Telemonitoring activities of daily living in home healthcare services to support aging in place
Bibliographic record
Abstract
Abstract Context Assessing older adults’ abilities to carry out their activities of daily living (ADLs) is a key determinant in the provision of homecare services for aging in place. Amidst a growing aging population and lack of human resources, continuous remote monitoring technology appears promising to support health and social care professionals (HSCPs) in identifying service needs. However, implementation studies conducted in real-life settings are lacking. This study is part of an on-going action design research project aimed at developing an ambient telemonitoring system monitoring ADLs to support clinical decision making. It focused on the initial step of implementation and aimed to understand 1) which HSCPs would want to use the system, 2) for which care recipient they requested it, and 3) for which reasons. Methods A multiple embedded case study utilizing mixed methods was conducted across 3 healthcare establishments in Quebec, Canada. Descriptive statistics from surveys and medical records was conducted to describe the profile of HSCPs and their care recipients. An inductive qualitative analysis was carried out through interviews with 23 HSCPs, in charge of 31 care recipients, to deepen our understanding of the reasons why they requested the system. Results HSCPs were primarily women (89%) occupational therapists (43%). Home care recipients were also primarily women (74%), with documented refusal of homecare services (65%), diagnosed with cognitive decline (94%), and living in a single-family home or apartment (68%). Overall, interviews revealed HSCPs challenges in getting the necessary information to assess their care recipients needs, despite the presence of in-home services and other strategies in place (e.g. informal carer support). Moreover, the telemonitoring system was perceived as promising for risk management. Conclusions There is an interest for the use of ADL telemonitoring technology in the delivery of home healthcare services for aging in place. Key messages • To facilitate its integration in practice, we explored the need for, and value of, ADLs telemonitoring technology by health and social care professionals (HSCPs) in real-life contexts. • By studying the integration of innovative technologies in home healthcare practices, such as ADLs telemonitoring, we aim to support HSCPs practice in fulfilling older adults desire to age in place.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".