Technologies That Assess the Location of Physical Activity and Sedentary Behavior: A Systematic Review

Assessing nature exposure: a study on the reliability and validity of a Portuguese version of the nature exposure scale

Introduction

Engaging with nature has been widely acknowledged for its positive impact on well-being. Traditionally, assessments of nature exposure focus on estimating green space exposure and outdoor time. However, the Nature Exposure Scale (NES) offers a unique approach by evaluating the quality of nature experiences, encompassing both deliberate and spontaneous encounters.

Methods

This study aimed to assess the validity and reliability of the NES in the context of Portugal. Exploratory Factor Analysis (PCA) and Confirmatory Factor Analysis (CFA) were employed to examine the underlying structure of the scale. Additionally, reliability assessments, along with tests of convergent and divergent validity, were conducted. Data were collected from a sample of 558 adults and 241 adolescents between 2016 and 2018.

Results

The findings revealed strong internal consistency of the NES, supported by acceptable correlation values and robust factor loadings within a unidimensional model. The scale effectively predicted variations in nature exposure across diverse professional activities.

Discussion

In summary, the 4-item NES emerged as a reliable tool for assessing nature exposure in various settings, effectively bridging a gap in the Portuguese context. The scale demonstrated potential for cross-cultural research and was particularly adept at predicting nature exposure in different professional contexts. The results suggest that the NES can enhance our understanding of the impact of nature on well-being in diverse cultural settings.

Conclusion

The study underscores the reliability of the NES in assessing nature exposure in Portugal, paving the way for further exploration in Portuguese-speaking regions. The scale holds promise for advancing research on the relationship between nature and well-being, contributing valuable insights across different cultural contexts.

How many days of indoor positioning system data are required to characterise typical movement behaviours of office workers?

Indoor Positioning Systems (IPS) appear to offer great potential to study the movement and interaction of people and their working environment, including office workplaces. But little is known about appropriate durations for data collection. In this study, location observations collected from 24 office workers on a 1220 m² office floor over a 3-month period, were analysed to determine how many days are required to estimate their typical movement and spatial behaviours. The analysis showed that up to 8 days of data was sufficient to characterise participants' typical daily movement behaviours and 10 days were required to estimate their typical spatial mobility. However, the results also indicate that 5 weeks of data collection are required to gather the necessary 10 days of data from each participant. These findings will help researchers and workplace professionals to understand the capabilities and requirements of IPS when considering their use in indoor work environments.

Impact of Hospital Characteristics and Governance Structure on the Adoption of Tracking Technologies for Clinical and Supply Chain Use: Longitudinal Study of US Hospitals

Background

Despite the increasing adoption rate of tracking technologies in hospitals in the United States, few empirical studies have examined the factors involved in such adoption within different use contexts (eg, clinical and supply chain use contexts). To date, no study has systematically examined how governance structures impact technology adoption in different use contexts in hospitals. Given that the hospital governance structure fundamentally governs health care workflows and operations, understanding its critical role provides a solid foundation from which to explore factors involved in the adoption of tracking technologies in hospitals.

Objective

This study aims to compare critical factors associated with the adoption of tracking technologies for clinical and supply chain uses and examine how governance structure types affect the adoption of tracking technologies in hospitals.

Methods

This study was conducted based on a comprehensive and longitudinal national census data set comprising 3623 unique hospitals across 50 states in the United States from 2012 to 2015. Using mixed effects population logistic regression models to account for the effects within and between hospitals, we captured and examined the effects of hospital characteristics, locations, and governance structure on adjustments to the innate development of tracking technology over time.

Results

From 2012 to 2015, we discovered that the proportion of hospitals in which tracking technologies were fully implemented for clinical use increased from 36.34% (782/2152) to 54.63% (1316/2409), and that for supply chain use increased from 28.58% (615/2152) to 41.3% (995/2409). We also discovered that adoption factors impact the clinical and supply chain use contexts differently. In the clinical use context, compared with hospitals located in urban areas, hospitals in rural areas (odds ratio [OR] 0.68, 95% CI 0.56-0.80) are less likely to fully adopt tracking technologies. In the context of supply chain use, the type of governance structure influences tracking technology adoption. Compared with hospitals not affiliated with a health system, implementation rates increased as hospitals affiliated with a more centralized health system—1.9-fold increase (OR 1.87, 95% CI 1.60-2.13) for decentralized or independent hospitals, 2.4-fold increase (OR 2.40, 95% CI 2.07-2.80) for moderately centralized health systems, and 3.1-fold increase for centralized health systems (OR 3.07, 95% CI 2.67-3.53).

Conclusions

As the first of such type of studies, we provided a longitudinal overview of how hospital characteristics and governance structure jointly affect adoption rates of tracking technology in both clinical and supply chain use contexts, which is essential for developing intelligent infrastructure for smart hospital systems. This study informs researchers, health care providers, and policy makers that hospital characteristics, locations, and governance structures have different impacts on the adoption of tracking technologies for clinical and supply chain use and on health resource disparities among hospitals of different sizes, locations, and governance structures.

Using Wearable Cameras to Categorize the Type and Context of Screen-Based Behaviors Among Adolescents: Observational Study

BACKGROUND

Automated wearable cameras present a new opportunity to accurately assess human behavior. However, this technology is seldom used in the study of adolescent's screen exposure, and the field is reliant on poor-quality self-report data.

OBJECTIVE

This study aimed to examine adolescents' screen exposure by categorizing the type and context of behaviors using automated wearable cameras.

METHODS

Adolescents (mean age 15.4 years, SD 1.6 years; n=10) wore a camera for 3 school evenings and 1 weekend day. The camera captured an image every 10 seconds. Fieldwork was completed between February and March 2020, and data were analyzed in August 2020. Images were date and time stamped, and coded for screen type, content, and context.

RESULTS

Data representing 71,396 images were analyzed. Overall, 74.0% (52,842/71,396) of images contained screens and 16.8% (11,976/71,396) of images contained multiple screens. Most screen exposures involved television sets (25,950/71,396, 36.3%), smartphones (20,851/71,396, 29.2%), and laptop computers (15,309/71,396, 21.4%). The context of screen use differed by device type, although most screen exposures occurred at home (62,455/64,856, 96.3%) and with solitary engagement (54,430/64,856, 83.9%). The immediate after-school period saw high laptop computer use (4785/15,950, 30.0%), while smartphone use (2059/5320, 38.7%) peaked during prebedtime hours. Weekend screen exposure was high, with smartphone use (1070/1927, 55.5%) peaking in the early morning period and fluctuating throughout the day.

CONCLUSIONS

There was evidence for high screen use during the after-school and weekend period, mostly through solitary engagement, and within the home environment. The findings may inform the basis of larger studies aimed at examining screen exposure in free-living conditions.

