Comparison of physical activity measures using mobile phone-based CalFit and Actigraph

Agent-based modelling: A stochastic approach to assessing personal exposure to environmental pollutants - Insights from the URBANOME project

In the context of the URBANOME project, aiming to assess European citizens' exposure to air pollutants (PM10, PM2.5, NO2) and noise, an extensive data collection process was undertaken. This involved the distribution of stationary home sensors, portable sensors, and smartphone applications, alongside participants logging their activities while using these devices. By leveraging socioeconomic and socio-demographic statistical data for the residents of Thessaloniki, we developed an agent-based model to estimate exposure levels based on the movement patterns, locations, and data collected from the URBANOME campaign. The model highlights that an individual's exposure is closely linked to the type of activities they perform, their location, age, and gender. Whether exposure occurs indoors, or outdoors is important for determining intake levels. Activity selections were found to be strongly influenced by income, age, and social connections, indicating that socio-economic factors significantly shape exposure patterns. The analysis also revealed considerable differences between PM measurements taken from fixed monitoring stations and the sensors used in the campaign. Notably, even agents residing in the same household displayed distinct exposure levels, underscoring the variability within localized environments. Preliminary results from the URBANOME campaign were compared with the ABM outputs, showing differences in median values of up to 20 % of both noise and inhalation intakes. This research emphasizes the importance of using such models for developing future scenarios in large cities aimed at fostering green transitions and enhancing citizens' quality of life. These models provide valuable insights for designing strategies to reduce exposure and improve urban living conditions.

Accuracy of steps measured by smartphones-based WeRun compared with ActiGraph-GT3X accelerometer in free-living conditions

Objectives

The purpose of this study was to evaluate the accuracy and reliability of steps tracked by smartphone-based WeChat app compared with Actigraph-GT3X accelerometer in free-living conditions.

Design

A cross-sectional study and repeated measures.

Methods

A total of 103 employees in the Pudong New Area of Shanghai, China, participated in this study. The participants wore an ActiGraph-GT3X accelerometer during the period of August to September 2019 (Time 1), December 2019 (Time 2) and September 2020 (Time 3). Each time, they wore the ActiGraph-GT3X accelerometer continuously for 7 days to assess their 7-day step counts. The smartphone-based WeRun step counts were collected in the corresponding period when subjects wore accelerometers. The subjects were invited to complete basic demographic characteristics questionnaires and to perform physical examination to obtain health-related results such as height, body weight, body fat percentage, waist circumference, hip circumference, and blood pressure.

Results

Based on 103 participants' 21 days of data, we found that the Spearman correlation coefficient between them was 0.733 (P < 0.01). The average number of WeRun steps measured by smartphones was 8,975 (4,059) per day, which was higher than those measured by accelerometers (8,462 ± 3,486 per day, P < 0.01). Demographic characteristics and different conditions can affect the consistency of measurements. The consistency was higher in those who were male, older, master's degree and above educated, and traveled by walking. Steps measured by smartphone and accelerometer in working days and August showed stronger correlation than other working conditions and time. Mean absolute percent error (MAPE) for step counts ranged from 0.5 to 15.9%. The test-retest reliability coefficients of WeRun steps ranged from 0.392 to 0.646. A multiple regression analysis adjusted for age, gender, and MVPA/step counts measured during Time 1 showed that body composition (body weight, BMI, body fat percentage, waist circumference, and hip circumference) was correlated with moderate-to-vigorous intensity physical activity, but it was not correlated with WeRun step counts.

Conclusions

The smartphone-based WeChat app can be used to assess physical activity step counts and is a reliable tool for measuring steps in free-living conditions. However, WeRun step counts' utilization is potentially limited in predicting body composition.

Association of inflammation and lung function decline caused by personal PM2.5 exposure: a machine learning approach in time-series data

Numerous studies focused on the association between lung function impairment and inflammation caused by fine particulate matter (PM_2.5), but the causal relationships are difficult to clarify. In the current study, twenty healthy Chinese young adults who participated in 7 days of observation every four seasons were enrolled, and autoregression models (AM) and classification and regression trees (CART) in a machine learning framework were applied to analyze the association among PM_2.5 exposure, inflammation, and lung function from a data structure perspective. There were strong cross-correlations between personal dose of PM_2.5 (Dw) and lung functions (vital capacity (VC), forced vital capacity (FVC), etc.). These cross-correlation coefficients were associated with inflammatory indicators (uteroglobin (UG), serum amyloid (SAA), and fractional exhaled nitric oxide (FeNO)). CART reported that inflammatory indicators UG and SAA had the predictive ability of the directional association between Dw and FVC at 1-day lag and that high levels of UG and SAA predicted that PM_2.5 exposure induced lung function decline. Consistently, lower lung function indicators at a 2-day lag after personal PM_2.5 exposure predicted the high value of inflammatory indicator FeNO. Taken together, we applied machine learning algorithms to analyze repeated measurement data, finding that inflammation and lung function decline caused by PM_2.5 could affect each other.

Does Google Fit provide valid energy expenditure measurements of functional tasks compared to those of Fibion accelerometer in healthy individuals? A cross-sectional study

BACKGROUND AND AIMS

Smartphone applications (e.g., Google Fit) may be a good alternative tool for accelerometers in estimating energy expenditure of physical activities because they are affordable, easy to use, and freely downloadable on smartphones. We aimed to determine the concurrent validity of the Fibion and Google Fit for measuring energy expenditure of functional tasks in healthy individuals.

METHODS

In this cross-sectional study, 28 healthy individuals (21.25 ± 1.84 years) performed certain tasks (lying, standing, 6-min walk test, treadmill walking, stair climbing and cycling) for ∼90 min, while wearing a Fibion accelerometer on their thigh and having the Google Fit application in a smartphone placed in their trouser pocket. Concurrent validity between the energy expenditure data of the Google Fit and Fibion was assessed using the Spearman rho correlation coefficient (data were not normally distributed), Bland-Altman plots and linear regression.

RESULTS

Neither energy expenditure for the whole duration nor for the tasks, except sitting + treadmill walking (r = 0.419, p = 0.027), showed significant correlations between the Google Fit and Fibion measurements. A proportional bias was evident for almost all comparisons.

CONCLUSIONS

The Google Fit did not provide valid energy expenditure measurements compared to the Fibion for most of the investigated tasks in healthy individuals.

Does surrounding greenness moderate the relationship between apparent temperature and physical activity? Findings from the PHENOTYPE project

BACKGROUND

Physical activity can be affected by both meteorological conditions and surrounding greenness, but few studies have evaluated the effects of these environmental factors on physical activity simultaneously. This multi-city comparative study aimed to assess the synergetic effects of apparent temperature and surrounding greenness on physical activity in four European cities. Specifically, we aimed to identify an interaction between surrounding greenness and apparent temperature in the effects on physical activity.

