Dataset Information

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

ABSTRACT: The location in which physical activity and sedentary behavior are performed can provide valuable behavioral information, both in isolation and synergistically with other areas of physical activity and sedentary behavior research. Global positioning systems (GPS) have been used in physical activity research to identify outdoor location; however, while GPS can receive signals in certain indoor environments, it is not able to provide room- or subroom-level location. On average, adults spend a high proportion of their time indoors. A measure of indoor location would, therefore, provide valuable behavioral information.This systematic review sought to identify and critique technology which has been or could be used to assess the location of physical activity and sedentary behavior.To identify published research papers, four electronic databases were searched using key terms built around behavior, technology, and location. To be eligible for inclusion, papers were required to be published in English and describe a wearable or portable technology or device capable of measuring location. Searches were performed up to February 4, 2015. This was supplemented by backward and forward reference searching. In an attempt to include novel devices which may not yet have made their way into the published research, searches were also performed using three Internet search engines. Specialized software was used to download search results and thus mitigate the potential pitfalls of changing search algorithms.A total of 188 research papers met the inclusion criteria. Global positioning systems were the most widely used location technology in the published research, followed by wearable cameras, and radio-frequency identification. Internet search engines identified 81 global positioning systems, 35 real-time locating systems, and 21 wearable cameras. Real-time locating systems determine the indoor location of a wearable tag via the known location of reference nodes. Although the type of reference node and location determination method varies between manufacturers, Wi-Fi appears to be the most popular method.The addition of location information to existing measures of physical activity and sedentary behavior will provide important behavioral information.

SUBMITTER: Loveday A

PROVIDER: S-EPMC4705371 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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

Loveday Adam A Sherar Lauren B LB Sanders James P JP Sanderson Paul W PW Esliger Dale W DW

Journal of medical Internet research 20150805 8

<h4>Background</h4>The location in which physical activity and sedentary behavior are performed can provide valuable behavioral information, both in isolation and synergistically with other areas of physical activity and sedentary behavior research. Global positioning systems (GPS) have been used in physical activity research to identify outdoor location; however, while GPS can receive signals in certain indoor environments, it is not able to provide room- or subroom-level location. On average, ...[more]

PMID: 26245157

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Project description:BackgroundSouth Asians are one of the fastest growing ethnic groups in western countries with a high incidence of chronic diseases like metabolic syndrome and cardiovascular disease occurring at younger ages and lower body weight compared with white Europeans. Physically active lifestyle and reduced sedentary time are modifiable risk factors that can decrease burden of chronic diseases. Population-level surveys based on self-report show South Asians engage in low levels of physical activity. Because of known limitations with self-report data, we aimed to synthesize available evidence to generate a physical activity /sedentary time profile of South Asians from studies using accelerometry.MethodsWe systematically searched Medline, EMBASE, CINAHL, PsycINFO, and SportDiscus. We included studies applying accelerometry to measure physical activity /sedentary time under free-living. Studies with an exclusive focus on drugs or including participants with health conditions/physical disability, and special populations (athletes/pregnant women) were excluded. Two authors independently adjudicated inclusion of citations at title/abstract and full text. We applied a standardized data abstraction form to extract relevant data. We evaluated methodological quality using Newcastle Ottawa Quality Assessment Scale. Due to variability and inconsistencies in measurement and reporting of physical activity /sedentary time, we only provide a narrative synthesis.FindingsWe identified only 14 studies(n = 1,338). Despite using similar accelerometry assumptions, we noted variability in reported outcomes for physical activity and sedentary time. Sedentary time ranged from 482(98) to 587 min/day. Mean light physical activity ranged from 211.69(67. 38) to 574(227) min/day. Moderate to vigorous physical activity among South Asian women ranged from 17-41 min/day and among men, 32-43 min/day.ConclusionSouth Asians exhibited higher levels of physical activity when compared to the Canadian population level survey but not when compared to the American population level survey. Overall, fewer studies, and small sample sizes led to considerable variability limiting any effective comparisons. Results highlight the importance of conducting methodologically robust studies based on random sampling to advance the field, and to capture true levels of sedentary time and physical activity in the South Asian population.