Using Wearable Cameras to Investigate Health-Related Daily Life Experiences: A Literature Review of Precautions and Risks in Empirical Studies

Automated, wearable cameras can benefit health-related research by capturing accurate and objective information about individuals' daily experiences. However, wearable cameras present unique privacy- and confidentiality-related risks due to the possibility of the images capturing identifying or sensitive information from participants and third parties. Although best practice guidelines for ethical research with wearable cameras have been published, limited information exists on the risks of studies using wearable cameras. The aim of this literature review was to survey risks related to using wearable cameras, and precautions taken to reduce those risks, as reported in empirical research. Forty-five publications, comprising 36 independent studies, were reviewed, and findings revealed that participants' primary concerns with using wearable cameras included physical inconvenience and discomfort in certain situations (e.g., public settings). None of the studies reviewed reported any serious adverse events. Although it is possible that reported findings do not include all risks experienced by participants in research with wearable cameras, our findings suggest a low level of risk to participants. However, it is important that investigators adopt recommended precautions, which can promote autonomy and reduce risks, including participant discomfort.

Activity space, office space: Measuring the spatial movement of office workers

A key to the development of more effective interventions to promote movement and reduce physical inactivity in office workplaces may be to measure and locate individual's spatial movement. Using an activity space estimation method, high resolution location data collected from 15 office workers over 12 days were used to estimate and analyse the location and extent of their daily spatial movement whilst in an office work-based setting. The results indicated that the method, kernel density estimation, combined with location data offers significant opportunities to not only measure and compare spatial movement behaviours but also simultaneously identify the locations where the behaviours occur. Combined with other data streams, this method will allow researchers to further investigate the influence of different environmental characteristics on these behaviours, potentially leading the development of more effective, longer lasting interventions to promote movement and reduce stationary behaviour, ultimately improving the health of office workers.

An empirical example of analysis using a two-stage modeling approach: within-subject association of outdoor context and physical activity predicts future daily physical activity levels

People differ from each other to the extent to which momentary factors, such as context, mood, and cognitions, influence momentary health behaviors. However, statistical models to date are limited in their ability to test whether the association between two momentary variables (i.e., subject-level slopes) predicts a subject-level outcome. This study demonstrates a novel two-stage statistical modeling strategy that is capable of testing whether subject-level slopes between two momentary variables predict subject-level outcomes. An empirical case study application is presented to examine whether there are differences in momentary moderate-to-vigorous physical activity (MVPA) levels between the outdoor and indoor context in adults and whether these momentary differences predict mean daily MVPA levels 6 months later. One hundred and eight adults from a multiwave longitudinal study provided 4 days of ecological momentary assessment (during baseline) and accelerometry data (both at baseline and 6 month follow-up). Multilevel data were analyzed using an open-source program (MixWILD) to test whether momentary strength between outdoor context and MVPA during baseline was associated with average daily MVPA levels measured 6 months later. During baseline, momentary MVPA levels were higher in outdoor contexts as compared to indoor contexts (b = 0.07, p < .001). Participants who had more momentary MVPA when outdoors (vs. indoors) during baseline (i.e., a greater subject-level slope) had higher daily MVPA at the 6 month follow-up (b = 0.09, p < .05). This empirical example shows that the subject-level momentary association between specific context (i.e., outdoors) and health behavior (i.e., physical activity) may contribute to overall engagement in that behavior in the future. The demonstrated two-stage modeling approach has extensive applications in behavioral medicine to analyze intensive longitudinal data collected from wearable sensors and mobile devices.

Location-Based Sedentary Time and Physical Activity in People Living With Coronary Artery Disease

PURPOSE

Sedentary time (ST) and lack of physical activity increase the risk of adverse outcomes for those living with coronary artery disease (CAD). Little is known about how much ST, light physical activity (LPA), and moderate to vigorous physical activity (MVPA) that CAD participants not attending cardiac rehabilitation engage in, the locations where they engage in these behaviors, and how far from home the locations are.

METHODS

Participants completed a survey and wore an accelerometer and global positioning system receiver for 7 d at baseline and 6 mo later.

RESULTS

Accelerometer analyses (n = 318) showed that participants averaged 468.4 ± 102.7 of ST, 316.1 ± 86.5 of LPA, and 32.9 ± 28.9 of MVPA min/d at baseline. ST and LPA remained stable at 6 mo, whereas MVPA significantly declined. The global positioning system (GPS) analyses (n = 315) showed that most of participant ST, LPA, and MVPA time was spent at home followed by other residential, retail/hospitality, and work locations at baseline and 6 mo. When not at home, the average distance to a given location ranged from approximately 9 to 18 km.

CONCLUSIONS

Participants with CAD spent the majority of their time being sedentary. Home was the location used the most to engage in ST, LPA, and MVPA. When not home, ST, LPA, and MVPA were distributed across a variety of locations. The average distance from home to a given location suggests that proximity to home may not be a barrier from an intervention perspective.

Virtual Fence System Based on IoT Paradigm to Prevent Occupational Accidents in the Construction Sector

Many occupational accidents in construction sites are caused by the intrusion of a worker into a hazardous area. Technological solutions based on RFID, BIM, or UWB can reduce accidents, but they still have some limitations.The aim of the current paper is to design and evaluate a new system of “virtual fences” based on Bluetooth Low-Energy (BLE) to avoid intrusions. First of all, the system was designed using a number of beacons, a Bayesian filter, a finite state machine, and an indicator. Secondly, its safety attributes were evaluated based on a scientific questionnaire by an expert panel following the staticized groups’ methodology. Results showed that the proposal is inexpensive and easy to integrate and configure. The selected experts evaluated positively all the attributes of the system, and provided valuable insights for further improvements. From the experts’ discussions, we concluded that successful adoption of this “virtual fence” system based on BLE beacons should consider the influence of factors such as cost savings, top management support, social acceptance, and compatibility and integration with existing systems, procedures, and company culture. In addition, legislation updates according to technical advances would help with successful adoption of any new safety system.

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

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

Accuracy of Sedentary Behavior-Triggered Ecological Momentary Assessment for Collecting Contextual Information: Development and Feasibility Study

BACKGROUND

Sedentary behavior has received much attention in the scientific community over the past decade. There is growing evidence that sedentary behavior is negatively associated with physical and mental health. However, an in-depth understanding of the social and environmental context of sedentary behavior is missing. Information about sedentary behavior, such as how everyday sedentary behavior occurs throughout the day (eg, number and length of sedentary bouts), where, when, and with whom it takes place, and what people are doing while being sedentary, is useful to inform the development of interventions aimed at reducing sedentary time. However, examining everyday sedentary behavior requires specific methods.

OBJECTIVE

The purpose of this paper is (1) to introduce sedentary behavior-triggered Ecological Momentary Assessment (EMA) as a methodological advancement in the field of sedentary behavior research and (2) to examine the accuracy of sedentary behavior-triggered EMA in 3 different studies in healthy adults. Moreover, we compare the accuracy of sedentary behavior-triggered EMA to simulations of random-trigger designs.

METHODS

Sedentary behavior-triggered EMA comprises a continuous assessment of sedentary behavior via accelerometers and repeated contextual assessments via electronic diaries (ie, an application on a smartphone). More specifically, the accelerometer analyzes and transfers data regarding body position (a sitting or lying position, or an upright position) via Bluetooth Low Energy (BLE) to a smartphone in real time and triggers the deployment of questionnaires. Each time a participant spends a specified time (eg, 20 minutes) in a sedentary position, the e-diary triggers contextual assessments. To test the accuracy of this method, we calculated a percentage score for all triggered prompts in relation to the total number of bouts that could trigger a prompt.