METHODS

Data were collected from 352 adult residents of Barcelona (Spain), Stoke-on-Trent (United Kingdom), Doetinchem (The Netherlands), and Kaunas (Lithuania) as part of the PHENOTYPE study. Participants wore a smartphone for seven consecutive days between May-December 2013 and provided additional sociodemographic survey data. Hourly average physical activity (Metabolic Equivalent of Task (MET)) and surrounding greenness (NDVI) were derived from the Calfit mobile application collecting accelerometer and location data. Hourly apparent temperature was calculated from temperature and relative humidity, which were obtained from local meteorological stations along with other meteorological covariates (rainfall, windspeed, and sky darkness). We assessed the interaction effects of apparent temperature and surrounding greenness on hourly physical activity for each city using linear mixed models, while adjusting for meteorological, demographic, and time-related variables.

RESULTS

We found significant interactions between apparent temperature and surrounding greenness on hourly physical activity in three of four cities, aside from the coastal city of Barcelona. Significant quadratic effects of apparent temperature were found in the highest level of surrounding greenness for Stoke-on-Trent and Doetinchem, with 4% decrease in median MET observed for a 10°C departure from optimal temperature (15.2°C and 14.6°C, respectively). Significant linear effects were found for higher levels of surrounding greenness in Kaunas, whereby an increase of 10°C was associated with ∼4% increase in median MET.

CONCLUSION

Apparent temperature and surrounding greenness interacted in the effect on hourly physical activity across three of four European cities, with varying effect between cities. While quadratic effects of temperature suggest diminishing levels of physical activity in the highest greenness levels in cities of temperate climates, the variation in surrounding greenness between cities could be further explored, particularly by looking at indoor-outdoor locations. The study findings support the need for evidence-based physical activity promotion and urban design.

Assessing personal exposure using Agent Based Modelling informed by sensors technology

Technology innovations create possibilities to capture exposure-related data at a great depth and breadth. Considering, though, the substantial hurdles involved in collecting individual data for whole populations, this study introduces a first approach of simulating human movement and interaction behaviour, using Agent Based Modelling (ABM). A city scale ABM was developed for urban Thessaloniki, Greece that feeds into population-based exposure assessment without imposing prior bias, basing its estimations onto emerging properties of the behaviour of the computerised autonomous decision makers (agents) that compose the city-system. Population statistics, road and buildings networks data were transformed into human, road and building agents, respectively. Survey outputs with time-use patterns were associated with human agent rules, aiming to model representative to real-world behaviours. Moreover, time-geography of exposure data, derived from a local sensors campaign, was used to inform and enhance the model. As a prevalence of an agent-specific decision-making, virtual individuals of different sociodemographic backgrounds express different spatiotemporal behaviours and their trajectories are coupled with spatially resolved pollution levels. Personal exposure was evaluated by assigning PM concentrations to human agents based on coordinates, type of location and intensity of encountered activities. Study results indicated that PM2.5 inhalation adjusted exposure between housemates can differ by 56.5% whereas exposure between two neighbours can vary by as much as 87%, due to the prevalence of different behaviours. This study provides details of a new methodology that permits the cost-effective construction of refined time-activity diaries and daily exposure profiles, taking into account different microenvironments and sociodemographic characteristics. The proposed method leads to a refined exposure assessment model, addressing effectively vulnerable subgroups of population. It can be used for evaluating the probable impacts of different public health policies prior to implementation reducing, therefore, the time and expense required to identify efficient measures.

A Mapping Review of Physical Activity Recordings Derived From Smartphone Accelerometers

BACKGROUND

Smartphones with embedded sensors, such as accelerometers, are promising tools for assessing physical activity (PA), provided they can produce valid and reliable indices. The authors aimed to summarize studies on the PA measurement properties of smartphone accelerometers compared with research-grade PA monitors or other objective methods across the intensity spectrum, and to report the effects of different smartphone placements on the accuracy of measurements.

METHODS

A systematic search was conducted on July 1, 2019 in PubMed, Embase, SPORTDiscus, and Scopus, followed by screening.

RESULTS

Nine studies were included, showing moderate-to-good agreements between PA indices derived from smartphone accelerometers and research-grade PA monitors and/or indirect calorimetry. Three studies investigated measurement properties across smartphone placements, with small differences. Large heterogeneity across studies hampered further comparisons.

CONCLUSIONS

Despite moderate-to-good agreements between PA indices derived from smartphone accelerometers and research-grade PA monitors and/or indirect calorimetry, the validity of smartphone monitoring is currently challenged by poor intermonitor reliability between smartphone brands/versions, heterogeneity in protocols used for validation, the sparsity of studies, and the need to address the effects of smartphone placement.

Smartphone Accelerometry: A Smart and Reliable Measurement of Real-Life Physical Activity in Multiple Sclerosis and Healthy Individuals

Background: Mobility impairment is common in persons with multiple sclerosis (pwMS) and can be assessed with clinical tests and surveys that have restricted ecological validity. Commercial research-based accelerometers are considered to be more valuable as they measure real-life mobility. Smartphone accelerometry might be an easily accessible alternative.

Objective: To explore smartphone accelerometry in comparison to clinical tests, surveys, and a wrist-worn ActiGraph in pwMS and controls.

Methods: Sixty-seven pwMS and 70 matched controls underwent mobility tests and surveys. Real-life data were collected with a smartphone and an ActiGraph over 7 days. We explored different smartphone metrics in a technical validation course and computed afterward correlation between ActiGraph (steps per minute), smartphone accelerometry (variance of vector magnitude), clinical tests, and surveys. We also determined the ability to separate between patients and controls as well as between different disability groups.

Results: Based on the technical validation, we found the variance of the vector magnitude as a reliable estimate to discriminate wear time and no wear-time of the smartphone. Due to a further association with different activity levels, it was selected for real-life analyses. In the cross-sectional study, ActiGraph correlated moderately (r = 0.43, p < 0.05) with the smartphone but less with clinical tests (rho between |0.211| and |0.337|). Smartphone data showed stronger correlations with age (rho = −0.487) and clinical tests (rho between |0.565| and |0.605|). ActiGraph only differed between pwMS and controls (p < 0.001) but not between disability groups. At the same time, the smartphone showed differences between pwMS and controls, between RRMS and PP-/SPMS, and between participants with/without ambulatory impairment (all p < 0.001).

Conclusions: Smartphone accelerometry provides better estimates of mobility and disability than a wrist-worn standard accelerometer in a free-living context for both controls and pwMS. Given the fact that no additional device is needed, smartphone accelerometry might be a convenient outcome of real-life ambulation in healthy individuals and chronic diseases such as MS.

