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Large-scale physical activity data reveal worldwide activity inequality
Abstract
To be able to curb the global pandemic of physical inactivity and the associated 5.3 million deaths per year, we need to understand the basic principles that govern physical activity. However, there is a lack of large-scale measurements of physical activity patterns across free-living populations worldwide. Here we leverage the wide usage of smartphones with built-in accelerometry to measure physical activity at the global scale. We study a dataset consisting of 68 million days of physical activity for 717,527 people, giving us a window into activity in 111 countries across the globe. We find inequality in how activity is distributed within countries and that this inequality is a better predictor of obesity prevalence in the population than average activity volume. Reduced activity in females contributes to a large portion of the observed activity inequality. Aspects of the built environment, such as the walkability of a city, are associated with a smaller gender gap in activity and lower activity inequality. In more walkable cities, activity is greater throughout the day and throughout the week, across age, gender, and body mass index (BMI) groups, with the greatest increases in activity found for females. Our findings have implications for global public health policy and urban planning and highlight the role of activity inequality and the built environment in improving physical activity and health.
Supplementary resource (1)
Data
July 2017
... Physical activity data, such as step counts, are among the most common digital biomarkers, and constitute the majority of the extracted MyHeart Counts data, with 4920 users of step tracking compared to 626 users of sleep tracking. However, inequalities in physical activity are well-documented in literature [4,49,76]. Althoff et al. [4] reveal variability in physical activity worldwide, where reduced activity in females explains a large portion of the observed activity inequality. Overall, the World Health Organization reports that "girls, women, older adults, people of low socioeconomic position, people with disabilities and chronic diseases, marginalized populations, indigenous people and the inhabitants of rural communities often have less access to safe, accessible, affordable and appropriate spaces and places in which to be physically active" [76]. ...
... However, inequalities in physical activity are well-documented in literature [4,49,76]. Althoff et al. [4] reveal variability in physical activity worldwide, where reduced activity in females explains a large portion of the observed activity inequality. Overall, the World Health Organization reports that "girls, women, older adults, people of low socioeconomic position, people with disabilities and chronic diseases, marginalized populations, indigenous people and the inhabitants of rural communities often have less access to safe, accessible, affordable and appropriate spaces and places in which to be physically active" [76]. ...
... Hence our findings might not be directly applicable across protected attributes and geographical contexts. Finally, while activity tracking is the most common functionality in PI systems and prior work has highlighted worldwide physical activity inequality [4], our dataset does not capture more advanced health features, such as heart monitoring and fertility tracking. Still, it is important to recognize that different use cases might incorporate different biases. ...
Personal informatics (PI) systems, powered by smartphones and wearables, enable people to lead healthier lifestyles by providing meaningful and actionable insights that break down barriers between users and their health information. Today, such systems are used by billions of users for monitoring not only physical activity and sleep but also vital signs and women's and heart health, among others. Despite their widespread usage, the processing of sensitive PI data may suffer from biases, which may entail practical and ethical implications. In this work, we present the first comprehensive empirical and analytical study of bias in PI systems, including biases in raw data and in the entire machine learning life cycle. We use the most detailed framework to date for exploring the different sources of bias and find that biases exist both in the data generation and the model learning and implementation streams. According to our results, the most affected minority groups are users with health issues, such as diabetes, joint issues, and hypertension, and female users, whose data biases are propagated or even amplified by learning models, while intersectional biases can also be observed.
... Therefore, it is an important public health problem [1,6,7]. In Europe and worldwide, according to recent surveys, a significant proportion of young people and adults have sedentary or low physical activity habits, and therefore do not comply with WHO [1] recommendations [8][9][10]. ...
... These data are of concern in adolescents [11], since it has been observed that the behavior patterns related to physical activity in adolescents are associated with those that they will later maintain in adulthood [4,12]. In response to this alarming global pandemic [10], WHO has suggested monitoring levels of physical activity and implementing strategies that can help increase these levels [2]. For this it is necessary to take into account a series of determinants or factors that influence the practice of exercise, which can be considered as barriers, depending on age and gender, and others such as professional or domestic activity, loads academic, smoking habit, important vital moment, climatic conditions, among others [3,[13][14][15][16][17]. ...
... Therefore, it stands out that in late adolescence a good habit of physical activity is maintained. Although worldwide surveys indicate a low level of physical activity practice [1,2,6,[8][9][10]. It is noteworthy that the female gender practices less physical activity, and as indicated by other surveys they have more sedentary habits than men [4,8,9,[18][19][20]. ...
... Shameli et al. [23] study how competitions affect physical activity using a dataset of competitions within the Argus smartphone app. Althoff et al. [3] study activity distribution from smartphones of 717,527 people across 111 countries. Using the same dataset, Pierson et al. [22] use Cyclic Hidden Markov Models to detect and model activity cycles. ...
... Example 4.1: Let ⃗ s raw = (5, 0, 0, 2, 3, 4, 3, 0), w = 3, stat = {sum, mean}. We split ⃗ s raw into three subvectors (5, 0, 0), (2,3,4) and (3, 0). Thus ⃗ s {sum,mean} = (sum(5, 0, 0), mean(5, 0, 0), sum(2, 3, 4), mean(2, 3, 4), sum(3, 0), mean(3, 0)) = (5, 1.67, 9, 3, 3, 1.5). ...
... Example 4.2: For ⃗ s raw = (5, 0, 0, 2, 3, 4, 3, 0), w = 3, b = 3, max steps = 6. We split ⃗ s raw into three subvectors (5, 0, 0), (2,3,4) and (3,0). Each subvector will have three buckets, i.e., {0}, [1,3], and [4,6]. ...
Wearable devices have gained huge popularity in today's world. These devices collect large-scale health data from their users, such as heart rate and step count data, that is privacy sensitive, however it has not yet received the necessary attention in the academia. In this paper, we perform the first systematic study on quantifying privacy risks stemming from step count data. In particular, we propose two attacks including attribute inference for gender, age and education and temporal linkability. We demonstrate the severity of the privacy attacks by performing extensive evaluation on a real life dataset and derive key insights. We believe our results can serve as a step stone for deriving a privacy-preserving ecosystem for wearable devices in the future.
... Policy factors are another influence on PA participation behavior and are a crucial feature of public health [30]. Policies can range from the restrictive, such as banning exposure to sugar-based product advertisements to increasing health promotion resources such as parks and community exercise facilities [30]. ...
... Policy factors are another influence on PA participation behavior and are a crucial feature of public health [30]. Policies can range from the restrictive, such as banning exposure to sugar-based product advertisements to increasing health promotion resources such as parks and community exercise facilities [30]. These policies may increase PA participation among children [30]. ...
... Policies can range from the restrictive, such as banning exposure to sugar-based product advertisements to increasing health promotion resources such as parks and community exercise facilities [30]. These policies may increase PA participation among children [30]. We anticipate that modifying these factors will increase the PA participation in children and decrease obesity and sedentary lifestyle-related health risk factors. ...
Background
Reduced physical activity (PA) is one of the significant health concerns in adults and children alike. Despite the proven benefits of PA, most children, globally, do not meet the weekly criteria of enough PA to maintain health. The proposed systematic review is the review of the factors and will provide information on the factors associated with PA participation in children.
Methods
The proposed systematic review will be conducted based on the methodology from the Cochrane Handbook for Systematic Reviews of Interventions. We will include observational studies (cross-sectional, case–control, and cohort studies), randomized controlled trials (RCTs), and non-randomized study designs for information on factors associated with PA participation among children. Studies with participants in the age range of 5–18 years, indulging in physical activity of 60 min per day for a minimum of 3 days a week, will be included. Studies including differently abled children, children under medical treatment, and those taking medications for illnesses such as neurological, cardiac, and mental health conditions will be excluded from the review. We will search MEDLINE (via PubMed and Web of Science), Scopus, EMBASE, CINAHL, Cochrane CENTRAL, and PEDro for English language publications published from the inception till October 2022. For additional studies, we will search websites such as the Australian Association for Adolescent Health International Association for Adolescent Health and a reference list of the included publications. Selection of studies, data extraction, and quality assessment of the included studies will be performed in duplicate. Quality assessment of the included studies will be performed using the Cochrane Risk of Bias tool (ROB-II) for RCTs, New-Castle Ottawa, for observational studies, and ROBINS-I (Risk of Bias for Non-Randomized studies of Interventions) for non-randomized study designs.
Discussion
The proposed systematic review and meta-analysis will present a summary of the available evidence on factors associated with PA participation in children. The findings of this review will provide new insights into how exercise providers can improve PA participation among children and can also help healthcare workers, clinicians, researchers, and policymakers to plan long-term interventions targeting child health.
Systematic review registration
PROSPERO CRD42021270057.
... Our replication efforts pointed to challenges in comparing results across different population-level physical activity studies that use different measurement modalities. We found that AoU's Fitbit-based assessments (2016-2020) resulted in daily step counts of ~2,900 steps higher than the smartphone-measured step counts reported by Althoff et al (2013Althoff et al ( -2014 5 . In contrast, we found that the overall proportion of active individuals (≥150 min/week of MVPA or ≥75 min/week of VPA 13 ) in the AoU cohort was 33% lower than in the BRFSS study 10 which employed self-reports. ...
... Our findings on activity level variations across the US, as well as how the discrepancy in activity between males and females impacts overall activity distributions within each state, were consistent with prior literature 5,10 . We found that participants in the warmer, southern US regions (e.g., Texas and Arizona) walked 660 fewer daily steps, on average, compared to those in the cooler, northeastern US regions (e.g., New York and Massachusetts). ...
Data from digital health technologies can provide insights into a population’s daily activity levels and behaviors. Using Fitbit wearable data from 7,431 participants enrolled in the All of Us Research Program, we examined activity inequality across the US and its association with obesity. We found that there were 660 fewer daily steps on average in the southern states than in the northeastern states. In addition, with every 0.01 increase in activity inequality, a metric used to quantify activity disparity, the predicted obesity prevalence of a state increased by 5%. Surprisingly, no association was discovered between a city's walkability and activity inequality, suggesting that extrinsic factors such as the built environment may not necessarily address the gap in activity levels in the US. This study demonstrates the value of digital health technologies in exploring and understanding public health while highlighting the need to examine complex confounding factors such as socioeconomic status, eating habits, and lifestyle that may contribute to activity inequality in the US.
... Insufficient PA also has been estimated as the fourth leading risk factor for global mortality [10] and is estimated to cost global healthcare systems $50 billion annually [11]. It has been a prominent and increasingly prevalent global public health problem [11,12]. Since the end of the twentieth century, PA related recommendations highlighted moderate-to vigorous-intensity PA (MVPA), including walking, cycling, running, and gardening, mainly for adults [13]. ...
... The results showed that in 2016, the prevalence of age-standardized insufficient PA was 27.5% globally, higher for women than men [16]. Another worldwide study found that insufficient PA is higher in high-income countries than in low-income [12]. Further, insufficient PA exists in several domains in life: at work, transport, or recreation, and different domains-specific PA may affect health differently [17]. ...
