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      Inferring Destinations and Activity Types of Older Adults From GPS Data: Algorithm Development and Validation

      , BASc , 1 , 2 , , MD, FRCPC, FGSA 2 , 3 , 4 , 5 , 6 , , MD, FRCPC 7 , 8 , , MA 5 , 9 , , PhD 10 , , PhD 1 , 2 , 11

      (Reviewer), (Reviewer)

      JMIR Aging

      JMIR Publications

      outdoor mobility, older adults, GPS, life space, activity types, machine learning

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          Abstract

          Background

          Outdoor mobility is an important aspect of older adults’ functional status. GPS has been used to create indicators reflecting the spatiotemporal dimensions of outdoor mobility for applications in health and aging. However, outdoor mobility is a multidimensional construct. There is, as of yet, no classification algorithm that groups and characterizes older adults’ outdoor mobility based on its semantic aspects (ie, mobility intentions and motivations) by integrating geographic and domain knowledge.

          Objective

          This study assesses the feasibility of using GPS to determine semantic dimensions of older adults’ outdoor mobility, including destinations and activity types.

          Methods

          A total of 5 healthy individuals, aged 65 years or older, carried a GPS device when traveling outside their homes for 4 weeks. The participants were also given a travel diary to record details of all excursions from their homes, including date, time, and destination information. We first designed and implemented an algorithm to extract destinations and infer activity types (eg, food, shopping, and sport) from the GPS data. We then evaluated the performance of the GPS-derived destination and activity information against the traditional diary method.

          Results

          Our results detected the stop locations of older adults from their GPS data with an F1 score of 87%. On average, the extracted home locations were within a 40.18-meter (SD 1.18) distance of the actual home locations. For the activity-inference algorithm, our results reached an F1 score of 86% for all participants, suggesting a reasonable accuracy against the travel diary recordings. Our results also suggest that the activity inference’s accuracy measure differed by neighborhood characteristics (ie, Walk Score).

          Conclusions

          We conclude that GPS technology is accurate for determining semantic dimensions of outdoor mobility. However, further improvements may be needed to develop a robust application of this system that can be adopted in clinical practice.

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          Most cited references 29

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          What makes a community age-friendly: A review of international literature.

          This paper undertakes a comprehensive review of the growing international literature on age-friendly communities. It examines a range of approaches and identifies the key attributes associated with creating a sustainable environment for seniors. The authors critically evaluate emerging policy trends and models and suggest directions for future research attention. The discussion provides important information and insights for the development of ageing policy and planning in Australia.
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            Validation of Walk Score for estimating access to walkable amenities.

            Proximity to walkable destinations or amenities is thought to influence physical activity behaviour. Previous efforts attempting to calculate neighbourhood walkability have relied on self-report or time-intensive and costly measures. Walk Score is a novel and publicly available website that estimates neighbourhood walkability based on proximity to 13 amenity categories (eg, grocery stores, coffee shops, restaurants, bars, movie theatres, schools, parks, libraries, book stores, fitness centres, drug stores, hardware stores, clothing/music stores). The purpose of this study is to test the validity and reliability of Walk Score for estimating access to objectively measured walkable amenities. Walk Scores of 379 residential/non-residential addresses in Rhode Island were manually calculated. Geographic information systems (GIS) was used to objectively measure 4194 walkable amenities in the 13 Walk Score categories. GIS data were aggregated from publicly available data sources. Sums of amenities within each category were matched to address data, and Pearson correlations were calculated between the category sums and address Walk Scores. Significant correlations were identified between Walk Score and all categories of aggregated walkable destinations within a 1-mile buffer of the 379 residential and non-residential addresses. Test-retest reliability correlation coefficients for a subsample of 100 addresses were 1.0. These results support Walk Score as a reliable and valid measure of estimating access to walkable amenities. Walk Score may be a convenient and inexpensive option for researchers interested in exploring the relationship between access to walkable amenities and health behaviours such as physical activity.
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              The Life Space Questionnaire: A Measure of the Extent of Mobility of Older Adults

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                Author and article information

                Contributors
                Journal
                JMIR Aging
                JMIR Aging
                JA
                JMIR Aging
                JMIR Publications (Toronto, Canada )
                2561-7605
                Jul-Dec 2020
                28 July 2020
                : 3
                : 2
                Affiliations
                [1 ] Institute of Biomaterials and Biomedical Engineering University of Toronto Toronto, ON Canada
                [2 ] KITE Research Institute Toronto Rehabilitation Institute University Health Network Toronto, ON Canada
                [3 ] Department of Medicine Baycrest Health Sciences Toronto, ON Canada
                [4 ] Department of Medicine University of Toronto Toronto, ON Canada
                [5 ] Rotman Research Institute Baycrest Health Sciences Toronto, ON Canada
                [6 ] Institute of Health Policy, Management and Evaluation University of Toronto Toronto, ON Canada
                [7 ] Department of Psychiatry Sunnybrook Health Sciences Centre Toronto, ON Canada
                [8 ] Department of Psychiatry University of Toronto Toronto, ON Canada
                [9 ] Institute of Medical Science University of Toronto Toronto, ON Canada
                [10 ] Laboratoire en Informatique Cognitive et Environnements de Formation Research Institute Department of Science and Technology TELUQ University Montreal, QC Canada
                [11 ] Department of Occupational Therapy and Occupational Science University of Toronto Toronto, ON Canada
                Author notes
                Corresponding Author: Sayeh Bayat sayeh.bayat@ 123456mail.utoronto.ca
                Article
                v3i2e18008
                10.2196/18008
                7420517
                32720647
                ©Sayeh Bayat, Gary Naglie, Mark J Rapoport, Elaine Stasiulis, Belkacem Chikhaoui, Alex Mihailidis. Originally published in JMIR Aging (http://aging.jmir.org), 28.07.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Aging, is properly cited. The complete bibliographic information, a link to the original publication on http://aging.jmir.org, as well as this copyright and license information must be included.

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