Use of global positioning systems to study physical activity and the environment: a systematic review

The integration of geographic methods and ecological momentary assessment in public health research: A systematic review of methods and applications

With the widespread prevalence of mobile devices, ecological momentary assessment (EMA) can be combined with geospatial data acquired through geographic techniques like global positioning system (GPS) and geographic information system. This technique enables the consideration of individuals' health and behavior outcomes of momentary exposures in spatial contexts, mostly referred to as "geographic ecological momentary assessment" or "geographically explicit EMA" (GEMA). However, the definition, scope, methods, and applications of GEMA remain unclear and unconsolidated. To fill this research gap, we conducted a systematic review to synthesize the methodological insights, identify common research interests and applications, and furnish recommendations for future GEMA studies. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines to systematically search peer-reviewed studies from six electronic databases in 2022. Screening and eligibility were conducted following inclusion criteria. The risk of bias assessment was performed, and narrative synthesis was presented for all studies. From the initial search of 957 publications, we identified 47 articles included in the review. In public health, GEMA was utilized to measure various outcomes, such as psychological health, physical and physiological health, substance use, social behavior, and physical activity. GEMA serves multiple research purposes: 1) enabling location-based EMA sampling, 2) quantifying participants' mobility patterns, 3) deriving exposure variables, 4) describing spatial patterns of outcome variables, and 5) performing data linkage or triangulation. GEMA has advanced traditional EMA sampling strategies and enabled location-based sampling by detecting location changes and specified geofences. Furthermore, advances in mobile technology have prompted considerations of additional sensor-based data in GEMA. Our results highlight the efficacy and feasibility of GEMA in public health research. Finally, we discuss sampling strategy, data privacy and confidentiality, measurement validity, mobile applications and technologies, and GPS accuracy and missing data in the context of current and future public health research that uses GEMA.

Where Are Czech Adolescents Active? The Patterns of Movement and Transport Behavior in Different Active Living Domains

To understand the environmental determinants of physical activity (PA), precise spatial localization is crucial. This cross-sectional study focuses on the spatiotemporal distribution of PA among Czech adolescents (n = 171) using Global Positioning System loggers and accelerometers. The results showed that adolescents spent most of their time in sedentary behavior, with 57.2% and 58.5% of monitored time at home and school, respectively. The park and playground had the lowest proportion of sedentary behavior but also the lowest amount of moderate to vigorous PA (MVPA). However, when considering the time spent in each domain, the highest proportion of MVPA was seen in publicly accessible playgrounds (13.3% of the time). Chi-square analysis showed that the relative distribution of different PA intensities did not differ across spatial domains. Based on these results, the authors propose 2 key strategies for increasing MVPA in adolescents: Increase the time spent in activity-supportive environments, such as parks and playgrounds, and design techniques to increase MVPA at home and school settings.

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.

Finding women in fishing communities around Lake Victoria: "Feasibility and acceptability of using phones and tracking devices"

INTRODUCTION

Women in fishing communities have both high HIV prevalence and incidence, hence they are a priority population for HIV prevention and treatment interventions. However, their mobility is likely to compromise the effectiveness of interventions. We assessed the acceptability, feasibility and of using phones and global positioning system (GPS) devices for tracking mobility, to inform future health research innovations.

METHODS

A mult-site formative qualitative study was conducted in six purposively selected Fishing Communities on the shores of Lake Victoria in Kenya, Tanzania, and Uganda. Participants were selected based on duration of stay in the community and frequency of movement. Sixty-four (64) women participated in the study (16 per fishing community). Twenty-four (24) participants were given a study phone; 24 were asked to use their own phones and 16 were provided with a portable GPS device to understand what is most preferred. Women were interviewed about their experiences and recommendations on carrying GPS devices or phones. Twenty four (24) Focus Group Discussions with 8-12 participants were conducted with community members to generate data on community perceptions regarding GPS devices and phones acceptability among women. Data were analyzed thematically and compared across sites/countries.

RESULTS

Women reported being willing to use tracking devices (both phones and GPS) because they are easy to carry. Their own phone was preferred compared to a study phone and GPS device because they were not required to carry an additional device, worry about losing it or be questioned about the extra device by their sexual partner. Women who carried GPS devices suggested more sensitization in communities to avoid domestic conflicts and public concern. Women suggested changing the GPS colour from white to a darker colour and, design to look like a commonly used object such as a telephone Subscriber Identity Module (SIM) card, a rosary/necklace or a ring for easy and safe storage.

CONCLUSION

Women in the study communities were willing to have their movements tracked, embraced the use of phones and GPS devices for mobility tracking. Devices need to be redesigned to be more discrete, but they could be valuable tools to understanding movement patterns and inform design of interventions for these mobile populations.

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.

Disparities in mobile phone ownership reflect inequities in access to healthcare

Human movement and population connectivity inform infectious disease management. Remote data, particularly mobile phone usage data, are frequently used to track mobility in outbreak response efforts without measuring representation in target populations. Using a detailed interview instrument, we measure population representation in phone ownership, mobility, and access to healthcare in a highly mobile population with low access to health care in Namibia, a middle-income country. We find that 1) phone ownership is both low and biased by gender, 2) phone ownership is correlated with differences in mobility and access to healthcare, and 3) reception is spatially unequal and scarce in non-urban areas. We demonstrate that mobile phone data do not represent the populations and locations that most need public health improvements. Finally, we show that relying on these data to inform public health decisions can be harmful with the potential to magnify health inequities rather than reducing them. To reduce health inequities, it is critical to integrate multiple data streams with measured, non-overlapping biases to ensure data representativeness for vulnerable populations.

Willingness to participate in in-the-moment surveys triggered by online behaviors

Surveys are a fundamental tool of empirical research, but they suffer from errors: in particular, respondents can have difficulties recalling information of interest to researchers. Recent technological developments offer new opportunities to collect data passively (i.e., without participant's intervention), avoiding recall errors. One of these opportunities is registering online behaviors (e.g., visited URLs) through tracking software ("meter") voluntarily installed by a sample of individuals on their browsing devices. Nevertheless, metered data are also affected by errors and only cover part of the objective information, while subjective information is not directly observable. Asking participants about such missing information by means of web surveys conducted in the moment an event of interest is detected by the meter has the potential to fill the gap. However, this method requires participants to be willing to participate. This paper explores the willingness to participate in in-the-moment web surveys triggered by online activities recorded by a participant-installed meter. A conjoint experiment implemented in an opt-in metered panel in Spain reveals overall high levels of willingness to participate among panelists already sharing metered data, ranging from 69% to 95%. The main aspects affecting this willingness are related to the incentive levels offered. Limited differences across participants are observed, except for household size and education. Answers to open questions also confirm that the incentive is the key driver of the decision to participate, whereas other potential problematic aspects such as the limited time to participate, privacy concerns, and discomfort caused by being interrupted play a limited role.

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.

Comparison of GPS imputation methods in environmental health research

Assessment of personal exposure in the external environment commonly relies on global positioning system (GPS) measurements. However, it has been challenging to determine exposures accurately due to missing data in GPS trajectories. In environmental health research using GPS, missing data are often discarded or are typically imputed based on the last known location or linear interpolation. Imputation is said to mitigate bias in exposure measures, but methods used are hardly evaluated against ground truth. Widely used imputation methods assume that a person is either stationary or constantly moving during the missing interval. Relaxing this assumption, we propose a method for imputing locations as a function of a person's likely movement state (stop, move) during the missing interval. We then evaluate the proposed method in terms of the accuracy of imputed location, movement state, and daily mobility measures such as the number of trips and time spent on places visited. Experiments based on real data collected by participants (n=59) show that the proposed approach outperforms existing methods. Imputation to the last known location can lead to large deviation from the actual location when gap distance is large. Linear interpolation is shown to result in large errors in mobility measures. Researchers should be aware that the different treatment of missing data can affect the spatiotemporal accuracy of GPS-based exposure assessments.

