A Pilot Study Examining Patient Attitudes and Intentions to Adopt Assistive Technologies Into Type 2 Diabetes Self-Management
Bibliographic record
Abstract
Approximately half of individuals living with type 2 diabetes mellitus (T2DM) have suboptimal self-management, which could be improved by using assistive technologies in self-management regimes. This study examines patient attitudes and intentions to adopt assistive technologies into T2DM self-management. Forty-four participants (M = 58.7 years) with T2DM were recruited from diabetes education classes in the southwestern Ontario, Canada, between February and April 2014. Participants completed a self-reported in-person survey assessing demographic characteristics, current diabetes management, and attitudes toward using assistive technologies in their diabetes self-management. Demographics, disease characteristics, and current technology use and preferences of the cohort were examined, followed by a correlational analysis of descriptive characteristics and attitudes and intentions to use technology in self-management. The majority of (but not all) participants felt that using Internet applications (65%) and smartphone (53.5%) applications for self-management was a good idea. The majority of participants did not currently use an Internet (92.5%) or mobile (96%) application for self-management. Of participants, 77% intended to use an Internet application to manage their diabetes in the future and 58% intended to use mobile applications. Younger age was associated with more positive attitudes (r = -.432, P = .003) and intentions (r = -.425, P = .005) to use assistive technologies in diabetes self-management. Findings suggest that patients, especially those younger in age, are favorable toward adopting assistive technologies into management practice. However, attitudes among older adults are less positive, and few currently make use of such technologies in any age group.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".