P1‐309: USE OF A WEB APPLICATION (DOBS) ON A MOBILE DEVICE TO RECORD DEMENTIA BEHAVIOURAL OBSERVATIONS BY FRONT‐LINE DEMENTIA CARE STAFF: A USABILITY STUDY
Bibliographic record
Abstract
A principle of assessment of behavioural symptoms in dementia is to chart observed behaviours at regular intervals, to help establish their frequency, severity, and any patterns to the behaviours. We have developed a web-based mobile Dementia Observation (DObs) application designed for use by front-line staff, with the goal of improving the ease, completeness, and accuracy of behavioral data collection. The aim of this study was to evaluate the usability of the DObs application on a mobile device by front-line dementia care workers during their usual clinical work-flow. Participants were nursing staff on the Toronto Rehab inpatient dementia unit. Participants were observed while performing their usual duties and using DObs on a mobile Android device to record behavioural observations in 30 minute intervals on a selected patient. At the end of the observed shift, participants also completed the system usability scale (SUS), a perceived usefulness scale, and a computer self-efficacy scale (C-SES). Qualitative data was collected via post-test questions. Five clinical staff participated in this study with a mean C-SES score of 8.5/10. Participants completed 100% of the initiation tasks correctly, but only 66% of the observation tasks and 40% of the completion tasks. On average, participants entered observation data 1.5 times per interval, responded to notifications within 5 seconds, and took 30 seconds to enter an observation. The perceived usefulness of the app was 3.8/5 and the System Usability Scale (SUS) score 76/100. Many of the failed tasks were related to challenges in incorporating the mobile device into the clinical workflow and technical failures related to an unfamiliar mobile device. Mobile technology offers an opportunity to improve the assessment and treatment of responsive behaviours in dementia. Two broad areas of improvement were identified: first, with respect to the usability of the DObs mobile application itself, and second, with respect to the adaptability and comfort of staff in using a mobile device for clinical data collection at the bedside. Our next study will examine the validity and reliability of the DObs mobile application in different long-term care environments.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".