Abstract Background The global prevalence of insufficient physical activity (PA) was reported to be 27.5% in 2016, and there were stable levels of insufficient PA worldwide between 2001 and 2016. The global target of a 10% reduction in insufficient PA by 2025 will not be met if the trends remain. The relevant data for trends in China were still scarce. This study aimed to determine nationwide temporal trends in insufficient PA among adults in China from 2010 to 2018. Methods 645 903 adults aged 18 years or older were randomly selected from four nationally representative cross-sectional surveys of the China Chronic Disease and Risk Factor Surveillance conducted in 2010, 2013, 2015, and 2018. PA was measured using the Global Physical Activity Questionnaire. Temporal changes in insufficient PA prevalence and participation of domain-specific moderate- to vigorous-intensity PA (MVPA) were analyzed using logistic regression. Results From 2010 to 2018, the age-adjusted prevalence of insufficient PA in China increased from 17.9% (95% confidence interval 16.3% to 19.5%) in 2010 to 22.3% (20.9% to 23.8%) in 2018 (P for trend
... However, when social demographic variables are included in the analysis, the relationship varies. The relationship was stronger for women than for men, and the association varied by race and ethnicity [20,21]. Male cardiovascular disease and respiratory disease mortality rates decreased with increasing green space, but no significant associations were found for women [22]. ...
Demographic data is widely used in both built environment and population health studies. Traditional data sources include national, state, and local surveys as well as archived data from longitudinal studies and newly emerging sources such as digitally accessible administrative data and real-time data from mobile devices. The value of these diverse data sets hinges on their accuracy, completeness, reliability, relevance, and timeliness. This chapter reviews the literature published in this field, provides a selective overview of the extant published research based on such data, and offers suggestions for the continuing access and use of such datasets.
... However, not everyone can be physically active. Inequality in physical activity is widespread, both within and across populations (Grzywacz & Marks, 2001;Gidlow et al., 2006;Althoff et al., 2017;Sher & Wu, 2021;Kang et al., 2021). Researching inequality patterns of physical activity and what may explain these patterns is important as it can provide valuable insights into why health disparities persist among social groups and across countries. ...
Physical activity improves health and well-being, but not everyone can be equally active. Previous research has suggested that racial minorities are less active than their white counterparts and immigrants are less active than their native-born counterparts. In this article, we adopt an intersectional and life course approach to consider how race and immigrant status may intersect to affect physical activity across the life span. This new approach also allows us to test the long-standing habitual versus structural debate in physical activity.
... Unlike the males being more susceptible to stroke [30,31], we observed that the global burdens by gender were different, with more deaths and higher levels of DALY, ASMR, and ASDR in the females. A potential reason might be that the females do fewer physical activities [32,33]. Nevertheless, the males need to be paid more attention based on our ndings of deaths and DALYs by gender. ...
Background
Low physical activity (LPA) has been linked to the risk of stroke. Previous studies on the disease burden of stroke attributable to LPA are incomplete and lagged. We aim to assess spatiotemporal trends in the global burden of stroke attributable to LPA from 1990 to 2019.
Methods
Based on the global burden of Disease, Injuries, and Risk Factors Study, our research examined deaths, the Disability-Adjusted Life Years (DALYs), the Age-Standardized Mortality Rate (ASMR), the Age-Standardized DALY Rate (ASDR), and the Estimated Annual Percentage Change (EAPC) for stroke attributable to LPA.
Results
Deaths and DALYs were on the rise worldwide from 1990 to 2019, with increases of 72.72% for the former and 67.41% for the latter; ASMR and ASDR decreased, with the ASMR-related EAPC of -1.61 (95% CI:-1.71–-1.5) and ASDR-related EAPC of -1.35 (95% CI:-1.43–-1.27); the females had more numbers of deaths and DALYs, and the majorities of deaths and DALYs were shared by those aged ≥70. The highest-burden rates were shared by North Africa, Middle East, and Tropical Latin America; the ASMR-related EAPC was associated with the ASMR in 1990 (R=-0.26, P<0.001) and the Socio-Demographic Index (SDI) across different countries in 2019 (R=-0.61, P<0.001), respectively, and such patterns were similar to what ASDR and the ASDR-related EAPC had; the Human Development Index (HDI) in 2019 was associated with the ASMR-related EAPC (R=0.63, P<0.001) and the ASDR-related EAPC across different countries (R=-0.62, P< 0.001), respectively.
Conclusions
Globally, deaths and DALYs of stroke attributable to LPA have deteriorated over the recent three decades. Special attention should be given to the effects of physical activity on health, and patients with stroke attributable to LPA worldwide should energetically be cared about, especially among those aged ≥ 70 and females in the regions of East Asia, North Africa, and the Middle East.
... Smartphone usage is ubiquitous throughout society with 85% of adults owning a smartphone [1]. However, there is some variation in ownership, with 95% of adults younger than 49 years having a smartphone compared to 61% in adults greater than 65 years of age [2]. ...
Study Objectives
We sought to develop behavioral sleep measures from passively sensed human-smartphone interactions and retrospectively evaluate their associations with sleep disturbance, anxiety, and depressive symptoms in a large cohort of real-world patients receiving virtual behavioral medicine care.
Methods
Behavioral sleep measures from smartphone data were developed: daily longest period of smartphone inactivity (inferred sleep period [ISP]); 30-day expected period of inactivity (expected sleep period [ESP]); regularity of the daily ISP compared to the ESP (overlap percentage); and smartphone usage during inferred sleep (disruptions, wakefulness during sleep period). These measures were compared to symptoms of sleep disturbance, anxiety, and depression using linear mixed-effects modeling. More than 2,300 patients receiving standard of care virtual mental healthcare across more than 111,000 days were retrospectively analyzed.
Results
Mean ESP duration was 8.4 hours (SD = 2.3), overlap percentage 75% (SD = 18%) and disrupted time windows 4.85 (SD = 3). There were significant associations between overlap percentage (p < 0.001) and disruptions (p < 0.001) with sleep disturbance symptoms after accounting for demographics. Overlap percentage and disruptions were similarly associated with anxiety and depression symptoms (all p < 0.001).
Conclusions
Smartphone behavioral measures appear useful to longitudinally monitor sleep and benchmark depressive and anxiety symptoms in patients receiving virtual behavioral medicine care. Patterns consistent with better sleep practices (i.e., greater regularity of ISP, fewer disruptions) were associated with lower levels of reported sleep disturbances, anxiety, and depression.
... [8][9][10] A large body of research has been conducted to understand the MVPA adherence gap. [11][12][13][14][15][16] One factor that has yet to be evaluated rigorously is participants' beliefs about the role of MVPA in long-term weight control. According to the theory of planned behavior, favorable evaluations about the importance of MVPA for weight control may impact MVPA engagement and weight loss by enhancing intentions to engage in MVPA behavior. ...
Background
Previous research has established the importance of moderate‐to‐vigorous physical activity (MVPA) for weight control. One area of unexplored investigation is the relationship between individuals' perceptions of the importance of MVPA for weight control and MVPA engagement. This study examined the associations between the perceived importance of MVPA and MVPA engagement, weight loss, barriers to PA, and exercise enjoyment in adults enrolled in a long‐term behavioral weight loss (BWL) intervention.
Methods
Adults ( N = 301) with overweight/obesity (BMI = 27–45 kg/m ² ) completed an 18‐month BWL intervention, followed by a no‐intervention 18‐month follow‐up. At baseline, 6 months, 18 months (i.e., post‐treatment), and 36 months (i.e., follow‐up), participants ranked the importance of six strategies for weight control: keeping a food record, MVPA, light PA, self‐weighing, small portions, and low‐calorie diet. Observed MVPA (measured by accelerometer), percent weight loss, perceived barriers to PA, and exercise enjoyment were also measured at each assessment.
Results
Results showed that most participants perceived MVPA as a primary weight control strategy (first, second, or third most important) throughout the intervention, regardless of the weight control goal (weight loss vs. maintenance). Individuals who ranked MVPA as a primary strategy for weight control at concurrent time points, compared to those who did not, engaged in significantly more MVPA at post‐treatment, had greater weight loss at follow‐up, endorsed fewer barriers to PA at post‐treatment and follow‐up, and reported greater exercise enjoyment at baseline and post‐treatment.
Conclusion
Perceived importance of MVPA was related to subjective experiences of MVPA, MVPA adherence, and weight loss in a long‐term BWL intervention.
... This was not the case in our study since step count was measured with the smartphone's accelerometer. A better alternative would be to collect data from wearables, which can be worn by the user the whole time, resulting in a more accurate step count (Althoff et al. 2017). However, we decided to create an app that is accessible, can reach many people, and therefore only required a smartphone. ...
In this paper, we aim to improve existing health recommender systems by defining relevant contextual and motivational variables to recommend physical activities and collect appreciation feedback. Current health recommenders do not sufficiently include users’ context and motivational theory when personalizing health suggestions. To bridge these gaps, we conducted a 21-day longitudinal user study with 36 participants using our Android app with collected sensor data and Ecological Momentary Assessments to collect daily activities, mood, and motivation. This study resulted in a dataset of 724 activities. Two approaches to determine feature relevance were followed: variable importances analysis on 40 input variables, and statistical analysis of mean differences in outcome variables across contexts. Our findings suggest recommending activity duration, intensity, location, and type by incorporating: company, situation (e.g., free time or work), happiness, calmness, energy level, physical complaints, and motivation. As such, we propose opportunities for future health recommenders to integrate these data with contextual pre-filtering techniques, extended with our suggestions for automatically collected weather, location types, step count, and time. We also propose to use mood and motivation as appreciation feedback to focus on user well-being and boost motivation.
... H UMAN mobility patterns have attracted widespread attention [1], [2], [3] to investigate when and where people's activities happen, and aroused multi-disciplinary applications such as urban planning [4], pollution abatement [5], and epidemic prevention [6]. For example, reliable human mobility models can inform policy-making of crucial projects, such as planning for expanding urban land [7] and making effective controlling strategies of gross polluters [5]. ...
Human mobility patterns have shown significant applications in policy-decision scenarios and economic behavior researches. The human mobility simulation task aims to generate human mobility trajectories given a small set of trajectory data, which have aroused much concern due to the scarcity and sparsity of human mobility data. Existing methods mostly rely on the static relationships of locations, while largely neglect the dynamic spatiotemporal effects of locations. On the one hand, spatiotemporal correspondences of visit distributions reveal the spatial proximity and the functionality similarity of locations. On the other hand, the varying durations in different locations hinder the iterative generation process of the mobility trajectory. Therefore, we propose a novel framework to model the dynamic spatiotemporal effects of locations, namely SpatioTemporal-Augmented gRaph neural networks (STAR). The STAR framework designs various spatiotemporal graphs to capture the spatiotemporal correspondences and builds a novel dwell branch to simulate the varying durations in locations, which is finally optimized in an adversarial manner. The comprehensive experiments over four real datasets for the human mobility simulation have verified the superiority of STAR to state-of-the-art methods. Our code will be made publicly available.
... There is rapid growth of enriched and systematized clinical data in electronic health records (EHRs) and other health-related information databases via digital technologies, such as wearable devices, smartphones, and edge computing [181]. In parallel, tremendous growth and maturation of computational science has stimulated the field of bioinformatics and applied artificial intelligence (AI) in the past decade. ...