Momentary mood response to natural outdoor environments in four European cities

Exposure to natural outdoor environments (NOE) has been shown in population-level studies to reduce anxiety and psychological distress. This study investigated how exposure to one's everyday natural outdoor environments over one week influenced mood among residents of four European cities including Barcelona (Spain), Stoke-on-Trent (United Kingdom), Doetinchem (The Netherlands) and Kaunas (Lithuania). Participants (n = 368) wore a smartphone equipped with software applications to track location and mood (using mobile ecological momentary assessment (EMA) software), for seven consecutive days. We estimated random-effects ordered logistic regression models to examine the association between mood (positive and negative affect), and exposure to green space, represented by two binary variables indicating exposure versus no exposure to NOE using GPS tracking and satellite and aerial imagery, 10 and 30 min prior to participants' completing the EMA. Models were adjusted for home city, day of the week, hour of the day, EMA survey type, residential NOE exposure, and sex, age, education level, mental health status and neighbourhood socioeconomic status. In addition, we tested for heterogeneity of effect by city, sex, age, residential NOE exposure and mental health status. Within 10 min of NOE exposure, compared to non-exposure, we found that overall there was a positive relationship with positive affect (OR: 1.39, 95% CI: 1.06, 1.81) of EMA surveys, and non-significant negative association with negative affect (OR: 0.80, 95% CI: 0.58, 1.10). When stratifying, associations were consistently found for Stoke-on-Trent inhabitants and men, while findings by age group were inconsistent. Weaker and less consistent associations were found for exposure 30 min prior to EMA. Our findings support increasing evidence of psychological and mental health benefits of exposure to natural outdoor environments, especially among urban populations such as those included in our study.

Built Environment Interventions to Increase Active Travel: a Critical Review and Discussion

PURPOSE OF REVIEW

To review the literature on built environment interventions to increase active travel, focusing on work since 2000 and on methodological choices and challenges affecting studies.

RECENT FINDINGS

Increasingly, there is evidence that built environment interventions can lead to more walking or cycling. Evidence is stronger for cycling than for walking interventions, and there is a relative lack of evidence around differential impacts of interventions. Some of the evidence remains methodologically weak, with much work in the 'grey' literature. While evidence in the area continues to grow, data gaps remain. Greater use of quasi-experimental techniques, improvements in routine monitoring of smaller schemes, and the use of new big data sources are promising. More qualitative research could help develop a more sophisticated understanding of behaviour change.

Personal assessment of the external exposome during pregnancy and childhood in Europe

The human exposome affects child development and health later in life, but its personal external levels, variability, and correlations are largely unknown. We characterized the personal external exposome of pregnant women and children in eight European cities. Panel studies included 167 pregnant women and 183 children (aged 6-11 years). A personal exposure monitoring kit composed of smartphone, accelerometer, ultraviolet (UV) dosimeter, and two air pollution monitors were used to monitor physical activity (PA), fine particulate matter (PM_2.5), black carbon, traffic-related noise, UV-B radiation, and natural outdoor environments (NOE). 77% of women performed the adult recommendation of ≥150 min/week of moderate to vigorous PA (MVPA), while only 3% of children achieved the childhood recommendation of ≥60 min/day MVPA. 11% of women and 17% of children were exposed to daily PM_2.5 levels higher than recommended (≥25μg/m³). Mean exposure to noise ranged from Lden 51.1 dB in Kaunas to Lden 65.2 dB in Barcelona. 4% of women and 23% of children exceeded the recommended maximum of 2 Standard-Erythemal-Dose of UV-B at least once a week. 33% of women and 43% of children never reached the minimum NOE contact recommendation of ≥30 min/week. The variations in air and noise pollution exposure were dominated by between-city variability, while most of the variation observed for NOE contact and PA was between-participants. The correlations between all personal exposures ranged from very low to low (Rho < 0.30). The levels of personal external exposures in both pregnant women and children are above the health recommendations, and there is little correlation between the different exposures. The assessment of the personal external exposome is feasible but sampling requires from one day to more than one year depending on exposure due to high variability between and within cities and participants.

ExpoApp: An integrated system to assess multiple personal environmental exposures

To assess environmental exposures at the individual level, new assessment methods and tools are required. We developed an exposure assessment system (ExpoApp) for smartphones. ExpoApp integrates: (i) geo-location and accelerometry measurements from a waist attached smartphone, (ii) data from portable monitors, (iii) geographic information systems, and (iv) individual's information. ExpoApp calculates time spent in microenvironments, physical activity level, inhalation rate, and environmental exposures and doses (e.g., green spaces, inhaled ultrafine particles- UFP). We deployed ExpoApp in a panel study of 158 adults from five cities (Amsterdam and Utrecht- the Netherlands, Basel- Switzerland, Norwich- UK, and Torino- Italy) with an UFP monitor. To evaluate ExpoApp, participants also carried a reference accelerometer (ActiGraph) and completed a travel-activity diary (TAD). System reliability and validity of measurements were evaluated by comparing the monitoring failure rate and the agreement on time spent in microenvironments and physical activity with the reference tools. There were only significant failure rate differences between ExpoApp and ActiGraph in Norwich. Agreement on time in microenvironments and physical activity level between ExpoApp and reference tools was 86.6% (86.5-86.7) and 75.7% (71.5-79.4), respectively. ExpoApp estimated that participants inhaled 16.5 × 10¹⁰ particles/day of UFP and had almost no contact with green spaces (24% of participants spent ≥30 min/day in green spaces). Participants with more contact with green spaces had higher inhaled dose of UFP, except for the Netherlands, where the relationship was the inverse. ExpoApp is a reliable system and provides accurate individual's measurements, which may help to understand the role of environmental exposures on the origin and course of diseases.

The impact of particulate matter on allergy risk among adults: integrated exposure assessment

Exposure assessment is an important part in environmental epidemiology for determining the associations of environmental factors with health effects. One of the greatest challenges for personal exposure assessment is associated with peoples' mobility during the day and spatial and temporal dynamics of air pollution. In this study, the impact of PM₁₀ (particulate matter less than 10 μm) on allergy risk among adults was assessed using objective methods of exposure assessment. The primary objective of the present study was to estimate personal exposure to PM₁₀ based on individual daily movement patterns. Significant differences between the concentration of PM₁₀ in different microenvironments (MEs) and personal exposure to PM₁₀ were determined. Home exposure accounted for the largest part of PM₁₀ exposure. Thirty-five percent of PM₁₀ exposure was received in other non-home MEs. Allergy risk increased significantly with increasing exposure to PM₁₀. Adults exposed to the highest levels of PM₁₀ exposure had a twice-higher risk of allergies than adults exposed to the lowest levels of PM₁₀ exposure. The study results have practical relevance for exposure assessment to environmental factors and its impact on health effects.

A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study

The links between increased participation in Physical Activity (PA) and improvements in health are well established. As this body of evidence has grown, so too has the search for measures of PA with high levels of methodological effectiveness (i.e. validity, reliability and responsiveness to change). The aim of this “review of reviews” was to provide a comprehensive overview of the methodological effectiveness of currently employed measures of PA, to aid researchers in their selection of an appropriate tool. A total of 63 review articles were included in this review, and the original articles cited by these reviews were included in order to extract detailed information on methodological effectiveness.

Self-report measures of PA have been most frequently examined for methodological effectiveness, with highly variable findings identified across a broad range of behaviours. The evidence-base for the methodological effectiveness of objective monitors, particularly accelerometers/activity monitors, is increasing, with lower levels of variability observed for validity and reliability when compared to subjective measures. Unfortunately, responsiveness to change across all measures and behaviours remains under-researched, with limited information available.

Other criteria beyond methodological effectiveness often influence tool selection, including cost and feasibility. However, researchers must be aware of the methodological effectiveness of any measure selected for use when examining PA. Although no “perfect” tool for the examination of PA in adults exists, it is suggested that researchers aim to incorporate appropriate objective measures, specific to the behaviours of interests, when examining PA in free-living environments.

