Combining sensor tracking with a GPS-based mobility survey to better measure physical activity in trips: public transport generates walking

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.

Micro urban spaces and mental well-being: Measuring the exposure to urban landscapes along daily mobility paths and their effects on momentary depressive symptomatology among older population

The urban environment plays an important role for the mental health of residents. Researchers mainly focus on residential neighbourhoods as exposure context, leaving aside the effects of non-residential environments. In order to consider the daily experience of urban spaces, a people-based approach focused on mobility paths is needed. Applying this approach, (1) this study investigated whether individuals' momentary mental well-being is related to the exposure to micro-urban spaces along the daily mobility paths within the two previous hours; (2) it explored whether these associations differ when environmental exposures are defined considering all location points or only outdoor location points; and (3) it examined the associations between the types of activity and mobility and momentary depressive symptomatology. Using a geographically-explicit ecological momentary assessment approach (GEMA), momentary depressive symptomatology of 216 older adults living in the Ile-de-France region was assessed using smartphone surveys, while participants were tracked with a GPS receiver and an accelerometer for seven days. Exposure to multiple elements of the streetscape was computed within a street network buffer of 25 m of each GPS point over the two hours prior to the questionnaire. Mobility and activity type were documented from a GPS-based mobility survey. We estimated Bayesian generalized mixed effect models with random effects at the individual and day levels and took into account time autocorrelation. We also estimated fixed effects. A better momentary mental wellbeing was observed when residents performed leisure activities or were involved in active mobility and when they were exposed to walkable areas (pedestrian dedicated paths, open spaces, parks and green areas), water elements, and commerce, leisure and cultural attractors over the previous two hours. These relationships were stronger when exposures were defined based only on outdoor location points rather than all location points, and when we considered within-individual differences compared to between-individual differences.

Assessing the association between overcrowding and human physiological stress response in different urban contexts: a case study in Salzburg, Austria

Overcrowding in densely populated urban areas is increasingly becoming an issue for mental health disorders. Yet, only few studies have examined the association between overcrowding in cities and physiological stress responses. Thus, this study employed wearable sensors (a wearable camera, an Empatica E4 wristband and a smartphone-based GPS) to assess the association between overcrowding and human physiological stress response in four types of urban contexts (green space, transit space, commercial space, and blue space). A case study with 26 participants was conducted in Salzburg, Austria. We used Mask R-CNN to detect elements related to overcrowding such as human crowds, sitting facilities, vehicles and bikes from first-person video data collected by wearable cameras, and calculated a change score (CS) to assess human physiological stress response based on galvanic skin response (GSR) and skin temperature from the physiological data collected by the wristband, then this study used statistical and spatial analysis to assess the association between the change score and the above elements. The results demonstrate the feasibility of using sensor-based measurement and quantitative analysis to investigate the relationship between human stress and overcrowding in relation to different urban elements. The findings of this study indicate the importance of considering human crowds, sitting facilities, vehicles and bikes to assess the impact of overcrowding on human stress at street level.

Associations of air pollution mixtures with ambulatory blood pressure: The MobiliSense sensor-based study

Air pollution is acknowledged as a determinant of blood pressure (BP), supporting the hypothesis that air pollution, via hypertension and other mechanisms, has detrimental effects on human health. Previous studies evaluating the associations between air pollution exposure and BP did not consider the effect that air pollutant mixtures may have on BP. We investigated the effect of exposure to single species or their synergistic effects as air pollution mixture on ambulatory BP. Using portable sensors, we measured personal concentrations of black carbon (BC), nitrogen dioxide (NO2), nitrogen monoxide (NO), carbon monoxide (CO), ozone (O3), and particles with aerodynamic diameters below 2.5 μm (PM2.5). We simultaneously collected ambulatory BP measurements (30-min intervals, N = 3319) of 221 participants over one day of their lives. Air pollution concentrations were averaged over 5 min to 1 h before each BP measurement, and inhaled doses were estimated across the same exposure windows using estimated ventilation rates. Fixed-effect linear models as well as quantile G-computation techniques were applied to associate air pollutants' individual and combined effects with BP, adjusting for potential confounders. In mixture models, a quartile increase in air pollutant concentrations (BC, NO2, NO, CO, and O3) in the previous 5 min was associated with a 1.92 mmHg (95% CI: 0.63, 3.20) higher systolic BP (SBP), while 30-min and 1-h exposures were not associated with SBP. However, the effects on diastolic BP (DBP) were inconsistent across exposure windows. Unlike concentration mixtures, inhalation mixtures in the previous 5 min to 1 h were associated with increased SBP. Out-of-home BC and O3 concentrations were more strongly associated with ambulatory BP outcomes than in-home concentrations. In contrast, only the in-home concentration of CO reduced DBP in stratified analyses. This study shows that exposure to a mixture of air pollutants (concentration and inhalation) was associated with elevated SBP.