Neurological and psychiatric diseases have high degrees of genetic and patho-physiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases , with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice. Conceptual overview of precision medicine The term 'precision medicine' (PM) has been on the lips and minds of scientists and clinicians alike in recent years. Yet the exact scope and scientific theoretical framework of PM is complex and escapes static boundaries. Despite the landmark announcement of the US Precision Medicine Initiative (PMI) in 2015 [1], how PM should be applied at the individual level, and translated from one disease to another, continues to be debated. The fundamental concept of PM is defined as 'prevention and treatment strategies that take individual variability into account' [1]. Despite this seemingly clear-cut definition, the biomedical communities are grappling with the implementation of transformational programs in real-world settings and whether traditionally defined disease entities require redefinition. The human brain is a highly complex system and is inherently difficult to model due to the dynamic and intricate interactions among its parts. Many of the properties that characterize complex and dynamical systems are relevant in the context of the brain, such as nonlinearity, emergence, spontaneous order, adaptation, and feedback loops. Neurological and psychiatric diseases are often multifactorial, involving different biological systems within a single disease spectrum and resulting from nonlinear interplay of risk genes, dynamic biological determinants, and environmental factors [2-5]. From this complex systems dynamic arise significant individual variabilities in the underlying biology, even when symptomatic and syndromic phenotypes are similar [2-5]. A PM paradigm is pivotal for tackling unmet needs in neurological and psychiatric diseases, which often lack effective treatments and represent a growing burden to healthcare systems and societies worldwide [6,7]. Pharmacological standard-of-care for complex brain disorders is very limited; in the case of brain proteinopathies (including protein misfolding disorders), or pathologically defined 'primary neurodegenerative diseases', approved treatments have been mostly drugs with time-limited efficacy and high interindividual variability in response. Moreover, no disease prediction Highlights Many CNS diseases lack curative or disease-modifying treatments and represent a growing burden to healthcare systems and societies worldwide. These diseases are often multifactorial and complex in nature, with significant individual variability in the underlying genetics and biology. We posit that the solution to tackling the unmet needs in neurological and psychiatric diseases requires a paradigm shift from a focus on late-stage syndromic phenotypes to targeting preclinical/early prodromal stages. Precision medicine (PM) approaches in neurology and psychiatry could provide screening solutions, deploy time-sensitive detection/diagnosis, and tailor treatment strategies to an individual's specific clinical-genetic-biological characteristics and risk factors. We highlight Alzheimer's disease as a case in point for PM oriented across neu-rology and psychiatry and as a compelling model towards PM-oriented drug R&D and healthcare practices.
... Higher PA in middle-aged adults contrasts to the negative association of PA with age found in most countries. 17,41,42 Although PA was lower in the oldest age group (65+ y), this difference was minimal and did not apply to participating METmin. Furthermore, if work PA is excluded, a positive linear association emerges with higher PA in the oldest age group. ...
Background:
Surveillance of domain-specific physical activity (PA) helps to target interventions to promote PA. We examined the sociodemographic correlates of domain-specific PA in New Zealand adults.
Methods:
A nationally representative sample of 13,887 adults completed the International PA Questionnaire-long form in 2019/20. Three measures of total and domain-specific (leisure, travel, home, and work) PA were calculated: (1) weekly participation, (2) mean weekly metabolic energy equivalent minutes (MET-min), and (3) median weekly MET-min among those who undertook PA. Results were weighted to the New Zealand adult population.
Results:
The average contribution of domain-specific activity to total PA was 37.5% for work activities (participation = 43.6%; median participating MET-min = 2790), 31.9% for home activities (participation = 82.2%; median participating MET-min = 1185), 19.4% for leisure activities (participation = 64.7%; median participating MET-min = 933), and 11.2% for travel activities (participation = 64.0%; median MET-min among participants = 495). Women accumulated more home PA and less work PA than men. Total PA was higher in middle-aged adults, with diverse patterns by age within domains. Māori accumulated less leisure PA than New Zealand Europeans but higher total PA. Asian groups reported lower PA across all domains. Higher area deprivation was negatively associated with leisure PA. Sociodemographic patterns varied by measure. For example, gender was not associated with total PA participation, but men accumulated higher MET-min when taking part in PA than women.
Conclusions:
Inequalities in PA varied by domain and sociodemographic group. These results should be used to inform interventions to improve PA.
... Lower levels of activity among women compared to men are frequent worldwide. This gender inequality is reduced when aspects of the built environment, such as walkability, are improved [40]. A possible explanation for the increased PA among combat unit soldiers is that combat soldiers may be more motivated compared to other soldiers since their acceptance to such units requires meeting strict physical demands. ...
Physical activity (PA) within the military can have large effects on the soldier’s health, productivity, and ability to meet tasks. This study aims to identify the factors associated with PA adherence during military service, applying the socioecological model, which classifies the factors influencing health behaviors into individual, social, and environmental levels. This cross-sectional survey was carried out among 500 soldiers aged 18 to 49 years in the Israeli Defense Forces. Statistical analysis to assess associations between PA and individual, social, and environmental factors included correlations, variance analyses, and multivariable linear regression. PA rates were higher among men soldiers in combat positions. Individual level factors, such as intention to perform PA (β = 0.42, p < 0.001), and self-efficacy regarding PA (β = 0.20, p < 0.001) were associated with PA among men and women. However, social norms were associated with PA only among men (β = 0.24, p < 0.001). The physical environment was not associated with PA adherence (β = −0.04, p = 0.210). Conclusions: Developing interventions on the individual level for all military personnel and interventions on the social level, mainly for men, could help increase levels of PA in the military.
... To counteract this, it is important to stay regularly physically active. However, according to recent findings, not all people are equally at risk of being inactive or insufficiently active, as residency and gender both seem to matter [29][30][31] within the European region [29] in general. In Austria, region-specific differences regarding PA behaviour pointing to an east-west gradient were monitored [23]. ...
The aim of this study is to compare data on the health status, self-reported exercise and non-exercise physical activity as well as fitness parameters, such as grip strength, of people in retirement in two cities that are both considered urban centres according to the statistical office of the European Union (EUROSTAT), but differ by geographic location. Self-reported physical activity questionnaires and objective assessments of physical fitness indicators collected by sports scientists were used and examined for differences. A total of 210 people (66.3 years ± 2.3) in Salzburg (n = 90) and Vienna (n = 120) was analysed. While no differences were found in self-reported health, there were differences in self-reported exposure to self-reported exercise and non-exercise physical activity, with the Viennese population being more inactive than their more western comparison group. In addition, the objective indicators for muscle strength, balance and flexibility of the lower extremities differed significantly in favour of the more western Austrian population. We recommend assessing the situation of older people in Austria regarding their physical activity and fitness on a regional basis, even if they live in cities of the same category. Future projects should therefore aim to consider specific regional needs during development and incorporate both subjective and objective indicators when monitoring the success of such programs.
... Current scientific evidence suggests that adequate exercise, in terms of quantity and quality, can even partially reverse some negative effects of aging [38]. Unfortunately, the global socioeconomic disparity is also reflected in global activity inequality, which represents a predictor of obesity prevalence [39]. ...
Background: Falls in older people have a significant impact on public health. The scientific literature has provided evidence about the necessity for older adults to be physically active, since it reduces the incidence of falls, several diseases, and deaths, and can even slow down some effects of aging. The primary aim of our study is to identify if physical performances and risk of falling are related to 1-, 2-, 3-, 4-, and 5-year mortality. Its secondary aim is to establish if people with both severely impaired physical performance and a high risk of falling also present impairment in other geriatric domains. Methods: In this prospective study, we enrolled subjects aged 65 years or more, subjected them to comprehensive assessment (including assessment of risk of falling, physical capacities, comorbidities, autonomies in daily living, cognitive abilities, mood, and nutritional status), and followed them for 5 years. Results: We included 384 subjects, 280 of whom were women (72.7%), with a median age of 81 years. Our results showed that physical performances and risk of falling are highly correlated to each other (rho = 0.828). After divided the sample into three groups (people without augmented risk of falling and able to perform adequate physical activity; people with moderate risk of falling and/or disability; people with severe risk of falling and/or disability), we found that the more severe the disability and risk of falling were, the more compromised the other geriatric domains were. Moreover, the survival probability progressively increased following the same trend, amounting to only 41% in severely compromised people, 51.1% in moderately compromised people, and 62.8% in people without physical compromise nor an augmented falling risk (p = 0.0124). Conclusions: Poor physical performance combined with a high risk of falling, correlated with each other, are associated with higher mortality and impairment in multiple domains in older adults.
... It might be the case that these samples lack the heterogeneity of walking behaviors observed in the poststroke population. Additionally, activity levels, socioeconomic, and ethnic disparities in poststroke care across geographical locations might influence the walking patterns of the samples engaged in research at different sites [21][22][23][24][25] . Therefore, combining data across different sites might increase the heterogeneity of behaviors measured in a research study, which ultimately can improve the generalizability of research findings to the overall post-stroke population. ...
Background: Walking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions.
Objective: We used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: 1) identify clusters of walking behaviors in people post-stroke and neurotypical controls, and 2) determine the generalizability of these walking clusters across different research sites. We hypothesized that participants post-stroke will have different walking impairments resulting in different clusters of walking behaviors, which are also different from control participants.
Methods: We collected data from 81 post-stroke participants across four research sites and included data from 31 control participants. Using sparse K-means clustering, we identified walking clusters based on 17 spatiotemporal and force variables. We analyzed the biomechanical features within each cluster to characterize cluster-specific walking behaviors. We also assessed the generalizability of the clusters using a leave-one-out approach.
Results: We identified five clusters: a fast and asymmetric cluster, a moderate speed and symmetric cluster with short stance times, a moderate speed and asymmetric cluster, a slow cluster with frontal plane force asymmetries, and a slow and symmetric cluster. The moderate speed and asymmetric cluster did not generalize across sites.
Conclusions: Although post-stroke walking patterns are heterogenous, these patterns can be systematically classified into distinct clusters based on spatiotemporal and force data. Future interventions could target the key features that characterize each cluster to increase the efficacy of interventions to improve mobility in people post-stroke.
... Enhanced models of the interaction between long-duration tissue loading and biological responses will help us study soft tissue injuries, tissue remodeling, and muscle fatigue. More generally, solutions to complex, movement-related health challenges, such as the global inactivity pandemic, will require multidisciplinary collaborations that incorporate biomechanics, psychology, sociology, and environmental factors (Althoff et al., 2017;Crum and Langer, 2007;Hicks et al., 2023;King et al., 2020). ...
Over the past half-century, musculoskeletal simulations have deepened our knowledge of human and animal movement. This article outlines ten steps to becoming a musculoskeletal simulation expert so you can contribute to the next half-century of technical innovation and scientific discovery. We advocate looking to the past, present, and future to harness the power of simulations that seek to understand and improve mobility. Instead of presenting a comprehensive literature review, we articulate a set of ideas intended to help researchers use simulations effectively and responsibly by understanding the work on which today's musculoskeletal simulations are built, following established modeling and simulation principles, and branching out in new directions.
... Third, the decrease in physical activity involvement has increased the number of cases of infectious diseases and caused life expectancy to be shorter. Everything is commonly reported among rural communities compared to urban communities (Althoff et al., 2017). Thus, the factors of seriousness, intense desire and the influence of individual behaviour are the primary motivations for the individual's involvement in recreation. ...
Motivation in outdoor recreational participation has an important position as it helps to determine why individuals engage in recreational behavior, the way individuals do, understand the benefits of participation, and serves as basic knowledge in assisting the planning process and environmental management. Each involvement is influenced by motivating factors that can generate behavior and achievement to experience the pleasure and self-satisfaction inherent in recreation. Thus, the aim of this study was to identify motivating factors influencing outdoor recreational involvement among rural and urban communities. A set of questionnaires was used to obtain quantitative data sets and were distributed to 384 respondents who were among individuals living in rural and urban areas. The analysis revealed four broad categories of motivation using the Recreation Experience Scales (REP) 'scales', namely social interaction, physical health and fitness, rest, and interaction with the environment. Respondents rated activities with family members, relaxing the mind, enjoying a peaceful surrounding area, and improving personal health and fitness as the main motivating factors influencing outdoor recreation involvement.
