Outdoor life in dementia: How predictable are people with dementia in their mobility?
Open accessAbstract
Introduction: People with dementia (PWD) often become disoriented, which increases their risk of getting lost. This article explores the extent to which we can predict future whereabouts of PWD by learning from their past mobility patterns using Global Positioning System (GPS) tracking devices. Methods: Seven older adults with dementia and eight healthy older adults completed 8 weeks of GPS data collection. We computed the probability that an appropriate algorithm can correctly predict the participant's future destinations using spatial and temporal patterns in each participant's GPS trajectories. Results: Relying on both spatial and temporal patterns, our results suggest that a 4‐week record of mobility patterns displays 95% potential predictability across the dementia group, which is significantly higher than 92% potential predictability among the controls, t(13) = –3.39, P < .01, d = –1.75. That is, we can hope to be able to predict destinations of PWD about 95% of the time and destinations of controls about 92% of the time. Discussions: Our findings on predictability of mobility patterns among PWD offer new perspectives on predictive mobility models that can be used to locate missing persons with dementia.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In one study, destinations for dementia patients (with 95% accuracy) are more predictable than the control population (with 92% accuracy).
“It was found that the destinations for the dementia patients (with 95% accuracy) are more predictable than the control population (with 92% accuracy).”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [113]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment
Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., Cummings, J. L., & Chertkow, H. (2005) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2005.53221.x · Cited by 25,553 · see citing works
Assessment of Older People: Self-Maintaining and Instrumental Activities of Daily Living
Lawton, M. P., & Brody, E. M. (1969) · The Gerontologist · doi:10.1093/geront/9.3_part_1.179 · Cited by 18,540 · see citing works
Quan, H., Li, B., Couris, C. M., Fushimi, K., Graham, P. J., Hider, P., Januel, J., & Sundararajan, V. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwq433 · Cited by 5,858 · see citing works
Progress in Development of the Index of ADL
Katz, S., Downs, T. D., Cash, H., & Grotz, R. C. (1970) · The Gerontologist · doi:10.1093/geront/10.1_part_1.20 · Cited by 4,129 · see citing works
Limits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234 · see citing works
The Meaning of "Aging in Place" to Older People
Wiles, J. L., Leibing, A., Guberman, N., Reeve, J., & Allen, R. E. S. (2011) · The Gerontologist · doi:10.1093/geront/gnr098 · Cited by 1,979 · see citing works
Standardizing Assessment of Elderly People in Acute Care: The interRAI Acute Care Instrument
Gray, L., Bernabei, R., Berg, K., Finne‐Soveri, H., Fries, B. E., Hirdes, J. P., Jónsson, P. V., Morris, J. N., Steel, K., & Ariño‐Blasco, S. (2008) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2007.01590.x · Cited by 967 · see citing works
The Lawton instrumental activities of daily living scale
Graf, C. (2008) · AJN, American Journal of Nursing · doi:10.1097/01.naj.0000314810.46029.74 · Cited by 832 · see citing works
Assessing Mobility in Older Adults: The UAB Study of Aging Life-Space Assessment
Peel, C., Baker, P. S., Roth, D. L., Brown, C. J., Bodner, E. V., & Allman, R. M. (2005) · Physical Therapy · doi:10.1093/ptj/85.10.1008 · Cited by 671 · see citing works
Spatial navigation deficits — overlooked cognitive marker for preclinical Alzheimer disease?