Assessing the Exposome with External Measures: Commentary on the State of the Science and Research Recommendations

The exposome comprises all environmental exposures that a person experiences from conception throughout the life course. Here we review the state of the science for assessing external exposures within the exposome. This article reviews (a) categories of exposures that can be assessed externally, (b) the current state of the science in external exposure assessment, (c) current tools available for external exposure assessment, and (d) priority research needs. We describe major scientific and technological advances that inform external assessment of the exposome, including geographic information systems; remote sensing; global positioning system and geolocation technologies; portable and personal sensing, including smartphone-based sensors and assessments; and self-reported questionnaire assessments, which increasingly rely on Internet-based platforms. We also discuss priority research needs related to methodological and technological improvement, data analysis and interpretation, data sharing, and other practical considerations, including improved assessment of exposure variability as well as exposure in multiple, critical life stages.

Physical Activity Barriers and Facilitators Among US Pacific Islanders and the Feasibility of Using Mobile Technologies for Intervention: A Focus Group Study With Tongan Americans

BACKGROUND

Pacific Islanders experience an elevated risk of health conditions such as obesity and diabetes, which are related to a lack of physical activity (PA). However, little attention has been paid to understanding the determinants of PA and promoting PA among this racial/ethnic group in the United States.

METHODS

We conducted focus group discussions with Tongan Americans, one of the major Pacific Islander groups in the United States, to gain a better understanding of their PA participation patterns, their barriers and facilitators, their attitudes toward PA, and their perceptions of how mobile technologies such as smartphones could help increase their PA levels.

RESULTS

Results indicate that although the participants understand the various benefits of PA, they do not engage in much leisure-time PA for exercise purposes. A lack of time is cited as an important reason for insufficient PA participation. In addition, most participants report familiarity with smartphones, positive views of mobile technology, and interest in using smartphones to measure and promote PA.

CONCLUSION

Multiple barriers were related with the low level of PA among Tongan Americans. Mobile technology is a promising way of enhancing PA among Tongan Americans and potentially other Pacific Islander subgroups. Culturally tailored strategies could significantly enhance the effectiveness of PA intervention.

Accelerometric estimates of physical activity vary unstably with data handling

Background

Because of unreliable self-report, accelerometry is increasingly used to objectively monitor physical activity (PA). However, results of accelerometric studies vary depending on the chosen cutpoints between activity intensities. Population-specific activity patterns likely affect the size of these differences. To establish their size and stability we apply three sets of cutpoints, including two calibrated to a single reference, to our accelerometric data and compare PA estimates.

Methods

1402 German adolescents from the GINIplus and LISAplus cohorts wore triaxial accelerometers (Actigraph GT3x) for one week (mean 6.23 days, 14.7 hours per day) at the hip. After validation of wear, we applied three sets of cutpoints for youth, including the most common standard (Freedson, 2005) and two calibrated to a single reference, (Romanzini uni- and triaxial, from Romanzini, 2014) to these data, estimating daily sedentary, light, moderate, vigorous and moderate-to-vigorous PA (MPA, VPA, MVPA). Stability of differences was assessed by comparing Romanzini’s two sets of cutpoints.

Results

Relative agreement between cutpoints was closer for activity of lower intensities (largest difference for sedentary behaviour 9%) but increased for higher intensities (largest difference for light activity 40%, MPA 102%, VPA 88%; all p<0.01). Romanzini’s uniaxial and triaxial cutpoints agreed no more closely with each other than with Freedson’s.

Conclusions

Estimated PA differed significantly between different sets of cutpoints, even when those cutpoints agreed perfectly on another dataset (i.e. Romanzini’s.) This suggests that the detected differences in estimated PA depend on population-specific activity patterns, which cannot be easily corrected for: converting activity estimates from one set of cutpoints to another may require access to raw data. This limits the utility of accelerometry for comparing populations in place and time. We suggest that accelerometric research adopt a standard for data processing, and apply and present the results of this standard in addition to those from any other method.

An evaluation of wearable sensors and their placements for analyzing construction worker's trunk posture in laboratory conditions

This study investigates the effect of sensor placement on the analysis of trunk posture for construction activities using two off-the-shelf systems. Experiments were performed using a single-parameter monitoring wearable sensor (SPMWS), the ActiGraph GT9X Link, which was worn at six locations on the body, and a multi-parameter monitoring wearable sensor (MPMWS), the Zephyr BioHarness™3, which was worn at two body positions. One healthy male was recruited and conducted 10 experiment sessions to repeat measurements of trunk posture within our study. Measurements of upper-body thoracic bending posture during the lifting and lowering of raised deck materials in a laboratory setting were compared against video-captured observations of posture. The measurements from the two sensors were found to be in agreement during slow-motion symmetric bending activities with a target bending of ≤45°. However, for asymmetric bending tasks, when the SPMWS was placed on the chest, its readings were substantially different from those of the MPMWS worn on the chest or under the armpit.

Natural outdoor environments and mental health: Stress as a possible mechanism

INTRODUCTION

Better mental health has been associated with exposure to natural outdoor environments (NOE). However, comprehensive studies including several indicators of exposure and outcomes, potential effect modifiers and mediators are scarce.

OBJECTIVES

We used novel, objective measures to explore the relationships between exposure to NOE (i.e. residential availability and contact) and different indicators of mental health, and possible modifiers and mediators.

METHODS

A nested cross-sectional study was conducted in: Barcelona, Spain; Stoke-on-Trent, United Kingdom; Doetinchem, Netherlands; Kaunas, Lithuania. Participants' exposure to NOE (including both surrounding greenness and green and/or blue spaces) was measured in terms of (a) amount in their residential environment (using Geographical Information Systems) and (b) their contact with NOE (using smartphone data collected over seven days). Self-reported information was collected for mental health (psychological wellbeing, sleep quality, vitality, and somatisation), and potential effect modifiers (gender, age, education level, and city) and mediators (perceived stress and social contacts), with additional objective NOE physical activity (potential mediator) derived from smartphone accelerometers.

RESULTS

Analysis of data from 406 participants showed no statistically significant associations linking mental health and residential NOE exposure. However, NOE contact, especially surrounding greenness, was statistically significantly tied to better mental health. There were indications that these relationships were stronger for males, younger people, low-medium educated, and Doetinchem residents. Perceived stress was a mediator of most associations, and physical activity and social contacts were not.

CONCLUSIONS

Our findings indicate that contact with NOE benefits mental health. Our results also suggest that having contact with NOE that can facilitate stress reduction could be particularly beneficial.