Application of machine learning to predict transport modes from GPS, accelerometer, and heart rate data

BACKGROUND

There has been an increased focus on active transport, but the measurement of active transport is still difficult and error-prone. Sensor data have been used to predict active transport. While heart rate data have very rarely been considered before, this study used random forests (RF) to predict transport modes using Global Positioning System (GPS), accelerometer, and heart rate data and paid attention to methodological issues related to the prediction strategy and post-processing.

METHODS

The RECORD MultiSensor study collected GPS, accelerometer, and heart rate data over seven days from 126 participants living in the Ile-de-France region. RF models were built to predict transport modes for every minute (ground truth information on modes is from a GPS-based mobility survey), splitting observations between a Training dataset and a Test dataset at the participant level instead at the minute level. Moreover, several window sizes were tested for the post-processing moving average of the predicted transport mode.

RESULTS

The minute-level prediction rate of being on trips vs. at a visited location was 90%. Final prediction rates of transport modes ranged from 65% for public transport to 95% for biking. Using minute-level observations from the same participants in the Training and Test sets (as RF spontaneously does) upwardly biases prediction rates. The inclusion of heart rate data improved prediction rates only for biking. A 3 to 5-min bandwidth moving average was optimum for a posteriori homogenization.

CONCLUSION

Heart rate only very slightly contributed to better predictions for specific transport modes. Moreover, our study shows that Training and Test sets must be carefully defined in RF models and that post-processing with carefully chosen moving average windows can improve predictions.

Acute ambulatory blood pressure response to short-term black carbon exposure: The MobiliSense sensor-based study

Documented relationships between black carbon (BC) exposure and blood pressure (BP) have been inconsistent. Very few studies measured both BC exposure and ambulatory BP across the multiple daily environments visited in the general population, and none adjusted for personal noise exposure, a major confounder. Our study addresses these gaps by considering 245 adults living in the Grand Paris region. Personal exposure to BC was monitored for 2 days using AE51 microaethalometers. Ambulatory BP was measured every 30 min after waking up using Arteriograph 24 monitors (n = 6772). Mixed effect models with a random intercept at the individual level and time-autocorrelation structure adjusted for personal noise exposure were used to evaluate the associations between BC exposure (averaged from 5 min to 1 h before each BP measurement) and BP. To increase the robustness of findings, we eliminated confounding by unmeasured time-invariant personal variables, by modelling the associations with fixed-effect models. All models were adjusted for potential confounders and short-term time trends. Results from mixed models show that a 1-μg/m³ increase in 5-minute averaged BC exposure was associated with an increase of 0.57 mmHg in ambulatory systolic blood pressure (SBP) (95 % CI: 0.30, 0.83) and with an increase of 0.36 mmHg in diastolic blood pressure (DBP) (95 % CI: 0.14, 0.58). The slope of the exposure-response relationship gradually decreased for both SBP and DBP with the increase in the averaging period of BC exposure from 5 min to 1 h preceding each BP measurement. Findings from the fixed-effect models were consistent with these results. There was no effect modification by noise in the associations, across all exposure windows. We found evidence of a relationship between BC exposure and acute increase in ambulatory SBP and DBP after adjustment for personal noise exposure, with potential implications for the development of adverse cardiovascular outcomes.

