A systematic review of the evolution of GPS use in active living research: A state of the evidence for research, policy, and practice

A potential environmental paradox in India: Associations between air pollution precautions and sedentary behaviour among children and youth

The negative impact of ambient air pollution on movement behaviours in the global south is a significant concern. Yet, evidence about this complex relationship is limited. This study assessed how precautions taken to prevent ambient air pollution exposure are associated with sedentary behaviour among children and youth in India. Participants aged 5-17 years (N = 986) from 41 schools in 28 urban and rural areas across India completed online surveys to provide information on movement behaviours, including precautions taken to avoid exposure to air pollution, perception of built environment, and sedentary behaviour. Multivariate gamma regression models were developed, adjusting for sociodemographic variables with sedentary behaviour as the primary criterion variable. Apart from an overall sample model, six segregated models were built to understand age, gender, and geographical variations. Children and youth who reported taking precautions to prevent ambient air pollution exposure were associated with significantly higher daily minutes of sedentary behaviour in both the overall sample (β = 0.085, 95 % CI = 0.001, 0.169) and the 13 to 17 age group (β = 0.110, 95 % CI = 0.007, 0.227). However, being able to access outdoor physical activity facilities before or after school was associated with lower sedentary behaviour in the following models: overall, rural, 5 to 12 and 13 to 17 age groups, and boys and girls. To our knowledge, this is the first study to depict a potential paradoxical relationship between precautions taken to avoid exposure to ambient air pollution and higher sedentary behaviour among children and youth in India i.e., a health-preserving behaviour is perpetuating another chronic disease risk factor.

Daily Path Areas and Location Use During and After Cardiac Rehabilitation

PURPOSE

Little research has focused on the potential impact that the environment plays in shaping cardiac rehabilitation (CR) patient sedentary time (ST) and physical activity (PA). To address this, the current study generated daily path areas (DPAs) based on the locations they visited during and after they completed CR.

METHODS

Patients in CR (n = 66) completed a survey and wore an accelerometer and Global Positioning System receiver for 7 days early (first month), late (last 2 weeks of program), and 3 months after completing CR.

RESULTS

Individual DPAs were approximately 24 km 2 at baseline and remained stable over time. Location-based analyses showed that most patients' ST and PA time was spent at home, followed by other residential, commercial, work, and CR locations. However, the time spent in certain locations (eg, parks and recreation locations) fluctuated during and after CR by intensity.

CONCLUSIONS

CR patient DPA was stable over time. Within this space, they primarily engaged in ST and PA at home. However, when not home, the distribution of location use varied across a number of locations that extended well beyond their neighborhoods. Therefore, proximity to home may not be a barrier for CR patients in relation to their ST and PA.

Smartphone Google Location History: A Novel Approach to Outdoor Physical Activity Research

BACKGROUND

Outdoor physical activity (PA) is an important component of overall health; however, it is difficult to measure. Passively collected smartphone location data like Google Location History (GLH) present an opportunity to address this issue.

OBJECTIVES

To evaluate the use of GLH data for measuring outdoor PA.

METHODS

We collected GLH data for 357 individuals from the Washington State Twin Registry. We first summarized GLH measurements relevant to outdoor PA. Next, we compared accelerometer measurements to GLH classified PA for a subset of 25 participants who completed 2 weeks of global positioning system and accelerometer monitoring. Finally, we examined the association between GLH measured walking and obesity.

RESULTS

Participants provided a mean (SD) average 52 (18.8) months of GLH time-activity data, which included a mean (SD) average of 2421 (1632) trips per participant. GLH measurements were classified as the following: 79,994 unique walking trips (11.6% of all trips), 564,558 (81.8%) trips in a passenger vehicle, 11,974 cycling trips (1.7%), and 890 running trips (0.1%). Sixty-two percent of these trips had location accuracy >80%. In the accelerometry evaluation, GLH walking trips had a corresponding mean vector magnitude of 3150 counts per minute, compared with 489 counts per minute for vehicle trips. In adjusted cross-sectional analyses, we observed an inverse association between both walking minutes and trips per month and the odds of being obese (odds ratio = 0.78; 95% CI, 0.60-0.96, and odds ratio = 0.91; 95% CI, 0.82-0.98, respectively).