RESULTS

Based on the accelerometer recordings, 29.3% (5062/17278) of all sedentary bouts were classified as moderate-to-long (20-40 minutes) and long bouts (≥ 41 minutes). On average, the accuracy by participant was 82.77% (3339/4034; SD 21.01%, range 71.00-88.22%) on the study level. Compared to simulations of random prompts (every 120 minutes), the number of triggered prompts was up to 47.9% (n=704) higher through the sedentary behavior-triggered EMA approach. Nearly 40% (799/2001) of all prolonged sedentary bouts (≥ 20 minutes) occurred during work, and in 57% (1140/2001) of all bouts, the participants were not alone.

CONCLUSIONS

Sedentary behavior-triggered EMA is an accurate method for collecting contextual information on sedentary behavior in daily life. Given the growing interest in sedentary behavior research, this sophisticated approach offers a real advancement as it can be used to collect social and environmental contextual information or to unravel dynamic associations. Furthermore, it can be modified to develop sedentary behavior-triggered mHealth interventions.

Breaking Up Sedentary Behavior Optimally to Enhance Mood

INTRODUCTION

Experimental evidence shows that breaking-up sedentary behavior is positively associated with positive mental health outcomes. However, whether sedentary breaks influence mood in everyday life is largely unknown. Moreover, it is unclear which break patterns are most beneficial to enhance mood.

METHODS

To investigate the degree to which sedentary break patterns influence mood dimensions in everyday life, we conducted an Ecological Momentary Assessment study in 92 university employees over 5 d. We continuously measured physical behavior (physical activity and sedentary behavior) objectively via accelerometers and assessed mood 10 times a day on smartphone diaries. We defined distinct break patterns, such as variations in frequency (number of breaks), duration (length of breaks), intensity (metabolic equivalent), and context (home or work) and used multilevel modeling to analyze the within-subject effects of sedentary break patterns on mood.

RESULTS

Break intensity was positively associated with subsequent valence (P < 0.01), energetic arousal (P < 0.01), and calmness (P < 0.05). Break frequency was positively associated with subsequent valence and energetic arousal (all P's < 0.01), but break duration was not associated with mood. Exploratory analyses indicated that breaking-up sedentary behavior was more beneficial at home than at work.

CONCLUSIONS

These ecologically valid findings suggest breaking-up sedentary behavior as a promising strategy to enhance mood in everyday life. In particular, breaking-up sedentary behavior frequently and intensively, for example, by walking instead of standing, may be most beneficial. We call for future studies to substantiate these accounts and to identify both practical and optimal break patterns among different samples. This evidence informs official public health recommendations aiming to "minimize sedentary time in everyday life."

Validating Accelerometers for the Assessment of Body Position and Sedentary Behavior

There is growing evidence that sedentary behavior is a risk factor for somatic and mental health. However, there is still a lack of objective field methods, which can assess both components of sedentary behavior: the postural (sitting/lying) and the movement intensity part. The purpose of the study was to compare the validity of different accelerometers (ActivPAL [thigh], ActiGraph [hip], move [hip], and move [thigh]). 20 adults (10 females; age 25.68 ± 4.55 years) participated in a structured protocol with a series of full- and semistandardized sessions under laboratory conditions. Direct observation via video recording was used as a criterion measure of body positions (sitting/lying vs. nonsitting/lying). By combining direct observation with metabolic equivalent tables, protocol activities were also categorized as sedentary or nonsedentary. Cohen’s kappa was calculated as an overall validity measure to compare accelerometer and video recordings. Across all conditions, for the measurement of sitting/lying body positions, the ActivPAL ([thigh], ĸ = .85) and Move 4 ([thigh], ĸ = .97) showed almost perfect agreement, whereas the Move 4 ([hip], ĸ = .78) and ActiGraph ([hip], ĸ = .67) showed substantial agreement. For the sedentary behavior part, across all conditions, the ActivPAL ([thigh], ĸ = .90), Move 4 ([thigh], ĸ = .95) and Move 4 ([hip], ĸ = .84) revealed almost perfect agreement, whereas the ActiGraph ([hip], ĸ = .69) showed substantial agreement. In particular, thigh-worn devices, namely the Move and the ActivPAL, achieved up to excellent validity in measuring sitting/lying body positions and sedentary behavior and are recommended for future studies.

The health impact of nature exposure and green exercise across the life course: a pilot study

BACKGROUND

Both nature exposure and green exercise (GE) provide numerous health benefits. However, there are no studies examining the impact of childhood GE on adult health.

METHODS

45 healthy adults (aged 69.8 ± 8.4 years) took part in the study, wearing a Firstbeat heart rate variability (HRV) monitor for 24 hours. Participants also completed questionnaires assessing childhood and adulthood nature exposure and GE, as well as current connectedness to nature (CN), perceived stress and well-being. Pearson's correlations and linear regression were used to examine relationships between variables.

RESULTS

Childhood nature exposure and GE significantly predicted adult nature exposure and GE (β .317, p < 0.05) as well as CN (β = .831, p < 0.01). After controlling for childhood nature exposure and GE, CN was negatively associated with the percentage of stress over the 24-hour period (r = -.363; p < 0.05) and positively associated with HRV during sleep (r = .415; p < 0.05).

CONCLUSIONS

CN is important for adult health; however childhood nature exposure and GE are essential to developing this connection.

The Current and Retrospective Intentional Nature Exposure Scales: Development and Factorial Validity

Both nature exposure and green exercise (GE) can improve health. However, there are no scales examining frequency of engagement; or that consider interaction with nature. There are also no scales assessing these variables during childhood. The aim of this study was to develop a modified (NES-II) and retrospective (RNES-II) version of the Nature Exposure Scale to incorporate GE and to examine their factor structure and reliability. Exploratory factor analysis (EFA) explored the factor structure of the scales; followed by confirmatory factor analysis to confirm the model fit. Fit indices for the one factor five item NES-II and RNES-II models identified by EFA were poor. Use of modification indices resulted in a good model fit; NES-II: χ(5, n = 385) = 2.638; χ_normed = 0.879; CFI= 1.000; RMSEA < 0.001 with 90%CI = 0.000–0.082; SRMR = 0.009; AIC = 36.638. RNES-II: χ(2, n = 385) = 7.149; χ_normed = 3.574; CFI = 0.995; RMSEA = 0.082 with 90%CI = 0.023–0.151; SRMR = 0.015; AIC = 43.149. Both models demonstrated very good reliability (α = 0.84; 89 respectively). These findings indicate that the scales can be used to assess current and retrospective nature exposure. However, due to the removal of item one, the authors recommend that the scales be named the ‘intentional nature exposure scale’ and ‘retrospective intentional nature exposure scale’.