InterMob: a 24-month randomised controlled trial comparing the effectiveness of an intervention including behavioural change techniques and free transport versus an intervention including air pollution awareness-raising on car use reduction among regular car users living in Grenoble, France

Background

Frequent car use contributes to health and environmental issues such as air pollution, climate change and obesity. Active and sustainable mobility (bike, walk, public transport, car sharing) may address these issues. Different strategies have been implemented in past research, involving hard levers, aimed at modifying the economical or geographical context (e.g., free public transport), and soft levers, aimed at modifying psychological processes (e.g., personalised transport advice). However, few studies have combined both hard and soft levers. In addition, few have used robust methodologies (e.g., randomised controlled trials), followed behavioural changes in the long-term, and been anchored in behaviour change theories. InterMob aims to address these limits by implementing a 24-month randomised controlled trial including hard and soft levers. The objectives of InterMob are to a) evaluate the effectiveness of an experimental arm versus an active controlled arm, and b) identify the processes of mobility change.

Methods

Regular car users living in Grenoble (N = 300) will be recruited and randomised to one of the two arms. The experimental arm consists in a six-month intervention combining hard levers (free access to transport/bikes), and soft levers (e.g., personalised transport advice). The control arm consists in a six-month intervention aimed at raising awareness on air pollution and its health effects. Both arms will include eight evaluation weeks (spread out over 24 months) based on a GPS, an accelerometer, and a pollution sensor. Moreover, participants will complete mobility logbooks and surveys measuring psychological constructs, socio-economical, and socio-spatial characteristics.

Discussion

InterMob will assess the effectiveness of two interventions aimed at reducing car use within regular car users in the short-, mid- and long-term. Moreover, InterMob will allow to better understand the psychological processes of behaviour change, and the socio-economical and geographical conditions under which the intervention is efficient in reducing car use. Finally, the benefits of mobility change in terms of physical activity, quality of life, and exposure to pollution will be quantified.

Trial registration

ClinicalTrials.gov: NCT05096000 on 27/10/2021 (retrospectively registered).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14099-4.

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.

Developing a One-Stop Platform Transportation Planning Service to Help Older Adults Move Around in Their Community Where, When, and How They Wish: Protocol for a Living Lab Study

Background

 Multiple mobility-related challenges frequently appear with aging. As a result, many older adults have difficulty getting around, to go, for example, to doctors’ appointments or leisure activities. Although various means of transportation are currently available, older adults do not necessarily use them, partly because they do not know which ones are adapted to their needs and preferences. To foster older adults’ autonomy and freedom in their decision-making about transportation, it is crucial to help them make informed decisions about the means that suit them best.

Objective

Our aim is to develop Mobilainés, a one-stop platform transportation planning service combining different transport modes and services to help older adults move around in their community where, when, and how they wish. More specifically, we aim to (1) define older adults’ mobility needs and preferences in order to conceptualize a one-stop platform; (2) cocreate a prototype of the one-stop platform; and (3) test the prototype with users in a real-life context.

Methods

This ongoing study uses a “Living Lab” co-design approach. This approach differs from traditional research on aging by facilitating intersectoral knowledge sharing and innovative solutions by and with older adults themselves. A steering committee of 8 stakeholders from the public, scientific, and private sectors, as well as older citizens, will meet quarterly throughout the study. The design comprises three phases, each with several iterative subphases. Phase 1 is exploration: through co-design workshops and literature reviews, members of the intersectoral committee will define older adults’ mobility needs and preferences to support the conceptualization of the one-stop platform. Phase 2 is experimentation: 4 personas will be produced that reflect the different needs and preferences of typical older adult end users of the platform; for development of a prototype, scenarios and mockups (static designs of the web application) will be created through co-design sessions with older adults (N=12) embodying these personas. Phase 3 is evaluation: we will test the usability of the prototype and document changes in mobility, such as the ability to move around satisfactorily and to participate in meaningful activities, by and with older adults (N=30) who use the prototype. The steering committee will identify ways to support the adoption, implementation, and scaling up of Mobilainés to ensure its sustainability. Qualitative and quantitative data will be triangulated according to each subphase objective.

Results

 The first phase began in September 2019. The study is scheduled for completion by mid-2023.