Project description:Background:Dialysis patients have reduced moderate to vigorous physical activity, and light physical activity. This has been shown in self-reported surveys and objective accelerometer studies. Less attention has been directed toward sedentary behavior, which is characterized by low energy expenditure (?1.5 metabolic equivalents). Furthermore, locations where physical activity and sedentary behavior occur are largely unknown for dialysis patients. Objectives:The objectives of this study were (1) to determine the minutes per day of moderate to vigorous physical activity, light physical activity, and sedentary behavior for hemodialysis patients; (2) to describe differences in moderate to vigorous physical activity, light physical activity, and sedentary behavior comparing dialysis versus nondialysis days; and (3) to describe the locations where moderate to vigorous physical activity, light physical activity, and sedentary behavior occur using global positioning system (GPS) data. Design:Cross-sectional study. Setting:The study was performed at a tertiary care hospital in Nova Scotia, Canada. Patients:A total of 50 adult in-center hemodialysis patients consented to the study. Measurements:Physical activity and sedentary behavior were measured with an Actigraph-GT3X accelerometer. Location was determined using a Qstarz BT-Q1000X GPS receiver. Methods:Minutes of daily activity were described as was percentage of wear time for each activity level across different locations during waking hours. Physical activity intensity, quantity, and location were also analyzed according to dialysis vs nondialysis days. Results:Forty-three patients met requirements for accelerometer analysis, of whom 42 had GPS data. Median wear time was 836.5 min/day (interquartile range [IQR]: 788.3-918.3). Median minutes of daily wear time spent in sedentary behavior, light physical activity, and moderate to vigorous physical activity was 636 minutes (IQR: 594.1-730.1), 178 minutes (IQR: 144-222.1), and 1.6 minutes (IQR: 0.6-7.7), respectively. Proportion of daily wear time spent in sedentary behavior, light physical activity, and moderate to vigorous physical activity was 78.4% (IQR: 70.7-84.0), 21.5% (IQR: 16.0-26.9), and 0.2% (IQR: 0.1-1.1), respectively. Home was the dominant location for total linked accelerometer-GPS time (59.4%, IQR: 46.9-69.5) as well as for each prespecified level of activity. Significantly more sedentary behavior and less light physical activity occurred on dialysis days compared with nondialysis days (P ? .01, respectively). Moderate to vigorous physical activity did not differ significantly between dialysis and nondialysis days. Limitations:Small sample size from a single academic center may limit generalizability. Difficult to engage population as less than half of eligible dialysis patients provided consent. Physical activity may have been underestimated as devices were not worn for all waking hours or aquatic activities, and hip-based accelerometers may not capture stationary exercise. Conclusions:Ambulatory, in-center hemodialysis patients exhibit substantial sedentary behavior and minimal physical activity across a limited range of locations. Given the sedentary tendencies of this population, focus should be directed on increasing physical activity at any location frequented. Home-based exercise programs may serve as a potential adjunct to established intradialytic-based therapies given the amount of time spent in the home environment.

Project description:Autism Spectrum Disorder affects up to 2.5% of children and is associated with harmful health outcomes (e.g. obesity). Low levels of physical activity and high levels of sedentary behaviors may contribute to harmful health outcomes. To systematically review the prevalence and correlates of physical activity and sedentary behaviors in children with Autism Spectrum Disorder, electronic databases (PsycINFO, SPORTDiscus, EMBASE, Medline) were searched from inception to November 2015. The review was registered with PROSPERO (CRD42014013849). Peer-reviewed, English language studies were included. Two reviewers screened potentially relevant articles. Outcomes of interest were physical activity and sedentary behaviour levels and their potential correlates. Data were collected and analysed in 2015. Of 35 included studies, 15 reported physical activity prevalence, 10 reported physical activity correlates, 18 reported sedentary behavior prevalence, and 10 reported sedentary behavior correlates. Estimates of children's physical activity (34-166 mins/day, average 86 mins/day) and sedentary behavior (126-558 mins/day in screen time, average 271 mins/day; 428-750 mins/day in total sedentary behavior, average 479 mins/day) varied across studies. Age was consistently inversely associated, and sex inconsistently associated with physical activity. Age and sex were inconsistently associated with sedentary behavior. Sample sizes were small. All but one of the studies were classified as having high risk of bias. Few correlates have been reported in sufficient studies to provide overall estimates of associations. Potential correlates in the physical environment remain largely unexamined. This review highlights varying levels of physical activity and sedentary behavior in children with Autism Spectrum Disorder. Research is needed to consistently identify the correlates of these behaviors. There is a critical need for interventions to support healthy levels of these behaviors.