Associations between the Home Physical Environment and Children's Home-Based Physical Activity and Sitting

It is important to understand the correlates of children's physical activity (PA) and sitting at home, where children spend significant time. The home social environment has an important influence; however, much less is known about the home physical environment. Therefore, the study aimed to assess relationships between the physical environment and children's sitting and PA at home. In total, 235 child-parent dyads were included in the analyses. Children spent 67% of their time at home sitting. Linear regression analyses examined associations between physical home environmental factors obtained via an audit and children's (55% girl, 10.2 ± 0.7) objective PA and sitting at home. Following adjustment for socio-demographics and social environmental factors, an open plan living area (OPLA), musical instrument accessibility and availability, and perceived house size were negatively and positively associated, whereas media equipment accessibility and availability was positively and negatively associated with sitting and standing, respectively. Additionally, an OPLA was positively associated with total and moderate-to-vigorous PA. Furthermore, sitting breaks were positively associated with objective garden size and negatively associated with digital TV. The physical home environment may have an important influence on children's sitting, standing and PA at home; therefore, interventions that target this environment are needed.

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

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

Feasibility of Automated Cameras to Measure Screen Use in Adolescents

INTRODUCTION

The influence of screens and technology on adolescent well-being is controversial and there is a need to improve methods to measure these behaviors. This study examines the feasibility and acceptability of using automated wearable cameras to measure evening screen use in adolescents.

METHODS

A convenience sample of adolescents (aged 13-17 years, n=15) wore an automated camera for 3 evenings from 5:00pm to bedtime. The camera (Brinno TLC120) captured an image every 15 seconds. Fieldwork was completed between October and December 2017, and data analyzed in 2018. Feasibility was examined by quality of the captured images, wear time, and whether images could be coded in relation to contextual factors (e.g., type of screen and where screen use occurred). Acceptability was examined by participant compliance to the protocol and from an exit interview.

RESULTS

Data from 39 evenings were analyzed (41,734 images), with a median of 268 minutes per evening. The camera was worn for 78% of the evening on Day 1, declining to 51% on Day 3. Nearly half of the images contained a screen in active use (46%), most commonly phones (13.7%), TV (12.6%), and laptops (8.2%). Multiple screen use was evident in 5% of images. Within the exit interview, participants raised no major concerns about wearing the camera, and data loss because of deletions or privacy concerns was minimal (mean, 14 minutes, 6%).

CONCLUSIONS

Automated cameras offer a feasible, acceptable method of measuring prebedtime screen behavior, including environmental context and aspects of media multitasking in adolescents.

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

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

Location monitoring of physical activity and participation in community dwelling older people: a scoping review

Background: Community participation and physical activity are important for the health of older adults. This review aimed to identify studies which have measured physical activity and community participation in older adults using Global positioning systems.Materials and methods: This scoping review searched key databases using predetermined subject headings and keywords. Two independent reviewers selected studies based on a systematic procedure following current guidelines. Inclusion criteria for studies were: participants aged over 50 years living independently in the community that reported on physical activity and/or participation inclusive of physical and social activity, and including a quantitative measure of location. All searches were limited to English. The primary review question was; "What studies have monitored the location of physical activity in an older population?" with secondary enquiries investigating the types of global positioning system devices, barriers and facilitators for activity and community participation.Results: The search returned 3723 articles (following duplicate removal) and 45 met the inclusion criteria. Studies from 12 countries published over a 12-year period were included. Participants were mainly healthy (n = 23) followed by having a cognitive impairment (n = 10). There were 14 different global positioning system devices used, assessing a variety of outcomes (n = 24). Seventeen studies identified facilitators and barriers to participation and physical activity in an older population. The most common facilitators were safety, weather and access to multi-purpose facilities. The most common barriers were weather, safety, low income/high deprivation areas and use of motor vehicles.Conclusion: This scoping review identified a variety of locational monitoring of older people using global positioning devices. Global positioning systems are a valuable tool to obtain accurate activity locations of older people. There is a need for clear guidelines regarding the use of global positioning system devices and specified outcomes in primary research to enable comparison across studies.Implications for rehabilitationPhysical activity and community participation are vital for healthy ageing.The environment can act as a facilitator or barrier to physical activity and community participation for older adults.Interventions need to target facilitators (weather, safety, facility access and social components) to maximize physical activity and community participation in older people.Interventions should be designed to reduce the barriers (weather, safety, low income and motor vehicle dependency) that prevent older adults from actively participating in their community.

Can Wearable Cameras be Used to Validate School-Aged Children's Lifestyle Behaviours?

Wearable cameras combined with accelerometers have been used to estimate the accuracy of children's self-report of physical activity, health-related behaviours, and the contexts in which they occur. There were two aims to this study; the first was to validate questions regarding self-reported health and lifestyle behaviours in 9⁻11-year-old children using the child's health and activity tool (CHAT), an accelerometer and a wearable camera. Second, the study sought to evaluate ethical challenges associated with taking regular photographs using a wearable camera through interviews with children and their families. Fourteen children wore an autographer and hip-worn triaxial accelerometer for the waking hours of one school and one weekend day. For both of these days, children self-reported their behaviours chronologically and sequentially using the CHAT. Data were examined using limits of agreement and percentage agreement to verify if reference methods aligned with self-reported behaviours. Six parent⁻child dyads participated in interviews. Seven, five, and nine items demonstrated good, acceptable, and poor validity, respectively. This demonstrates that the accuracy of children's recall varies according to the behaviour or item being measured. This is the first study to trial the use of wearable cameras in assessing the concurrent validity of children's physical activity and behaviour recall, as almost all other studies have used parent proxy reports alongside accelerometers. Wearable cameras carry some ethical and technical challenges, which were examined in this study. Parents and children reported that the autographer was burdensome and in a few cases invaded privacy. This study demonstrates the importance of adhering to an ethical framework.

Associations of context-specific sitting time with markers of cardiometabolic risk in Australian adults

Background

High volumes of sitting time are associated with an elevated risk of type 2 diabetes and cardiovascular disease, and with adverse cardiometabolic risk profiles. However, previous studies have predominately evaluated only total sitting or television (TV) viewing time, limiting inferences about the specific cardiometabolic health impacts of sitting accumulated in different contexts. We examined associations of sitting time in four contexts with cardiometabolic risk biomarkers in Australian adults.

Methods

Participants (n = 3429; mean ± SD age 58 ± 10 years) were adults without clinically diagnosed diabetes or cardiovascular disease from the 2011–2012 Australian Diabetes, Obesity and Lifestyle (AusDiab) study. Multiple linear regressions examined associations of self-reported context-specific sitting time (occupational, transportation, TV-viewing and leisure-time computer use) with a clustered cardiometabolic risk score (CMR) and with individual cardiometabolic risk biomarkers (waist circumference, BMI, resting blood pressure, triglycerides, HDL- and LDL-cholesterol, and fasting and 2-h post-load plasma glucose).

Results

Higher CMR was significantly associated with greater TV-viewing and computer sitting time (b [95%CI] = 0.07 [0.04, 0.09] and 0.06 [0.03, 0.09]), and tended to be associated with higher occupational and transport sitting time (0.01 [− 0.01, 0.03] and 0.03 [− 0.00, 0.06]), after adjustment for potential confounders. Furthermore, keeping total sitting time constant, accruing sitting via TV-viewing and computer use was associated with significantly higher CMR (0.05 [0.02, 0.08] and 0.04 [0.01, 0.06]), accruing sitting in an occupational context was associated with significantly lower CMR (− 0.03 [− 0.05, − 0.01]), while no significant association was seen for transport sitting (0.00 [− 0.03, 0.04]). Results varied somewhat between the respective biomarkers; however, higher sitting time in each domain tended to be associated detrimentally with individual biomarkers except for fasting glucose (non-significant associations) and systolic blood pressure (a beneficial association was observed). Overall, associations were stronger for TV-viewing and computer use, and weaker for occupational sitting.

