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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
... A recent large-scale digital health study on 717,527 smartphone users across 111 countries demonstrated a correspondence between activity inequality and obesity and a city's walkability, with findings of gender differences in activity and obesity 5 . Such findings, if found to be robust and replicable within the population of the United States (US), can serve as a quantifiable benchmark for assessing disparities in physical activity in the US and their relation to sex, age, geographic location, built environment, and other potential key influencing factors. ...
... In addition, we found that the mode of daily steps for Arizona (7196) was higher than in Texas (6839), whereas the mean of daily steps in Arizona (7568) was lower than in Texas (7648). The higher mode in Arizona compared to Texas indicates that the distribution skewness varies across states and, hence, reinforces the utility of activity inequality evaluation, which can uncover activity variance within a region 5 . ...
... Variability in the gender-activity gap across US states We hypothesized that the skewness in each state's activity distribution (Fig. 1b, c) may be attributable to gender differences in activity levels given the findings by Althoff et al. 5 . Across all populations, males had approxi-mately1200 more daily steps on average than females. ...
Large-scale and detailed analyses of activity in the United States (US) remain limited. In this work, we leveraged the comprehensive wearable, demographic, and survey data from the All of Us Research Program, the largest and most diverse population health study in the US to date, to apply and extend the previous global findings on activity inequality within the context of the US. We found that daily steps differed by sex at birth, age, body characteristics, geography, and built environment. Quantifying activity inequality using the modified Gini index, we found a strong correlation with obesity prevalence (R2 = 0.804) and a moderate correlation with perceived walkability (R2 = 0.426) and the activity gender gap (R2 = 0.385). This study demonstrates the value of digital health technologies in exploring and understanding public health practices while highlighting the need to examine complexities, including biopsychosocial factors that may contribute to activity inequality.
... In activity logging applications such as Fitbit, Under Armour Record, and Argus, users might take one of many possible actions from a large and diverse space of potential actions at any point in time. Users continuously track many actions of their lives including exercise, diet, sleep, and commuting behavior with the goal of improving self-knowledge and personal well-being [6,7,45,46]. User modeling is critical to making activity logging applications more useful by providing users with personalized experiences matching their specific objectives [12,19,23,27,54]. ...
... To illustrate critical properties of real-world actions we use a dataset of logged activities from a mobile activity logging application, Argus by Azumio, used in previous work on activity logging [6,7,45]. This smartphone app allows users to track their various daily activities including drink, sleep, heart rate, running, weight, food, walking, biking, workout, and stretching actions. ...
... This dataset includes over four thousand active users taking 1.2 million actions over the course of seven months (all users logged at least two unique actions per day on average). Due to the popularity of the app, this set of users is very diverse in terms of age, gender, health status, country of origin, and other features [7]. We note that the following properties of real-world actions also hold in other datasets including Under Armour activity logging app data (Section 6.1). ...
Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million actions taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, our model improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions.
... We conduct an observational study using data from a mobile activity tracking application, Argus by Azumio [5,7,38] (Table 1). This smartphone app allows users to track various daily activities including running, walking, cardio, heart rate, weight, sleep, drink, and food logging activities (the app also supports other more rarely used activities such as measuring stress or logging yoga which we aggregate and call "other" activities; less than 2% of total logging). ...
... This long observation period and large scale uniquely enables us to study re-engagement patterns after prolonged user absence. Due to the popularity of the app, its users are relatively diverse in terms of age, gender, weight status, country of origin, and other features [7]. ...
... This work studies user engagement and re-engagement patterns after long periods of inactivity in the context of activity tracking applications. We extend previous work (e.g., [1,5,7,27,35,38,47]) by discovering that users regularly re-engage after long periods of inactivity structuring user engagement into multiple lives with distinct characteristics. We demonstrate that these multiple lives are driven by user intent and external influences. ...