CONCLUSIONS

GLH data provide a novel method for measuring long-term, retrospective outdoor PA that can provide new opportunities for PA research.

The "uneven road" to food: Socioeconomic disparities in the mobility burden of food purchasing behavior in major US cities, 2019-2023

Socioeconomic factors contribute to distinct patterns of food-purchasing behaviors, placing a higher burden of mobility on vulnerable, deprived populations. Traditional approaches often overlook the dynamics of human activity as contextual influences, simulating a perceived food environment that contradicts the actual use thereof. The rise of large-scale mobile phone data presents a unique opportunity to capture real behavioral patterns and their mobility implications at a fine-grained level. Using a Time-Weighted Kernel Density Estimation (TWKDE) model on mobile phone data, this study introduces two novel measures - the Spatial Engel's Coefficient (SEC) index and the Distance-to-Activity Curve (DAC) - to assess the equity of food-purchasing travel across nine U.S. cities over five years, analyzed by socioeconomic status, time period, and location. Our findings reveal that lower socioeconomic status is strongly associated with greater mobility burdens in food-purchasing travel. This mobility gap between the highest and lowest socioeconomic groups was further exacerbated during the COVID-19 pandemic, manifesting in the form of spatial segregation of opportunities within cities. This paper contributes to the literature by developing novel activity-based tools that offer a more nuanced understanding of the behavioral characteristics of food-purchasing activities. These empirical insights can help policymakers identify the communities facing the greatest mobility burdens and guide targeted, place-based interventions to promote equity in food access.

Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review

The youth mental health crisis is exacerbated by limited access to care and resources. Mobile health (mHealth) platforms using predictive artificial intelligence (AI) can improve access and reduce barriers, enabling real-time responses and precision prevention. This systematic review evaluates predictive AI approaches in mHealth platforms for forecasting mental health symptoms among youth (13-25 years). We searched studies from Embase, PubMed, Web of Science, PsycInfo, and CENTRAL, to identify relevant studies. From 11 studies identified, three studies predicted multiple symptoms, with depression being the most common (63%). Most platforms used smartphones and 25% integrated wearables. Key predictors included smartphone usage (N=5), sleep metrics (N=6), and physical activity (N=5). Nuanced predictors like usage locations and sleep stages improved prediction. Logistic regression was most used (N=6), followed by Support Vector Machines (N=3) and ensemble methods (N=4). F-scores for anxiety and depression ranged from 0.73 to 0.84, and AUCs from 0.50 to 0.74. Stress models had AUCs of 0.68 to 0.83. Bayesian model selection and Shapley values enhanced robustness and interpretability. Barriers included small sample sizes, privacy concerns, missing data, and underrepresentation bias. Rigorous evaluation of predictive performance, generalizability, and user engagement is critical before mHealth platforms are integrated into psychiatric care.

GPS Suitability for Physical Frailty Assessment

The ageing of the population needs the automation of patient monitoring. The objective of this is twofold: to improve care and reduce costs. Frailty, as a state of increased vulnerability resulting from several diseases, can be seen as a pandemic for older people. One of the most common detection tests is gait speed. This article compares the gait speed measured outdoors using smartphones with that measured using manual tests conducted in medical centres. In the experiments, the walking speed was measured over a straight path of 80 m. Additionally, the speed was measured over 2.4 m in the middle of the path, given that this is the minimum distance used in medical frailty tests. To eliminate external factors, the participants were healthy individuals, the weather was good, and the path was flat and free of obstacles. The results obtained are promising. The measurements taken with common smartphones over a straight path of 80 m are within the same order of error as those observed in the manual tests conducted by practitioners.