Project description:BackgroundThe effectiveness of digital health interventions is commonly assumed to be related to the level of user engagement with the digital health intervention, including measures of both digital health intervention use and users' subjective experience. However, little is known about the relationships between the measures of digital health intervention engagement and physical activity or sedentary behavior.ObjectiveThis study aims to describe the direction and strength of the association between engagement with digital health interventions and physical activity or sedentary behavior in adults and explore whether the direction of association of digital health intervention engagement with physical activity or sedentary behavior varies with the type of engagement with the digital health intervention (ie, subjective experience, activities completed, time, and logins).MethodsFour databases were searched from inception to December 2019. Grey literature and reference lists of key systematic reviews and journals were also searched. Studies were eligible for inclusion if they examined a quantitative association between a measure of engagement with a digital health intervention targeting physical activity and a measure of physical activity or sedentary behavior in adults (aged ≥18 years). Studies that purposely sampled or recruited individuals on the basis of pre-existing health-related conditions were excluded. In addition, studies were excluded if the individual engaging with the digital health intervention was not the target of the physical activity intervention, the study had a non-digital health intervention component, or the digital health interventions targeted multiple health behaviors. A random effects meta-analysis and direction of association vote counting (for studies not included in meta-analysis) were used to address objective 1. Objective 2 used vote counting on the direction of the association.ResultsOverall, 10,653 unique citations were identified and 375 full texts were reviewed. Of these, 19 studies (26 associations) were included in the review, with no studies reporting a measure of sedentary behavior. A meta-analysis of 11 studies indicated a small statistically significant positive association between digital health engagement (based on all usage measures) and physical activity (0.08, 95% CI 0.01-0.14, SD 0.11). Heterogeneity was high, with 77% of the variation in the point estimates explained by the between-study heterogeneity. Vote counting indicated that the relationship between physical activity and digital health intervention engagement was consistently positive for three measures: subjective experience measures (2 of 3 associations), activities completed (5 of 8 associations), and logins (6 of 10 associations). However, the direction of associations between physical activity and time-based measures of usage (time spent using the intervention) were mixed (2 of 5 associations supported the hypothesis, 2 were inconclusive, and 1 rejected the hypothesis).ConclusionsThe findings indicate a weak but consistent positive association between engagement with a physical activity digital health intervention and physical activity outcomes. No studies have targeted sedentary behavior outcomes. The findings were consistent across most constructs of engagement; however, the associations were weak.

Project description:BackgroundTraditional psychological theories are inadequate to fully leverage the potential of smartphones and improve the effectiveness of physical activity (PA) and sedentary behavior (SB) change interventions. Future interventions need to consider dynamic models taken from other disciplines, such as engineering (eg, control systems). The extent to which such dynamic models have been incorporated in the development of interventions for PA and SB remains unclear.ObjectiveThis review aims to quantify the number of studies that have used dynamic models to develop smartphone-based interventions to promote PA and reduce SB, describe their features, and evaluate their effectiveness where possible.MethodsDatabases including PubMed, PsycINFO, IEEE Xplore, Cochrane, and Scopus were searched from inception to May 15, 2019, using terms related to mobile health, dynamic models, SB, and PA. The included studies involved the following: PA or SB interventions involving human adults; either developed or evaluated integrated psychological theory with dynamic theories; used smartphones for the intervention delivery; the interventions were adaptive or just-in-time adaptive; included randomized controlled trials (RCTs), pilot RCTs, quasi-experimental, and pre-post study designs; and were published from 2000 onward. Outcomes included general characteristics, dynamic models, theory or construct integration, and measured SB and PA behaviors. Data were synthesized narratively. There was limited scope for meta-analysis because of the variability in the study results.ResultsA total of 1087 publications were screened, with 11 publications describing 8 studies included in the review. All studies targeted PA; 4 also included SB. Social cognitive theory was the major psychological theory upon which the studies were based. Behavioral intervention technology, control systems, computational agent model, exploit-explore strategy, behavioral analytic algorithm, and dynamic decision network were the dynamic models used in the included studies. The effectiveness of quasi-experimental studies involved reduced SB (1 study; P=.08), increased light PA (1 study; P=.002), walking steps (2 studies; P=.06 and P<.001), walking time (1 study; P=.02), moderate-to-vigorous PA (2 studies; P=.08 and P=.81), and nonwalking exercise time (1 study; P=.31). RCT studies showed increased walking steps (1 study; P=.003) and walking time (1 study; P=.06). To measure activity, 5 studies used built-in smartphone sensors (ie, accelerometers), 3 of which used the phone's GPS, and 3 studies used wearable activity trackers.ConclusionsTo our knowledge, this is the first systematic review to report on smartphone-based studies to reduce SB and promote PA with a focus on integrated dynamic models. These findings highlight the scarcity of dynamic model-based smartphone studies to reduce SB or promote PA. The limited number of studies that incorporate these models shows promising findings. Future research is required to assess the effectiveness of dynamic models in promoting PA and reducing SB.Trial registrationInternational Prospective Register of Systematic Reviews (PROSPERO) CRD42020139350; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=139350.

Dataset Information

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

Publications

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

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