Daily and hourly patterns of physical activity and sedentary behavior of older adults: Atherosclerosis risk in communities (ARIC) study

This cross-sectional study of older adults ≥ 65 years describes daily and hourly patterns of accelerometer-derived steps, sedentary, and physical activity behaviors and examines differences by day of the week and sociodemographic and health-related factors to identify time-use patterns. Data were from 459 Atherosclerosis Risk in Communities (ARIC) study participants (60% female; mean ± SD age = 78.3 ± 4.6 years; 20% Black) who wore a hip accelerometer ≥ 4 of 7 days, for ≥ 10 h/day in 2016. We used linear mixed models to examine daily patterns of steps, sedentary, low light, high light, and moderate-to-vigorous intensity physical activity (MVPA). Differences by sex, median age ( Collapse

Key Words

• Accelerometer
• Elderly
• Movement
• Steps
• Time-use

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MESH Headings Collapse

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Affiliation(s)

Erin E. Dooley The University of Alabama at Birmingham, Birmingham, AL, USA
Lisa A. Pompeii Baylor College of Medicine, Houston, TX, USA
Priya Palta Columbia University Irving Medical Center, New York, NY, USA
Pablo Martinez-Amezcua Columbia University Irving Medical Center, New York, NY, USA
Bjoern Hornikel The University of Alabama at Birmingham, Birmingham, AL, USA
Kelly R. Evenson Gillings School of Global Public Health, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jennifer A. Schrack Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Kelley Pettee Gabriel The University of Alabama at Birmingham, Birmingham, AL, USA

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Mobility Based on GPS Trajectory Data and Interviews: A Pilot Study to Understand the Differences between Lower- and Higher-Income Older Adults in Hong Kong

Few studies have examined mobility from a social exclusion perspective. Limited mobility can restrict opportunities to interact with others and therefore may lead to social exclusion. This pilot study was designed to test the feasibility of integrating Global Positioning System (GPS) trajectory data and interview data to understand the different mobility patterns between lower- and higher-income older adults in Hong Kong and the potential reasons for and impacts of these differences. Lower- (n = 21) and higher- (n = 24) income adults aged 60 years of age or older in Hong Kong were recruited based on purposive sampling. They were asked to wear a GPS device for 7 days. Seven measures of mobility (four dimensions) were created based on GPS data and compared between lower- and higher-income older adults, including extensity (standard deviation ellipse, standard distance between all locations), intensity (time spent out of home, doing activities), diversity (number of locations), and non-exclusivity (time spent in public open spaces and places with higher public service provisions). It then administered semi-structured interviews to understand the determined differences. The activity spaces for lower-income older adults were, on average, smaller than those for higher-income older adults, but lower-income older adults spent significantly more time participating in out-of-home activities. They were more likely to be exposed to environments with similar socioeconomic characteristics as their own. The interviews showed that limited social networks and expenditure on transport were the two main factors associated with lower-income older adults having relatively fewer activity spaces, which may lead to further social exclusion. We recommend using GPS in daily life as a feasible way to capture the mobility patterns and using interviews to deeply understand the different patterns between lower- and higher-income older adults. Policy strategies aiming to improve the mobility of older might be helpful for further improving the social inclusion of lower-income older adults.

Has the Sudden Health Emergency Impacted Public Awareness? Survey-Based Evidence from China

Public environmental cognition is an important basis for optimizing environmental management and reducing tensions between humans and land. Although the level of environmental cognition is a gradual process under normal conditions, it often changes qualitatively because of major public emergencies. During the 2019 new coronavirus epidemic (COVID-19), the most significant public health event in recent years, 24,188 national samples were obtained based on a network survey. The comprehensive evaluation method was used to assess the impact of major public events on public environmental cognition and the characteristics of spatial and temporal distribution. The findings are as follows. (1) During the epidemic period, sudden public health emergencies effectively promoted the national residents’ environmental awareness, whether urban residents or rural; most respondents generally agreed with the concept of “respect nature and cherish life”. (2) The environmental cognition of national residents was higher in the northwest and lower in the northeast of China, which is suitable for economic and social development and humanistic tradition. (3) There was a clear positive correlation between environmental awareness and education level. (4) During the epidemic, nervousness of respondents had a negative effect on environmental cognition. This study provides scientific support and a basis for decision making for the government to carry out environmental management optimization and improve the ecological and environmental cognition of the public, as well as devise effective intervention mechanisms with different time and space dimensions for similar future public health emergencies.

How Long Should GPS Recording Lengths Be to Capture the Community Mobility of An Older Clinical Population? A Parkinson's Example

Wearable global position system (GPS) technology can help those working with older populations and people living with movement disorders monitor and maintain their mobility level. Health research using GPS often employs inconsistent recording lengths due to the lack of a standard minimum GPS recording length for a clinical context. Our work aimed to recommend a GPS recording length for an older clinical population. Over 14 days, 70 older adults with Parkinson's disease wore the wireless inertial motion unit with GPS (WIMU-GPS) during waking hours to capture daily "time outside", "trip count", "hotspots count" and "area size travelled". The longest recording length accounting for weekend and weekdays was ≥7 days of ≥800 daily minutes of data (14 participants with 156, 483.9 min recorded). We compared the error rate generated when using data based on recording lengths shorter than this sample. The smallest percentage errors were observed across all outcomes, except "hotspots count", with daily recordings ≥500 min (8.3 h). Eight recording days will capture mobility variability throughout days of the week. This study adds empirical evidence to the sensor literature on the required minimum duration of GPS recording.

Differences in Park Walking, Comparing the Physically Inactive and Active Groups: Data from mHealth Monitoring System in Seoul

Despite the overall increase in physical activities and park uses, the discrepancies between physically inactive and active people have increasing widened in recent times. This paper aims to empirically measure the differences in walking activity in urban parks between the physically inactive and active. As for the dataset, 22,744 peoples' 550,234 walking bouts were collected from the mHealth system of the Seoul government, using the smartphone healthcare app, WalkOn, from September to November 2019, in Seocho-gu district, Seoul, Korea. We classified the physically inactive and active sample groups, based on their regular walking (≥150 min of moderate-to-vigorous walking activity a week), and analyzed their park walking activities. We found that while there was no significant difference in walking measures of non-park walking between the sample groups, the difference did exist in park walking. The park walking average in the physically active group had more steps (p = 0.021), longer time (p = 0.008), and higher intensity (p < 0.001) of walking than that in the inactive group. Each park also revealed differences in its on-site park walking quantity and quality, based on which we could draw the list of 'well-walked parks', which held more bouts and more moderate-to-vigorous physical activities (MVPAs) than other parks in Seocho-gu district. This paper addresses how park walking of physically inactive and active people is associated with multiple differences in everyday urban walking.

Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study

BACKGROUND

Smartphone location data can be used for observational health studies (to determine participant exposure or behavior) or to deliver a location-based health intervention. However, missing location data are more common when using smartphones compared to when using research-grade location trackers. Missing location data can affect study validity and intervention safety.

OBJECTIVE

The objective of this study was to investigate the distribution of missing location data and its predictors to inform design, analysis, and interpretation of future smartphone (observational and interventional) studies.

METHODS

We analyzed hourly smartphone location data collected from 9665 research participants on 488,400 participant days in a national smartphone study investigating the association between weather conditions and chronic pain in the United Kingdom. We used a generalized mixed-effects linear model with logistic regression to identify whether a successfully recorded geolocation was associated with the time of day, participants' time in study, operating system, time since previous survey completion, participant age, sex, and weather sensitivity.