Conclusions

 This innovative transportation planning service will merge existing transportation options in one place. By meeting a wide variety of older adults’ needs and preferences, Mobilainés will help them feel comfortable and safe when moving around, which should increase their participation in meaningful activities and reduce the risk of social isolation.

International Registered Report Identifier (IRRID)

DERR1-10.2196/33894

Air pollution and metabolic disorders: Dynamic versus static measures of exposure among Hispanics/Latinos and non-Hispanics

INTRODUCTION

Exposure to air pollution disproportionately affects racial/ethnic minorities that could contribute to health inequalities including metabolic disorders. However, most existing studies used a static assessment of air pollution exposure (mostly using the residential address) and do not account for activity space when modelling exposure to air pollution. The aim of this study is to understand how exposure to air pollution impacts metabolic disorders biomarkers, how this effect differs according to ethnicity, and for the first time compare these findings with two methods of exposure assessment: dynamic and static measures.

METHODS

Among the Community of Mine study, a cross-sectional study conducted in San Diego County, insulin resistance, diabetes, hypertension, obesity, dyslipidemia, and metabolic syndrome (MetS) were assessed. Exposure to air pollution (PM_2.5, NO₂, traffic) was calculated using static measures around the home, and dynamic measures of mobility derived from Global Positioning Systems (GPS) traces using kernel density estimators to account for exposure variability across space and time. Associations of air pollution with metabolic disorders were quantified using generalized estimating equation models to account for the clustered nature of the data.

RESULTS

Among 552 participants (mean age 58.7 years, 42% Hispanic/Latino), Hispanics/Latinos had a higher exposure to PM_2.5 compared to non-Hispanics using static measures. In contrast, Hispanics/Latinos had less exposure to PM_2.5 using dynamic measures. For all participants, higher dynamic exposure to PM_2.5 and NO₂ was associated with increased insulin resistance and cholesterol levels, and increased risk of obesity, dyslipidemia and MetS (RR 1.17, 95% CI: 1.07-1.28; RR 1.21, 95% CI: 1.12-1.30, respectively). The association between dynamic PM_2.5 exposure and MetS differed by Hispanic/Latino ethnicity.

CONCLUSION

These results highlight the importance of considering people's daily mobility in assessing the impact of air pollution on health.

MobiliSense cohort study protocol: do air pollution and noise exposure related to transport behaviour have short-term and longer-term health effects in Paris, France?

INTRODUCTION

MobiliSense explores effects of air pollution and noise related to personal transport habits on respiratory and cardiovascular health. Its objectives are to quantify the contribution of personal transport/mobility to air pollution and noise exposures of individuals; to compare exposures in different transport modes; and to investigate whether total and transport-related personal exposures are associated with short-term and longer-term changes in respiratory and cardiovascular health.

METHODS AND ANALYSIS

MobiliSense uses sensors of location, behaviour, environmental nuisances and health in 290 census-sampled participants followed-up after 1/2 years with an identical sensor-based strategy. It addresses knowledge gaps by: (1) assessing transport behaviour over 6 days with GPS receivers and GPS-based mobility surveys; (2) considering personal exposures to both air pollution and noise and improving their characterisation (inhaled doses, noise frequency components, etc); (3) measuring respiratory and cardiovascular outcomes (smartphone-assessed respiratory symptoms, lung function with spirometry, resting blood pressure, ambulatory brachial/central blood pressure, arterial stiffness and heart rate variability) and (4) investigating short-term and longer-term (over 1-2 years) effects of transport.

ETHICS AND DISSEMINATION

The sampling and data collection protocol was approved by the National Council for Statistical Information, the French Data Protection Authority and the Ethical Committee of Inserm. Our final aim is to determine, for communicating with policy-makers, how scenarios of changes in personal transport behaviour affect individual exposure and health.