Validating novel air pollution sensors to improve exposure estimates for epidemiological analyses and citizen science

Low cost, personal air pollution sensors may reduce exposure measurement errors in epidemiological investigations and contribute to citizen science initiatives. Here we assess the validity of a low cost personal air pollution sensor. Study participants were drawn from two ongoing epidemiological projects in Barcelona, Spain. Participants repeatedly wore the pollution sensor - which measured carbon monoxide (CO), nitric oxide (NO), and nitrogen dioxide (NO₂). We also compared personal sensor measurements to those from more expensive instruments. Our personal sensors had moderate to high correlations with government monitors with averaging times of 1-h and 30-min epochs (r ~ 0.38-0.8) for NO and CO, but had low to moderate correlations with NO₂ (~0.04-0.67). Correlations between the personal sensors and more expensive research instruments were higher than with the government monitors. The sensors were able to detect high and low air pollution levels in agreement with expectations (e.g., high levels on or near busy roadways and lower levels in background residential areas and parks). Our findings suggest that the low cost, personal sensors have potential to reduce exposure measurement error in epidemiological studies and provide valid data for citizen science studies.

Living Close to Natural Outdoor Environments in Four European Cities: Adults' Contact with the Environments and Physical Activity

This study investigated whether residential availability of natural outdoor environments (NOE) was associated with contact with NOE, overall physical activity and physical activity in NOE, in four different European cities using objective measures. A nested cross-sectional study was conducted in Barcelona (Spain); Stoke-on-Trent (United Kingdom); Doetinchem (The Netherlands); and Kaunas (Lithuania). Smartphones were used to collect information on the location and physical activity (overall and NOE) of around 100 residents of each city over seven days. We used Geographic Information Systems (GIS) to determine residential NOE availability (presence/absence of NOE within 300 m buffer from residence), contact with NOE (time spent in NOE), overall PA (total physical activity), NOE PA (total physical activity in NOE). Potential effect modifiers were investigated. Participants spent around 40 min in NOE and 80 min doing overall PA daily, of which 11% was in NOE. Having residential NOE availability was consistently linked with higher NOE contact during weekdays, but not to overall PA. Having residential NOE availability was related to NOE PA, especially for our Barcelona participants, people that lived in a city with low NOE availability.

Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study

Background

The use of embedded smartphone sensors offers opportunities to measure physical activity (PA) and human movement. Big data—which includes billions of digital traces—offers scientists a new lens to examine PA in fine-grained detail and allows us to track people’s geocoded movement patterns to determine their interaction with the environment.

Objective

The objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data.

Methods

The criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement.

Results

A total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [−0.07 to 0.78] kcal/min, t₈₂=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t₈₉=5.27, P<.001). The absolute magnitude of criterion biases increased as a function of locomotive speed (F_1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F_1,4=1.26, P<.29). Furthermore, 95% limits of agreement were consistent for criterion and convergent biases, and EE from Movn was strongly correlated with both reference measures (criterion r=.91, convergent r=.92, both P<.001). Movn overestimated EE during free-living activities (bias=1.00 [0.98-1.02] kcal/min, t₆₁₂₃=101.49, P<.001), and biases were larger during high-intensity activities (F_3,6120=1550.51, P<.001). In addition, 95% limits of agreement for convergent biases were heterogeneous across free-living activity intensity levels, but Movn and ActiGraph measures were strongly correlated (r=.87, P<.001). Integration of GPS and accelerometer data within a geographic information system (GIS) enabled creation of individual temporospatial maps.

Conclusions

The Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics.

Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity

Physical activity is closely linked with health and well-being; however, many Americans do not engage in regular exercise. Older adults and those with low socioeconomic status are especially at risk for poor health, largely due to their sedentary lifestyles. Fitness technology, including trackers and smartphone applications (apps), has become increasingly popular for measuring and encouraging physical activity in recent years. However, many questions remain regarding the effectiveness of this technology for promoting behavior change. Behavior change techniques such as goal setting, feedback, rewards, and social factors are often included in fitness technology. However, it is not clear which components are most effective and which are actually being used by consumers. We discuss additional strategies not typically included in fitness technology devices or apps that are promising for engaging inactive, vulnerable populations. These include action planning, restructuring negative attitudes, enhancing environmental conditions, and identifying other barriers to regular physical activity. We consider which strategies are most conducive to motivating behavior change among sedentary adults. Overall, fitness technology has the potential to significantly impact public health, research, and policies. We suggest ways in which app developers and behavior change experts can collaborate to develop successful apps. Advances are still needed to help inactive individuals determine how, when, where, and with whom they can increase their physical activity.

Benefits of Mobile Phone Technology for Personal Environmental Monitoring

Background

Tracking individuals in environmental epidemiological studies using novel mobile phone technologies can provide valuable information on geolocation and physical activity, which will improve our understanding of environmental exposures.

Objective

The objective of this study was to assess the performance of one of the least expensive mobile phones on the market to track people's travel-activity pattern.

Methods

Adults living and working in Barcelona (72/162 bicycle commuters) carried simultaneously a mobile phone and a Global Positioning System (GPS) tracker and filled in a travel-activity diary (TAD) for 1 week (N=162). The CalFit app for mobile phones was used to log participants’ geographical location and physical activity. The geographical location data were assigned to different microenvironments (home, work or school, in transit, others) with a newly developed spatiotemporal map-matching algorithm. The tracking performance of the mobile phones was compared with that of the GPS trackers using chi-square test and Kruskal-Wallis rank sum test. The minute agreement across all microenvironments between the TAD and the algorithm was compared using the Gwet agreement coefficient (AC1).

Results

The mobile phone acquired locations for 905 (29.2%) more trips reported in travel diaries than the GPS tracker (P<.001) and had a median accuracy of 25 m. Subjects spent on average 57.9%, 19.9%, 9.0%, and 13.2% of time at home, work, in transit, and other places, respectively, according to the TAD and 57.5%, 18.8%, 11.6%, and 12.1%, respectively, according to the map-matching algorithm. The overall minute agreement between both methods was high (AC1 .811, 95% CI .810-.812).

Conclusions

The use of mobile phones running the CalFit app provides better information on which microenvironments people spend their time in than previous approaches based only on GPS trackers. The improvements of mobile phone technology in microenvironment determination are because the mobile phones are faster at identifying first locations and capable of getting location in challenging environments thanks to the combination of assisted-GPS technology and network positioning systems. Moreover, collecting location information from mobile phones, which are already carried by individuals, allows monitoring more people with a cheaper and less burdensome method than deploying GPS trackers.

Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy

Background

Children with physical impairments are at a greater risk for obesity and decreased physical activity. A better understanding of physical activity pattern and energy expenditure (EE) would lead to a more targeted approach to intervention.

Objective

This study focuses on studying the use of machine-learning algorithms for EE estimation in children with disabilities. A pilot study was conducted on children with Duchenne muscular dystrophy (DMD) to identify important factors for determining EE and develop a novel algorithm to accurately estimate EE from wearable sensor-collected data.

Methods

There were 7 boys with DMD, 6 healthy control boys, and 22 control adults recruited. Data were collected using smartphone accelerometer and chest-worn heart rate sensors. The gold standard EE values were obtained from the COSMED K4b2 portable cardiopulmonary metabolic unit worn by boys (aged 6-10 years) with DMD and controls. Data from this sensor setup were collected simultaneously during a series of concurrent activities. Linear regression and nonlinear machine-learning–based approaches were used to analyze the relationship between accelerometer and heart rate readings and COSMED values.