RESULTS

For most participants, the app collected a median of 2 out of a maximum of 24 locations (1760/9665, 18.2% of participants), no location data (1664/9665, 17.2%), or complete location data (1575/9665, 16.3%). The median locations per day differed by the operating system: participants with an Android phone most often had complete data (a median of 24/24 locations) whereas iPhone users most often had a median of 2 out of 24 locations. The odds of a successfully recorded location for Android phones were 22.91 times higher than those for iPhones (95% CI 19.53-26.87). The odds of a successfully recorded location were lower during weekends (odds ratio [OR] 0.94, 95% CI 0.94-0.95) and nights (OR 0.37, 95% CI 0.37-0.38), if time in study was longer (OR 0.99 per additional day in study, 95% CI 0.99-1.00), and if a participant had not used the app recently (OR 0.96 per additional day since last survey entry, 95% CI 0.96-0.96). Participant age and sex did not predict missing location data.

CONCLUSIONS

The predictors of missing location data reported in our study could inform app settings and user instructions for future smartphone (observational and interventional) studies. These predictors have implications for analysis methods to deal with missing location data, such as imputation of missing values or case-only analysis. Health studies using smartphones for data collection should assess context-specific consequences of high missing data, especially among iPhone users, during the night and for disengaged participants.

Smart Watch Versus Classic Receivers: Static Validity of Three GPS Devices in Different Types of Built Environments

In order to study the relationship between human physical activity and the design of the built environment, it is important to measure the location of human movement accurately. In this study, we compared an inexpensive GPS receiver (Holux RCV-3000) and a frequently used Garmin Forerunner 35 smart watch, with a device that has been validated and recommended for physical activity research (Qstarz BT-Q1000XT). These instruments were placed on six geodetic points, which represented a range of different environments (e.g., residential, open space, park). The coordinates recorded by each device were compared with the known coordinates of the geodetic points. There were no differences in accuracy among the three devices when averaged across the six sites. However, the Garmin was more accurate in the city center and the Holux was more accurate in the park and housing estate areas compared to the other devices. We consider the location accuracy of the Holux and the Garmin to be comparable to that of the Qstarz. Therefore, we consider these devices to be suitable instruments for locating physical activity. Researchers must also consider other differences among these devices (such as battery life) when determining if they are suitable for their research studies.

Using big data for evaluating development outcomes: A systematic map

BACKGROUND

Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data-that is digitally generated, passively produced and automatically collected-offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs.

OBJECTIVES

Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts.

SEARCH METHODS

A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist.

SELECTION CRITERIA

The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes.

DATA COLLECTION AND ANALYSIS

Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only.

MAIN RESULTS

The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine-generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs.

AUTHORS' CONCLUSIONS

This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficacies related to the use of big data. There are a number of outcomes which are not being studied using big data, either due to the lack to applicability such as education or due to lack of awareness about the new methods and data sources. The map points to a number of gaps as well as opportunities where future researchers can conduct research.

Sociodemographic inequities and active transportation in adults from Latin America: an eight-country observational study

Background

Active transportation is a crucial sort of physical activity for developing sustainable environments and provides essential health benefits. This is particularly important in Latin American countries because they present the highest burden of non-communicable diseases relative to other worldwide regions. This study aimed to examine the patterns of active transportation and its association with sociodemographic inequities in Latin American countries.

Methods

This cross-sectional study was conducted in eight countries. Participants (n = 8547, 18–65 years) self-reported their active transportation (walking, cycling, and total) using the International Physical Activity Questionnaire. Sex, age, ethnicity, socioeconomic level, education level, public and private transport use, and transport mode were used as sociodemographic inequities.

Results

Participants spent a total of 19.9, 3.1, and 23.3 min/day with walking, cycling, and total active transportation, respectively. Mixed and other ethnicity (Asian, Indigenous, Gypsy, and other), high socioeconomic level as well as middle and high education level presented higher walking than Caucasian, low socioeconomic and education level. Private transport mode and use of ≥ 6 days/week of private transport showed lower walking than public transport mode and ≤ 2 days/week of private transport. Use of ≥ 3 days/week of public transport use presented higher walking than ≤ 2 days/week of public transport. Men had higher cycling for active transportation than women. Use of ≥ 3 days/week of public transport use presented higher cycling than ≤ 2 days/week of public transport. ≥6 days/week showed lower cycling than ≤ 2 days/week of private transport use. Men (b: 5.57: 95 %CI: 3.89;7.26), black (3.77: 0.23;7.31), mixed (3.20: 1.39;5.00) and other ethnicity (7.30: 2.55;12.04), had higher total active transportation than women and Caucasian. Private transport mode (-7.03: -11.65;-2.41) and ≥ 6 days/week of private transport use (-4.80: -6.91;-0.31) showed lower total active transportation than public transport mode and ≤ 2 days/week of private transport use. Use of 3–5 (5.10: 1.35;8.85) and ≥ 6 days/week (8.90: 3.07;14.73) of public transport use presented higher total active transportation than ≤ 2 days/week of public transport use. Differences among countries were observed.

Conclusions

Sociodemographic inequities are associated differently with active transportation across Latin American countries. Interventions and policies that target the promotion of active policies transportation essential to consider sociodemographic inequities.

Trial registration

ClinicalTrials.Gov NCT02226627. Retrospectively registered on August 27, 2014.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-021-01524-0.

Patient-centered Outcomes Research in Interstitial Lung Disease: An Official American Thoracic Society Research Statement

Background: In the past two decades, many advances have been made to our understanding of interstitial lung disease (ILD) and the way we approach its treatment. Despite this, many questions remain unanswered, particularly those related to how the disease and its therapies impact outcomes that are most important to patients. There is currently a lack of guidance on how to best define and incorporate these patient-centered outcomes in ILD research. Objectives: To summarize the current state of patient-centered outcomes research in ILD, identify gaps in knowledge and research, and highlight opportunities and methods for future patient-centered research agendas in ILD. Methods: An international interdisciplinary group of experts was assembled. The group identified top patient-centered outcomes in ILD, reviewed available literature for each outcome, highlighted important discoveries and knowledge gaps, and formulated research recommendations. Results: The committee identified seven themes around patient-centered outcomes as the focus of the statement. After a review of the literature and expert committee discussion, we developed 28 research recommendations. Conclusions: Patient-centered outcomes are key to ascertaining whether and how ILD and interventions used to treat it affect the way patients feel and function in their daily lives. Ample opportunities exist to conduct additional work dedicated to elevating and incorporating patient-centered outcomes in ILD research.

GPS-based built environment measures associated with adult physical activity

Studies often rely on home locations to access built environment (BE) influences on physical activity (PA). We use GPS and accelerometer data collected for 288 individuals over a two-week period to examine eight GPS-derived BE characteristics and moderate-to-vigorous PA (MVPA) and light-to-moderate-vigorous PA (LMVPA). NDVI, parks, blue space, pedestrian-orientated intersections, and population density were associated with increased odds of LMVPA and MVPA, while traffic air pollution and noise were associated with decreased odds of LMVPA and MVPA. Associations varied by population density and when accounting for multiple BE measures. These findings provide further information on where individuals choose to be physically active.