... The significant difference observed in our study can be explained by our sample's lower PA baseline; consequently, there was a greater chance for improvement. Several studies have identified a link between baseline PA levels and mHealth intervention effectiveness [54,[56][57][58][59]. Additionally, the implementation of preventative measures such as social distancing and lockdown restrictions during the COVID-19 pandemic between 2020 and 2021 might reduce the level of PA in the postpandemic period. ...
... Physical inactivity is a global health problem and a primary risk factor for non-communicable diseases, which now account for the majority of deaths worldwide [1,2]. Large-scale research has shown that women are less physically active than men [3,4], with this gender gap beginning in late adolescence and continuing throughout the lifespan, with women becoming more inactive as they age [5]. ...
Background:
Research shows that inactive young women are attracted to using mobile phone applications (apps) to increase physical activity. Apps can promote physical activity by delivering a range of behaviour change techniques to influence determinants of user behaviour. Previous qualitative research has examined user experiences with techniques in physical activity apps, however there is little research specifically among young women. This study aimed to explore young women's experiences using commercial physical activity apps to change their behaviour.
Methods:
Young women were recruited online to use a randomly assigned app for two weeks to achieve a personal goal. Using photovoice, a qualitative participatory research method, participants generated insights about their experiences through photographs and semi-structured interviews. Thematic analysis was conducted on photograph and interview data.
Results:
Thirty-two female participants, aged 18-24 years, completed the study. Behaviour change techniques tended to cluster around four key themes: logging and monitoring physical activity; reminders and prompts; workout videos and written instructions; and social features. Social support also strongly influenced participants' experiences.
Conclusions:
Results suggest that behaviour change techniques influenced physical activity in line with social cognitive models, and these models are useful to understand how apps can target user behaviour for young women. The findings identified factors important for young women that seemed to moderate their experiences, such as social norms about women's appearance, which should be further explored within the context of behaviour change models and app design.
... Introduction Physical inactivity is a major public health concern [1][2][3][4]. Regular engagement in physical activity (PA) confers numerous benefits including improved learning [5][6][7][8], life skills, and social/emotional development [9][10][11][12][13], academic performance [14,15], and overall health and well-being [16][17][18][19]. Despite the many benefits, less than 40% of Canadian children and youth are meeting the recommended 60 minutes of daily moderate-to-vigorous PA [20,21]. ...
Background:
Physical Literacy (PL) is a synthesis construct that ties together movement competencies with affective, motivational, and knowledge-based elements. It is considered foundational to the development of physical activity-related outcomes. Many diverse organizations and programs have embraced the concept and are implementing programs targeting each of those core elements. However, research has lagged behind its interest and adoption. Among the more prominent gaps is the design and evaluation of programs that aim to increase PL within special populations such as new immigrants or refugee youth.
Methods:
The Immigrant-focused Physical Literacy for Youth (IPLAY) program is a co-developed evidence-informed 8-week PL program designed for new immigrant and refugee youths who have recently settled in Calgary, Alberta, Canada. This study aims to use a convergent parallel mixed-methods approach to collect, analyse, and interpret quantitative and qualitative data in the evaluation and iteration of the IPLAY program.
Discussion:
PL programs can be used as a tool to build confidence and physical competencies among newcomer youth. Furthermore, academic-community collaborations in the design and delivery of PL programs can help improve the access and interest for PL programs among newcomer youth. These partnerships are critical and timely considering the recent and upcoming waves of immigration to "arrival cities" across Canada.
... However, with the rapid increase in industrialization during the 20th century, humans have become sedentary, with an accompanying increase in the incidence and prevalence of cancer, cardiovascular disease, and dementia 3 . Modern Americans achieve a mean of 4700 daily steps, and the majority do not meet the modest recommendation of 150 minutes of exercise per week 4,5 . ...
Background: Physical activity is strongly protective against the development of chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity. Our randomized crossover trial has continued to digitally enroll participants, allowing increasing statistical power for greater precision in subsequent analyses.
Methods: We offered enrollment to adults aged >=18 years with access to an iPhone and the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomly allocated to four 7-day interventions. Interventions consisted of: 1) daily personalized e-coaching based on the individuals baseline activity patterns, 2) daily prompts to complete 10,000 steps, 3) hourly prompts to stand following inactivity, and 4) daily instructions to read guidelines from the American Heart Association website. The trial was completed in a free-living setting, where neither the participants or investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis. This trial is registered with ClinicalTrials.gov, NCT03090321.
Findings: Between January 1, 2017 and April 1, 2022, 4500 participants consented to enroll in the trial, of whom 2458 completed 7-days of baseline monitoring (mean daily steps 4232+/-73) and at least one day of one of the four interventions. The greater statistical power afforded by continued passive enrollment revealed that e-coaching prompts, tailored to an individual, increased step count significantly more than other interventions (402+/-71 steps, P=7.1x10-8).
Interpretation: Digital studies can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we show that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort.
Funding: Stanford Data Science Initiative and Catalyst Program, Apple, Google
Walkability is a critical component of built environments, yet there is still diverse conceptualization and measurement of the construct. The Walk Score ® metric is one measure of walkability, which is widely used in scholarly, industry, and public domains. With increased interest in the use of Walk Score ® as a research tool, it is necessary to examine the operationalization and scope of the measure. This scoping review examined how researchers utilized Walk Score ® in walkability research, with specific attention to identifying limitations related to health outcomes as well as the use of the metric in non-health research contexts. Findings from the review provide a novel and critically nuanced understanding of how the assumptions and limitations of Walk Score ® are addressed relative to socio-ecological aspects of walkability.
Seasonal variations in glycemic trends remain largely unstudied despite the growing prevalence of diabetes. To address this gap, our objective is to investigate temporal changes in glycemic trends by analyzing intensively sampled blood glucose data from 137 patients (ages 2 to 76, primarily type 1 diabetes) over the course of 9 months to 4.5 years. From over 91,000 days of continuous glucose monitor data, we found that glycemic control decreases significantly around the holidays, with the largest decline observed on New Year’s Day among the patients with already poor glycemic control (i.e., <55% time in the target range). We also observed seasonal variations in glycemic trends, with patients having worse glycemic control in the months of November to February (i.e., mid-fall and winter, in the United States), and better control in the months of April to August (i.e., mid-spring and summer). These insights are critical to inform targeted interventions that can improve diabetes outcomes.
Aims
Physical activity is associated with decreased incidence of the chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity.
Methods and results
We offered enrolment to community-living iPhone-using adults aged ≥18 years in the USA, UK, and Hong Kong who downloaded the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomized to four 7-day interventions. Interventions consisted of: (i) daily personalized e-coaching based on the individual’s baseline activity patterns, (ii) daily prompts to complete 10 000 steps, (iii) hourly prompts to stand following inactivity, and (iv) daily instructions to read guidelines from the American Heart Association (AHA) website. After completion of one 7-day intervention, participants subsequently randomized to the next intervention of the crossover trial. The trial was completed in a free-living setting, where neither the participants nor investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis (modified in that participants had to complete 7 days of baseline monitoring and at least 1 day of an intervention to be included in analyses). This trial is registered with ClinicalTrials.gov, NCT03090321.
Conclusion
Between 1 January 2017 and 1 April 2022, 4500 participants consented to enrol in the trial (a subset of the approximately 50 000 participants in the larger MyHeart Counts study), of whom 2458 completed 7 days of baseline monitoring (mean daily steps 4232 ± 73) and at least 1 day of one of the four interventions. Personalized e-coaching prompts, tailored to an individual based on their baseline activity, increased step count significantly (+402 ± 71 steps from baseline, P = 7.1⨯10−8). Hourly stand prompts (+292 steps from baseline, P = 0.00029) and a daily prompt to read AHA guidelines (+215 steps from baseline, P = 0.021) were significantly associated with increased mean daily step count, while a daily reminder to complete 10 000 steps was not (+170 steps from baseline, P = 0.11). Digital studies have a significant advantage over traditional clinical trials in that they can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we present a novel finding that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. These data suggest that participants are more likely to react positively and increase their physical activity when prompts are personalized. Further studies are needed to determine the effects of digital interventions on long-term outcomes.
Background: Prior investigators have examined the relationship between neighborhood public transportation access and physical activity among adolescents, but research is lacking on the association with obesity in this age group. This study examines the association between neighborhood public transportation access and adolescent BMI using a national sample. Methods: We used cross-sectional data from the Family Life, Activity, Sun, Health, and Eating study, a national survey (2014) that assessed physical activity and diet, among adolescents (aged 12-17 years, N = 1737) and their parents. We ran crude and adjusted linear regression models to test the association between neighborhood-level public transportation access (less prevalent and prevalent) and individual participant-level BMI z-scores. Results: The analytic sample included 336 adolescents (50% female; 69% had healthy weight; 28% had overweight or obesity). Adjusted models showed a positive relationship between high public transportation access and adolescent z-BMI (b = 0.25, confidence interval [95% CI]: -0.01 to 0.50). In stratified analyses, high public transportation access was associated with higher z-BMI for high school students (b = 0.57, 95% CI: 0.23-0.91), males (b = 0.48, 95% CI: 0.09-0.87), and adolescents in households with an income below $99,999 (0.29, 95% CI: 0.02-0.56). Conclusion: Neighborhood public transportation access is associated with adolescent BMI, but the direction of this association varies across urban adolescent demographic subgroups. Further research is needed to clarify the relationships between individual and social-environmental factors that impact public transportation access and its association with adolescent BMI.
Introduction
Total hip replacement (THR) is performed in an increasing number of individuals around the world and while improvements in pain reduction and long-term enhancement of muscle strength are well documented, the improvement in daily activity does not follow the same trend. This study aimed to determine the feasibility of a 5-week intervention where a personalised outdoor walking distance is monitored using a commercial activity monitor (Fitbit Charge 4).
Method
Data was collected on gait and activities of daily living using patient reported outcome measures. Following the completion of the intervention period, participants took part in a semi-structured interview to voice their opinion on the use of the activity monitor, their experiences, and any challenges in order to assess the feasibility of the intervention. All quantitative data were presented descriptively, using appropriate summary statistics. Interviews were analysed using thematic analysis.
Results
Five participants who had undergone total hip replacement surgery within the postoperative period of 3 to 6 months were recruited from the local community.
Conclusion
The findings suggest that the intervention was feasible and that it encouraged all participants to increase their daily activity. Therefore, it can be concluded that a follow-up effectiveness trial is warranted.