Conclusions

Higher context-specific sitting times tended to be detrimentally associated, albeit modestly, with CMR and several cardiometabolic risk biomarkers. There was some evidence suggesting that the context in which people sit is relevant above and beyond total sitting time. Methodological issues notwithstanding, these findings may assist in identifying priorities for sitting-reduction initiatives, in order to achieve optimal cardiometabolic health benefits.

Electronic supplementary material

The online version of this article (10.1186/s12966-018-0748-3) contains supplementary material, which is available to authorized users.

A Cardiopulmonary Monitoring System for Patient Transport Within Hospitals Using Mobile Internet of Things Technology: Observational Validation Study

Background

During intrahospital transport, adverse events are inevitable. Real-time monitoring can be helpful for preventing these events during intrahospital transport.

Objective

We attempted to determine the viability of risk signal detection using wearable devices and mobile apps during intrahospital transport. An alarm was sent to clinicians in the event of oxygen saturation below 90%, heart rate above 140 or below 60 beats per minute (bpm), and network errors. We validated the reliability of the risk signal transmitted over the network.

Methods

We used two wearable devices to monitor oxygen saturation and heart rate for 23 patients during intrahospital transport for diagnostic workup or rehabilitation. To determine the agreement between the devices, records collected every 4 seconds were matched and imputation was performed if no records were collected at the same time by both devices. We used intraclass correlation coefficients (ICC) to evaluate the relationships between the two devices.

Results

Data for 21 patients were delivered to the cloud over LTE, and data for two patients were delivered over Wi-Fi. Monitoring devices were used for 20 patients during intrahospital transport for diagnostic work up and for three patients during rehabilitation. Three patients using supplemental oxygen before the study were included. In our study, the ICC for the heart rate between the two devices was 0.940 (95% CI 0.939-0.942) and that of oxygen saturation was 0.719 (95% CI 0.711-0.727). Systemic error analyzed with Bland-Altman analysis was 0.428 for heart rate and –1.404 for oxygen saturation. During the study, 14 patients had 20 risk signals: nine signals for eight patients with less than 90% oxygen saturation, four for four patients with a heart rate of 60 bpm or less, and seven for five patients due to network error.

Conclusions

We developed a system that notifies the health care provider of the risk level of a patient during transportation using a wearable device and a mobile app. Although there were some problems such as missing values and network errors, this paper is meaningful in that the previously mentioned risk detection system was validated with actual patients.

Establishing a Demographic, Development and Environmental Geospatial Surveillance Platform in India: Planning and Implementation

BACKGROUND

Inadequate administrative health data, suboptimal public health infrastructure, rapid and unplanned urbanization, environmental degradation, and poor penetration of information technology make the tracking of health and well-being of populations and their social determinants in the developing countries challenging. Technology-integrated comprehensive surveillance platforms have the potential to overcome these gaps.

OBJECTIVE

This paper provides methodological insights into establishing a geographic information system (GIS)-integrated, comprehensive surveillance platform in rural North India, a resource-constrained setting.

METHODS

The International Clinical Epidemiology Network Trust International established a comprehensive SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in rural Palwal, a district in Haryana, North India. The surveillance platform evolved by adopting four major steps: (1) site preparation, (2) data construction, (3) data quality assurance, and (4) data update and maintenance system. Arc GIS 10.3 and QGIS 2.14 software were employed for geospatial data construction. Surveillance data architecture was built upon the geospatial land parcel datasets. Dedicated software (SOMAARTH-1) was developed for handling high volume of longitudinal datasets. The built infrastructure data pertaining to land use, water bodies, roads, railways, community trails, landmarks, water, sanitation and food environment, weather and air quality, and demographic characteristics were constructed in a relational manner.

RESULTS

The comprehensive surveillance platform encompassed a population of 0.2 million individuals residing in 51 villages over a land mass of 251.7 sq km having 32,662 households and 19,260 nonresidential features (cattle shed, shops, health, education, banking, religious institutions, etc). All land parcels were assigned georeferenced location identification numbers to enable space and time monitoring. Subdivision of villages into sectors helped identify socially homogenous community clusters (418/676, 61.8%, sectors). Water and hygiene parameters of the whole area were mapped on the GIS platform and quantified. Risk of physical exposure to harmful environment (poor water and sanitation indicators) was significantly associated with the caste of individual household (P=.001), and the path was mediated through the socioeconomic status and density of waste spots (liquid and solid) of the sector in which these households were located. Ground-truthing for ascertaining the land parcel level accuracies, community involvement in mapping exercise, and identification of small habitations not recorded in the administrative data were key learnings.

CONCLUSIONS

The SOMAARTH DDESS experience allowed us to document and explore dynamic relationships, associations, and pathways across multiple levels of the system (ie, individual, household, neighborhood, and village) through a geospatial interface. This could be used for characterization and monitoring of a wide range of proximal and distal determinants of health.

Unique Views on Obesity-Related Behaviors and Environments: Research Using Still and Video Images

OBJECTIVES

To document challenges to and benefits from research involving the use of images by capturing examples of such research to assess physical activity- or nutrition-related behaviors and/or environments.

METHODS

Researchers (i.e., key informants) using image capture in their research were identified through knowledge and networks of the authors of this paper and through literature search. Twenty-nine key informants completed a survey covering the type of research, source of images, and challenges and benefits experienced, developed specifically for this study.

RESULTS

Most respondents used still images in their research, with only 26.7% using video. Image sources were categorized as participant generated (n = 13; e.g., participants using smartphones for dietary assessment), researcher generated (n = 10; e.g., wearable cameras with automatic image capture), or curated from third parties (n = 7; e.g., Google Street View). Two of the major challenges that emerged included the need for automated processing of large datasets (58.8%) and participant recruitment/compliance (41.2%). Benefit-related themes included greater perspectives on obesity with increased data coverage (34.6%) and improved accuracy of behavior and environment assessment (34.6%).

CONCLUSIONS

Technological advances will support the increased use of images in the assessment of physical activity, nutrition behaviors, and environments. To advance this area of research, more effective collaborations are needed between health and computer scientists. In particular development of automated data extraction methods for diverse aspects of behavior, environment, and food characteristics are needed. Additionally, progress in standards for addressing ethical issues related to image capture for research purposes is critical.