"Psyosphere": A GPS Data-Analysing Tool for the Behavioural Sciences

Positioning technologies, such as GPS are widespread in society but are used only sparingly in behavioural science research, e.g., because processing positioning technology data can be cumbersome. The current work attempts to unlock positioning technology potential for behavioural science studies by developing and testing a research tool to analyse GPS tracks. This tool—psyosphere—is published as open-source software, and aims to extract behaviours from GPSs data that are more germane to behavioural research. Two field experiments were conducted to test application of the research tool. During these experiments, participants played a smuggling game, thereby either smuggling tokens representing illicit material past border guards or not. Results suggested that participants varied widely in variables, such as course and speed variability and distance from team members in response to the presence of border guards. Subsequent analyses showed that some of these GPS-derived behavioural variables could be linked to self-reported mental states, such as fear. Although more work needs to be done, the current study demonstrates that psyosphere may enable researchers to conduct behavioural experiments with positioning technology, outside of a laboratory setting.

Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews

Background: Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the methodological complexity of demonstrating their measurable impact on human health. Even though new big data sources have shown unprecedented potential for disease diagnosis and outbreak detection, we need to investigate results in the existing literature to gain a comprehensive understanding of their impact on and benefits to human health. Objective: A systematic review of systematic reviews on identifying digital data sources and their impact area on people's health, including challenges, opportunities, and good practices. Methods: A multidatabase search was performed. Peer-reviewed papers published between January 2010 and November 2020 relevant to digital data sources on health were extracted, assessed, and reviewed. Results: The 64 reviews are covered by three domains, that is, universal health coverage (UHC), public health emergencies, and healthier populations, defined in WHO's General Programme of Work, 2019-2023, and the European Programme of Work, 2020-2025. In all three categories, social media platforms are the most popular digital data source, accounting for 47% (N = 8), 84% (N = 11), and 76% (N = 26) of studies, respectively. The second most utilized data source are electronic health records (EHRs) (N = 13), followed by websites (N = 7) and mass media (N = 5). In all three categories, the most studied impact of digital data sources is on prevention, management, and intervention of diseases (N = 40), and as a tool, there are also many studies (N = 10) on early warning systems for infectious diseases. However, they could also pose health hazards (N = 13), for instance, by exacerbating mental health issues and promoting smoking and drinking behavior among young people. Conclusions: The digital data sources presented are essential for collecting and mining information about human health. The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems. This study also identified the apparent gap in systematic reviews investigating the novel big data streams, Internet of Things (IoT) data streams, and sensor, mobile, and GPS data researched using artificial intelligence, complex network, and other computer science methods, as in this domain systematic reviews are not common.

Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review

OBJECTIVE

The study sought to explore to what extent geolocation data has been used to study serious mental illness (SMI). SMIs such as bipolar disorder and schizophrenia are characterized by fluctuating symptoms and sudden relapse. Currently, monitoring of people with an SMI is largely done through face-to-face visits. Smartphone-based geolocation sensors create opportunities for continuous monitoring and early intervention.

MATERIALS AND METHODS

We searched MEDLINE, PsycINFO, and Scopus by combining terms related to geolocation and smartphones with SMI concepts. Study selection and data extraction were done in duplicate.

RESULTS

Eighteen publications describing 16 studies were included in our review. Eleven studies focused on bipolar disorder. Common geolocation-derived digital biomarkers were number of locations visited (n = 8), distance traveled (n = 8), time spent at prespecified locations (n = 7), and number of changes in GSM (Global System for Mobile communications) cell (n = 4). Twelve of 14 publications evaluating clinical aspects found an association between geolocation-derived digital biomarker and SMI concepts, especially mood. Geolocation-derived digital biomarkers were more strongly associated with SMI concepts than other information (eg, accelerometer data, smartphone activity, self-reported symptoms). However, small sample sizes and short follow-up warrant cautious interpretation of these findings: of all included studies, 7 had a sample of fewer than 10 patients and 11 had a duration shorter than 12 weeks.

CONCLUSIONS

The growing body of evidence for the association between SMI concepts and geolocation-derived digital biomarkers shows potential for this instrument to be used for continuous monitoring of patients in their everyday lives, but there is a need for larger studies with longer follow-up times.

Global Positioning System Analysis of Physical Demands in Elite Women's Beach Handball Players in an Official Spanish Championship

This cross-sectional study aims to analyze the physical demands of elite beach handball players during an official competition. Nine elite female (mean age: 24.6 ± 4.0 years; body weight: 62.4 ± 4.6 kg; body height: 1.68 ± 0.059 m; training experience: 5 years; training: 6 h/week) beach handball players of the Spanish National Team were recruited for this study. A Global Positioning System was incorporated on each player’s back to analyze their movement patterns. Speed and distance were recorded at a sampling frequency of 15 Hz, whereas acceleration was recorded at 100 Hz by means of a built-in triaxial accelerometer. The main finding of the study is that 53% of the distance travelled is done at speeds between 1.5 and 5 km/h and 30% of the distance is between 9 and 13 km/h (83% of the total distance covered), which shows the intermittent efforts that beach handball involves at high intensity, as reflected in the analysis of the internal load with 62.82 ± 14.73% of the game time above 80% of the maximum heart rate. These data help to orientate training objectives to the physical demands required by the competition in order to optimize the players’ performance.

Smartphone App (2kmFIT-App) for Measuring Cardiorespiratory Fitness: Validity and Reliability Study

BACKGROUND

There is strong evidence suggesting that higher levels of cardiorespiratory fitness (CRF) are associated with a healthier metabolic profile, and that CRF can serve as a powerful predictor of morbidity and mortality. In this context, a smartphone app based on the 2-km walk test (UKK test) would provide the possibility to assess CRF remotely in individuals geographically distributed around a country or continent, and even between continents, with minimal equipment and low costs.

OBJECTIVE

The overall aim of this study was to evaluate the validity and reliability of 2kmFIT-App developed for Android and iOS mobile operating systems to estimate maximum oxygen consumption (VO2max) as an indicator of CRF. The specific aims of the study were to determine the validity of 2kmFIT-App to track distance and calculate heart rate (HR).

METHODS

Twenty participants were included for field-testing validation and reliability analysis. The participants completed the UKK test twice using 2kmFIT-App. Distance and HR were measured with the app as well as with accurate methods, and VO2max was estimated using the UKK test equation.

RESULTS

The validity results showed the following mean differences (app minus criterion): distance (-70.40, SD 51.47 meters), time (-0.59, SD 0.45 minutes), HR (-16.75, SD 9.96 beats/minute), and VO2max (3.59, SD 2.01 ml/kg/min). There was moderate validity found for HR (intraclass correlation coefficient [ICC] 0.731, 95% CI -0.211 to 0.942) and good validity found for VO2max (ICC 0.878, 95% CI -0.125 to 0.972). The reliability results showed the following mean differences (retest minus test): app distance (25.99, SD 43.21 meters), app time (-0.15, SD 0.94 seconds), pace (-0.18, SD 0.33 min/km), app HR (-4.5, 13.44 beats/minute), and app VO2max (0.92, SD 3.04 ml/kg/min). There was good reliability for app HR (ICC 0.897, 95% CI 0.742-0.959) and excellent validity for app VO2max (ICC 0.932, 95% CI 0.830-0.973). All of these findings were observed when using the app with an Android operating system, whereas validity was poor when the app was used with iOS.

CONCLUSIONS

This study shows that 2kmFIT-App is a new, scientifically valid and reliable tool able to objectively and remotely estimate CRF, HR, and distance with an Android but not iOS mobile operating system. However, certain limitations such as the time required by 2kmFIT-App to calculate HR or the temperature environment should be considered when using the app.

