Improvement in Quality of Life With the Use of a Technological System Among Patients With Chronic Disease Followed Up in Primary Care (TeNDER Project): Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Among chronic diseases, cognitive, neurological, and cardiovascular impairments are becoming increasingly prevalent, generating a shift in health and social needs. Technology can create an ecosystem of care integrated with microtools based on biosensors for motion, location, voice, and expression detection that can help people with chronic diseases. A technological system capable of identifying symptoms, signs, or behavioral patterns could provide notification of the development of complications of disease. This would help the self-care of patients with chronic disease and save health care costs, promoting the autonomy and empowerment of patients and their caregivers, improving their quality of life (QoL), and providing health professionals with monitoring tools. OBJECTIVE: The main objective of this study is to evaluate the effectiveness of a technological system (the TeNDER system) to improve quality of life in patients with chronic diseases: Alzheimer disease, Parkinson disease, and cardiovascular disease. METHODS: A multicenter, randomized, parallel-group clinical trial will be conducted with a follow-up of 2 months. The scope of the study will be the primary care health centers of the Community of Madrid belonging to the Spanish public health system. The study population will be patients diagnosed with Parkinson disease, Alzheimer disease, and cardiovascular disease; their caregivers; and health professionals. The sample size will be 534 patients (380 in the intervention group). The intervention will consist of the use of the TeNDER system. The system will monitor the patients by means of biosensors, and their data will be integrated into the TeNDER app. With the information provided, the TeNDER system will generate health reports that can be consulted by patients, caregivers, and health professionals. Sociodemographic variables and technological affinity will be measured, as will views on the usability of and satisfaction with the TeNDER system. The dependent variable will be the mean difference in QoL score between the intervention and control groups at 2 months. To study the effectiveness of the TeNDER system in improving QoL in patients, an explanatory linear regression model will be constructed. All analyses will be performed with the 95% CI and robust estimators. RESULTS: Ethics approval for this project was received on September 11, 2019. The trial was registered on August 14, 2020. Recruitment commenced in April 2021, and the expected results will be available during 2023 or 2024. CONCLUSIONS: This clinical trial among patients with highly prevalent chronic illnesses and the people most involved in their care will provide a more realistic view of the situation experienced by people with long-term illness and their support networks. The TeNDER system is in continuous development based on a study of the needs of the target population and on feedback during its use from the users: patients, caregivers, and primary care health professionals. TRIAL REGISTRATION: ClinicalTrials.gov NCT05681065; https://clinicaltrials.gov/ct2/show/NCT05681065. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47331.
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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 | 0.033 | 0.026 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".