Quantification of Free-Living Community Mobility in Healthy Older Adults Using Wearable Sensors

Introduction: Understanding determinants of community mobility disability is critical for developing interventions aimed at preventing or delaying disability in older adults. In an effort to understand these determinants, capturing and measuring community mobility has become a key factor. The objectives of this paper are to present and illustrate the signal processing workflow and outcomes that can be extracted from an activity and community mobility measurement approach based on GPS and accelerometer sensor data and 2) to explore the construct validity of the proposed measurement approach using data collected from healthy older adults in free-living conditions. Methods: Personal, functional impairment and environmental variables were obtained by self-report questionnaires in 75 healthy community-living older adults (mean age = 66 ± 7 years old) living on the island of Montreal, QC, Canada. Participants wore, for 14 days during waking hours on the hip, a data logger incorporating a GPS receiver with a 3-axis accelerometer. Time at home ratio (THR), Trips out (TO), Destinations (D), Maximal distance of destinations (MDD), Active time ratio (ATR), Steps (S), Distance in a vehicle (DV), Time in a vehicle (TV), Distance on foot (DF), Time on foot (TF), Ellipse area (EA), and Ellipse maximum distance (EMD) were extracted from the recordings. Results: After applying quality control criteria, the original data set was reduced from 75 to 54 participants (28% attrition). Results from the remaining sample show that under free-living conditions in healthy older adults, location, activity and community mobility outcomes vary across individuals and certain personal variables (age, income, living situation, professional status, vehicle access) have potential mitigating effects on these outcomes. There was a significant (yet small) relationship (rho < 0.40) between self-reported life space and MDD, DV, EA, and EMD. Conclusion: Wearability and usability of the devices used to capture free-living community mobility impact participant compliance and the quality of the data. The construct validity of the proposed approach appears promising but requires further studies directed at populations with mobility impairments.

Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study

Background

Mobile devices are increasingly used to collect location-based information from individuals about their physical activities, dietary intake, environmental exposures, and mental well-being. Such research, which typically uses wearable devices or mobile phones to track location, benefits from the growing availability of fine-grained data regarding human mobility. However, little is known about the comparative geospatial accuracy of such devices.

Objective

In this study, we compared the data quality of location information collected from two mobile devices that determine location in different ways—a global positioning system (GPS) watch and a mobile phone with Google’s Location History feature enabled.

Methods

A total of 21 chronically ill participants carried both devices, which generated digital traces of locations, for 28 days. A mobile phone–based brief ecological momentary assessment (EMA) survey asked participants to manually report their location at 4 random times throughout each day. Participants also took part in qualitative interviews and completed surveys twice during the study period in which they reviewed recent mobile phone and watch trace data to compare the devices’ trace data with their memory of their activities on those days. Trace data from the devices were compared on the basis of (1) missing data days, (2) reasons for missing data, (3) distance between the route data collected for matching day and the associated EMA survey locations, and (4) activity space total area and density surfaces.

Results

The watch resulted in a much higher proportion of missing data days (P<.001), with missing data explained by technical differences between the devices as well as participant behaviors. The mobile phone was significantly more accurate in detecting home locations (P=.004) and marginally more accurate (P=.07) for all types of locations combined. The watch data resulted in a smaller activity space area and more accurately recorded outdoor travel and recreation.

Conclusions

The most suitable mobile device for location-based health research depends on the particular study objectives. Furthermore, data generated from mobile devices, such as GPS phones and smartwatches, require careful analysis to ensure quality and completeness. Studies that seek precise measurement of outdoor activity and travel, such as measuring outdoor physical activity or exposure to localized environmental hazards, would benefit from the use of GPS devices. Conversely, studies that aim to account for time within buildings at home or work, or those that document visits to particular places (such as supermarkets, medical facilities, or fast food restaurants), would benefit from the greater precision demonstrated by the mobile phone in recording indoor activities.

Persuasive technology to support active and healthy ageing: An exploration of past, present, and future

The age of the population worldwide is rapidly increasing, bringing social and economic challenges. Persuasive technology can alleviate the burden on traditional healthcare services when used to support healthy behaviors, for instance in the prevention and treatment of chronic diseases. Additionally, healthy behaviors are key factors for active and healthy ageing by delaying or even reversing functional decline. In this manuscript, we present a multi-perspective analysis of technologies that can be used in the support of active and healthy ageing in the daily life. First, we take the perspective of physical and mental health, by focusing on the promotion of physical activity and emotional wellbeing. From a temporal perspective, we look at how technology evolved from past, present and future. The overview of the literature is structured in four main sections: (1) measurement of current behavior (monitoring), (2) analysis of the data gathered to derive meaningful information (analyzing & reasoning), (3) support the individual in the adoption or maintenance of a behavior (coaching), and (4) tools or interfaces that provide the information to the individual to stimulate the desired behavior (applications). Finally, we provide recommendations for the design, development and implementation of future technological innovations to support Active and Healthy Ageing in daily life.

A Novel Algorithm for Determining the Contextual Characteristics of Movement Behaviors by Combining Accelerometer Features and Wireless Beacons: Development and Implementation

BACKGROUND

Unfortunately, global efforts to promote "how much" physical activity people should be undertaking have been largely unsuccessful. Given the difficulty of achieving a sustained lifestyle behavior change, many scientists are reexamining their approaches. One such approach is to focus on understanding the context of the lifestyle behavior (ie, where, when, and with whom) with a view to identifying promising intervention targets.

OBJECTIVE

The aim of this study was to develop and implement an innovative algorithm to determine "where" physical activity occurs using proximity sensors coupled with a widely used physical activity monitor.

METHODS

A total of 19 Bluetooth beacons were placed in fixed locations within a multilevel, mixed-use building. In addition, 4 receiver-mode sensors were fitted to the wrists of a roving technician who moved throughout the building. The experiment was divided into 4 trials with different walking speeds and dwelling times. The data were analyzed using an original and innovative algorithm based on graph generation and Bayesian filters.

RESULTS

Linear regression models revealed significant correlations between beacon-derived location and ground-truth tracking time, with intraclass correlations suggesting a high goodness of fit (R²=.9780). The algorithm reliably predicted indoor location, and the robustness of the algorithm improved with a longer dwelling time (>100 s; error <10%, R²=.9775). Increased error was observed for transitions between areas due to the device sampling rate, currently limited to 0.1 Hz by the manufacturer.

CONCLUSIONS

This study shows that our algorithm can accurately predict the location of an individual within an indoor environment. This novel implementation of "context sensing" will facilitate a wealth of new research questions on promoting healthy behavior change, the optimization of patient care, and efficient health care planning (eg, patient-clinician flow, patient-clinician interaction).

Revealed Preference Methods for Studying Bicycle Route Choice-A Systematic Review

One fundamental aspect of promoting utilitarian bicycle use involves making modifications to the built environment to improve the safety, efficiency and enjoyability of cycling. Revealed preference data on bicycle route choice can assist greatly in understanding the actual behaviour of a highly heterogeneous group of users, which in turn assists the prioritisation of infrastructure or other built environment initiatives. This systematic review seeks to compare the relative strengths and weaknesses of the empirical approaches for evaluating whole journey route choices of bicyclists. Two electronic databases were systematically searched for a selection of keywords pertaining to bicycle and route choice. In total seven families of methods are identified: GPS devices, smartphone applications, crowdsourcing, participant-recalled routes, accompanied journeys, egocentric cameras and virtual reality. The study illustrates a trade-off in the quality of data obtainable and the average number of participants. Future additional methods could include dockless bikeshare, multiple camera solutions using computer vision and immersive bicycle simulator environments.

