Effects of Home Telemonitoring to Support Improved Care for Chronic Obstructive Pulmonary Diseases
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of a home telemonitoring technology on patients with chronic obstructive pulmonary disease in terms of care satisfaction, patient empowerment, improved quality of life, and utilization of hospital and home care. DESIGN: A quasi-experimental retrospective and prospective design was developed with a matched control group to compare the effects of telemonitoring (the experimental group, n = 23) with the traditional homecare offering (the control group, n = 23). MEASUREMENTS: Satisfaction, patient empowerment, and quality of life were measured using validated Likert scales, whereas the data on care utilization were collected from the participating patients' medical record. RESULTS: Mixed results were observed. The clinical effects of home telemonitoring were very positive in terms of patients' satisfaction and empowerment. The perceptions of care providers as well as those of patients were congruent in this respect. Also, the study suggests that telemonitoring may have a positive effect on quality of life for patients with chronic obstructive pulmonary diseases. In contrast, the results were disappointing in terms of resource savings for the use of both homecare and hospital care. CONCLUSION: Capturing the full potential of these new technologies will require a much more fundamental reorganization of work than just a simple deployment of the technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".