Inferring mobility measures from GPS traces with missing data

With increasing availability of smartphones with Global Positioning System (GPS) capabilities, large-scale studies relating individual-level mobility patterns to a wide variety of patient-centered outcomes, from mood disorders to surgical recovery, are becoming a reality. Similar past studies have been small in scale and have provided wearable GPS devices to subjects. These devices typically collect mobility traces continuously without significant gaps in the data, and consequently the problem of data missingness has been safely ignored. Leveraging subjects' own smartphones makes it possible to scale up and extend the duration of these types of studies, but at the same time introduces a substantial challenge: to preserve a smartphone's battery, GPS can be active only for a small portion of the time, frequently less than $10\%$, leading to a tremendous missing data problem. We introduce a principled statistical approach, based on weighted resampling of the observed data, to impute the missing mobility traces, which we then summarize using different mobility measures. We compare the strengths of our approach to linear interpolation (LI), a popular approach for dealing with missing data, both analytically and through simulation of missingness for empirical data. We conclude that our imputation approach better mirrors human mobility both theoretically and over a sample of GPS mobility traces from 182 individuals in the Geolife data set, where, relative to LI, imputation resulted in a 10-fold reduction in the error averaged across all mobility features.

Comparing GPS-Based Community Mobility Measures with Self-report Assessments in Older Adults with Parkinson's Disease

BACKGROUND

Real-life community mobility (CM) measures for older adults, especially those with Parkinson's disease (PD), are important tools when helping individuals maintain optimal function and quality of life. This is one of the first studies to compare an objective global positioning system (GPS) sensor and subjective self-report CM measures in an older clinical population.

METHODS

Over 14 days, 54 people in Ontario, Canada with early to mid-stage PD (mean age = 67.5 ± 6.3 years; 47 men; 46 retired) wore a wireless inertial measurement unit with GPS (WIMU-GPS), and completed the Life Space Assessment and mobility diaries. We assessed the convergent validity, reliability and agreement on mobility outcomes using Spearman's correlation, intraclass correlation coefficient, and Bland-Altman analyses, respectively.

RESULTS

Convergent validity was attained by the WIMU-GPS for trip frequency (rs = .69, 95% confidence interval [CI] = 0.52-0.81) and duration outside (rs = .43, 95% CI = 0.18-0.62), but not for life space size (rs = .39, 95% CI = 0.14-0.60). The Life Space Assessment exhibited floor and ceiling effects. Moderate agreements were observed between WIMU-GPS and diary for trip frequency and duration (intraclass correlation coefficients = 0.71, 95% CI = 0.51-0.82; 0.67, 95% CI = 0.42-0.82, respectively). Disagreement was more common among nonretired individuals.

CONCLUSIONS

WIMU-GPS could replace diaries for trip frequency and duration assessments in older adults with PD. Both assessments are best used for retired persons. However, the Life Space Assessment may not reflect actual mobility.

Automated Detection of Exercise Sessions in Patients With Peripheral Artery Disease: EVIDENCE FOR AN EXERCISE DOSE RESPONSE TO TRAINING

PURPOSE

Monitoring home exercise using accelerometry in patients with peripheral artery disease (PAD) may provide a tool to improve adherence and titration of the exercise prescription. However, methods for unbiased analysis of accelerometer data are lacking. The aim of the current post hoc analysis was to develop an automated method to analyze accelerometry output collected during home-based exercise.

METHODS

Data were obtained from 54 patients with PAD enrolled in a clinical trial that included a home-based exercise intervention using diaries and an accelerometer. Peak walking time was assessed on a graded treadmill at baseline and 6 mo. In 35 randomly selected patient data sets, visual inspection of accelerometer output confirmed exercise sessions throughout the 6 mo. An algorithm was developed to detect exercise sessions and then compared with visual inspection of sessions to mitigate the heterogeneity in session intensity across the population. Identified exercise sessions were characterized on the basis of total step count and activity duration. The methodology was then applied to data sets for all 54 patients.

RESULTS

The ability of the algorithm to detect exercise sessions compared with visual inspection of the accelerometer output resulted in a sensitivity of 85% and specificity of 90%. Algorithm-detected exercise sessions (total) and intensity (steps/wk) were correlated with change in peak walking time (r = 0.28; r = 0.43).

CONCLUSIONS

An algorithm to assess data from an accelerometer successfully detected home-based exercise sessions. Algorithm-identified exercise sessions were correlated with improvements in performance after 6 mo of training in patients with PAD, supporting the effectiveness of monitored home-based exercise.

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

This is the first systematic review to comprehensively capture Global Positioning Systems' (GPS) utilization in active living research by investigating the influence of physical contexts and social environment on all intensities of physical activity and sedentary behavior among all age groups. An extensive search of peer-reviewed literature was conducted using six databases. Out of 2026 articles identified, 129 studies met the inclusion criteria. After describing the evolution of GPS use across four themes (study designs and methods, physical contexts and social environment, active transportation, and behaviors), evidence-based recommendations for active living research, policy, and practice were generated.

Tracking Children's Physical Activity Patterns across the School Year: A Mixed-Methods Longitudinal Case Study

Despite the breadth of health benefits associated with regular physical activity (PA), many children in the UK are not sufficiently active enough to meet health guidelines, and tend to become less active as they mature into and throughout adolescence. Research has indicated that children's school, home and neighbourhood environments can all significantly influence their opportunities to engage in moderate-to-vigorous physical activity (MVPA). However, less is known about how children's MVPA patterns within these key environments may change across the school year. The current mixed-methods case study aims to explore this issue by tracking key stage 2 (KS2) and key stage 3 (KS3) children's MVPA patterns across the school year. Fifty-eight children (29 boys, 29 girls, KS2 = 34, KS3 = 24) wore an integrated global positioning systems (GPS) and heart rate (HR) monitor over four consecutive days in the first term of school (autumn), before these measurements were repeated in the two remaining school terms (winter-summer). A subsample of children (n = 6-8 per group) were invited to take part in one of six focus groups each term to further explore their PA behaviours and identify the barriers and facilitators to PA. The children's MVPA was significantly lower (p = 0.046) in term 2 (winter/spring term) than during the warmer terms (autumn and summer). All the locations showed reductions in MVPA in term 2, except indoor MVPA, which increased, and MVPA on foot in the neighbourhood, which remained consistent. Focus groups revealed location, friends, and the variety of options to be associated with MVPA, and poor weather, parental permission, and time limitations to be barriers to MVPA. This mixed-methodological, repeated-measures design study highlights differences in the activity patterns and perceptions of children over the school year. Future studies should implement longitudinal, multi-method approaches to gain deeper insight into how children's PA behaviours differ over time. Consequently, this can inform future health policies promoting children's PA throughout the year.

Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey

Introduction

Common approaches to measure health behaviors rely on participant responses and are subject to bias. Technology-based alternatives, particularly using GPS, address these biases while opening new channels for research. This study describes the development and implementation of a GPS-based approach to detect health facility visits in rural Pune district, India.

Methods

Participants were mothers of under-five year old children within the Vadu Demographic Surveillance area. Participants received GPS-enabled smartphones pre-installed with a location-aware application to continuously record and transmit participant location data to a central server. Data were analyzed to identify health facility visits according to a parameter-based approach, optimal thresholds of which were calibrated through a simulation exercise. Lists of GPS-detected health facility visits were generated at each of six follow-up home visits and reviewed with participants through prompted recall survey, confirming visits which were correctly identified. Detected visits were analyzed using logistic regression to explore factors associated with the identification of false positive GPS-detected visits.

Results

We enrolled 200 participants and completed 1098 follow-up visits over the six-month study period. Prompted recall surveys were completed for 694 follow-up visits with one or more GPS-detected health facility visits. While the approach performed well during calibration (positive predictive value (PPV) 78%), performance was poor when applied to participant data. Only 440 of 22 251 detected visits were confirmed (PPV 2%). False positives increased as participants spent more time in areas of high health facility density (odds ratio (OR) = 2.29, 95% confidence interval (CI) = 1.62-3.25). Visits detected at facilities other than hospitals and clinics were also more likely to be false positives (OR = 2.78, 95% CI = 1.65-4.67) as were visits detected to facilities nearby participant homes, with the likelihood decreasing as distance increased (OR = 0.89, 95% CI = 0.82-0.97). Visit duration was not associated with confirmation status.

