Low-Fidelity Prototype of a Sensor-Dependent Interaction Platform: Formative Evaluation With Informal Caregivers of Older Adults With Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Unobtrusive sensing technologies developed for monitoring deviant behaviors in older adult care require integration with an interaction platform to facilitate the flow of information between older adults and their caregivers. However, the continuous monitoring capabilities generate a considerable amount of data that must be interpreted, filtered, and personalized before being communicated to the informal caregivers based on their specific care needs and requirements. OBJECTIVE: For the effective implementation of unobtrusive sensing solutions (USSs) in the care of older adults with cognitive impairment, we aimed to explore the expectations and preconditions regarding the implementation of USSs from the perspective of informal caregivers. Subsequently, we designed and evaluated a low-fidelity prototype of an interaction platform for its conceptual workflow and usability, incorporating persuasive system design features based on the needs and requirements of informal caregivers. METHODS: Overall, 6 informal caregivers of older adults with cognitive impairment living alone participated in this qualitative interview study. We explored the expectation and preconditions regarding implementation through open-ended questions and conducted a formative evaluation (usability study with a think-aloud approach) to evaluate the conceptual workflow and used persuasive system design features in the interaction platform. Overall, a combination of inductive and thematic analyses was used to analyze the interviews. RESULTS: The results of this study present both positive and negative outcome expectations regarding the implementation of USSs, highlighting benefits such as objective decision-making and peace of mind and concerns about information overload and the potential substitution of human contact. Strategic information communication agreements between informal and formal caregivers were deemed crucial for the successful implementation of USSs in care. Overall, informal caregivers had a positive experience with the low-fidelity prototype of the interaction platform, particularly valuing the personalization feature. CONCLUSIONS: In conclusion, to achieve successful implementation, a holistic design approach is necessary, and equal consideration should be given to the personalization-privacy paradox to balance users' needs and privacy.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".