Sensing pedestrian flows for real-time assessment of non-pharmaceutical policy interventions during the COVID-19 pandemic

Introduction

To combat and mitigate the transmission of the SARS-CoV-2 virus, reducing the number of social contacts within a population is highly effective. Non-pharmaceutical policy interventions, e.g. stay-at-home orders, closing schools, universities, and (non-essential) businesses, are expected to decrease pedestrian flows in public areas, leading to reduced social contacts. The extent to which such interventions show the targeted effect is often measured retrospectively by surveying behavioural changes. Approaches that use data generated through mobile phones are hindered by data confidentiality and privacy regulations and complicated by selection effects. Furthermore, access to such sensitive data is limited. However, a complex pandemic situation requires a fast evaluation of the effectiveness of the introduced interventions aiming to reduce social contacts. Location-based sensor systems installed in cities, providing objective measurements of spatial mobility in the form of pedestrian flows, are suited for such a purpose. These devices record changes in a population's behaviour in real-time, do not have privacy problems as they do not identify persons, and have no selection problems due to ownership of a device.

Objective

This work aimed to analyse location-based sensor measurements of pedestrian flows in 49 metropolitan areas at 100 locations in Germany to study whether such technology is suitable for the real-time assessment of behavioural changes during a phase of several different pandemic-related policy interventions.

Methods

Spatial mobility data of pedestrian flows was linked with policy interventions using the date as a unique linkage key. Data was visualised to observe potential changes in pedestrian flows before or after interventions. Furthermore, differences in time series of pedestrian counts between the pandemic and the pre-pandemic year were analysed.

Results

The sensors detected changes in mobility patterns even before policy interventions were enacted. Compared to the pre-pandemic year, pedestrian counts were 85% lower.

Conclusions

The study illustrated the practical value of sensor-based real-time measurements when linked with non-pharmaceutical policy intervention data. This study's core contribution is that the sensors detected behavioural changes before enacting or loosening non-pharmaceutical policy interventions. Therefore, such technologies should be considered in the future by policymakers for crisis management and policy evaluation.

Personal exposure to concentrations and inhalation of black carbon according to transport mode use: The MobiliSense sensor-based study

INTRODUCTION

Epidemiological evidence suggests that motorized vehicle users have a higher air pollutant exposure (especially from vehicle exhaust) than active (walking or cycling) transport users. However, studies often relied on insufficiently diverse sample and ignored that minute ventilation has an effect on individuals' inhaled dose. This study examined commuters' breathing zone concentration and inhaled doses of black carbon (BC) when travelling by different transport modes in the Grand Paris region.

METHODS

Personal exposure to BC was continuously measured with MicroAethalometer (MicroAeth AE51) portable monitors strapped on participants' shoulder with tube inlet at the level of the neck (breathing zone), and inhaled doses were derived from several methods estimating ventilation [based on metabolic equivalents from accelerometry [METs], heart rate, and breathing rate]. Trip stages and transport modes were assessed from GPS and mobility survey data. Breathing zone concentrations and inhaled doses of BC were compared across transport modes at the trip stage level (n = 7495 for 283 participants) using linear mixed effect models with a random intercept at individual level.

RESULTS

Trip stages involving public transport and private motorized transport were associated with a 2.20 µg/m³ (95% CI: 1.99, 2.41) and 2.29 µg/m³ (95% CI: 2.10, 2.48) higher breathing zone concentration to BC than walking, respectively. Trip stages with other active modes had a 0.41 µg (95% CI: 0.25, 0.57) higher inhaled dose, while those involving public transport and private motorized transport had a 0.25 µg (95% CI: -0.35, -0.15) and 0.19 µg (95 %CI: -0.28, -0.10) lower inhaled dose of BC per 30 min than walking.

CONCLUSION

The ranking of transport modes in terms of personal exposure was markedly different when breathing zone concentrations and inhaled doses were considered. Future studies should take both into account to explore the relationship of air pollutants in transport microenvironments with physiological response.

