Public transit use and physical activity in community-dwelling older adults: Combining GPS and accelerometry to assess transportation-related physical activity

Voss, Christine; Sims‐Gould, Joanie; Ashe, Maureen C.; McKay, Heather; Pugh, Caitlin; Winters, Meghan

doi:10.1016/j.jth.2016.02.011

Cited by 36 publications

(22 citation statements)

References 22 publications

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“…In studies that focus on physical activity, GPS tracking is typically combined with accelerometer data. Such studies relate physical activity of, for instance older adults or adolescents, to the walkability of neighborhoods, park visits, and modes of travel such as commuting or school-travel, and public transport (Carlson et al, 2016;Chaix et al, 2014;King et al, 2011;Marquet and Miralles-Guasch, 2015;Stewart et al, 2016;Voss et al, 2014Voss et al, , 2016Winters et al, 2015). Other studies use GPS tracking to measure activity space (Hirsch et al, 2014Lee et al, 2016).…”

Section: Mixed-methods Approachmentioning

confidence: 99%

Numbers and narratives: Developing a mixed-methods approach to understand mobility in later life

Meijering

Weitkamp

2016

Social Science & Medicine

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The aim of this methods-focused article is to explore the potential benefits of integrating GPS, diary and in-depth interview data to gain richer insights into the everyday mobility practices of older adults. Eighteen adults, aged 65-90 years, living in the Netherlands, participated in the study. Our findings illustrate how quantitative (GPS) and qualitative (interviews and diary-based) approaches together can generate different insights and layers of understanding from each individual method in order to enhance the overall study findings. Our findings demonstrate that our methodological approach generates new insights with respect to GPS-measured and self-reported mobility, time-geographies, and micro-geographies of older adults in the Netherlands. In conclusion, our mixed-methods approach contributes to a better understanding of the everyday mobility practices of older adults, and could be used in other demographic groups.

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Section: Mixed-methods Approachmentioning

confidence: 99%

Numbers and narratives: Developing a mixed-methods approach to understand mobility in later life

Meijering

Weitkamp

2016

Social Science & Medicine

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“…Some previous studies analysed the relationships among travel behaviour, step count and living functions of older people. Voss et al [4] used a global positioning system (GPS) and accelerometers to identify the relationships between the usage of public transportation and the level of physical activity. Travel using public transportation was more highly associated with a higher physical activity compared to travel by non-public transportation.…”

Section: Reference Reviewmentioning

confidence: 99%

“…Examples include a study analysing the travel behaviour in the central area of Kumamoto City, Kumamoto Prefecture, japan [7], and one examining people's preferred escape routes from a natural disaster [8]. Of these studies, only Voss et al [4] analysed the behaviour of older citizens.…”

Section: Reference Reviewmentioning

confidence: 99%

Impact analysis of reduced fare programme for older people on step counts per day and travel behaviour

Kamada¹,

Matsunaka²,

Oba³

et al. 2017

Int. J. TDI

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This study verifies the impact of the reduced fare programme for senior citizens on the step count per day, visited places and the modal share of public transportation. We targeted citizens over 65 years in Toyama City, japan. The city operates a Special Pass Project, which allows passholders to receive a public transportation discount fare to the city centre from any station or stop in the city. We selected participants randomly and collected global positioning system log data and step counts for one month. Participants were instructed to wear the provided instruments, which we configured in advance. We analysed and compared the results between passholders and non-passholders. Passholders walked more than non-passholders. In addition, passholders visited more places in the city centre. Moreover, the modal share of public transportation between the city centre and home was higher for passholders than for non-passholders. These results confirm that the reduced fare programme supports active lifestyles for senior citizens.

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“…Yet, more consistent empirical evidence is needed to support such associations (Loukaitou‐Sideris, ; Sallis, Bauman, & Pratt, ). In addition, few PA studies to date using GPS and accelerometer data have taken into account motorized‐transport modes like traveling in a vehicle (e.g., Voss et al, ). According to Gordon‐Larsen, Nelson, and Beam (), the high utilization of non‐motorized transport (e.g., walking and biking) for commuting is mostly exhibited among the group of young adults who meet PA recommendations, whereas the higher percentage of young adults who do not meet PA recommendations is associated with higher utilization of motorized‐transport modes (e.g., public transit and private car) for traveling to workplaces or schools.…”

Section: Introductionmentioning

confidence: 99%

Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques

Lee

Kwan

2018

Transactions in GIS

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Due to the advancement of tracking technology, a large quantity of movement data has been collected and analyzed in various research domains. In human mobility and physical activity (PA) research, GPS trajectories and the capabilities of geographic information systems (GIS) facilitate a better understanding of the associations between PA and various environmental factors taking individuals' daily travels into account. PA research, however, needs to widen its focus from the intensity of PA to types of PA, which may provide useful clues for understanding specific health behaviors in particular geographic contexts. This study proposes and develops an algorithm to automatically classify PA types and in-vehicle status using GPS and accelerometer data. Walking, standing, jogging, biking and sedentary/in-vehicle statuses are identified through hierarchical classification processes based on machine learning and geospatial techniques. The proposed algorithm achieved high predictive accuracy on real-world GPS and accelerometer data. It can greatly reduce participants' and researchers' burdens by automatically identifying PA types and in-vehicle status for human mobility research, which is also known as travel mode imputation in transportation research. | 1523 LEE and KWAN

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Public transit use and physical activity in community-dwelling older adults: Combining GPS and accelerometry to assess transportation-related physical activity

Cited by 36 publications

References 22 publications

Numbers and narratives: Developing a mixed-methods approach to understand mobility in later life

Numbers and narratives: Developing a mixed-methods approach to understand mobility in later life

Impact analysis of reduced fare programme for older people on step counts per day and travel behaviour

Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques

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