Conclusions

The optimal parameter combination for health facility visits simulated by field workers substantially overestimated health visits from participant GPS data. This study provides useful insights into the challenges in detecting health facility visits where providers are numerous, highly clustered within urban centers and located near residential areas of the population which they serve.

Quality of hybrid location data drawn from GPS-enabled mobile phones: Does it matter?

Despite their increasing popularity in human mobility studies, few studies have investigated the geo-spatial quality of GPS-enabled mobile phone data in which phone location is determined by special queries designed to collect location data with predetermined sampling intervals (hereafter "active mobile phone data"). We focus on two key issues in active mobile phone data-systematic gaps in tracking records and positioning uncertainty-and investigate their effects on human mobility pattern analyses. To address gaps in records, we develop an imputation strategy that utilizes local environment information, such as parcel boundaries, and recording time intervals. We evaluate the performance of the proposed imputation strategy by comparing raw versus imputed data with participants' online survey responses. The results indicate that imputed data are superior to raw data in identifying individuals' frequently visited places on a weekly basis. To assess the location accuracy of active mobile phone data, we investigate the spatial and temporal patterns of the positional uncertainty of each record and examine via Monte Carlo simulation how inaccurate location information might affect human mobility pattern indicators. Results suggest that the level of uncertainty varies as a function of time of day and the type of land use at which the position was determined, both of which are closely related to the location technology used to determine the location. Our study highlights the importance of understanding and addressing limitations of mobile phone derived positioning data prior to their use in human mobility studies.

Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies

An accurate assessment of pollutants’ exposure and precise evaluation of the clinical outcomes pose two major challenges to the contemporary environmental health research. The common methods for exposure assessment are based on residential addresses and are prone to many biases. Pollution levels are defined based on monitoring stations that are sparsely distributed and frequently distanced far from residential addresses. In addition, the degree of an association between outdoor and indoor air pollution levels is not fully elucidated, making the exposure assessment all the more inaccurate. Clinical outcomes’ assessment, on the other hand, mostly relies on the access to medical records from hospital admissions and outpatients’ visits in clinics. This method differentiates by health care seeking behavior and is therefore, problematic in evaluation of an onset, duration, and severity of an outcome. In the current paper, we review a number of novel solutions aimed to mitigate the aforementioned biases. First, a hybrid satellite-based modeling approach provides daily continuous spatiotemporal estimations with improved spatial resolution of 1 × 1 km2 and 200 × 200 m2 grid, and thus allows a more accurate exposure assessment. Utilizing low-cost air pollution sensors allowing a direct measurement of indoor air pollution levels can further validate these models. Furthermore, the real temporal-spatial activity can be assessed by GPS tracking devices within the individuals’ smartphones. A widespread use of smart devices can help with obtaining objective measurements of some of the clinical outcomes such as vital signs and glucose levels. Finally, human biomonitoring can be efficiently done at a population level, providing accurate estimates of in-vivo absorbed pollutants and allowing for the evaluation of body responses, by biomarkers examination. We suggest that the adoption of these novel methods will change the research paradigm heavily relying on ecological methodology and support development of the new clinical practices preventing adverse environmental effects on human health.

Association of psychosocial and perceived environmental factors with park-based physical activity among elderly in two cities in China and Germany

Background

Urban parks play an important role in promoting physical activity (PA) among adults and especially among older city residents. According to the socioecological approach the association of physical environments and psychosocial factors in the context of park-based PA of elderly have not been systematically examined until now, let alone the relevance of the city (urban area) on a cross-cultural level. This study investigated selected aspects of (1) the association of psychosocial and park environmental factors with park-based physical activity (PBPA) of older people; and (2) the moderating effect of city on the association of these factors with PBPA.

Methods

A face-to-face survey was conducted of a mixed-culture sample from different urban surroundings in Hong Kong (HK) and Leipzig (L). In six parks of each city physically active elderly (> = 60 years; HK: n = 306; L: n = 311) were recruited. Multiple linear regressions were used to analyse the association between psychosocial factors and perceived environmental factors with PBPA and the moderating effect of city.

Results

Controlled for demographic variables, all other psychosocial factors were significantly related to PBPA, except social support. In terms of environmental factors, PBPA was positively associated with safety, attractiveness, features and negatively associated with park time distance. Controlled for demographic variables, psychosocial and environmental factors, the moderating effect of city on the associations of park features and park time distance with PBPA was not significant in HK. In contrast, there was a significant positive relationship for park features and a negative relationship for park time distance with PBPA in L.

Conclusions

Psychosocial and perceived environmental factors significantly influence PBPA of older people. City moderates the associations of these factors and independently contributes to park-based PA of the elderly. The different interactions of environmental factors and urban area for PBPA of elderly can support policy makers on the municipal level in choosing adequate strategies for promoting PA of older people in parks.

Neighborhoods to Nucleotides - Advances and gaps for an obesity disparities systems epidemiology model

Purpose of Review

Disparities in obesity rates in the US continue to increase. Here we review progress and highlight gaps in understanding disparities in obesity with a focus on the Hispanic/Latino population from a systems epidemiology framework. We review seven domains: environment, behavior, biomarkers, nutrition, microbiome, genomics, and epigenomics/transcriptomics. We focus on recent advances that include at least two or more of these domains, and then provide a real world example of data collection efforts that reflect these domains.

Recent Findings

Research into DNA methylation related to discrimination and microbiome relating to eating behaviors and food content is furthering understanding of why disparities in obesity persist. Environmental and neighborhood level research is uncovering the importance of exposures such as air and noise pollution and systematic or structural racism for obesity and related outcomes through behaviors such as sleep.

Exploring Children's Physical Activity Behaviours According to Location: A Mixed-Methods Case Study

The school environment is ideally placed to facilitate physical activity (PA) with numerous windows of opportunity from break and lunch times, to lesson times and extracurricular clubs. However, little is known about how children interact with the school environment to engage in PA and the other locations they visit daily, including time spent outside of the school environment i.e., evening and weekend locations. Moreover, there has been little research incorporating a mixed-methods approach that captures children’s voices alongside objectively tracking children’s PA patterns. The aim of this study was to explore children’s PA behaviours according to different locations. Sixty children (29 boys, 31 girls)—35 key stage 2 (aged 9–11) and 25 key stage 3 (aged 11–13)—wore an integrated global positioning systems (GPS) and heart rate (HR) monitor over four consecutive days. A subsample of children (n = 32) were invited to take part in one of six focus groups to further explore PA behaviours and identify barriers and facilitators to PA. Children also completed a PA diary. The KS2 children spent significantly more time outdoors than KS3 children (p = 0.009). Boys engaged in more light PA (LPA) when on foot and in school, compared with girls (p = 0.003). KS3 children engaged in significantly more moderate PA (MPA) at school than KS2 children (p = 0.006). Focus groups revealed fun, enjoyment, friends, and family to be associated with PA, and technology, costs, and weather to be barriers to PA. This mixed methodological study highlights differences in the PA patterns and perceptions of children according to age and gender. Future studies should utilize a multi-method approach to gain a greater insight into children’s PA patterns and inform future health policies that differentiate among a range of demographic groups of children.

Methodologies for assessing contextual exposure to the built environment in physical activity studies: A systematic review

Growing research has integrated Global Positioning Systems (GPS), Geographic Information Systems (GIS), and accelerometry in studying effects of built environment on physical activity outcomes. This systematic review aimed to summarize current geospatial methods of assessing contextual exposure to the built environment in these studies. Based on reviewing 79 eligible articles, methods were identified and grouped into three main categories based on similarities in their approaches as follows: domain-based (67% of studies), buffer-based (22%), and activity space-based (11%). Additionally, technical barriers and potential sources of uncertainties in each category were discussed and recommendations on methodological improvements were made.