Association between yoga and related contextual factors with moderate-to-vigorous physical activity among children and youth aged 5 to 17 years across five Indian states

Physical inactivity is one of the four key preventable risk factors, along with unhealthy diet, tobacco use, and alcohol consumption, underlying most noncommunicable diseases. Promoting physical activity is particularly important among children and youth, whose active living behaviours often track into adulthood. Incorporating yoga, an ancient practice that originated in India, can be a culturally-appropriate strategy to promote physical activity in India. However, there is little evidence on whether yoga practice is associated with moderate-to-vigorous physical activity (MVPA) accumulation. Thus, this study aims to understand how yoga practice is associated with MVPA among children and youth in India. Data for this study were obtained during the coronavirus disease lockdown in 2021. Online surveys capturing MVPA, yoga practice, contextual factors, and sociodemographic characteristics, were completed by 5 to 17-year-old children and youth in partnership with 41 schools across 28 urban and rural locations in five states. Linear regression analyses were conducted to assess the association between yoga practice and MVPA. After controlling for age, gender, and location, yoga practice was significantly associated with MVPA among children and youth (β = 0.634, p < 0.000). These findings highlight the value of culturally-appropriate activities such as yoga, to promote physical activity among children and youth. Yoga practice might have a particularly positive impact on physical activity among children and youth across the world, owing to its growing global prevalence.

Neighborhood Access to the Built Environment and Allostatic Load: A Systematic Review of the Use of Geographic Information Systems

Objectives: This paper systematically reviews how spatial analysis has been used to measure relationships between access to the built environment and Allostatic Load (AL) or biomarkers relevant to the stress pathway. Geographic Information Systems (GIS) facilitate objective measurement of built environment access that may explain unequal health outcomes linked to living in stressful environments. Methods: Systematic review, search date 13 July 2022 with methods published a priori. Included studies that quantitatively assessed associations between GIS measures of neighborhood attributes and biomarkers of stress. Results: 23 studies from 14 countries were included having used GIS measures to assess relationships between access to the built environment and biomarkers relevant to AL, with 17 being cross-sectional and 6 longitudinal. Just 2 studies explicitly assessed associations between GIS measures and AL, but 21 explored biomarkers relevant to the stress pathway. GIS was used to calculate density (how much of x within y) and proximity (how far from a to b) measures. Conclusion: GIS measures of greenspace, the food environment, area-level demographics, and land-use measures were found to influence biomarkers relevant to the stress pathway, highlighting the utility of this approach. GIS use is extremely limited when measuring the built environment and its influence on AL but has been widely used to consider effects on individual biomarkers of stress. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=348355], identifier [CRD42022348355].

Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research

Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data vary from study to study, making comparisons and reproducibility challenging.

OBJECTIVES

The objectives of this systematic review were to (1) identify best practices for GPS data collection and processing; (2) quantify reporting of best practices in published studies; and (3) discuss examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage of GPS data in health studies.

DESIGN

A systematic review.

DATA SOURCES

Electronic databases searched (24 October 2023) were PubMed, Scopus and Web of Science (PROSPERO ID: CRD42022322166).

ELIGIBILITY CRITERIA

Included peer-reviewed studies published in English met at least one of the criteria: (1) protocols involving GPS for exposure/context and human health research purposes and containing empirical data; (2) linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health; (4) derived variable methods using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary).

DATA EXTRACTION AND SYNTHESIS

We examined 157 manuscripts for reporting of best practices including wear time, sampling frequency, data validity, noise/signal loss and data linkage to assess risk of bias.

RESULTS

We found that 6% of the studies did not disclose the GPS device model used, only 12.1% reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and only 68.2% reported the inclusion criteria for their data.

CONCLUSIONS

Our recommendations for reporting on GPS usage, processing and linkage may be transferrable to other geospatial devices, with the hope of promoting transparency and reproducibility in this research.

PROSPERO REGISTRATION NUMBER

CRD42022322166.