Improving interpretation of publically reported statistics on health and healthcare: the Figure Interpretation Assessment Tool (FIAT-Health)

BACKGROUND

Policy-makers, managers, scientists, patients and the general public are confronted daily with figures on health and healthcare through public reporting in newspapers, webpages and press releases. However, information on the key characteristics of these figures necessary for their correct interpretation is often not adequately communicated, which can lead to misinterpretation and misinformed decision-making. The objective of this research was to map the key characteristics relevant to the interpretation of figures on health and healthcare, and to develop a Figure Interpretation Assessment Tool-Health (FIAT-Health) through which figures on health and healthcare can be systematically assessed, allowing for a better interpretation of these figures.

METHODS

The abovementioned key characteristics of figures on health and healthcare were identified through systematic expert consultations in the Netherlands on four topic categories of figures, namely morbidity, healthcare expenditure, healthcare outcomes and lifestyle. The identified characteristics were used as a frame for the development of the FIAT-Health. Development of the tool and its content was supported and validated through regular review by a sounding board of potential users.

RESULTS

Identified characteristics relevant for the interpretation of figures in the four categories relate to the figures' origin, credibility, expression, subject matter, population and geographical focus, time period, and underlying data collection methods. The characteristics were translated into a set of 13 dichotomous and 4-point Likert scale questions constituting the FIAT-Health, and two final assessment statements. Users of the FIAT-Health were provided with a summary overview of their answers to support a final assessment of the correctness of a figure and the appropriateness of its reporting.

CONCLUSIONS

FIAT-Health can support policy-makers, managers, scientists, patients and the general public to systematically assess the quality of publicly reported figures on health and healthcare. It also has the potential to support the producers of health and healthcare data in clearly communicating their data to different audiences. Future research should focus on the further validation of the tool in practice.

Using Bluetooth proximity sensing to determine where office workers spend time at work

Background

Most wearable devices that measure movement in workplaces cannot determine the context in which people spend time. This study examined the accuracy of Bluetooth sensing (10-second intervals) via the ActiGraph GT9X Link monitor to determine location in an office setting, using two simple, bespoke algorithms.

Methods

For one work day (mean±SD 6.2±1.1 hours), 30 office workers (30% men, aged 38±11 years) simultaneously wore chest-mounted cameras (video recording) and Bluetooth-enabled monitors (initialised as receivers) on the wrist and thigh. Additional monitors (initialised as beacons) were placed in the entry, kitchen, photocopy room, corridors, and the wearer’s office. Firstly, participant presence/absence at each location was predicted from the presence/absence of signals at that location (ignoring all other signals). Secondly, using the information gathered at multiple locations simultaneously, a simple heuristic model was used to predict at which location the participant was present. The Bluetooth-determined location for each algorithm was tested against the camera in terms of F-scores.

Results

When considering locations individually, the accuracy obtained was excellent in the office (F-score = 0.98 and 0.97 for thigh and wrist positions) but poor in other locations (F-score = 0.04 to 0.36), stemming primarily from a high false positive rate. The multi-location algorithm exhibited high accuracy for the office location (F-score = 0.97 for both wear positions). It also improved the F-scores obtained in the remaining locations, but not always to levels indicating good accuracy (e.g., F-score for photocopy room ≈0.1 in both wear positions).

Conclusions

The Bluetooth signalling function shows promise for determining where workers spend most of their time (i.e., their office). Placing beacons in multiple locations and using a rule-based decision model improved classification accuracy; however, for workplace locations visited infrequently or with considerable movement, accuracy was below desirable levels. Further development of algorithms is warranted.

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.

Brain Activation in Response to Personalized Behavioral and Physiological Feedback From Self-Monitoring Technology: Pilot Study

Background

The recent surge in commercially available wearable technology has allowed real-time self-monitoring of behavior (eg, physical activity) and physiology (eg, glucose levels). However, there is limited neuroimaging work (ie, functional magnetic resonance imaging [fMRI]) to identify how people’s brains respond to receiving this personalized health feedback and how this impacts subsequent behavior.

Objective

Identify regions of the brain activated and examine associations between activation and behavior.

Methods

This was a pilot study to assess physical activity, sedentary time, and glucose levels over 14 days in 33 adults (aged 30 to 60 years). Extracted accelerometry, inclinometry, and interstitial glucose data informed the construction of personalized feedback messages (eg, average number of steps per day). These messages were subsequently presented visually to participants during fMRI. Participant physical activity levels and sedentary time were assessed again for 8 days following exposure to this personalized feedback.

Results

Independent tests identified significant activations within the prefrontal cortex in response to glucose feedback compared with behavioral feedback (P<.001). Reductions in mean sedentary time (589.0 vs 560.0 minutes per day, P=.014) were observed. Activation in the subgyral area had a moderate correlation with minutes of moderate-to-vigorous physical activity (r=0.392, P=.043).

Conclusion

Presenting personalized glucose feedback resulted in significantly more brain activation when compared with behavior. Participants reduced time spent sedentary at follow-up. Research on deploying behavioral and physiological feedback warrants further investigation.

Exploring the Potential of a Wearable Camera to Examine the Early Obesogenic Home Environment: Comparison of SenseCam Images to the Home Environment Interview

BACKGROUND

The obesogenic home environment is usually examined via self-report, and objective measures are required.

OBJECTIVE

This study explored whether the wearable camera SenseCam can be used to examine the early obesogenic home environment and whether it is useful for validation of self-report measures.

METHODS

A total of 15 primary caregivers of young children (mean age of child 4 years) completed the Home Environment Interview (HEI). Around 12 days after the HEI, participants wore the SenseCam at home for 4 days. A semistructured interview assessed participants' experience of wearing the SenseCam. Intraclass correlation coefficients (ICCs), percent agreement, and kappa statistics were used as validity estimates for 54 home environment features.

RESULTS

Wearing the SenseCam was generally acceptable to those who participated. The SenseCam captured all 54 HEI features but with varying detail; 36 features (67%) had satisfactory validity (ICC or kappa ≥0.40; percent agreement ≥80 where kappa could not be calculated). Validity was good or excellent (ICC or kappa ≥0.60) for fresh fruit and vegetable availability, fresh vegetable variety, display of food and drink (except sweet snacks), family meals, child eating lunch or dinner while watching TV, garden and play equipment, the number of TVs and DVD players, and media equipment in the child's bedroom. Validity was poor (ICC or kappa <0.40) for tinned and frozen vegetable availability and variety, and sweet snack availability.

CONCLUSIONS

The SenseCam has the potential to objectively examine and validate multiple aspects of the obesogenic home environment. Further research should aim to replicate the findings in a larger, representative sample.

Sensor Fusion to Infer Locations of Standing and Reaching Within the Home in Incomplete Spinal Cord Injury

OBJECTIVE

The objective of rehabilitation after spinal cord injury is to enable successful function in everyday life and independence at home. Clinical tests can assess whether patients are able to execute functional movements but are limited in assessing such information at home. A prototype system is developed that detects stand-to-reach activities, a movement with important functional implications, at multiple locations within a mock kitchen.