Results

Existing calorimetry equations using linear regression and nonlinear machine-learning–based models, developed for healthy adults and young children, give low correlation to actual EE values in children with disabilities (14%-40%). The proposed model for boys with DMD uses ensemble machine learning techniques and gives a 91% correlation with actual measured EE values (root mean square error of 0.017).

Conclusions

Our results confirm that the methods developed to determine EE using accelerometer and heart rate sensor values in normal adults are not appropriate for children with disabilities and should not be used. A much more accurate model is obtained using machine-learning–based nonlinear regression specifically developed for this target population.

Feasibility and Performance Test of a Real-Time Sensor-Informed Context-Sensitive Ecological Momentary Assessment to Capture Physical Activity

Background

Objective physical activity monitors (eg, accelerometers) have high rates of nonwear and do not provide contextual information about behavior.

Objective

This study tested performance and value of a mobile phone app that combined objective and real-time self-report methods to measure physical activity using sensor-informed context-sensitive ecological momentary assessment (CS-EMA).

Methods

The app was programmed to prompt CS-EMA surveys immediately after 3 types of events detected by the mobile phone’s built-in motion sensor: (1) Activity (ie, mobile phone movement), (2) No-Activity (ie, mobile phone nonmovement), and (3) No-Data (ie, mobile phone or app powered off). In addition, the app triggered random (ie, signal-contingent) ecological momentary assessment (R-EMA) prompts (up to 7 per day). A sample of 39 ethnically diverse high school students in the United States (aged 14-18, 54% female) tested the app over 14 continuous days during nonschool time. Both CS-EMA and R-EMA prompts assessed activity type (eg, reading or doing homework, eating or drinking, sports or exercising) and contextual characteristics of the activity (eg, location, social company, purpose). Activity was also measured with a waist-worn Actigraph accelerometer.

Results

The average CS-EMA + R-EMA prompt compliance and survey completion rates were 80.5% and 98.5%, respectively. More moderate-to-vigorous intensity physical activity was recorded by the waist-worn accelerometer in the 30 minutes before CS-EMA activity prompts (M=5.84 minutes) than CS-EMA No-Activity (M=1.11 minutes) and CS-EMA No-Data (M=0.76 minute) prompts (P’s<.001). Participants were almost 5 times as likely to report going somewhere (ie, active or motorized transit) in the 30 minutes before CS-EMA Activity than R-EMA prompts (odds ratio=4.91, 95% confidence interval=2.16-11.12).

Conclusions

Mobile phone apps using motion sensor–informed CS-EMA are acceptable among high school students and may be used to augment objective physical activity data collected from traditional waist-worn accelerometers.

An Automatic User-Adapted Physical Activity Classification Method Using Smartphones

In recent years, an increasing number of people have become concerned about their health. Most chronic diseases are related to lifestyle, and daily activity records can be used as an important indicator of health. Specifically, using advanced technology to automatically monitor actual activities can effectively prevent and manage chronic diseases. The data used in this paper were obtained from acceleration sensors and gyroscopes integrated in smartphones. We designed an efficient Adaboost-Stump running on a smartphone to classify five common activities: cycling, running, sitting, standing, and walking and achieved a satisfactory classification accuracy of 98%. We designed an online learning method, and the classification model requires continuous training with actual data. The parameters in the model then become increasingly fitted to the specific user, which allows the classification accuracy to reach 95% under different use environments. In addition, this paper also utilized the OpenCL framework to design the program in parallel. This process can enhance the computing efficiency approximately ninefold.

Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment

INTRODUCTION

Smartphone applications (apps) facilitate the collection of data on multiple aspects of behavior that are useful for characterizing baseline patterns and for monitoring progress in interventions aimed at promoting healthier lifestyles. Individual-based models can be used to examine whether behavior, such as diet, corresponds to certain typological patterns. The objectives of this paper are to demonstrate individual-based modeling methods relevant to a person's eating behavior, and the value of such approach compared to typical regression models.

METHOD

Using a mobile app, 2 weeks of physical activity and ecological momentary assessment (EMA) data, and 6 days of diet data were collected from 12 university students recruited from a university in Kunming, a rapidly developing city in southwest China. Phone GPS data were collected for the entire 2-week period, from which exposure to various food environments along each subject's activity space was determined. Physical activity was measured using phone accelerometry. Mobile phone EMA was used to assess self-reported emotion/feelings. The portion size of meals and food groups was determined from voice-annotated videos of meals. Individual-based regression models were used to characterize subjects as following one of 4 diet typologies: those with a routine portion sizes determined by time of day, those with portion sizes that balance physical activity (energy balance), those with portion sizes influenced by emotion, and those with portion sizes associated with food environments.

RESULTS

Ample compliance with the phone-based behavioral assessment was observed for all participants. Across all individuals, 868 consumed food items were recorded, with fruits, grains and dairy foods dominating the portion sizes. On average, 218 hours of accelerometry and 35 EMA responses were recorded for each participant. For some subjects, the routine model was able to explain up to 47% of the variation in portion sizes, and the energy balance model was able to explain over 88% of the variation in portion sizes. Across all our subjects, the food environment was an important predictor of eating patterns. Generally, grouping all subjects into a pooled model performed worse than modeling each individual separately.

CONCLUSION

A typological modeling approach was useful in understanding individual dietary behaviors in our cohort. This approach may be applicable to the study of other human behaviors, particularly those that collect repeated measures on individuals, and those involving smartphone-based behavioral measurement.

Urban and transport planning, environmental exposures and health-new concepts, methods and tools to improve health in cities

BACKGROUND

The majority of people live in cities and urbanization is continuing worldwide. Cities have long been known to be society's predominant engine of innovation and wealth creation, yet they are also a main source of pollution and disease.

METHODS

We conducted a review around the topic urban and transport planning, environmental exposures and health and describe the findings.

RESULTS

Within cities there is considerable variation in the levels of environmental exposures such as air pollution, noise, temperature and green space. Emerging evidence suggests that urban and transport planning indicators such as road network, distance to major roads, and traffic density, household density, industry and natural and green space explain a large proportion of the variability. Personal behavior including mobility adds further variability to personal exposures, determines variability in green space and UV exposure, and can provide increased levels of physical activity. Air pollution, noise and temperature have been associated with adverse health effects including increased morbidity and premature mortality, UV and green space with both positive and negative health effects and physical activity with many health benefits. In many cities there is still scope for further improvement in environmental quality through targeted policies. Making cities 'green and healthy' goes far beyond simply reducing CO2 emissions. Environmental factors are highly modifiable, and environmental interventions at the community level, such as urban and transport planning, have been shown to be promising and more cost effective than interventions at the individual level. However, the urban environment is a complex interlinked system. Decision-makers need not only better data on the complexity of factors in environmental and developmental processes affecting human health, but also enhanced understanding of the linkages to be able to know at which level to target their actions. New research tools, methods and paradigms such as geographical information systems, smartphones, and other GPS devices, small sensors to measure environmental exposures, remote sensing and the exposome paradigm together with citizens observatories and science and health impact assessment can now provide this information.

CONCLUSION

While in cities there are often silos of urban planning, mobility and transport, parks and green space, environmental department, (public) health department that do not work together well enough, multi-sectorial approaches are needed to tackle the environmental problems. The city of the future needs to be a green city, a social city, an active city, a healthy city.