Assessing the Current Integration of Multiple Personalised Wearable Sensors for Environment and Health Monitoring

The ever-growing development of sensor technology brings new opportunities to investigate impacts of the outdoor environment on human health at the individual level. However, there is limited literature on the use of multiple personalized sensors in urban environments. This review paper focuses on examining how multiple personalized sensors have been integrated to enhance the monitoring of co-exposures and health effects in the city. Following PRISMA guidelines, two reviewers screened 4898 studies from Scopus, Web of Science, ProQuest, Embase, and PubMed databases published from January 2010 to April 2021. In this case, 39 articles met the eligibility criteria. The review begins by examining the characteristics of the reviewed papers to assess the current situation of integrating multiple sensors for health and environment monitoring. Two main challenges were identified from the quality assessment: choosing sensors and integrating data. Lastly, we propose a checklist with feasible measures to improve the integration of multiple sensors for future studies.

Digital Interventions on Healthy Lifestyle Management: Systematic Review

BACKGROUND

Digital interventions have tremendous potential to improve well-being and health care conveyance by improving adequacy, proficiency, availability, and personalization. They have gained acknowledgment in interventions for the management of a healthy lifestyle. Therefore, we are reviewing existing conceptual frameworks, digital intervention approaches, and associated methods to identify the impact of digital intervention on adopting a healthier lifestyle.

OBJECTIVE

This study aims to evaluate the impact of digital interventions on weight management in maintaining a healthy lifestyle (eg, regular physical activity, healthy habits, and proper dietary patterns).

METHODS

We conducted a systematic literature review to search the scientific databases (Nature, SpringerLink, Elsevier, IEEE Xplore, and PubMed) that included digital interventions on healthy lifestyle, focusing on preventing obesity and being overweight as a prime objective. Peer-reviewed articles published between 2015 and 2020 were included. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and a framework for an evidence-based systematic review. Furthermore, we improved the review process by adopting the Rayyan tool and the Scale for the Assessment of Narrative Review Articles.

RESULTS

Our initial searches identified 780 potential studies through electronic and manual searches; however, 107 articles in the final stage were cited following the specified inclusion and exclusion criteria. The identified methods for a successful digital intervention to promote a healthy lifestyle are self-monitoring, self-motivation, goal setting, personalized feedback, participant engagement, psychological empowerment, persuasion, digital literacy, efficacy, and credibility. In this study, we identified existing conceptual frameworks for digital interventions, different approaches to provide digital interventions, associated methods, and execution challenges and their impact on the promotion of healthy lifestyle management.

CONCLUSIONS

This systematic literature review selected intervention principles (rules), theories, design features, ways to determine efficient interventions, and weaknesses in healthy lifestyle management from established digital intervention approaches. The results help us understand how digital interventions influence lifestyle management and overcome the existing shortcomings. It serves as a basis for further research with a focus on designing, developing, testing, and evaluating the generation of personalized lifestyle recommendations as a part of digital health interventions.

Assessing the Policy Environment for Active Mobility in Cities-Development and Feasibility of the PASTA Cycling and Walking Policy Environment Score

The importance of setting a policy focus on promoting cycling and walking as sustainable and healthy modes of transport is increasingly recognized. However, to date a science-driven scoring system to assess the policy environment for cycling and walking is lacking. In this study, spreadsheet-based scoring systems for cycling and walking were developed, including six dimensions (cycling/walking culture, social acceptance, perception of traffic safety, advocacy, politics and urban planning). Feasibility was tested using qualitative data from pre-specified sections of semi-standardized interview and workshop reports from a European research project in seven cities, assessed independently by two experts. Disagreements were resolved by discussions of no more than 75 minutes per city. On the dimension "perception of traffic safety", quantitative panel data were used. While the interrater agreement was fair, feasibility was confirmed in general. Validity testing against social norms towards active travel, modal split and network length was encouraging for the policy area of cycling. Rating the policy friendliness for cycling and walking separately was found to be appropriate, as different cities received the highest scores for each. Replicating this approach in a more standardized way would pave the way towards a transparent, evidence-based system for benchmarking policy approaches of cities towards cycling and walking.

Physical activity and sedentary behaviour related to transport activity assessed with multiple body-worn accelerometers: the RECORD MultiSensor Study

OBJECTIVES

The study explored the physical activity and sedentary behaviours related to transport activity. The aim was to provide evidence to support public health and transport policies encouraging people to reach daily recommendations of physical activity.

STUDY DESIGN

The study design of this study is a cross-sectional study design.