Assessing Older Adults' Daily Mobility: A Comparison of GPS-Derived and Self-Reported Mobility Indicators

Interest in global positioning system (GPS)-based mobility assessment for health and aging research is growing, and with it the demand for validated GPS-based mobility indicators. Time out of home (TOH) and number of activity locations (#ALs) are two indicators that are often derived from GPS data, despite lacking consensus regarding thresholds to be used to extract those as well as limited knowledge about their validity. Using 7 days of GPS and diary data of 35 older adults, we make the following three main contributions. First, we perform a sensitivity analysis to investigate how using spatial and temporal thresholds to compute TOH and #ALs affects the agreement between self-reported and GPS-based indicators. Second, we show how daily self-reported and GPS-derived mobility indicators are compared. Third, we explore whether the type and duration of self-reported activity events are related to the degree of correspondence between reported and GPS event. Highest indicator agreement was found for temporal interpolation (Tmax) of up to 5 h for both indicators, a radius (Dmax) to delineate home between 100 and 200 m for TOH, and for #ALs a spatial extent (Dmax) between 125 and 200 m, and temporal extent (Tmin) between 5 and 6 min to define an activity location. High agreement between self-reported and GPS-based indicators is obtained for TOH and moderate agreement for #ALs. While reported event type and duration impact on whether a reported event has a matching GPS event, indoor and outdoor events are detected at equal proportions. This work will help future studies to choose optimal threshold settings and will provide knowledge about the validity of mobility indicators.

Sedentary Behaviors in Today's Youth: Approaches to the Prevention and Management of Childhood Obesity: A Scientific Statement From the American Heart Association

This scientific statement is about sedentary behavior and its relationship to obesity and other cardiometabolic outcomes in youth. A deleterious effect of sedentary behavior on cardiometabolic health is most notable for screen-based behaviors and adiposity; however, this relation is less apparent for other cardiometabolic outcomes or when sedentary time is measured with objective movement counters or position monitors. Increasing trends of screen time are concerning; the portability of screen-based devices and abundant access to unlimited programming and online content may be leading to new patterns of consumption that are exposing youth to multiple pathways harmful to cardiometabolic health. This American Heart Association scientific statement provides an updated perspective on sedentary behaviors specific to modern youth and their impact on cardiometabolic health and obesity. As we reflect on implications for practice, research, and policy, what emerges is the importance of understanding the context in which sedentary behaviors occur. There is also a need to capture the nature of sedentary behavior more accurately, both quantitatively and qualitatively, especially with respect to recreational screen-based devices. Further evidence is required to better inform public health interventions and to establish detailed quantitative guidelines on specific sedentary behaviors in youth. In the meantime, we suggest that televisions and other recreational screen-based devices be removed from bedrooms and absent during meal times. Daily device-free social interactions and outdoor play should be encouraged. In addition, parents/guardians should be supported to devise and enforce appropriate screen time regulations and to model healthy screen-based behaviors.

Accelerometer and GPS Data to Analyze Built Environments and Physical Activity

Purpose: Most built environment studies have quantified characteristics of the areas around participants' homes. However, the environmental exposures for physical activity (PA) are spatially dynamic rather than static. Thus, merged accelerometer and global positioning system (GPS) data were utilized to estimate associations between the built environment and PA among adults. Methods: Participants (N = 142) were recruited on trails in Massachusetts and wore an accelerometer and GPS unit for 1-4 days. Two binary outcomes were created: moderate-to-vigorous PA (MVPA vs. light PA-to-sedentary); and light-to-vigorous PA (LVPA vs. sedentary). Five built environment variables were created within 50-meter buffers around GPS points: population density, street density, land use mix (LUM), greenness, and walkability index. Generalized linear mixed models were fit to examine associations between environmental variables and both outcomes, adjusting for demographic covariates. Results: Overall, in the fully adjusted models, greenness was positively associated with MVPA and LVPA (odds ratios [ORs] = 1.15, 95% confidence interval [CI] = 1.03, 1.30 and 1.25, 95% CI = 1.12, 1.41, respectively). In contrast, street density and LUM were negatively associated with MVPA (ORs = 0.69, 95% CI = 0.67, 0.71 and 0.87, 95% CI = 0.78, 0.97, respectively) and LVPA (ORs = 0.79, 95% CI = 0.77, 0.81 and 0.81, 95% CI = 0.74, 0.90, respectively). Negative associations of population density and walkability with both outcomes reached statistical significance, yet the effect sizes were small. Conclusions: Concurrent monitoring of activity with accelerometers and GPS units allowed us to investigate relationships between objectively measured built environment around GPS points and minute-by-minute PA. Negative relationships between street density and LUM and PA contrast evidence from most built environment studies in adults. However, direct comparisons should be made with caution since most previous studies have focused on spatially fixed buffers around home locations, rather than the precise locations where PA occurs.

Evidence of Green Areas, Cycle Infrastructure and Attractive Destinations Working Together in Development on Urban Cycling

The built environment influences and promotes cycling that has now become a challenge for sustainable urban mobility in many cities where this mode of transport carries little weight. This is the case for Granada (Spain), a medium-sized city in southern Europe, which as a university city and with lots of green areas, could find potential supportive factors to promote cycling. Website-apps with a Global Positioning System (GPS), such as Ciclogreen that encourage active accessibility try to promote cycling and are supported by the University of Granada. The aim of this work is to assess the capacity of green areas and some influential factors of their built environment to attract cycling routes. To this end, a spatial analysis was made and interpreted by a statistical model to check the correlation between these factors and a high number of cycling routes through or near the green areas. The results show a high number of cycling routes within urban surroundings that include green areas, cycle lanes, university facilities, and public car parks in proximity relationships. Identifying synergies among these urban factors and the information and incentive coming from a digital catalyst in shape on an app could be useful in urban planning for cycling.

Assessing Physical Activities Occurring on Sidewalks and Streets: Protocol for a Cross-Sectional Study

Background

A considerable proportion of outdoor physical activity (PA) is done on sidewalks and streets, necessitating the development of a reliable measure of PA performed in these settings. The Block Walk Method (BWM) is one of the more common approaches for this purpose. Although it utilizes reliable observation techniques and displays criterion validity, it remains relatively unchanged since its introduction in 2006. It is a nontechnical, labor-intensive, first generation method. Advancing the BWM would contribute significantly to our understanding of PA behavior.

Objective

This study will develop and test a new BWM that utilizes a wearable video device (WVD) and computer video analysis to assess PAs performed on sidewalks and streets. The specific aims are to improve the BWM by incorporating a WVD (eyeglasses with a high-definition video camera in the frame) into the methodology and advance this WVD-enhanced BWM by applying machine learning and recognition software to automatically extract information on PAs occurring on the sidewalks and streets from the videos.

Methods

Trained observers (1 wearing and 1 not wearing the WVD) will walk together at a set pace along predetermined 1000 ft sidewalk and street observation routes representing low, medium, and high walkable areas. During the walks, the non-WVD observer will use the traditional BWM to record the numbers of individuals standing, sitting, walking, biking, and running in observation fields along the routes. The WVD observer will continuously video the observation fields. Later, 2 investigators will view the videos to determine the number of individuals performing PAs in the observation fields. The video data will then be analyzed automatically using multiple deep convolutional neural networks (CNNs) to determine the number of humans in the observation fields and the type of PAs performed. Bland Altman methods and intraclass correlation coefficients (ICCs) will be used to assess agreement. Potential sources of error such as occlusions (eg, trees) will be assessed using moderator analyses.

