Remote Patient Monitoring and Digital Therapeutics Enhancing the Continuum of Care in Heart Failure: Nonrandomized Pilot Study
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is the primary cause of hospitalization among Canadian patients aged ≥65 years. Care for HF requires regular clinical follow-ups to prevent readmissions and facilitate medical therapy optimization. Multiple barriers lead to therapeutic medical inertia including limited human resources and regional inequities. Remote patient monitoring (RPM) and digital therapeutics (DTx) solutions have been developed to improve HF management, but their adoption remains limited and underexplored. The Continuum project emerged as a collaborative initiative involving a health care center, a software start-up, and an industrial partner. OBJECTIVE: We aimed to develop and test the feasibility of the Continuum intervention that seamlessly combined an RPM system with a DTx solution for HF within the same software. METHODS: A 3-month pre-post pilot study was conducted from October 2020 to June 2021. Patients with HF who owned a smartphone or tablet (having remote patient monitoring [RPM+]), had (1) access to a self-care app where they could enter their vital signs, weight, and HF symptoms and view educational content; (2) daily monitoring of their data by a nurse; and (3) a DTx module with automated HF medication suggestions based on national guidelines, made available to their treating medical team. Bluetooth devices were offered to facilitate data recording. Nurses on RPM monitoring could call patients and arrange appointments with their medical team. Patients without a mobile device or unable to use the app were followed in another group (without remote patient monitoring [RPM-]). RESULTS: In total, 52 patients were enrolled in this study (32 RPM+ and 20 RPM-). Among patients owning a mobile device, only 14% (5/37) could not use the app. In the RPM+ group, 47% (15/32) of the patients used the app for more than 80% (67 days) of the 12-week study period. The use of our digital solution was integrated into the regular nursing workday and only 34 calls had to be made by the nurse during the study period. Only 6% (2/32) of the patients in the RPM+ group experienced at least 1 all-cause hospitalization versus 35% (7/20) of the RPM- ones during the follow-up (6%, 2/32 vs 25%, 5/20 for HF hospitalization) and patients were more likely to have their HF therapy optimized if the DTx solution was available. Quality of life improved in patients compliant with the use of the mobile app (mean score variation +10.6, SD 14.7). CONCLUSIONS: This pilot study demonstrated the feasibility of implementing our digital solution, within the specific context of HF. The seamless integration of Continuum into nursing workflow, mobile app accessibility, and adoption by patients, were the 3 main key learning points of this study. Further investigation is required to assess the potential impacts on hospitalizations, drug optimization, and quality of life. TRIAL REGISTRATION: ClinicalTrials.gov NCT05377190; https://clinicaltrials.gov/study/NCT05377190 (pilot study #21.403).
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".