Uncovering Outdoor Mobility Behaviours of Older Adults With and Without Dementia from GPS Data
Bibliographic record
Abstract
One of the greatest health challenges of the growing ageing population is dementia. Currently, there is no way to stop the progression of dementia or reverse its physiologic changes, and thus, there is an acute need for (1) supporting people with dementia to maintain a healthy lifestyle, and (2) making early diagnosis of the disease. A key determinant of a healthy and active lifestyle is outdoor mobility. However, dementia affects complex activities such as navigation by impairing cognitive and functional abilities, making outdoor mobility particularly challenging for individuals. In addition, with certain dementias such as Alzheimer’s disease, navigational impairment is considered to be one of the earliest signs of the disease process. In this context, characterizing and supporting outdoor mobility for older adults with dementia is of great significance. Global positioning system (GPS) provides an avenue for addressing outdoor mobility challenges in dementia, and its application in dementia detection has become an area of increased interest. Therefore, the overarching aim of this thesis is to provide means to better understand and support outdoor mobility of people with dementia by using passively collected data from GPS. To operationalize this goal, Chapter 2 and 3 of this work present a new outlook on outdoor mobility patterns in dementia by developing a framework for classifying higher complexity mobility behaviours, ranging from temporal characteristics such as the timing of the user’s trips to semantic characteristics such as the user’s transportation modes and activity types. Chapter 4, then, evaluates the extent to which one can predict the future whereabouts of people with dementia by learning from their past mobility behaviours captured by the GPS-based framework. Finally, Chapter 5 explores driving behaviours, the most common form of transportation among older adults, by modelling older adults’ driving behaviours in naturalistic settings using GPS. In summary, this thesis is an attempt toward developing a comprehensive GPS Mobility Construct for understanding and supporting the complex and interconnected relationship between older adults with dementia and their environment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".