The current rapid urbanization process has led to an uneven distribution of infrastructure, which has brought about many environmental and social problems. Therefore, it is necessary to have a comprehensive understanding of the construction of urban infrastructure in order to better plan the direction of urban development and cope with social problems such as educational resources, public health, and aging. As a common spatial indicator in urban geography studies, accessibility is an important tool for monitoring and constructing urban development patterns, as well as an indicator of fairness in resource allocation reflecting sociological studies, and the most common method used in the existing literature to evaluate the fairness of infrastructure facilities is also accessibility analysis. The traditional accessibility review is too simple in its methodological analysis, and with the development of information technology, it does not include many new methods in its examination. Therefore, this thesis adopts a systematic review approach to comprehensively analyze the strengths and weaknesses of existing methods, their scope of application, and perspectives of concern, and to gain a clearer understanding of future accessibility method improvements. It is found that the subjective factors of residents, i.e., mobility and consumption level, are rarely considered when examining factors affecting accessibility; current accessibility measures mainly examine accessibility at a certain time slice, i.e., static accessibility, and less research is conducted on dynamic accessibility, which is particularly important for certain facilities, such as emergency medical facilities; accessibility is mainly studied for common green spaces, transportation, and medical facilities. Therefore, accessibility measures are more oriented to spatial accessibility, and less attention is paid to non-spatial accessibility.Keywordsinfrastructureaccessibilitysystemic overview
The cumulative health effect resulting from the disparity in physical activity engagement could be transformed into out-of-pocket health expenditure in future, which would widen the socioeconomic gap on all portions of the income spectrum. Recent study reveals the association between physical activity inequity and social inequity. However, the difficulty in accurately measuring the physical activity could deter further exploration of this issue. This correspondence use smartphone-derived big data to provide a more fine-grained depiction, which suggest that the inequity in physical activity can contribute to the social inequity several years later.
Introduction:
Few studies have explored dementia risk according to sex and gender including for transgender and non-binary adults. This study evaluated dementia risk factors and risk scores among cisgender, transgender, and non-binary adults.
Methods:
Observational data were drawn from the 2019 Behavioral Risk Factor Surveillance System. A matched-cohort approach was used to develop sex (male, female) and gender identity cohorts (cisgender men, cisgender women, transgender men, transgender women, and non-binary adults) for comparison. Dementia risk scores were calculated using established mid-life and late-life risk score algorithms.
Results:
Males had higher overall mid-life dementia risk, and lower late-life Alzheimer's disease risk compared to females. Transgender men, transgender women, and non-binary adults had higher overall late-life risk compared to both cisgender men and women.
Discussion:
Future research is needed to build the evidence base for specific risk factors that may be contributing to higher overall risk among understudied and underserved gender groups.
Highlights:
Using data from the 2019 Behavioral Risk Factor Surveillance System, this matched-cohort study found that those assigned female at birth had lower overall mid-life dementia risk and higher overall late-life Alzheimer's disease (AD) risk compared to those assigned male at birth. Transgender men, transgender women, and non-binary adults all showed higher overall late-life AD risk compared to cisgender men and cisgender women. Between-group differences were found in the incidence of specific risk and protective factors for dementia and AD.
The present study tested whether energy-minimizing behaviors evoke reward-related brain activity that promotes the repetition of these behaviors via reinforcement learning processes. Fifty-eight healthy young adults in a standing position performed a task where they could earn a reward either by sitting down or squatting while undergoing electroencephalographic (EEG) recording. Reward-prediction errors were quantified as the amplitude of the EEG-derived reward positivity. Results showed that reward positivity was larger on reward versus no reward trials, confirming the validity of our paradigm to measure evoked reward-related brain activity. However, results showed no evidence that sitting (vs. standing and squatting) trials led to larger reward positivity. Moreover, we found no evidence suggesting that this effect was moderated by typical physical activity, physical activity on the day of the study, or energy expenditure during the experiment. However, at the behavioral level, results showed that the probability of choosing the stimulus more likely to lead to sitting than standing increased as the number of trials increased. In addition, results revealed that the probability of changing the selected stimulus was higher when the previous trial was a stand trial relative to a sit trial. In sum, neural results showed no evidence supporting the theory that opportunities to minimize energy expenditure are rewarding. However, behavioral findings suggested participants tend to choose the less effortful behavioral alternative and were therefore consistent with the theory of effort minimization (TEMPA).
Background: Physical inactivity is important modifiable risk factor for major NCDs. Medical students and faculty who are physically active are more probable to prescribe physical activity to their patients, drastically improving clinical outcomes. Objectives: 1) To describe physical activity of participants in terms of pattern, type & levels. 2) To correlate physical activity with various measures of obesity. Methods: This cross-sectional study was conducted among 350 medical students and young faculty aged 18–35 years. Physical activity was assessed using WHO GPAQ. Physical activity pattern was described in work, travel and leisure domains. It was classified into vigorous and moderate intensity activity types. Levels were defined as insufficiently, moderately and highly active. Measurement of various obesity parameters was done. Data was analyzed using SPSS v25.0. Results: Of all respondents, 193 (55.14%) were males and 157 (44.86%) were females. The prevalence of insufficient physical activity was 20.57%. Respondents reported highest physical activity in leisure domain (1222.45 ± 1590.8 mean METM/W). BMI was significantly correlated with physical activity in transport (p = 0.018) and leisure (p
Background:
Promoting physical activity, such as habitual walking behaviors, in people with cognitive impairment may support their ability to remain independent with a good quality of life for longer. However, people with cognitive impairment participate in less physical activity compared to cognitively unimpaired older adults. The local area in which people live may significantly impact abilities to participate in physical activity. For example, people who live in more deprived areas may have less safe and walkable routes.
Objective:
To examine this further, this study aimed to explore associations between local area deprivation and physical activity in people with cognitive impairment and cognitively unimpaired older adults (controls).
Methods:
87 participants with cognitive impairment (mild cognitive impairment or dementia) and 27 older adult controls from the North East of England were included in this analysis. Participants wore a tri-axial wearable accelerometer (AX3, Axivity) on their lower backs continuously for seven days. The primary physical activity outcome was daily step count. Individuals' neighborhoods were linked to UK government area deprivation statistics. Hierarchical Bayesian models assessed the association between local area deprivation and daily step count in people with cognitive impairment and controls.
Results:
Key findings indicated that there was no association between local area deprivation and daily step count in people with cognitive impairment, but higher deprivation was associated with lower daily steps for controls.
Conclusion:
These findings suggest that cognitive impairment may be associated with lower participation in physical activity which supersedes the influence of local area deprivation observed in normal aging.
Introduction
The use of activity wristbands to monitor and promote schoolchildren's physical activity (PA) is increasingly widespread. However, their validity has not been sufficiently studied, especially among primary schoolchildren. Consequently, the main purpose was to examine the validity of the daily steps and moderate-to-vigorous PA (MVPA) scores estimated by the activity wristbands Fitbit Ace 2, Garmin Vivofit Jr 2, and the Xiaomi Mi Band 5 in primary schoolchildren under free-living conditions.
Materials and methods
An initial sample of 67 schoolchildren (final sample = 62; 50% females), aged 9–12 years old (mean = 10.4 ± 1.0 years), participated in the present study. Each participant wore three activity wristbands (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on his/her non-dominant wrist and a research-grade accelerometer (ActiGraph wGT3X-BT) on his/her hip as the reference standard (number of steps and time in MVPA) during the waking time of one day.
Results
Results showed that the validity of the daily step scores estimated by the Garmin Vivofit Jr 2 and Xiaomi Mi Band 5 were good and acceptable (e.g., MAPE = 9.6/11.3%, and lower 95% IC of ICC = 0.87/0.73), respectively, as well as correctly classified schoolchildren as meeting or not meeting the daily 10,000/12,000-step-based recommendations, obtaining excellent/good and good/acceptable results (e.g., Garmin Vivofit Jr 2 , k = 0.75/0.62; Xiaomi Mi Band 5, k = 0.73/0.53), respectively. However, the Fitbit Ace 2 did not show an acceptable validity (e.g., daily steps: MAPE = 21.1%, and lower 95% IC of ICC = 0.00; step-based recommendations: k = 0.48/0.36). None of the three activity wristbands showed an adequate validity for estimating daily MVPA (e.g., MAPE = 36.6–90.3%, and lower 95% IC of ICC = 0.00–0.41) and the validity for the MVPA-based recommendation tended to be considerably lower (e.g., k = −0.03–0.54).
Conclusions
The activity wristband Garmin Vivofit Jr 2 obtained the best validity for monitoring primary schoolchildren's daily steps, offering a feasible alternative to the research-grade accelerometers. Furthermore, this activity wristband could be used during PA promotion programs to provide accurate feedback to primary schoolchildren to ensure their accomplishment with the PA recommendations.
Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.
Maintaining an active lifestyle is a key health behavior in people with type 2 diabetes (T2D). This study assessed the feasibility and acceptability of a socio-ecological Nordic walking intervention (SENWI) to enhance healthy behaviors in primary healthcare settings. Participants included individuals with T2D (n = 33; age 70 (95% CI 69–74)) and healthcare professionals (HCPs, n = 3). T2D participants were randomly assigned to a SENWI, active comparator, or control group for twelve weeks. Feasibility and acceptability were evaluated based on a mixed methodology. Quantitative data reported adherence information, differences between follow-up and dropout participants and pre- and post-intervention on physical activity, sedentary behavior, and health outcomes. Qualitative data acquisition was performed using focus groups and semi-structured interviews and analyzed using thematic analysis. Thirty-three T2D invited participants were recruited, and twenty-two (66.7%) provided post-intervention data. The SENWI was deemed acceptable and feasible, but participants highlighted the need to improve options, group schedules, gender inequities, and the intervention’s expiration date. Healthcare professionals expressed a lack of institutional support and resources. Nevertheless, no significant difference between the SENWI follow-up and dropout participants or pre- and post- intervention was found (only between the active comparator and control group in the physical quality of life domain). Implementing the SENWI in primary healthcare settings is feasible and acceptable in real-world conditions. However, a larger sample is needed to assess the program’s effectiveness in improving healthy behaviors and its impact on health-related outcomes in the long term.
Dear Editor,
Bridging the gender gap of physical activity (PA) is an essential component of the government’s effort on promoting population health. The stark inequity in PA can indicate the poor performance of a government in tacking the population health issue. A recent study of JPH suggests that the gender gap of PA is associated with the multidimensional gender inequity (e.g. female’s employment, education and political participation).¹ The gender gap of PA can be a manifestation of long-standing health inequity resulting from the gender norm, gender role and solidified division of labor.² However, the intrinsic difficulty in accurately measuring PA prevents the exploration of the relationship between its gender gap and all aspects of public health outcomes. For example, the vast differences in PA between weekday and weekend can make the survey less accurate or reliable, and the volume of PA people perform is also often volatile.³ Accordingly, a new research method is needed to investigate the PA-related issue.
Circadian rhythm has been linked to both physical and mental health at an individual level in prior research. Such a link at population level has been long hypothesized but has never been tested, largely because of lack of data. To partly fix this literature gap, we need: a dataset on population-level circadian rhythms, a dataset on population-level health conditions, and strong associations between these two partly independent sets. Recent work has shown that affect on social media data relates to population-level circadian rhythms. Building upon that work, we extracted five circadian rhythm metrics from 6M Reddit posts across 18 major cities (for which the number of residents is highly correlated with the number of users), and paired them with three ground-truth health metrics (daily number of steps, sleep quantity, and sleep quality) extracted from 233K wearable users in these cities. We found that rhythms of online activity approximated sleeping patterns rather than, what the literature previously hypothesized, alertness levels. Despite that, we found that these rhythms, when computed in two specific times of the day (i.e., late at night and early morning), were still predictive of the three ground-truth health metrics: in general, healthier cities had morning spikes on social media, night dips, and expressions of positive affect. These results suggest that circadian rhythms on social media, if taken at two specific times of the day and operationalized with literature-driven metrics, can approximate the temporal evolution of people's shared underlying biological rhythm as it relates to physical activity (R2=0.492), sleep quantity (R2=0.765), and sleep quality (R2=0.624).