DESIGN

Ten individuals with incomplete spinal cord injuries performed a sequence of standing and reaching tasks. The system monitored their movements by combining two sources of information: a triaxial accelerometer, placed on the subject's thigh, detected sitting or standing, and a network of radio frequency tags, wirelessly connected to a wrist-worn device, detected reaching at three locations. A threshold-based algorithm detected execution of the combined tasks and accuracy was measured by the number of correctly identified events.

RESULTS

The system was shown to have an average accuracy of 98% for inferring when individuals performed stand-to-reach activities at each tag location within the same room.

CONCLUSIONS

The combination of accelerometry and tags yielded accurate assessments of functional stand-to-reach activities within a home environment. Optimization of this technology could simplify patient compliance and allow clinicians to assess functional home activities.

Do unfavourable alcohol, smoking, nutrition and physical activity predict sustained leisure time sedentary behaviour? A population-based cohort study

Comparing lifestyle of people remaining sedentary during longer periods of their life with those favourably changing their behaviour can provide cues to optimize interventions targeting sedentary behaviour. The objective of this study was to determine lifestyle predictors of sustained leisure time sedentary behaviour and assess whether these predictors were dependent on gender, age, socioeconomic position and occupational sedentary behaviour. Data from a large longitudinal population-based cohort of adults (aged 18-97years) in Stockholm responding to public health surveys in 2010 and 2014 were analysed (n=49,133). Leisure time sedentary behaviour was defined as >3h per day of leisure sitting time e.g. watching TV, reading or using tablet. Individuals classified as sedentary at baseline (n=9562) were subsequently categorized as remaining sedentary (n=6357) or reduced sedentary behaviour (n=3205) at follow-up. Lifestyle predictors were unfavourable alcohol consumption, smoking, nutrition, and physical activity. Odds ratios (OR) and corresponding 95% Confidence Intervals (CI) were calculated, adjusting for potential confounders. Unfavourable alcohol consumption (OR=1.22, CI:1.11-1.34), unfavourable candy- or cake consumption (OR=1.15, CI:1.05-1.25), and unfavourable physical activity in different contexts were found to predict sustained sedentary behaviour, with negligible differences according to gender, age, socioeconomic position and occupational sedentary behaviour. People with unfavourable lifestyle profiles regarding alcohol, sweets, or physical activity are more likely to remain sedentary compared to sedentary persons with healthier lifestyle. The impact of combining interventions to reduce leisure time sedentary behaviour with reducing alcohol drinking, sweet consumption and increasing physical activity should be tested as a promising strategy for behavioural modification.

A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future.

Novel technology to help understand the context of physical activity and sedentary behaviour

When used in large, national surveillance programmes, objective measurement tools provide prevalence estimates of low physical activity guideline compliance and high amounts of sedentary time. There are undoubtedly a plethora of reasons for this but one possible contributing factor is the current lack of behavioural context offered by accelerometers and posture sensors. Context includes information such as where the behaviour occurs, the type of activity being performed and is vital in allowing greater refinement of intervention strategies. Novel technologies are emerging with the potential to provide this information. Example data from three ongoing studies is used to illustrate the utility of these technologies. Study one assesses the concurrent validity of electrical energy monitoring and wearable cameras as measures of television viewing. This study found that on average the television is switched on for 202 min d^-1 but is visible in just 90 min of wearable camera images with a further 52 min where the participant is in their living room but the television is not visible in the image. Study two utilises indoor location monitoring to assess where older adult care home residents accumulate their sedentary time. This study found that residents were highly sedentary (sitting for an average of 720 min d^-1) and spent the majority of their time in their own rooms with more time spent in communal areas in the morning than in the afternoon. Lastly, study three discusses the use of proximity sensors to quantify exposure to a height adjustable desk. These studies are example applications of this technology, with many other technologies available and applications possible. The adoption of these technologies will provide researchers with a more complete understanding of the behaviour than has previously been available.

Survalytics: An Open-Source Cloud-Integrated Experience Sampling, Survey, and Analytics and Metadata Collection Module for Android Operating System Apps

BACKGROUND

We describe here Survalytics, a software module designed to address two broad areas of need. The first area is in the domain of surveys and app analytics: developers of mobile apps in both academic and commercial environments require information about their users, as well as how the apps are being used, to understand who their users are and how to optimally approach app development. The second area of need is in the field of ecological momentary assessment, also referred to as experience sampling: researchers in a wide variety of fields, spanning from the social sciences to psychology to clinical medicine, would like to be able to capture daily or even more frequent data from research subjects while in their natural environment.

OBJECTIVE

Survalytics is an open-source solution for the collection of survey responses as well as arbitrary analytic metadata from users of Android operating system apps.

METHODS

Surveys may be administered in any combination of one-time questions and ongoing questions. The module may be deployed as a stand-alone app for experience sampling purposes or as an add-on to existing apps. The module takes advantage of free-tier NoSQL cloud database management offered by the Amazon Web Services DynamoDB platform to package a secure, flexible, extensible data collection module. DynamoDB is capable of Health Insurance Portability and Accountability Act compliant storage of personal health information.

RESULTS

The provided example app may be used without modification for a basic experience sampling project, and we provide example questions for daily collection of blood glucose data from study subjects.

CONCLUSIONS

The module will help researchers in a wide variety of fields rapidly develop tailor-made Android apps for a variety of data collection purposes.

Validity of a multi-context sitting questionnaire across demographically diverse population groups: AusDiab3

Background

Sitting time questionnaires have largely been validated in small convenience samples. The validity of this multi-context sitting questionnaire against an accurate measure of sitting time is reported in a large demographically diverse sample allowing assessment of validity in varied demographic subgroups.

Methods

A subgroup of participants of the third wave of the Australian Diabetes, Obesity, and Lifestyle (AusDiab3) study wore activPAL3™ monitors (7 days, 24 hours/day protocol) and reported their sitting time for work, travel, television viewing, leisure computer use and “other” purposes, on weekdays and weekend days (n = 700, age 36-89 years, 45 % men). Correlations (Pearson’s r; Spearman’s ρ) of the self-report measures (the composite total, contextual measures and items) with monitor-assessed sitting time were assessed in the whole sample and separately in socio-demographic subgroups. Agreement was assessed using Bland-Altman plots.

Results

The composite total had a correlation with monitor-assessed sitting time of r = 0.46 (95 % confidence interval [CI]: 0.40, 0.52); this correlation did not vary significantly between demographic subgroups (all >0.4). The contextual measure most strongly correlated with monitor-assessed sitting time was work (ρ = 0.25, 95 % CI: 0.17, 0.31), followed by television viewing (ρ = 0.16, 95 % CI: 0.09, 0.24). Agreement of the composite total with monitored sitting time was poor, with a positive bias (B = 0.53, SE 0.04, p < 0.001) and wide limits of agreement (±4.32 h).

Conclusions

This multi-context questionnaire provides a total sitting time measure that ranks participants well for the purposes of assessing health associations but has limited accuracy relative to activPAL-assessed sitting time. Findings did not differ in demographic subgroups.

Electronic supplementary material

The online version of this article (doi:10.1186/s12966-015-0309-y) contains supplementary material, which is available to authorized users.