A Continuance Model for a Mobile/Web Based Self-Management System for Adolescent Diabetics: The Role of Loyalty Incentive
Bibliographic record
Abstract
It is estimated that 200 children per day worldwide develop Juvenile Diabetes (JD). There is no cure for JD, therefore treatment protocols focus on controlling the disease. Several information systems (IS) have been developed to help patients manage their chronic diseases, but often these systems suffer from reduced use over time or complete abandonment. Limited research has been conducted that examines continued usage in this domain. Through this study, our purpose is to build and evaluate a mobile/web based JD monitoring system combined with a rewards program designed to increase continued system use. We propose a comprehensive continuance intention model by combining the IS Continuance Model proposed by Bhattacherjee with DeLone and McLean’s IS Success Model. We also explore the role of the context specific constructs of Interaction Quality and Perceived Disease Management Effort and the moderating role of several individual factors on relations in the proposed model. We propose a longitudinal study utilizing a survey methodology to empirically validate the proposed model. Data analysis will utilize structural equation modeling using partial least squares. Participants in this survey consist of adolescent JD patients and their parents, allowing us to understand the factors which are most relevant to each stakeholder 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".