The Kunming CalFit study: modeling dietary behavioral patterns using smartphone data

Human behavioral interventions aimed at improving health can benefit from objective wearable sensor data and mathematical models. Smartphone-based sensing is particularly practical for monitoring behavioral patterns because smartphones are fairly common, are carried by individuals throughout their daily lives, offer a variety of sensing modalities, and can facilitate various forms of user feedback for intervention studies. We describe our findings from a smartphone-based study, in which an Android-based application we developed called CalFit was used to collect information related to young adults' dietary behaviors. In addition to monitoring dietary patterns, we were interested in understanding contextual factors related to when and where an individual eats, as well as how their dietary intake relates to physical activity (which creates energy demand) and psychosocial stress. 12 participants were asked to use CalFit to record videos of their meals over two 1-week periods, which were translated into nutrient intake by trained dietitians. During this same period, triaxial accelerometry was used to assess each subject's energy expenditure, and GPS was used to record time-location patterns. Ecological momentary assessment was also used to prompt subjects to respond to questions on their phone about their psychological state. The GPS data were processed through a web service we developed called Foodscoremap that is based on the Google Places API to characterize food environments that subjects were exposed to, which may explain and influence dietary patterns. Furthermore, we describe a modeling framework that incorporates all of these information to dynamically infer behavioral patterns that may be used for future intervention studies.

Variability in and agreement between modeled and personal continuously measured black carbon levels using novel smartphone and sensor technologies

Novel technologies, such as smartphones and small personal continuous air pollution sensors, can now facilitate better personal estimates of air pollution in relation to location. Such information can provide us with a better understanding about whether and how personal exposures relate to residential air pollution estimates, which are normally used in epidemiological studies. The aims of this study were to examine (1) the variability in personal air pollution levels during the day and (2) the relationship between modeled home and school estimates and continuously measured personal air pollution exposure levels in different microenvironments (e.g., home, school, and commute). We focused on black carbon as an indicator of traffic-related air pollution. We recruited 54 school children (aged 7-11) from 29 different schools around Barcelona as part of the BREATHE study, an epidemiological study of the relation between air pollution and brain development. For 2 typical week days during 2012-2013, the children were given a smartphone with CalFit software to obtain information on their location and physical activity level and a small sensor, the micro-aethalometer model AE51, to measure their black carbon levels simultaneously and continuously. We estimated their home and school exposure to PM2.5 filter absorbance, which is well-correlated with black carbon, using a temporally adjusted PM2.5 absorbance land use regression (LUR) model. We found considerable variation in the black carbon levels during the day, with the highest levels measured during commuting periods (geometric mean = 2.8 μg/m(3)) and the lowest levels at home (geometric mean = 1.3 μg/m(3)). Hourly temporally adjusted LUR model estimates for the home and school showed moderate to good correlation with measured personal black carbon levels at home and school (r = 0.59 and 0.68, respectively) and lower correlation with commuting trips (r = 0.32 and 0.21, respectively). The correlation between modeled home estimates and overall personal black carbon levels was 0.62. Personal black carbon levels vary substantially during the day. The correlation between modeled and measured black carbon levels was generally good, with the exception of commuting times. In conclusion, novel technologies, such as smartphones and sensors, provide insights in personal exposure to air pollution.

Development and validity of a 3-day smartphone assisted 24-hour recall to assess beverage consumption in a Chinese population: a randomized cross-over study

This paper addresses the need for diet assessment methods that capture the rapidly changing beverage consumption patterns in China. The objective of this study was to develop a 3-day smartphone-assisted 24-hour recall to improve the quantification of beverage intake amongst young Chinese adults (n=110) and validate, in a small subset (n=34), the extent to which the written record and smartphone-assisted recalls adequately estimated total fluid intake, using 24-hour urine samples. The smartphone-assisted method showed improved validity compared with the written record-assisted method, when comparing reported total fluid intake to total urine volume. However, participants reported consuming fewer beverages on the smartphone-assisted method compared with the written record-assisted method, primarily due to decreased consumption of traditional zero-energy beverages (i.e. water, tea) in the smartphone-assisted method. It is unclear why participants reported fewer beverages in the smartphone-assisted method than the written record -assisted method. One possibility is that participants found the smartphone method too cumbersome, and responded by decreasing beverage intake. These results suggest that smartphone-assisted 24-hour recalls perform comparably but do not appear to substantially improve beverage quantification compared with the current written record-based approach. In addition, we piloted a beverage screener to identify consumers of episodically consumed SSBs. As expected, a substantially higher proportion of consumers reported consuming SSBs on the beverage screener compared with either recall type, suggesting that a beverage screener may be useful in characterizing consumption of episodically consumed beverages in China's dynamic food and beverage landscape.

Integrating smart-phone based momentary location tracking with fixed site air quality monitoring for personal exposure assessment

Epidemiological studies investigating relationships between environmental exposures from air pollution and health typically use residential addresses as a single point for exposure, while environmental exposures in transit, at work, school or other locations are largely ignored. Personal exposure monitors measure individuals' exposures over time; however, current personal monitors are intrusive and cannot be operated at a large scale over an extended period of time (e.g., for a continuous three months) and can be very costly. In addition, spatial locations typically cannot be identified when only personal monitors are used. In this paper, we piloted a study that applied momentary location tracking services supplied by smart phones to identify an individual's location in space-time for three consecutive months (April 28 to July 28, 2013) using available Wi-Fi networks. Individual exposures in space-time to the traffic-related pollutants Nitrogen Oxides (NOX) were estimated by superimposing an annual mean NOX concentration surface modeled using the Land Use Regression (LUR) modeling technique. Individual's exposures were assigned to stationary (including home, work and other stationary locations) and in-transit (including commute and other travel) locations. For the individual, whose home/work addresses were known and the commute route was fixed, it was found that 95.3% of the time, the individual could be accurately identified in space-time. The ambient concentration estimated at the home location was 21.01 ppb. When indoor/outdoor infiltration, indoor sources of air pollution and time spent outdoors were taken into consideration, the individual's cumulative exposures were 28.59 ppb and 96.49 ppb, assuming a respective indoor/outdoor ratio of 1.33 and 5.00. Integrating momentary location tracking services with fixed-site field monitoring, plus indoor-outdoor air exchange calibration, makes exposure assessment of a very large population over an extended time period feasible.

Measuring and influencing physical activity with smartphone technology: a systematic review

BACKGROUND

Rapid developments in technology have encouraged the use of smartphones in physical activity research, although little is known regarding their effectiveness as measurement and intervention tools.

OBJECTIVE

This study systematically reviewed evidence on smartphones and their viability for measuring and influencing physical activity.

DATA SOURCES

Research articles were identified in September 2013 by literature searches in Web of Knowledge, PubMed, PsycINFO, EBSCO, and ScienceDirect.

STUDY SELECTION

The search was restricted using the terms (physical activity OR exercise OR fitness) AND (smartphone* OR mobile phone* OR cell phone*) AND (measurement OR intervention). Reviewed articles were required to be published in international academic peer-reviewed journals, or in full text from international scientific conferences, and focused on measuring physical activity through smartphone processing data and influencing people to be more active through smartphone applications.

