Remote Health Monitoring with Wearable Devices: Investigating the Current Literature, Barriers, Facilitators, and Future Application
Bibliographic record
Abstract
Subject: Issues within healthcare systems across the globe came to the forefront during the COVID-19 pandemic, emphasizing the inefficiency of traditional in-person healthcare visits. Inperson visits are expensive, episodic, and inaccessible for some patients, which impacts patient care quality. Healthcare system strain is obvious in seriously overcrowded Canadian emergency departments. A possible solution to these problems is remote health monitoring using wearable devices, which involves continuous collection of biometric data outside of the clinical setting. Remote health monitoring may increase healthcare accessibility and reduce hospital readmissions, healthcare costs, and clinician burnout. This thesis synthesizes knowledge from the emerging and promising field of remote health monitoring, highlighting evidence gaps and providing a foundation for implementing remote health monitoring in healthcare systems. Methods: This thesis aims to understand the current literature, barriers and facilitators, and future application of remote health monitoring with wearable devices. Chapter 1 is a scoping review on wearable devices for remote health monitoring in non-hospital settings, mapping currently available evidence and highlighting knowledge gaps to direct future research. Four electronic databases were searched: CINAHL, Scopus, Embase, and MEDLINE up to August 5, 2024, with a focus on studies that included clinically relevant outcomes. Chapter 2 is a scoping review that assessed physician attitudes towards wearable devices for remote health monitoring to understand key stakeholder perspectives on integrating these devices into patient care. MEDLINE, EMBASE, CINAHL, Scopus, and ProQuest Dissertations and Theses Global were searched up to October 5, 2023. Physician attitude data was analysed using an inductive qualitative content analysis approach. Chapter 3 is an exploration of how emergency department patients use primary care, telehealth and technology in a healthcare context to decide about using emergency care. Patients in the emergency department waiting room at the Northeast Community Health Centre, Edmonton, Alberta, Canada were briefly surveyed on these topics. Conclusions: Chapter 1 identified 80 studies that met eligibility criteria, and most used wearable devices to monitor changes in chronic disease, rather than to identify new diseases. There was an absence of randomized controlled trials in this review, and included studies had a diverse range of methodologies, calling for standardization in future work so that studies can be compared. Chapter 2 included 13 studies that met eligibility criteria, investigating physician attitudes towards remote health monitoring with wearable devices. An analysis of physician attitudes revealed a balanced number of benefits and concerns for this stakeholder group, including potential benefits of improved clinical decision making and patient engagement, and concerns about financial cost and the cost of training healthcare providers. These barriers and facilitators need to be considered in future studies and when implementing remote health monitoring with wearable devices in healthcare systems. Chapter 3 includes data from 50 emergency department patients, showing that although patients reported a history of using primary care, telehealth, and technology in a healthcare context, a small proportion used these resources prior to their current emergency department visit. Age differences existed for telehealth use and smartphone ownership, but not for use of smart devices (smartphones and/or smartwatches) to make health decisions. Results support the potential benefits of remote health monitoring with wearable devices to assist patients with healthcare access decisions, possibly reducing unnecessary emergency department visits. This thesis contributes to our knowledge about remote health monitoring with wearable devices, highlighting knowledge gaps, identifying barriers and facilitators to implementation, and providing evidence around the potential use of this technology with emergency department patients to reduce strain on acute care. These findings have implications for future research, particularly calling for more randomized controlled trials to affirm the clinical effectiveness of remote health monitoring with wearable devices. Valuable information on the barriers and facilitators of translating this technology into practice should be considered when this transition takes place. Remote health monitoring with wearables can increase healthcare service accessibility and individualized care, potentially alleviating strain on emergency departments. This thesis adds to current literature on wearable devices for remote health monitoring, providing a foundation for uptake in clinical practice, that may lead to increased accessibility and efficiency in relation to healthcare system operations.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".