Cascading Feedback: A Longitudinal Study of a Feedback Ecosystem for Telemonitoring Patients with Chronic Disease
Bibliographic record
Abstract
While telemonitoring technology is widely used in treatment of patients with chronic diseases, our understanding of how it influences patient-related outcomes is limited. Drawing upon feedback intervention theory, the paper develops a model that examines how a telemonitoring feedback ecosystem (patient, telemonitoring technology, care provider) is related to patient behavioral outcomes. More precisely, we study the cascading effects of two types of technology feedback (medical and compliance alerts) on the provision of three types of feedback (outcome, corrective, and personal) given by care providers, and how the feedback in turn is related to patient adaptation and ultimately to calls to 911. Using generalized linear mixed modeling, we tested our hypotheses with longitudinal data from 212 patients with chronic obstructive pulmonary disease (COPD) and/or chronic heart failure (CHF) over 26 weeks. Our results show that medical alerts had a positive association with all three types of provider feedback. By contrast, compliance alerts had curvilinear relationships with corrective and personal feedback. Our results also show that outcome feedback and personal feedback were associated with increases in patient adaptations. Patient adaptation was negatively related to the odds of calling 911. Interestingly, we found a significant negative interaction between outcome and corrective feedback and patient adaptation. Finally, our results show that while the frequency of feedback decreased over the life of the program, the amount of adaptations increased over the same period, which suggests that patient self-management improved over time. By examining a telemonitoring-based ecosystem with two stages of feedback, our study contributes to the chronic disease management literature as well as to other contexts where monitoring technologies deliver feedback that is mediated by a third party. Theoretical and practical implications of our study are discussed.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".