On the Necessity of Multidisciplinarity in the Development of at-Home Health Monitoring Platforms for Older Adults: Systematic Review
Bibliographic record
Abstract
Background: The growth of aging populations globally has increased the demand for new models of care. At-home, computerized health care monitoring is a growing paradigm, which explores the possibility of reducing workloads, lowering the demand for resource-intensive secondary care, and providing more precise and personalized care. Despite the potential societal benefit of autonomous monitoring systems when implemented properly, uptake in health care institutions is slow, and a great volume of research across disciplines encounters similar common barriers to real-world implementation. Objective: The goal of this systematic review was to construct an evaluation framework that can assess research in terms of how well it addresses already identified barriers to application and then use that framework to analyze the literature across disciplines and identify trends between multidisciplinarity and the likelihood of research being developed robustly. Methods: This paper introduces a scoring framework for evaluating how well individual pieces of research address key development considerations using 10 identified common barriers to uptake found during meta-review from different disciplines across the domain of health care monitoring. A scoping review is then conducted using this framework to identify the impact that multidisciplinarity involvement has on the effective development of new monitoring technologies. Specifically, we use this framework to measure the relationship between the use of multidisciplinarity in research and the likelihood that a piece of research will be developed in a way that gives it genuine practical application. Results: We show that viewpoints of multidisciplinarity; namely across computer science and medicine alongside public and patient involvement (PPI) have a significant positive impact in addressing commonly encountered barriers to application research and development according to the evaluation criteria. Using our evaluation metric, multidisciplinary teams score on average 54.3% compared with 35% for teams made up of medical experts and social scientists, and 2.68 for technical-based teams, encompassing computer science and engineering. Also identified is the significant effect that involving either caregivers or end users in the research in a co-design or PPI-based capacity has on the evaluation score (29.3% without any input and between 48.3% and 36.7% for end user or caregiver input respectively, on average). Conclusions: This review recommends that, to limit the volume of novel research arbitrarily re-encountering the same issues in the limitations of their work and hence improve the efficiency and effectiveness of research, multidisciplinarity should be promoted as a priority to accelerate the rate of advancement in this field and encourage the development of more technology in this domain that can be of tangible societal benefit.
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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 | 0.086 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 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".