Results

Outcomes from this study are pending; however, preliminary studies supporting the research protocol indicate that the BWM is reliable for determining the PA mode (Cramer V=.89; P<.001), the address where the PA occurred (Cohen kappa=.85; P<.001), and the number engaged in an observed PA (ICC=.85; P<.001). The number of individuals seen walking along routes was correlated with several environmental characteristics such as sidewalk quality (r=.39; P=.02) and neighborhood aesthetics (r=.49; P<.001). Furthermore, we have used CNNs to detect cars, bikes, and pedestrians as well as individuals using park facilities.

Conclusions

We expect the new approach will enhance measurement accuracy while reducing the burden of data collection. In the future, the capabilities of the WVD-CNN system will be expanded to allow for the determination of other characteristics captured in videos such as caloric expenditure and environmental conditions.

International Registered Report Identifier (IRRID)

PRR1-10.2196/12976

Activity spaces in studies of the environment and physical activity: A review and synthesis of implications for causality

Activity spaces are increasingly used to understand how people interact with their environment and engage in activity but their use may raise challenges regarding causal inference. We conducted a systematic review of findings and the methodological, analytical and conceptual issues relevant to causal inference. Studies were included if they comprised a spatial summary of locations visited, assessed any part of the causal pathway between the environment, physical activity and health, and used quantitative or qualitative methods. We searched seven electronic databases in January 2018 and screened 11910 articles for eligibility. Forty-seven studies were included for review. Studies answered research questions about features of or environmental features within activity spaces using a range of spatial and temporal summary techniques. The conceptual challenge of using activity spaces to strengthen causal inference was rarely considered, although some studies discussed circularity, temporality, and plausibility. Future studies should use longitudinal and experimental designs and consider the potential and actual use of spaces for physical activity, and their relationship with total levels of activity.

Putting the Boom, Boom, Boom into Physical Activity and Health: Music Festivals as a Positive Health Alternative to Couch Fandom

Background: Despite the popularity of outdoor music festivals in the UK, no evidence exists of the volume or intensity of movement that occurs through attendance at these festivals and the potential health benefits this may provide. The aim of this study was to accurately record the amount of physical activity and movement at the Glastonbury Festival and to compare it against recommended levels. Methods: 22 attendees wore an Actigraph activity monitor and GPS data-logger to the Glastonbury Festival. Distances travelled, speeds and durations were recorded. Activity levels were identified based on step count thresholds and the total duration spent in moderate to vigorous physical activity (MVPA) was calculated. Results: Mean total distance of 66.1 km was recorded with daily distance (11.01 km), movement duration (11 h 28 min) and steps/day (15,661). Total MVPA of 927 min occurred over the festival period. Conclusions: This study objectively recorded the volume of physical activity that occurred at an outdoor UK festival. Large movement distances and MVPA six times greater than the recommended guidelines for health benefits were found. It can be suggested that attendance at large-scale festivals can be used as a modality for attaining physical activity guidelines alongside commonly suggested fitness activities.

Validation of the Block Walk Method for Assessing Physical Activity occurring on Sidewalks/Streets

The block walk method (BWM) is one of the more common approaches for assessing physical activity (PA) performed on sidewalks/streets; however, it is non-technical, labor-intensive, and lacks validation. This study aimed to validate the BWM and examine the potential for using a wearable video device (WVD) to assess PA occurring on sidewalks/streets. Trained observers (one wearing and one not wearing the WVD) walked together and performed the BWM according to a previously developed protocol along routes in low, medium, and high walkable areas. Two experts then reviewed the videos. A total of 1150 (traditional) and 1087 (video review) individuals were observed during 900 min of observation. When larger numbers of individuals were observed, the traditional method overestimated the overall number of people as well as those walking and sitting/standing, while underestimating the number of runners. Valid estimates of PA occurring on sidewalks/streets can be obtained by the traditional BWM in low and medium walkability areas and/or with non-common activities (cycling); however, its validity is questionable when sidewalks/streets use volume is high. The use of WVDs in PA assessment has the potential to establish new levels of accuracy, reduce resource requirements, and open up the possibility for retrospective analysis.

How Short Is Long Enough? Modeling Temporal Aspects of Human Mobility Behavior Using Mobile Phone Data

Time-location data collected from location-sensing technologies have the potential to advance our understanding of human mobility. Existing human activity studies tend to ignore a critical issue in data collection-the time period for which the activity data will be collected. Our study investigated this significant gap in the literature on temporal aspects of human mobility behavior-how many days constitute a period long enough to capture individuals' highly organized activity episodes and how they vary among individuals with heterogeneous demographic and social-economic characteristics. To determine a minimum number of days to capture individuals' highly organized activity episodes in activity space, we examined a distribution of Kullback-Leibler divergence indexes. To evaluate the differences in the minimal number of observation days per subgroup whose demographic and economic characteristics are heterogenous, we used a Bayesian profile regression model. Our study showed that the estimated minimum number of days required to capture routine activity patterns was 13.5 days with a standard deviation of 6.64. We found that participant's age, employment status, size of household, and accessibility to downtown, food, and physical activity, as well as economic status of residential environment, are important factors that affect temporal aspects of mobility behavior.

How many days of global positioning system (GPS) monitoring do you need to measure activity space environments in health research?

This study examined the number of days of global positioning system (GPS) monitoring needed to measure attributes of an individual's routine activity space. Multiple alternative activity space representations (cumulative, mean daily), measures (kernel density, route buffer, convex hull), and attributes (area size, supermarkets, fast food restaurants, parks) were examined. Results suggested wide variability in required GPS days to obtain valid estimates of activity space attributes (1-23 days). In general, fewer days were needed for mean daily activity space representations, kernel density measures, and densities of environmental exposures (vs. counts). While kernel density measures reliably estimated between-person differences in attributes after just a few days, most variability in environmental attributes for convex hull and route buffer measures was within-person. Based on these results, a minimum of 14 days of valid GPS data is recommended to measure activity spaces.

Children's mobility and environmental exposures in urban landscapes: A cross-sectional study of 10-11 year old Scottish children

Research into how the environment affects health and related behaviour is typically limited in at least two ways: it represents the environment to which people are exposed using fixed areal units, and, it focuses on one or two environmental characteristics only. This study developed a methodology for describing children's mobility and the complexity of their environmental exposure across a 1934 km2 study area, including urban, suburban and rural zones. It conceptualised and modelled this area as a landscape, comprised of spatially discrete amenities, infrastructure features, differing land covers/use and broader environmental contexts. The model used a 25 m2 grid system (∼3 million cells). For each cell, there was detailed built-environment information. We joined data for 100 10/11-year-old children who had worn GPS trackers to provide individual-level mobility information for one week during 2015/16 to our model. Using negative binomial regression, we explored which landscape features were associated with a child visiting that space and time spent there. We examined whether relationships between the features across our study area and children's use of the space differed by their sociodemographic characteristics. We found that children often used specific amenities outside their home neighbourhood, even if they were also available close to home. They spent more time in cells containing roads/transportation stops, food/drink retail (Incidence rate ratio (IRR):4.02, 95%CI 2.33 to 6.94), places of worship (IRR:5.98, 95%CI 3.33 to 10.72) and libraries (IRR:7.40, 95%CI 2.13 to 25.68), independently of proximity to home. This has importance for the optimal location of place-based health interventions. If we want to target children, we need to understand that using fixed neighbourhood boundaries may not be the best way to do it. The variations we found in time spent in certain areas by sex and socio-economic position also raise the possibility that interventions which ignore these differences may exacerbate inequalities.