METHODS

Between 2013 and 2015, the RECORD MultiSensor Study collected data from 155 participants using two accelerometers worn on the thigh and trunk. In addition, data were collected from Global Positioning System (GPS) receivers and a GPS-based mobility survey. Relationships between transport modes and the durations and partition patterns of physical behaviours were established at the trip stage (n = 7692) and trip levels (n = 4683) using multilevel linear models with a random effect at the individual level and taking into account temporal autocorrelation.

RESULTS

Participants travelled for a median of 1 h 45 min per day. Trip stages and trips involving walking, other active modes or public transport were associated with a lower sitting duration and a higher moderate-to-vigorous physical activity (MVPA) duration than trips with a personal motorised vehicle. Using public transport was associated with a lower number of transitions between sedentary behaviours and non-sedentary behaviours, and with a higher number of transitions between non-sedentary behaviours and MVPA than relying on a private motorised vehicle.

CONCLUSIONS

This study is the first to assess the association of transport mode with physical activity and sedentary behaviours captured with thigh- and trunk-worn accelerometers at both the trip stage and trip levels. The results demonstrate that, in addition to active transport modes, encouraging people to use public transport increases physical activity and reduces sedentary time.

Linking physical and social environments with mental health in old age: a multisensor approach for continuous real-life ecological and emotional assessment

Background

Urban stress is mentioned as a plausible mechanism leading to chronic stress, which is a risk factor of depression. Yet, an accurate assessment of urban stressors in environmental epidemiology requires new methods. This article discusses methods for the sensor-based continuous assesment of geographic environments, stress and depressive symptoms in older age. We report protocols of the promoting mental well-being and healthy ageing in cities (MINDMAP) and Healthy Aging and Networks in Cities (HANC) studies nested in the RECORD Cohort as a background for a broad discussion about the theoretical foundation and monitoring tools of mobile sensing research in older age. Specifically, these studies allow one to compare how older people with and without depression perceive, navigate and use their environment; and how the built environments, networks of social contacts, and spatial mobility patterns influence the mental health of older people.

Methods

Our research protocol combines (1) Global Positioning System (GPS) and accelerometer tracking and a GPS-based mobility survey to assess participants’ mobility patterns, activity patterns and environmental exposures; (2) proximity detection to assess whether household members are close to each other; (3) ecological momentary assessment to track momentary mood and stress and environmental perceptions; and (4) electrodermal activity for the tentative prediction of stress. Data will be compared within individuals (at different times) and between persons with and without depressive symptoms.

Conclusion

The development of mobile sensing and survey technologies opens an avenue to improve understanding of the role of momentary stressors and resourcing features of residential and non-residential environments for older populations’ mental health. However, validation, privacy and ethical aspects are important issues to consider.

Transport-related walking among young adults: when and why?

BACKGROUND

The existing smartphones' technology allows for the objective measurement of a person's movements at a fine-grained level of geographic and temporal detail, and in doing so, it mitigates the issues associated with self-report biases and lack of spatial details. This study proposes and evaluates the advantages of using a smartphone app for collecting accurate, fine-grained, and objective data on people's transport-related walking.

METHODS

A sample of 142 participants (mostly young adults) was recruited in a large Australian university, for whom the app recorded all their travel activities over two weekdays during August-September 2014. We identified eight main activity nodes which operate as transport-related walking generators. We explored the participants' transport-related walking patterns around and between these activity nodes through the use of di-graphs to better understand patterns of incidental physical activity and opportunities for intervention to increase incidental walking.

RESULTS

We found that the educational node - in other samples may be represented by the workplace - is as important as the residential node for generating walking trips. We also found that the likelihood of transport-related walking trips is larger during the daytime, whereas at night time walking trips tend to be longer. We also showed that patterns of transport-related walking relate to the presence of 'chaining' trips in the afternoon period.

CONCLUSIONS

The findings of this study show how the proposed data collection and analytic approach can inform urban design to enhance walkability at locations that are likely to generate walking trips. This study's insights can help to shape public education and awareness campaigns that aim to encourage walking trips throughout the day by suggesting locations and times of the day when engaging in these forms of exercise is easiest and least intrusive.