Coughlan, G., Laczó, J., Hort, J., Minihane, A.-M., & Hornberger, M. (2018) · Nature Reviews Neurology · doi:10.1038/s41582-018-0031-x · Cited by 500 · see citing works
Validation of Katz index of independence in activities of daily living in Turkish older adults
Arık, G., Varan, H. D., Doğu, B. B., Karabulut, E., Kara, Ö., Kılıç, M. K., Kızılarslanoğlu, M. C., Sümer, F., Kuyumcu, M. E., Yeşil, Y., et al. (2015) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2015.08.019 · Cited by 299 · see citing works
Spatiotemporal Patterns of Urban Human Mobility
Hasan, S., Schneider, C., Ukkusuri, S. V., & González, M. C. (2012) · Journal of Statistical Physics · doi:10.1007/s10955-012-0645-0 · Cited by 282 · see citing works
iWander: An Android application for dementia patients
Sposaro, F., Danielson, J., & Tyson, G. (2010) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2010.5627669 · Cited by 178 · see citing works
Information and communication technology solutions for outdoor navigation in dementia
Teipel, S., Babiloni, C., Hoey, J., Kaye, J., Kirste, T., & Burmeister, O. K. (2016) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2015.11.003 · Cited by 120 · see citing works
Tung, J. Y., Rose, R. V., Gammada, E. Z., Lam, I., Roy, É., Black, S. E., & Poupart, P. (2013) · Gerontology · doi:10.1159/000355669 · Cited by 110 · see citing works
White, E. B., & Montgomery, P. (2014) · Aging & Mental Health · doi:10.1080/13607863.2014.924091 · Cited by 101 · see citing works
A look at deaths occurring in persons with dementia lost in the community
Rowe, M. A., & Bennett, V. (2003) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/153331750301800612 · Cited by 99 · see citing works
Outdoor wayfinding in dementia
Sheehan, B., Burton, E. J., & Mitchell, L. (2006) · Dementia · doi:10.1177/1471301206062254 · Cited by 88 · see citing works
Exploiting machine learning techniques for location recognition and prediction with smartphone logs
Cho, S. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.079 · Cited by 80 · see citing works
A 2.5-Year Longitudinal Assessment of Naturalistic Driving in Preclinical Alzheimer’s Disease
Roe, C. M., Stout, S. H., Rajasekar, G., Ances, B. M., Jones, J. M., Head, D. M., Benzinger, T. L. S., Williams, M., Davis, J. D., Ott, B. R., et al. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-181242 · Cited by 65 · see citing works
Öhman, A., & Nygård, L. (2005) · OTJR Occupational Therapy Journal of Research · doi:10.1177/153944920502500302 · Cited by 62 · see citing works
Interrater reliability of measurements of comorbid illness should be reported
Hall, S. F., Groome, P. A., Streiner, D. L., et al. (2006) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2006.02.006 · Cited by 58 · see citing works
Design of A GPS Monitoring System for Dementia Care and its Challenges in Academia-Industry Project
Wan, L., Müller, C., Randall, D. W., & Wulf, V. (2016) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2963095 · Cited by 54 · see citing works
Energy efficient LoRa GPS tracker for dementia patients
Hadwen, T., Smallbon, V., Zhang, Q., et al. (2017) · doi:10.1109/embc.2017.8036938 · Cited by 51 · see citing works
Understanding the cognitive underpinnings of functional impairments in early dementia: a review
Giebel, C. M., Challis, D., & Montaldi, D. (2015) · Aging & Mental Health · doi:10.1080/13607863.2014.1003282 · Cited by 50 · see citing works
What do we know about strategies to manage dementia‐related wandering? A scoping review
Neubauer, N., Azad‐Khaneghah, P., Cruz, A. M., & Liu, L. (2018) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.08.001 · Cited by 44 · see citing works
Strandenæs, M. G., Lund, A. D., & Rokstad, A. M. M. (2017) · Aging & Mental Health · doi:10.1080/13607863.2017.1304892 · Cited by 42 · see citing works
Examining the Limits of Predictability of Human Mobility
Kulkarni, V., Mahalunkar, A., Garbinato, B., & Kelleher, J. D. (2019) · Entropy · doi:10.3390/e21040432 · Cited by 41 · see citing works
Bayat, S., Naglie, G., Rapoport, M. J., Stasiulis, E., Widener, M. J., & Mihailidis, A. (2021) · Gerontology · doi:10.1159/000515391 · Cited by 41 · see citing works
Self-citationWojtusiak, J., & Nia, R. M. (2019) · Internet of Things · doi:10.1016/j.iot.2019.01.002 · Cited by 40 · see citing works
People with Dementia Who Become Lost
Rowe, M. A. (2003) · AJN American Journal of Nursing · doi:10.1097/00000446-200307000-00016 · Cited by 32 · see citing works
Bayat, S., Naglie, G., Rapoport, M. J., Stasiulis, E., Chikhaoui, B., & Mihailidis, A. (2020) · JMIR Aging · doi:10.2196/18008 · Cited by 18 · see citing works
Self-citationGPS Trajectories Based Personalized Safe Geofence for Elders with Dementia
Lin, Q., Liu, X., & Wang, W. (2018) · doi:10.1109/smartworld.2018.00111 · Cited by 14 · see citing works
Møller, A. K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50249-2_11 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works
- Authors
- Bayat, S., & Mihailidis, A.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- Bayat, Sayeh
- Venue
- Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
- Publisher
- Wiley
- Published
- 2021-01
- Volume
- 13
- Issue
- 1
- Pages
- e12187-e12187
- ISSN
- 2352-8729 (ISSN-L)
- PMC
- PMC8118112
- OpenAlex
- W3162323097
- Cited by
- 21
- References
- 35
- Citations by year
- 2025: 5 · 2024: 3 · 2023: 5 · 2022: 7 · 2021: 1
- FWCI
- 2.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- dementia, outdoor mobility, GPS tracking, destination prediction, spatial and temporal patterns, missing persons, disorientation, older adults
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
