Real time location system techonolgy and older adults with cognitive impairment
Bibliographic record
Abstract
Older adults are at high risk of cognitive impairment, with almost half of people over 80 years of age presenting with some degree of cognitive impairment (Edwards, 2003). Cognitive impairment within the older adult population is often attributable to dementia. Dementia is an irreversible slow and progressive decline of cognition, characterized by memory loss, impairment in judgement, and difficulty in conducting activities of daily functioning (Downing, Caprio, & Lyness, 2013; Edwards, 2003). As those with dementia require increasing support for daily functioning, many relocate into an assisted living environment. The umbrella term 'residential care' is often used to reflect a continuum of assisted living environments which are designed to facilitate and support an older adult's functional independence. Retirement homes typically foster independent living for those with mild to no cognitive impairment. Long-term care homes provide more intensive staffing support for those with moderate cognitive impairment or chronic illnesses unable to care for themselves. Lastly, in-hospital special care units are often utilized to stabilize those with severe cognitive impairment and behavioural manifestations. Within Canada, approximately 87% of those living within a residential care setting have dementia or cognitive impairment (Canadian Institutes of Health Research). As most of these residential care settings have limited staff to resident ratio, utilization of technology may prove to be an essential solution to alleviate resource gaps and augment care delivery. Lorenz, Freddolino, Comas-Herrara, Knapp, and Damant (2019) categorized seven technology functions in use for those with cognitive impairment: memory support, treatment, safety and security, training, care delivery, social interaction, and others. Technology has been most used at enhancing safety and security for those with cognitive impairment and living within the community (e.g., motion sensors, alarms, fall detectors). A systematic review conducted by Lynn et al. (2019) identified the technologies that are being used within long-term residential care settings. The technology categories included: telecare, light therapy, robotics (e.g., robotic companion), well-being and leisure (e.g., touch screen devices, watches to measure sleep cycles), simulated presence and orientation (e.g., audio/video recordings), and activities of daily living (e.g. handwashing, taking medication memory aids). There have been several reviews of evidence on the use of different technologies in the older adult population to measure a number of variables, such as detection of agitation and aggression (Khan, Ye, Taati, & Mihailidis, 2018), monitoring of treatment response of people with dementia (Husebo et al., 2019), prediction of falls risk (Dolatabadi, Van Ooteghem, Taati, & Iaboni, 2018), gait analysis of people with dementia (Iersel, Hoefsloot, Munneke, Bloem, & Olde Rikkert, 2004), and physical activity levels (Taraldsen, Chastin, Riphagen, Vereijken, & Helbostad, 2011). Although specific to the older adult, these reviews were not exclusive to those with cognitive impairment nor residential care settings, making the findings from the reviews challenging to apply to this sub-population of older adults. One technology not well studied in the older adult population is real-time locating systems (RTLS). RTLS is an indoor positioning system which has been used across hospital and residential care home settings (Akl, Taati, & Mihailidis, 2015; M.E. Bowen, Crenshaw, & Stanhope, 2018; Jansen, Diegelmann, Schnabel, & Hauer, 2017). RTLS can provide a vast amount of data on an individual's movements in location and time. RTLS consists of a software application and reference points that detect and synthesize positioning data from wireless transmitters worn by people or attached to objects. Healthcare providers can use the data obtained from the transmitters to help understand patterns of human movement and behaviour. There is a vast potential to use the RTLS health indices data to augment both healthcare decisions and measure clinical outcomes. There has been a rising interest in the use of RTLS in long-term care settings. Preliminary research has shown that clinically meaningful information can be extracted from this data – for example, detection of agitation (Bankole et al., 2012) and monitoring wandering behaviours (Mary Elizabeth Bowen & Rowe, 2019). To date, there has not been a review identifying how RTLS data is being used for the care of older adults with cognitive impairment living in a residential care setting. Purpose A review of the clinical applications of RTLS technology in older adults with cognitive impairment who live in a residential care environment is required, with the ultimate goal of identifying any evidence-based or clinically validated uses for the technology. As such, the review will aim to answer the following questions: 1. For older adults with cognitive impairment residing in a residential care setting, how are data from RTLS technologies being used? 2. In what ways have RTLS data been used to develop or validate health measures for the clinical care of people with cognitive impairment in residential care settings? See attachment for the full study protocol.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".