Feasibility of Using Mobile Technology to Improve Physical Activity Among People Living with Diabetes in Asia
Bibliographic record
Abstract
Background: Chronic conditions such as diabetes (DM) and cardiovascular disease are associated with disability and poor quality of life. Asians are 30% more likely than non-Hispanic Whites to develop type 2 DM. The important roles of wearable technological applications or devices in maintaining individuals’ motivation to attain their physical activity (PA) goal have been reported. However, evidence of the feasibility and impact of the mobile technology on the PA in Thailand is limited. This study aims to examine the feasibility and the impact of the immediate performance feedback feature of the wearable device on PA. Methods: This pilot study recruited persons aged 18 or older with diabetes from two diabetes clinics in Chiang Mai, Thailand. Participants were randomly assigned into three groups: the aware group (AW: can see the step count on the device screen), the unaware group (UW: the device screen was completely covered), and the control group (usual care). Participants in the AW and UW groups were asked to wear the device for 2 full days while the usual care group did not wear the device. All participants completed a questionnaire package at their first study visit. Data analysis of univariate and multivariate general linear models were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC). All significance levels were set at 5%. Results: One hundred and fourteen participants age ranged from 39 to 75 years old were included in this analysis. The majority were female (n = 86, 69%) and married (n = 103, 82%). No adverse events were reported among device users. Within 2 days, there were less than 10% missing data and more than 70% of participants liked the devices mainly because they could see the step count. More than 63% of participants who wore the device had an average of steps greater than 10,000 per day. Although the number of steps and sleep hours were not significantly different between the AW and UW groups, 68% of the AW participants found that the device help them with their exercise. Discussion: The results demonstrate the feasibility of the use of the wearable device among people living with chronic conditions. Participants found that the step count screen provided immediate physical performance feedback that was helpful with their exercise. The behavioral changes, however, could not be examined due to the short duration of the usage. Future studies that require longer device usage in larger sample sizes are needed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| 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".