The relationship between physical activity, physical fitness, and academic performance has been widely studied internationally. However, reports of this relationship are contradictory, and its impact is still a matter of controversy. For that reason, the current research determined the correlation between these three variables in a group of 56 female and 48 male Colombian students with an average age of 14.08 ± 0.89 years. A quantitative, correlational, and cross-sectional study was carried out. The physical activity was evaluated using PAQ-A questionnaire. For physical fitness, six tests from the Euro fitness battery were used. The academic performance in Spanish, Math, Natural, and Social Sciences were obtained from the grades for the participants at the end of the academic year. Correlations were determined by multivariate multiple linear regression. The obtained results suggest that aerobic endurance test had effect in the studied variables, whereas the correlation of the other tested predictors did not show any meaningful statistical result. In fact, academic performance is not affected by the physical fitness of the students at the time course grades were measured.
Cardiometabolic diseases are a major public-health concern owing to their increasing prevalence worldwide. These diseases are characterized by a high degree of interindividual variability with regards to symptoms, severity, complications and treatment responsiveness. Recent technological advances, and the growing availability of wearable and digital devices, are now making it feasible to profile individuals in ever-increasing depth. Such technologies are able to profile multiple health-related outcomes, including molecular, clinical and lifestyle changes. Nowadays, wearable devices allowing for continuous and longitudinal health screening outside the clinic can be used to monitor health and metabolic status from healthy individuals to patients at different stages of disease. Here we present an overview of the wearable and digital devices that are most relevant for cardiometabolic-disease-related readouts, and how the information collected from such devices could help deepen our understanding of metabolic diseases, improve their diagnosis, identify early disease markers and contribute to individualization of treatment and prevention plans.
The coronavirus-disease-2019 (COVID-19) pandemic has had a devastating physical and psychological impact on society, especially on students. In this study, we describe the levels of physical activity (Physical-Activity-Questionnaire-Short-Form (IPAQ-SF)), Burnout (School-Burnout-Inventory for students (SBI-U)) and engagement (Utrecht-Work-Engagement-Scale-9 items (UWES-9S)) in a cohort of Latin American higher education students during the COVID-19 pandemic in 2020. We also determined whether physical activity, Burnout, and engagement are related according to gender and area of study. Self-reported data from 571 Latin American students (64.79% women, 34.15% men; average age 25.24 ± 5.52 years) were collected via an online survey questionnaire. Spearman correlation analyses evaluated the associations between physical activity, Burnout, and engagement. Comparative analyses by gender and field of study were also performed. The results showed no correlation or association in the linear regression between the IPAQ-SF and SBI-U scores or between the IPAQ-SF and the UWES-9S scores. By gender, men had higher IPAQ-SF scores (p < 0.05) and reported higher intensity physical activity than women, but women had higher SBI-U scores (p < 0.05). No difference was found between men and women according to the UWES-9S scores (p = 0.28). There was also no difference in IPAQ-SF scores (p = 0.29) regarding the field of study. Our results suggest that women perform less physical activity than men, which is consistent with higher Burnout. However, physical activity was not associated with Burnout or engagement overall, which indicates that it was insufficient to prevent emotional stress in Latin American higher education students during a pandemic.
Objectives:
This study aimed to examine the association between country-level environmental correlates and the prevalence of active school travel (AST) in Asia and country-level differences in AST by age and sex.
Methods:
This ecological study involved 31 Asian countries. Dependent variables were AST prevalence, AST prevalence difference by age, and by sex. Independent variables were country-level environmental correlates extracted using publicly available datasets, classified into physical and social environments. Association estimates of each dependent variable and each of the independent variables were calculated using univariate linear regression. All variables were standardized to have a mean of 0 and a standard deviation of 1.
Results:
Results showed that 1 standard deviation (SD) difference in urban population percentage, night-time light, secondary-school enrolment, and prevalence of adult insufficient physical activity were negatively associated with AST prevalence (SD difference: -0.44 (-0.78 to -0.09), -0.40 (-0.76 to -0.04), -0.39 (-0.74 to -0.04), and -0.40 (-0.76 to -0.03), respectively). A 1 SD difference in car per people was associated with a -0.46 (-0.84 to -0.09) difference of AST prevalence by age. A 1 SD difference in PM2.5 concentration and of prevalence of adult insufficient physical activity were associated with a difference of 0.38 (0.01-0.74) and 0.42 (0.03-0.80) difference of AST prevalence by sex.
Conclusions:
This study shows that Asian countries with a greater number of people living in urban areas, lower levels of overall adult physical activity and higher levels of night-time light have a lower prevalence of adolescent AST. Country-level physical and social environmental correlates explained some of the regional variance in AST. Future policy actions and interventions for the region need to be contextually sensitive to the environmental correlates that vary between countries.
Background
Insufficient physical activity (PA) is a well-established risk factor for several noncommunicable diseases such as cardiovascular diseases, cancer, diabetes, depression, and dementia. The World Health Organization (WHO) advises that individuals engage in 150 minutes of moderate PA per week or 75 minutes of intense PA per week. According to the WHO’s latest report, 23% of adults fail to meet the minimum recommended level of PA. The percentage was even higher in a recent global study that showed 27% of adults were insufficiently active and reported a 5% increase in the prevalence trend of insufficient PA between 2001 and 2016. The study also showed the rate of insufficient PA among countries varied significantly. For instance, it was estimated that 40% were insufficiently active in the United States, and the percentage was even higher in Saudi Arabia (more than 50%). Governments are actively developing policies and methods to successfully establish a PA-inducing environment that encourages a healthy lifestyle in order to address the global steady decline in PA.
Objective
The purpose of this study was to determine the effectiveness of mobile health (mHealth) interventions, particularly SMS text messaging interventions, to improve PA and decrease BMI in healthy adults in the workplace.
Methods
In this parallel, 2-arm randomized controlled trial, healthy adults (N=327) were randomized to receive an mHealth intervention (tailored text messages combined with self-monitoring (intervention; n=166) or no intervention (control; n=161). Adults who were fully employed in an academic institution and had limited PA during working hours were recruited for the study. Outcomes, such as PA and BMI, were assessed at baseline and 3 months later.
Results
Results showed significant improvement in PA levels (weekly step counts) in the intervention group (β=1097, 95% CI 922-1272, P<.001). There was also a significant reduction in BMI (β=0.60, 95% CI 0.50-0.69, P<.001).
Conclusions
Combining tailored text messages and self-monitoring interventions to improve PA and lower BMI was significantly effective and has the potential to leverage current methods to improve wellness among the public.
Importance:
Previous studies have shown that individuals who regularly walk, particularly 8000 daily steps or more, experience lower mortality. However, little is known about the health benefits of walking intensively only a few days a week.
Objective:
To evaluate the dose-response association between the number of days an individual takes 8000 steps or more and mortality among US adults.
Design, setting, and participants:
This cohort study evaluated a representative sample of participants aged 20 years or older in the National Health and Nutrition Examination Surveys 2005-2006 who wore an accelerometer for 1 week and their mortality data through December 31, 2019. Data were analyzed from April 1, 2022, to January 31, 2023.
Exposures:
Participants were grouped by the number of days per week they took 8000 steps or more (0 days, 1-2 days, and 3-7 days).
Main outcomes and measures:
Multivariable ordinary least squares regression models were used to estimate adjusted risk differences (aRDs) for all-cause and cardiovascular mortality during the 10-year follow-up, adjusting for potential confounders (eg, age, sex, race and ethnicity, insurance status, marital status, smoking, comorbidities, and average daily step counts).
Results:
Among 3101 participants (mean [SD] age, 50.5 [18.4] years; 1583 [51.0%] women and 1518 [49.0%] men; 666 [21.5%] Black, 734 [23.7%] Hispanic, 1579 [50.9%] White, and 122 [3.9%] other race and ethnicity), 632 (20.4%) did not take 8000 steps or more any day of the week, 532 (17.2%) took 8000 steps or more 1 to 2 days per week, and 1937 (62.5%) took 8000 steps or more 3 to 7 days per week. Over the 10-year follow-up, all-cause and cardiovascular deaths occurred in 439 (14.2%) and 148 (5.3%) participants, respectively. Compared with participants who walked 8000 steps or more 0 days per week, all-cause mortality risk was lower among those who took 8000 steps or more 1 to 2 days per week (aRD, -14.9%; 95% CI -18.8% to -10.9%) and 3 to 7 days per week (aRD, -16.5%; 95% CI, -20.4% to -12.5%). The dose-response association for both all-cause and cardiovascular mortality risk was curvilinear; the protective association plateaued at 3 days per week. Different thresholds for the number of daily steps between 6000 and 10 000 yielded similar results.
Conclusions and relevance:
In this cohort study of US adults, the number of days per week taking 8000 steps or more was associated with a lower risk of all-cause and cardiovascular mortality in a curvilinear fashion. These findings suggest that individuals may receive substantial health benefits by walking just a couple days a week.
On the eve of the 2012 summer Olympic Games, the first Lancet Series on physical activity established that physical inactivity was a global pandemic, and global public health action was urgently needed. The present paper summarises progress on the topics covered in the first Series. In the past 4 years, more countries have been monitoring the prevalence of physical inactivity, although evidence of any improvements in prevalence is still scarce. According to emerging evidence on brain health, physical inactivity accounts for about 3·8% of cases of dementia worldwide. An increase in research on the correlates of physical activity in low-income and middle-income countries (LMICs) is providing a better evidence base for development of context-relevant interventions. A finding specific to LMICs was that physical inactivity was higher in urban (vs rural) residents, which is a cause for concern because of the global trends toward urbanisation. A small but increasing number of intervention studies from LMICs provide initial evidence that community-based interventions can be effective. Although about 80% of countries reported having national physical activity policies or plans, such policies were operational in only about 56% of countries. There are important barriers to policy implementation that must be overcome before progress in increasing physical activity can be expected. Despite signs of progress, efforts to improve physical activity surveillance, research, capacity for intervention, and policy implementation are needed, especially among LMICs.
The influence of the circadian clock on sleep scheduling has been studied extensively in the laboratory; however, the effects of society on sleep remain largely unquantified. We show how a smartphone app that we have developed, ENTRAIN, accurately collects data on sleep habits around the world. Through mathematical modeling and statistics, we find that social pressures weaken and/or conceal biological drives in the evening, leading individuals to delay their bedtime and shorten their sleep. A country's average bedtime, but not average wake time, predicts sleep duration. We further show that mathematical models based on controlled laboratory experiments predict qualitative trends in sunrise, sunset, and light level; however, these effects are attenuated in the real world around bedtime. Additionally, we find that women schedule more sleep than men and that users reporting that they are typically exposed to outdoor light go to sleep earlier and sleep more than those reporting indoor light. Finally, we find that age is the primary determinant of sleep timing, and that age plays an important role in the variability of population-level sleep habits. This work better defines and personalizes "normal" sleep, produces hypotheses for future testing in the laboratory, and suggests important ways to counteract the global sleep crisis.
Background
There is increasing interest in using smartphones as stand-alone physical activity monitors via their built-in accelerometers, but there is presently limited data on the validity of this approach.
Objective
The purpose of this work was to determine the validity and reliability of 3 Android smartphones for measuring physical activity among midlife and older adults.
