG), and 7-point self-monitoring of blood glucose were measured. Patients were provided with a stress reducing biofeedback mobile app and instructed to use it 3 times a day. The mobile app-Serenita-is an interactive relaxation app based on acquiring a photoplethysmography signal from the mobile phone's camera lens, where the user places his finger. The app collects information regarding the user's blood flow, heart rate, and heart rate variability and provides real-time feedback and individualized breathing instructions in order to modulate the stress level. All clinical and biochemical measures were repeated at 8 and 16 weeks of the study. The primary outcome was changes in measures at 8 weeks. RESULTS: Seven patients completed 8 weeks of the study and 4 completed 16 weeks. At week 8, weight dropped by an average of 4.0 Kg (SD 4.3), systolic BP by 8.6 mmHg (SD 18.6), HbA1c by 1.3% (SD 1.6), FPG by 4.3 mmol/l (4.2), and serum TG were unchanged. CONCLUSIONS: Stress reduction using a mobile app based on biofeedback may improve glycemic control, weight, and BP.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".