Comparing Location Data From Smartphone and Dedicated Global Positioning System Devices: Implications for Epidemiologic Research

In this study, we compared location data from a dedicated Global Positioning System (GPS) device with location data from smartphones. Data from the Interventions, Equity, and Action in Cities Team (INTERACT) Study, a study examining the impact of urban-form changes on health in 4 Canadian cities (Victoria, Vancouver, Saskatoon, and Montreal), were used. A total of 337 participants contributed data collected for about 6 months from the Ethica Data smartphone application (Ethica Data Inc., Toronto, Ontario, Canada) and the SenseDoc dedicated GPS (MobySens Technologies Inc., Montreal, Quebec, Canada) during the period 2017-2019. Participants recorded an average total of 14,781 Ethica locations (standard deviation, 19,353) and 197,167 SenseDoc locations (standard deviation, 111,868). Dynamic time warping and cross-correlation were used to examine the spatial and temporal similarity of GPS points. Four activity-space measures derived from the smartphone app and the dedicated GPS device were compared. Analysis showed that cross-correlations were above 0.8 at the 125-m resolution for the survey and day levels and increased as cell size increased. At the day or survey level, there were only small differences between the activity-space measures. Based on our findings, we recommend dedicated GPS devices for studies where the exposure and the outcome are both measured at high frequency and when the analysis will not be aggregate. When the exposure and outcome are measured or will be aggregated to the day level, the dedicated GPS device and the smartphone app provide similar results.

Quality appraisal of spatial epidemiology and health geography research: A scoping review of systematic reviews

A scoping review of peer-reviewed literature was conducted to understand how systematic reviews assess the methodological quality of spatial epidemiology and health geography research. Fifty-nine eligible reviews were identified and included. Variations in the use of quality appraisal tools were found. Reviews applied existing quality appraisal tools with no adaptations (n = 32; 54%), existing quality appraisal tools with adaptations (n = 9; 15%), adapted tools or methods from other reviews (n = 13; 22%), and developed new quality appraisal tools for the review (n = 5; 8%). Future research should focus on developing and validating a quality appraisal tool that evaluates the spatial methodology within studies.

Accounting for space, time, and behavior using GPS derived dynamic measures of environmental exposure

Time-weighted spatial averaging approaches (TWSA) are an increasingly utilized method for calculating exposure using global positioning system (GPS) mobility data for health-related research. They can provide a time-weighted measure of exposure, or dose, to various environments or health hazards. However, little work has been done to compare existing methodologies, nor to assess how sensitive these methods are to mobility data inputs (e.g., walking vs driving), the type of environmental data being assessed as the exposure (e.g., continuous surfaces vs points of interest), and underlying point-pattern clustering of participants (e.g., if a person is highly mobile vs predominantly stationary). Here we contrast three TWSA approaches that have been previously used or recently introduced in the literature: Kernel Density Estimation (KDE), Density Ranking (DR), and Point Overlay (PO). We feed GPS and accelerometer data from 602 participants through each method to derive time-weighted activity spaces, comparing four mobility behaviors: all movement, stationary time, walking time, and in-vehicle time. We then calculate exposure values derived from the various TWSA activity spaces with four environmental layer data types (point, line, area, surface). Similarities and differences across TWSA derived exposures for the sample and between individuals are explored, and we discuss interpretation of TWSA outputs providing recommendations for researchers seeking to apply these methods to health-related studies.

The 2022 India Report Card on physical activity for children and adolescents

Background

With strong evidence of physical inactivity's link to chronic disease and economic burden - particularly with childhood active living behaviors tracking into adulthood - it is imperative to promote physical activity among children and adolescents in India.

Objectives

To evaluate active living patterns among Indian children and adolescents.

Methods

The India Report Card (IRC) team, which consists of experts in India and Canada, systematically collected and appraised evidence on 11 indicators of active living, including 5 behavioral (Overall Physical Activity, Organized Sport Participation, Active Play, Active Transportation, Sedentary Behavior), 2 individual-level (Physical Fitness, Yoga) and 4 sources of influence (Family and Peers, School, Community and Built Environment, Government). Peer-reviewed articles were appraised based on national representativeness, sample size, and data quality. Grey literature was appraised based on comprehensiveness, validity of the sources, and representativeness. All indicators were assessed against parameters provided by the Active Healthy Kids Global Alliance.

Results

Active Transportation and Government Strategies were ranked highest with a B- and C+ grade, respectively. Overall Physical Activity and Schools were assigned a C grade, while Sedentary Behavior and Community and Built Environment were given D grades. Yoga was the lowest ranking indicator with a D- grade. Organized Sport Participation, Active Play, Family and Peers, and Physical Fitness were all graded incomplete.