MethodsA laboratory (study 1) and a free-living (study 2) protocol were conducted. In study 1, individuals engaged in prescribed activities including sedentary (eg, sitting), light (sweeping), moderate (eg, walking 3 mph on a treadmill), and vigorous (eg, jogging 5 mph on a treadmill) activity over a 2-hour period wearing both an ActiGraph and 3 Android smartphones (ie, HTC MyTouch, Google Nexus One, and Motorola Cliq). In the free-living study, individuals engaged in usual daily activities over 7 days while wearing an Android smartphone (Google Nexus One) and an ActiGraph.
ResultsStudy 1 included 15 participants (age: mean 55.5, SD 6.6 years; women: 56%, 8/15). Correlations between the ActiGraph and the 3 phones were strong to very strong (ρ=.77-.82). Further, after excluding bicycling and standing, cut-point derived classifications of activities yielded a high percentage of activities classified correctly according to intensity level (eg, 78%-91% by phone) that were similar to the ActiGraph’s percent correctly classified (ie, 91%). Study 2 included 23 participants (age: mean 57.0, SD 6.4 years; women: 74%, 17/23). Within the free-living context, results suggested a moderate correlation (ie, ρ=.59, P
Human movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal.
Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation
could greatly improve malaria control programs. Here, we use spatially explicit mobile phone data and malaria prevalence information
from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis identifies
importation routes that contribute to malaria epidemiology on regional spatial scales.
Neighborhood walkability can influence physical activity. We evaluated the validity of Walk Score(®) for assessing neighborhood walkability based on GIS (objective) indicators of neighborhood walkability with addresses from four US metropolitan areas with several street network buffer distances (i.e., 400-, 800-, and 1,600-meters). Address data come from the YMCA-Harvard After School Food and Fitness Project, an obesity prevention intervention involving children aged 5-11 years and their families participating in YMCA-administered, after-school programs located in four geographically diverse metropolitan areas in the US (n = 733). GIS data were used to measure multiple objective indicators of neighborhood walkability. Walk Scores were also obtained for the participant's residential addresses. Spearman correlations between Walk Scores and the GIS neighborhood walkability indicators were calculated as well as Spearman correlations accounting for spatial autocorrelation. There were many significant moderate correlations between Walk Scores and the GIS neighborhood walkability indicators such as density of retail destinations and intersection density (p < 0.05). The magnitude varied by the GIS indicator of neighborhood walkability. Correlations generally became stronger with a larger spatial scale, and there were some geographic differences. Walk Score(®) is free and publicly available for public health researchers and practitioners. Results from our study suggest that Walk Score(®) is a valid measure of estimating certain aspects of neighborhood walkability, particularly at the 1600-meter buffer. As such, our study confirms and extends the generalizability of previous findings demonstrating that Walk Score is a valid measure of estimating neighborhood walkability in multiple geographic locations and at multiple spatial scales.
Accurate assessment is required to assess current and changing physical activity levels, and to evaluate the effectiveness of interventions designed to increase activity levels. This study systematically reviewed the literature to determine the extent of agreement between subjectively (self-report e.g. questionnaire, diary) and objectively (directly measured; e.g. accelerometry, doubly labeled water) assessed physical activity in adults.
Eight electronic databases were searched to identify observational and experimental studies of adult populations. Searching identified 4,463 potential articles. Initial screening found that 293 examined the relationship between self-reported and directly measured physical activity and met the eligibility criteria. Data abstraction was completed for 187 articles, which described comparable data and/or comparisons, while 76 articles lacked comparable data or comparisons, and a further 30 did not meet the review's eligibility requirements. A risk of bias assessment was conducted for all articles from which data was abstracted.
Correlations between self-report and direct measures were generally low-to-moderate and ranged from -0.71 to 0.96. No clear pattern emerged for the mean differences between self-report and direct measures of physical activity. Trends differed by measure of physical activity employed, level of physical activity measured, and the gender of participants. Results of the risk of bias assessment indicated that 38% of the studies had lower quality scores.
The findings suggest that the measurement method may have a significant impact on the observed levels of physical activity. Self-report measures of physical activity were both higher and lower than directly measured levels of physical activity, which poses a problem for both reliance on self-report measures and for attempts to correct for self-report - direct measure differences. This review reveals the need for valid, accurate and reliable measures of physical activity in evaluating current and changing physical activity levels, physical activity interventions, and the relationships between physical activity and health outcomes.
Income inequality has generally been associated with differences in health. A psychosocial interpretation of health inequalities, in terms of perceptions of relative disadvantage and the psychological consequences of inequality raises several conceptual and empirical problems. Income inequality is accompanied by many differences in conditions of life at the individual and population levels, which may adversely influence health. Interpretation of links between income inequality and health must begin with the structural causes, of inequalities, and not just locus on perceptions of that inequality. Reducing health inequalities and improving public health in the 21st century requires strategic investment in neo-material conditions via more equitable distribution of public and private resources.
To gain some insight into how various behavioral (lifestyle) factors influence sleep duration, by investigation of the relationship of sleep time to waking activities using the American Time Use Survey (ATUS).
Cross-sectional data from ATUS, an annual telephone survey of a population sample of US citizens who are interviewed regarding how they spent their time during a 24-hour period between 04:00 on the previous day and 04:00 on the interview day.
Data were pooled from the 2003, 2004, and 2005 ATUS databases involving N=47,731 respondents older than 14 years of age.
N/A.
Adjusted multiple linear regression models showed that the largest reciprocal relationship to sleep was found for work time, followed by travel time, which included commute time. Only shorter than average sleepers (<7.5 h) spent more time socializing, relaxing, and engaging in leisure activities, while both short (<5.5 h) and long sleepers (> or =8.5 h) watched more TV than the average sleeper. The extent to which sleep time was exchanged for waking activities was also shown to depend on age and gender. Sleep time was minimal while work time was maximal in the age group 45-54 yr, and sleep time increased both with lower and higher age.
Work time, travel time, and time for socializing, relaxing, and leisure are the primary activities reciprocally related to sleep time among Americans. These activities may be confounding the frequently observed association between short and long sleep on one hand and morbidity and mortality on the other hand and should be controlled for in future studies.
Despite their importance for urban planning, traffic forecasting and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Lévy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.
With continued widespread acceptance of pedometers by both researchers and practitioners, evidence-based steps/day indices are needed to facilitate measurement and motivation applications of physical activity (PA) in public health. Therefore, the purpose of this article is to reprise, update, and extend the current understanding of dose-response relationships in terms of pedometer-determined PA. Any pedometer-based PA guideline presumes an accurate and standardized measure of steps; at this time, industry standards establishing quality control of instrumentation is limited to Japan where public health pedometer applications and the 10,000 steps.d slogan are traceable to the 1960s. Adult public health guidelines promote > or =30 min of at least moderate-intensity daily PA, and this translates to 3000-4000 steps if they are: 1) at least moderate intensity (i.e., > or =100 steps.min); 2) accumulated in at least 10-min bouts; and 3) taken over and above some minimal level of PA (i.e., number of daily steps) below which individuals might be classified as sedentary. A zone-based hierarchy is useful for both measurement and motivation purposes in adults: 1) <5000 steps.d (sedentary); 2) 5000-7499 steps.d (low active); 3) 7500-9999 steps.d (somewhat active); 4) > or =10,000-12,499 steps.d (active); and 5) > or =12,500 steps.d (highly active). Evidence to support youth-specific cutoff points is emerging. Criterion-referenced approaches based on selected health outcomes present the potential for advancing evidence-based steps/day standards in both adults and children from a measurement perspective. A tradeoff that needs to be acknowledged and considered is the impact on motivation when evidence-based cutoff points are interpreted by individuals as unattainable goals.
The global pandemic of physical inactivity requires a multisectoral, multidisciplinary public-health response. Scaling up interventions that are capable of increasing levels of physical activity in populations across the varying cultural, geographic, social, and economic contexts worldwide is challenging, but feasible. In this paper, we review the factors that could help to achieve this. We use a mixed-methods approach to comprehensively examine these factors, drawing on the best available evidence from both evidence-to-practice and practice-to-evidence methods. Policies to support active living across society are needed, particularly outside the health-care sector, as demonstrated by some of the successful examples of scale up identified in this paper. Researchers, research funders, and practitioners and policymakers in culture, education, health, leisure, planning, and transport, and civil society as a whole, all have a role. We should embrace the challenge of taking action to a higher level, aligning physical activity and health objectives with broader social, environmental, and sustainable development goals.
Smartphone apps claim to help conditions from addiction to schizophrenia, but few have been thoroughly tested.
Background:
Physical inactivity is a global pandemic responsible for over 5 million deaths annually through its effects on multiple non-communicable diseases. We aimed to document how objectively measured attributes of the urban environment are related to objectively measured physical activity, in an international sample of adults.
Methods:
We based our analyses on the International Physical activity and Environment Network (IPEN) adult study, which was a coordinated, international, cross-sectional study. Participants were sampled from neighbourhoods with varied levels of walkability and socioeconomic status. The present analyses of data from the IPEN adult study included 6822 adults aged 18-66 years from 14 cities in ten countries on five continents. Indicators of walkability, public transport access, and park access were assessed in 1·0 km and 0·5 km street network buffers around each participant's residential address with geographic information systems. Mean daily minutes of moderate-to-vigorous-intensity physical activity were measured with 4-7 days of accelerometer monitoring. Associations between environmental attributes and physical activity were estimated using generalised additive mixed models with gamma variance and logarithmic link functions.
Results:
Four of six environmental attributes were significantly, positively, and linearly related to physical activity in the single variable models: net residential density (exp[b] 1·006 [95% CI 1·003-1·009]; p=0·001), intersection density (1·069 [1·011-1·130]; p=0·019), public transport density (1·037 [1·018-1·056]; p=0·0007), and number of parks (1·146 [1·033-1·272]; p=0·010). Mixed land use and distance to nearest public transport point were not related to physical activity. The difference in physical activity between participants living in the most and least activity-friendly neighbourhoods ranged from 68 min/week to 89 min/week, which represents 45-59% of the 150 min/week recommended by guidelines.
Interpretation:
Design of urban environments has the potential to contribute substantially to physical activity. Similarity of findings across cities suggests the promise of engaging urban planning, transportation, and parks sectors in efforts to reduce the health burden of the global physical inactivity pandemic.
Funding:
Funding for coordination of the IPEN adult study, including the present analysis, was provided by the National Cancer Institute of National Institutes of Health (CA127296) with studies in each country funded by different sources.
In a variety of situations in psychological research, it is desirable to be able to make statistical comparisons between correlation coefficients measured on the same individuals. For example, an experimenter may wish to assess whether two predictors correlate equally with a criterion variable. In another situation, the experimenter may wish to test the hypothesis that an entire matrix of correlations has remained stable over time. The present article reviews the literature on such tests, points out some statistics that should be avoided, and presents a variety of techniques that can be used safely with medium to large samples. Several illustrative numerical examples are provided.
The article, written in 1973, examines what comparisons of income distributions can be made when Lorenz curves cross, employing the concept of third-order stochastic dominance.
As roughly 20 million Fitbit owners can attest, the idea of the "quantified self" is enticing. Consumers are turning to their smartphones and wearable devices to count their steps, their calories, or their hours of sleep; to help them quit smoking, drinking, or stressing; or to help manage chronic illness. And this life-tracking craze has produced something that many clinical researchers covet: a deluge of intimate data about individuals' moment-to-moment behavior, and the chance to influence that behavior in real time, through activities built into an app or strategically timed alerts and messages. Major university health centers and government funding agencies hope "mHealth" will finally make a dent in intractable public health problems, from obesity to tobacco use to depression. But harnessing the self-tracking trend to promote healthier behavior is far from a sure bet, as the first generation of mobile health researchers are discovering.