STUDY APPRAISAL AND SYNTHESIS METHODS

Two reviewers independently performed the selection of articles and examined titles and abstracts to exclude those out of scope. Data on study characteristics, technologies used to objectively measure physical activity, strategies applied to influence activity; and the main study findings were extracted and reported.

RESULTS

A total of 26 articles (with the first published in 2007) met inclusion criteria. All studies were conducted in highly economically advantaged countries; 12 articles focused on special populations (e.g. obese patients). Studies measured physical activity using native mobile features, and/or an external device linked to an application. Measurement accuracy ranged from 52 to 100% (n = 10 studies). A total of 17 articles implemented and evaluated an intervention. Smartphone strategies to influence physical activity tended to be ad hoc, rather than theory-based approaches; physical activity profiles, goal setting, real-time feedback, social support networking, and online expert consultation were identified as the most useful strategies to encourage physical activity change. Only five studies assessed physical activity intervention effects; all used step counts as the outcome measure. Four studies (three pre-post and one comparative) reported physical activity increases (12-42 participants, 800-1,104 steps/day, 2 weeks-6 months), and one case-control study reported physical activity maintenance (n = 200 participants; >10,000 steps/day) over 3 months.

LIMITATIONS

Smartphone use is a relatively new field of study in physical activity research, and consequently the evidence base is emerging.

CONCLUSIONS

Few studies identified in this review considered the validity of phone-based assessment of physical activity. Those that did report on measurement properties found average-to-excellent levels of accuracy for different behaviors. The range of novel and engaging intervention strategies used by smartphones, and user perceptions on their usefulness and viability, highlights the potential such technology has for physical activity promotion. However, intervention effects reported in the extant literature are modest at best, and future studies need to utilize randomized controlled trial research designs, larger sample sizes, and longer study periods to better explore the physical activity measurement and intervention capabilities of smartphones.

Using personal sensors to assess the exposome and acute health effects

Introduction: The exposome encompasses the totality of human environmental exposures. Recent developments in sensor technology have made it possible to better measure personal exposure to environmental pollutants and other factors. We aimed to discuss and demonstrate the recent developments in personal sensors to measure multiple exposures and possible acute health responses, and discuss the main challenges ahead. Methods: We searched for a range of sensors to measure air pollution, noise, temperature, UV, physical activity, location, blood pressure, heart rate and lung function and to obtain information on green space and emotional status/mood and put it on a person. Results and Conclusions: We discussed the recent developments and main challenges for personal sensors to measure multiple exposures. We found and put together a personal sensor set that measures a comprehensive set of personal exposures continuously over 24 h to assess part of the current exposome and acute health responses. We obtained data for a whole range of exposures and some acute health responses, but many challenges remain to apply the methodology for extended time periods and larger populations including improving the ease of wear, e.g., through miniaturization and extending battery life, and the reduction of costs. However, the technology is moving fast and opportunities will come closer for further wide spread use to assess, at least part of the exposome.

Potential of a smartphone as a stress-free sensor of daily human behaviour

Behaviour is one of the most powerful objective signals that connotes psychological functions regulated by neuronal network systems. This study searched for simple behaviours using smartphone sensors with three axes for measuring acceleration, angular speed and direction. We used quantitative analytic methodology of pattern recognition for work contexts, individual workers and seasonal effects in our own longitudinally recorded data. Our 13 laboratory members were involved in the care of common marmosets and domestic chicks, which lived in separate rooms. They attached a smartphone to their front waist-belts during feeding and cleaning in five care tasks. Behavioural characteristics such as speed, acceleration and azimuth, pitch, and roll angles were monitored. Afterwards, participants noted subjective scores of warmth sensation and work efficiency. The multivariate time series behavioral data were characterized by the subjective scores and environmental factors such as room temperature, season, and humidity, using the linear mixed model. In contrast to high-precision but stress-inducing sensors, the mobile sensors measuring daily behaviours allowed us to quantify the effects of the psychological states and environmental factors on the behavioural traits.

Positive health effects of the natural outdoor environment in typical populations in different regions in Europe (PHENOTYPE): a study programme protocol

INTRODUCTION

Growing evidence suggests that close contact with nature brings benefits to human health and well-being, but the proposed mechanisms are still not well understood and the associations with health remain uncertain. The Positive Health Effects of the Natural Outdoor environment in Typical Populations in different regions in Europe (PHENOTYPE) project investigates the interconnections between natural outdoor environments and better human health and well-being.

AIMS AND METHODS

The PHENOTYPE project explores the proposed underlying mechanisms at work (stress reduction/restorative function, physical activity, social interaction, exposure to environmental hazards) and examines the associations with health outcomes for different population groups. It implements conventional and new innovative high-tech methods to characterise the natural environment in terms of quality and quantity. Preventive as well as therapeutic effects of contact with the natural environment are being covered. PHENOTYPE further addresses implications for land-use planning and green space management. The main innovative part of the study is the evaluation of possible short-term and long-term associations of green space and health and the possible underlying mechanisms in four different countries (each with quite a different type of green space and a different use), using the same methodology, in one research programme. This type of holistic approach has not been undertaken before. Furthermore there are technological innovations such as the use of remote sensing and smartphones in the assessment of green space.

CONCLUSIONS

The project will produce a more robust evidence base on links between exposure to natural outdoor environment and human health and well-being, in addition to a better integration of human health needs into land-use planning and green space management in rural as well as urban areas.

Big Data and Disease Prevention: From Quantified Self to Quantified Communities

Big data is often discussed in the context of improving medical care, but it also has a less appreciated but equally important role to play in preventing disease. Big data can facilitate action on the modifiable risk factors that contribute to a large fraction of the chronic disease burden, such as physical activity, diet, tobacco use, and exposure to pollution. It can do so by facilitating the discovery of risk factors for disease at population, subpopulation, and individual levels, and by improving the effectiveness of interventions to help people achieve healthier behaviors in healthier environments. In this article, we describe new sources of big data in population health, explore their applications, and present two case studies illustrating how big data can be leveraged for prevention. We also discuss the many implementation obstacles that must be overcome before this vision can become a reality.

Improving estimates of air pollution exposure through ubiquitous sensing technologies

Traditional methods of exposure assessment in epidemiological studies often fail to integrate important information on activity patterns, which may lead to bias, loss of statistical power, or both in health effects estimates. Novel sensing technologies integrated with mobile phones offer potential to reduce exposure measurement error. We sought to demonstrate the usability and relevance of the CalFit smartphone technology to track person-level time, geographic location, and physical activity patterns for improved air pollution exposure assessment. We deployed CalFit-equipped smartphones in a free-living population of 36 subjects in Barcelona, Spain. Information obtained on physical activity and geographic location was linked to space-time air pollution mapping. We found that information from CalFit could substantially alter exposure estimates. For instance, on average travel activities accounted for 6% of people's time and 24% of their daily inhaled NO2. Due to the large number of mobile phone users, this technology potentially provides an unobtrusive means of enhancing epidemiologic exposure data at low cost.