Conclusions

Active Transportation, Government Strategies, and Overall Physical Activity have improved since the 2018 IRC, a positive trend that needs to be translated to other indicators. However, Sedentary Behavior has consistently worsened, with grades C, C-, and D-, in 2016, 2018, and 2022, respectively. Evidence generated by the 2022 IRC suggests opportunities for improvement not only in India, but also the 56 other countries taking part in Global Matrix 4.0.

Bring Your Own Location Data: Use of Google Smartphone Location History Data for Environmental Health Research

BACKGROUND

Environmental exposures are commonly estimated using spatial methods, with most epidemiological studies relying on home addresses. Passively collected smartphone location data, like Google Location History (GLH) data, may present an opportunity to integrate existing long-term time-activity data.

OBJECTIVES

We aimed to evaluate the potential use of GLH data for capturing long-term retrospective time-activity data for environmental health research.

METHODS

We included 378 individuals who participated in previous Global Positioning System (GPS) studies within the Washington State Twin Registry. GLH data consists of location information that has been routinely collected since 2010 when location sharing was enabled within android operating systems or Google apps. We created instructions for participants to download their GLH data and provide it through secure data transfer. We summarized the GLH data provided, compared it to available GPS data, and conducted an exposure assessment for nitrogen dioxide (NO2) air pollution.

RESULTS

Of 378 individuals contacted, we received GLH data from 61 individuals (16.1%) and 53 (14.0%) indicated interest but did not have historical GLH data available. The provided GLH data spanned 2010-2021 and included 34 million locations, capturing 66,677 participant days. The median number of days with GLH data per participant was 752, capturing 442 unique locations. When we compared GLH data to 2-wk GPS data (∼1.8 million points), 95% of GPS time-activity points were within 100m of GLH locations. We observed important differences between NO2 exposures assigned at home locations compared with GLH locations, highlighting the importance of GLH data to environmental exposure assessment.

DISCUSSION

We believe collecting GLH data is a feasible and cost-effective method for capturing retrospective time-activity patterns for large populations that presents new opportunities for environmental epidemiology. Cohort studies should consider adding GLH data collection to capture historical time-activity patterns of participants, employing a "bring-your-own-location-data" citizen science approach. Privacy remains a concern that needs to be carefully managed when using GLH data. https://doi.org/10.1289/EHP10829.

Development of a method for walking step observation based on large-scale GPS data

Background

Widespread use of smartphones has enabled the continuous monitoring of people’s movements and physical activity. Linking global positioning systems (GPS) data obtained via smartphone applications to physical activity data may allow for large-scale and retrospective evaluation of where and how much physical activity has increased or decreased due to environmental, social, or individual changes caused by policy interventions, disasters, and infectious disease outbreaks. However, little attention has been paid to the use of large-scale commercial GPS data for physical activity research due to limitations in data specifications, including limited personal attribute and physical activity information. Using GPS logs with step counts measured by a smartphone application, we developed a simple method for daily walking step estimation based on large-scale GPS data.

Methods

The samples of this study were users whose GPS logs were obtained in Sendai City, Miyagi Prefecture, Japan, during October 2019 (37,460 users, 36,059,000 logs), and some logs included information on daily step counts (731 users, 450,307 logs). The relationship between land use exposure and daily step counts in the activity space was modeled using the small-scale GPS logs with daily step counts. Furthermore, we visualized the geographic distribution of estimated step counts using a large set of GPS logs with no step count information.

Results

The estimated model showed positive relationships between visiting high-rise buildings, parks and public spaces, and railway areas and step counts, and negative relationships between low-rise buildings and factory areas and daily step counts. The estimated daily step counts tended to be higher in urban areas than in suburban areas. Decreased step counts were mitigated in areas close to train stations. In addition, a clear temporal drop in step counts was observed in the suburbs during heavy rainfall.

Conclusions

The relationship between land use exposure and step counts observed in this study was consistent with previous findings, suggesting that the assessment of walking steps based on large-scale GPS logs is feasible. The methodology of this study can contribute to future policy interventions and public health measures by enabling the retrospective and large-scale observation of physical activity by walking.