Accurate and timely estimates of population characteristics are a critical input to social and economic research and policy. In industrialized economies, novel sources of data are enabling new approaches to demographic profiling, but in developing countries, fewer sources of big data exist.We show that an individual's past history of mobile phone use can be used to infer his or her socioeconomic status. Furthermore, we demonstrate that the predicted attributes of millions of individuals can, in turn, accurately reconstruct the distribution of wealth of an entire nation or to infer the asset distribution of microregions composed of just a few households. In resource-constrained environments where censuses and household surveys are rare, this approach creates an option for gathering localized and timely information at a fraction of the cost of traditional methods.
Corresponding Author: Mitesh S. Patel, MD, MBA, MS, University of Pennsylvania, 13th Floor Blockley Hall, 423 Guardian Dr, Philadelphia, PA 19104 (mpatel@upenn.edu).
Author Contributions: Ms Case had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: All authors.
Acquisition, analysis, or interpretation of data: Case, Patel.
Drafting of the manuscript: All authors.
Critical revision of the manuscript for important intellectual content: Case, Patel.
Statistical analysis: Case, Patel.
Administrative, technical, or material support: Case, Burwick, Patel.
Study supervision: Volpp, Patel.
Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Volpp reported receiving research funding from Humana, Merck, Discovery, Weight Watchers, and CVS; consulting income from CVS and VALhealth; and being a principal at VALhealth. No other disclosures were reported.
Funding/Support: This study was funded in part through grant RC4 AG039114-01 from the National Institute on Aging. Dr Patel was supported by the US Department of Veteran Affairs and the Robert Wood Johnson Foundation.
Role of the Funder/Sponsor: The National Institute on Aging, the US Department of Veteran Affairs, and the Robert Wood Johnson Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Studies on the health effects of income inequality have generated great interest. The evidence on this association between countries is mixed,1-4 but income inequality and health have been linked within the United States,5-11 Britain,12 and Brazil.13 Questions remain over how to interpret these findings and the mechanisms involved. We discuss three interpretations of the association between income inequality and health: the individual income interpretation, the psychosocial environment interpretation, and the neo-material interpretation. Summary points Income inequality has generally been associated with differences in health A psychosocial interpretation of health inequalities, in terms of perceptions of relative disadvantage and the psychological consequences of inequality, raises several conceptual and empirical problems Income inequality is accompanied by many differences in conditions of life at the individual and population levels, which may adversely influence health Interpretation of links between income inequality and health must begin with the structural causes of inequalities, and not just focus on perceptions of that inequality Reducing health inequalities and improving public health in the 21st century requires strategic investment in neo-material conditions via more equitable distribution of public and private resources
Physical activity clearly is beneficial for health. Thus, it is unfortunate that so many people worldwide do not get sufficient activity to meet guidelines. This talk will focus on the public health problem of physical inactivity worldwide, by estimating how much of the world's major non-communicable diseases is due to inactivity.
Altering physical and social environments can change behaviors to improve population health
Although measures of inequality are increasingly used to compare nations, cities, and other social units, the properties of alternative measures have received little attention in the sociological literature. This paper considers both theoretical and methodological implications of several common measures of inequality. The Gini index is found to satisfy the basic criteria of scale invariance and the principle of transfers, but two other measures--the coefficent of variation and Theil's measure--are usually preferable. While none of these measures is strictly appropriate for interval-level data, valid comparisons can be made in special circumstances. The social welfare function is considered as an alternative approach for developing measures of inequality, and methods of estimation, testing, and decomposition are presented.
Background:
Physical activity (PA) has been consistently implicated in the etiology of obesity, whereas recent evidence on the importance of sedentary time remains inconsistent. Understanding of dose-response associations of PA and sedentary time with overweight and obesity in adults can be improved with large-scale studies using objective measures of PA and sedentary time. The purpose of this study was to examine the strength, direction and shape of dose-response associations of accelerometer-based PA and sedentary time with body mass index (BMI) and weight status in 10 countries, and the moderating effects of study site and gender.
Methods:
Data from the International Physical activity and the Environment Network (IPEN) Adult study were used. IPEN Adult is an observational multi-country cross-sectional study, and 12 sites in 10 countries are included. Participants wore an accelerometer for seven consecutive days, completed a socio-demographic questionnaire and reported height and weight. In total, 5712 adults (18-65 years) were included in the analyses. Generalized additive mixed models, conducted in R, were used to estimate the strength and shape of the associations.
Results:
A curvilinear relationship of accelerometer-based moderate-to-vigorous PA and total counts per minute with BMI and the probability of being overweight/obese was identified. The associations were negative, but weakened at higher levels of moderate-to-vigorous PA (>50 min per day) and higher counts per minute. No associations between sedentary time and weight outcomes were found. Complex site- and gender-specific findings were revealed for BMI, but not for weight status.
Conclusions:
On the basis of these results, the current Institute of Medicine recommendation of 60 min per day of moderate-to-vigorous PA to prevent weight gain in normal-weight adults was supported. No relationship between sedentary time and the weight outcomes was present, calling for further examination. If moderator findings are confirmed, the relationship between PA and BMI may be country- and gender-dependent, which could have important implications for country-specific health guidelines.
In psychological research, it is desirable to be able to make statistical comparisons between correlation coefficients measured on the same individuals. For example, an experimenter (E) may wish to assess whether 2 predictors correlate equally with a criterion variable. In another situation, the E may wish to test the hypothesis that an entire matrix of correlations has remained stable over time. The present article reviews the literature on such tests, points out some statistics that should be avoided, and presents a variety of techniques that can be used safely with medium to large samples. Several numerical examples are provided. (18 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Physical inactivity is the fourth leading cause of death worldwide. We summarise present global efforts to counteract this problem and point the way forward to address the pandemic of physical inactivity. Although evidence for the benefits of physical activity for health has been available since the 1950s, promotion to improve the health of populations has lagged in relation to the available evidence and has only recently developed an identifiable infrastructure, including efforts in planning, policy, leadership and advocacy, workforce training and development, and monitoring and surveillance. The reasons for this late start are myriad, multifactorial, and complex. This infrastructure should continue to be formed, intersectoral approaches are essential to advance, and advocacy remains a key pillar. Although there is a need to build global capacity based on the present foundations, a systems approach that focuses on populations and the complex interactions among the correlates of physical inactivity, rather than solely a behavioural science approach focusing on individuals, is the way forward to increase physical activity worldwide.
Physical inactivity is an important contributor to non-communicable diseases in countries of high income, and increasingly so in those of low and middle income. Understanding why people are physically active or inactive contributes to evidence-based planning of public health interventions, because effective programmes will target factors known to cause inactivity. Research into correlates (factors associated with activity) or determinants (those with a causal relationship) has burgeoned in the past two decades, but has mostly focused on individual-level factors in high-income countries. It has shown that age, sex, health status, self-efficacy, and motivation are associated with physical activity. Ecological models take a broad view of health behaviour causation, with the social and physical environment included as contributors to physical inactivity, particularly those outside the health sector, such as urban planning, transportation systems, and parks and trails. New areas of determinants research have identified genetic factors contributing to the propensity to be physically active, and evolutionary factors and obesity that might predispose to inactivity, and have explored the longitudinal tracking of physical activity throughout life. An understanding of correlates and determinants, especially in countries of low and middle income, could reduce the eff ect of future epidemics of inactivity and contribute to effective global prevention of non-communicable diseases.
To implement effective non-communicable disease prevention programmes, policy makers need data for physical activity levels and trends. In this report, we describe physical activity levels worldwide with data for adults (15 years or older) from 122 countries and for adolescents (13-15-years-old) from 105 countries. Worldwide, 31·1% (95% CI 30·9-31·2) of adults are physically inactive, with proportions ranging from 17·0% (16·8-17·2) in southeast Asia to about 43% in the Americas and the eastern Mediterranean. Inactivity rises with age, is higher in women than in men, and is increased in high-income countries. The proportion of 13-15-year-olds doing fewer than 60 min of physical activity of moderate to vigorous intensity per day is 80·3% (80·1-80·5); boys are more active than are girls. Continued improvement in monitoring of physical activity would help to guide development of policies and programmes to increase activity levels and to reduce the burden of non-communicable diseases.
Strong evidence shows that physical inactivity increases the risk of many adverse health conditions, including major non-communicable diseases such as coronary heart disease, type 2 diabetes, and breast and colon cancers, and shortens life expectancy. Because much of the world's population is inactive, this link presents a major public health issue. We aimed to quantify the eff ect of physical inactivity on these major non-communicable diseases by estimating how much disease could be averted if inactive people were to become active and to estimate gain in life expectancy at the population level.
For our analysis of burden of disease, we calculated population attributable fractions (PAFs) associated with physical inactivity using conservative assumptions for each of the major non-communicable diseases, by country, to estimate how much disease could be averted if physical inactivity were eliminated. We used life-table analysis to estimate gains in life expectancy of the population.
Worldwide, we estimate that physical inactivity causes 6% (ranging from 3·2% in southeast Asia to 7·8% in the eastern Mediterranean region) of the burden of disease from coronary heart disease, 7% (3·9-9·6) of type 2 diabetes, 10% (5·6-14·1) of breast cancer, and 10% (5·7-13·8) of colon cancer. Inactivity causes 9% (range 5·1-12·5) of premature mortality, or more than 5·3 million of the 57 million deaths that occurred worldwide in 2008. If inactivity were not eliminated, but decreased instead by 10% or 25%, more than 533 000 and more than 1·3 million deaths, respectively, could be averted every year. We estimated that elimination of physical inactivity would increase the life expectancy of the world's population by 0·68 (range 0·41-0·95) years.
Physical inactivity has a major health eff ect worldwide. Decrease in or removal of this unhealthy behaviour could improve health substantially.
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Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
We identified individual-level diurnal and seasonal mood rhythms in cultures across the globe, using data from millions of public Twitter messages. We found that individuals awaken in a good mood that deteriorates as the day progresses--which is consistent with the effects of sleep and circadian rhythm--and that seasonal change in baseline positive affect varies with change in daylength. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends.
When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970's, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine, and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods-or developing methods related to matching-do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.
U.S. adults may have lower levels of ambulatory physical activity compared with adults living in other countries.
The purpose of this study was to provide descriptive, epidemiological data on the average number of steps per day estimated to be taken by U.S. adults and to identify predictors of pedometer-measured physical activity on the basis of demographic characteristics and self-reported behavioral characteristics.
The America On the Move study was conducted in 2003. Individuals (N = 2522) aged 13 yr and older consented to fill out a survey, including 1921 adults aged 18 yr and older. Valid pedometer data were collected on 1136 adults with Accusplit AE120 pedometers. Data were weighted to reflect the general U.S. population according to several variables (age, gender, race/ethnicity, education, income, level of physical activity, and number of 5- to 17-yr-old children in the household). Differences in steps per day between subgroups were analyzed using unpaired t-tests when only two subgroups were involved or one-way ANOVA if multiple subgroups were involved.
Adults reported taking an average of 5117 steps per day. Male gender, younger age, higher education level, single marital status, and lower body mass index were all positively associated with steps per