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

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.

SUBMITTER: Bayat S

PROVIDER: S-EPMC7420517 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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

Bayat Sayeh S Naglie Gary G Rapoport Mark J MJ Stasiulis Elaine E Chikhaoui Belkacem B Mihailidis Alex A

JMIR aging 20200728 2

<h4>Background</h4>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 ...[more]

PMID: 32720647

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Project description:BackgroundHaving destinations within walking distance can encourage older people to walk. Yet, not all destinations may be equally important. Little is known about the types of destinations fostering older adults' walking for transport in small and medium-sized towns and rural communities. The aim of this study was to explore the associations between the availability of different destinations and walking for transport among older adults living in communities with less than 100,000 inhabitants.MethodsBetween May and September 2019, self-reported data from 2242 older adults (≥ 65 years) living in the Metropolitan Region Northwest (Germany) were collected within the project AFOOT - Securing urban mobility of an aging population. Data from 2137 study participants were eligible for this analysis. Logistic regression models were used to investigate the relationship between the perceived destination availability of 19 different destinations within a 20-min or 10-min walk from home, respectively, and the engagement in walking for transport. Crude and adjusted models were run separately for each destination and distance category. Exploratory subgroup analyses examined the associations between the availability of destinations within a 20-min walk from home and walking for transport stratified by gender, use of a walking aid, and car availability.ResultsThe availability of each of the investigated destinations within a 20-min walk and of nearly all of these destinations within a 10-min walk from home was significantly positively associated with walking for transport in crude models. Most associations remained significant after adjustment for covariates. The strongest associations were found for the availability of small stores, pharmacy, and bakery. The availability of a bus stop showed the weakest associations and was not significantly associated with walking for transport after adjustment for covariates.ConclusionsThe provision of local amenities within walking distance may be a promising approach to foster older adults' walking for transport in smaller communities with less than 100,000 inhabitants and to enable active and healthy aging in place. Further quantitative and qualitative research is needed to validate these findings and to better understand older adults' walking behavior.

Project description:BACKGROUND:As societies become more complex, larger populations suffer from insomnia. In 2014, the US Centers for Disease Control and Prevention declared that sleep disorders should be dealt with as a public health epidemic. However, it is hard to provide adequate treatment for each insomnia sufferer, since various behavioral characteristics influence symptoms of insomnia collectively. OBJECTIVE:We aim to develop a neural-net based unsupervised user clustering method towards insomnia sufferers in order to clarify the unique traits for each derived groups. Unlike the current diagnosis of insomnia that requires qualitative analysis from interview results, the classification of individuals with insomnia by using various information modalities from smart bands and neural-nets can provide better insight into insomnia treatments. METHODS:This study, as part of the precision psychiatry initiative, is based on a smart band experiment conducted over 6 weeks on individuals with insomnia. During the experiment period, a total of 42 participants (19 male; average age 22.00 [SD 2.79]) from a large university wore smart bands 24/7, and 3 modalities were collected and examined: sleep patterns, daily activities, and personal demographics. We considered the consecutive daily information as a form of images, learned the latent variables of the images via a convolutional autoencoder (CAE), and clustered and labeled the input images based on the derived features. We then converted consecutive daily information into a sequence of the labels for each subject and finally clustered the people with insomnia based on their predominant labels. RESULTS:Our method identified 5 new insomnia-activity clusters of participants that conventional methods have not recognized, and significant differences in sleep and behavioral characteristics were shown among groups (analysis of variance on rank: F4,37=2.36, P=.07 for the sleep_min feature; F4,37=9.05, P<.001 for sleep_efficiency; F4,37=8.16, P<.001 for active_calorie; F4,37=6.53, P<.001 for walks; and F4,37=3.51, P=.02 for stairs). Analyzing the consecutive data through a CAE and clustering could reveal intricate connections between insomnia and various everyday activity markers. CONCLUSIONS:Our research suggests that unsupervised learning allows health practitioners to devise precise and tailored interventions at the level of data-guided user clusters (ie, precision psychiatry), which could be a novel solution to treating insomnia and other mental disorders.

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

Publications

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

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