Mobile health applications that track activities, such as exercise, sleep, and diet, are becoming widely used. While these activity tracking applications have the potential to improve our health, user engagement and retention are critical factors for their success. However, long-term user engagement patterns in real-world activity tracking applications are not yet well understood. Here we study user engagement patterns within a mobile physical activity tracking application consisting of 115 million logged activities taken by over a million users over 31 months. Specifically, we show that over 75% of users return and re-engage with the application after prolonged periods of inactivity, no matter the duration of the inactivity. We find a surprising result that the re-engagement usage patterns resemble those of the start of the initial engagement period, rather than being a simple continuation of the end of the initial engagement period. This evidence points to a conceptual model of multiple lives of user engagement, extending the prevalent single life view of user activity. We demonstrate that these multiple lives occur because the users have a variety of different primary intents or goals for using the app. We find evidence for users being more likely to stop using the app once they achieved their primary intent or goal (e.g., weight loss). However, these users might return once their original intent resurfaces (e.g., wanting to lose newly gained weight). Based on insights developed in this work, including a marker of improved primary intent performance, our prediction models achieve 71% ROC AUC. Overall, our research has implications for modeling user re-engagement in health activity tracking applications and has consequences for how notifications, recommendations as well as gamification can be used to increase engagement.
... Individuals who exercise regularly are more prone to maintaining good health over an extended period. [65][66][67] Disparities in physical activity levels are expected to emerge among nations, driven by factors like female inactivity and environmental considerations such as the walkability of urban areas. Public intervention proposes that enhancing the walkability or cyclability of cities would enhance overall well-being. ...
... Public intervention proposes that enhancing the walkability or cyclability of cities would enhance overall well-being. [67,68] COPD patients' daily actual work decreases in the early stages of the illness and eventually disappears, with significant clinical outcomes, when compared to healthy, age-matched controls. Higher adult lung volumes are linked to high-impact health work during puberty but not the type of flight. ...
... Higher adult lung volumes are linked to high-impact health work during puberty but not the type of flight. [67,69] Reduced lung capacity decline and intensification in COPD patients are linked to normal levels of physical activity, [70] as is a lower risk of developing COPD in smokers. Family might also be involved. ...
Respiratory infections, a global health priority according to the World Health Organization, cause around 7.5 million deaths annually, constituting 14% of global mortality. Beyond severe health implications, these diseases exacerbate social disparities and impose a substantial economic burden. Chronic obstructive pulmonary disease (COPD) combines chronic bronchitis (airway inflammation) and emphysema (air sac destruction) caused by prolonged exposure to irritants, and poor lifestyle choices lead to airway blockage and breathing difficulties. Lifestyle choices significantly influence health trajectories, evidenced by a consistent increase in “positive comfort” over time. A Chinese study highlights the correlation between adopting a healthy lifestyle and increased life expectancy. European health initiatives address these challenges, emphasizing early detection through large-scale health camps to identify new cases and assess severity. Exacerbation and infections are primary triggers, with bacteria and viruses requiring antibiotic interventions. Awareness campaigns targeting causes, symptoms, and prevention, including childhood infection initiatives with influenza and pneumococcal vaccinations, are crucial. Motivating smoking cessation and encouraging whole grain, fruit, and vegetable consumption mitigate lung oxidative damage. Promoting physical activity and addressing environmental pollution contribute to overall lung health. Timely nutritional evaluations for newly diagnosed cases manage obesity and malnutrition and prevent further lung function deterioration. There is growing attention toward the influence of poor lifestyle choices like sedentary lifestyle, environmental exposure, and unhealthy dietary patterns on the increased risk of COPD development besides smoking. This essay explores these factors, recognizing the intricate interplay between lifestyle and COPD prevention and management.
... Additional conditions that contribute to disparities include the built environment, which is defined as the human-made buildings and infrastructure that provide physical settings for individuals to live, work, learn, and engage in recreational activities [23]. Studies exploring the built environment have reported that minorities are more likely to live in communities with poorer walkability and limited access to resources [24][25][26], consequently resulting in worse health outcomes. The impacts of environmental determinants on PA extend beyond just the built environment, encompassing other environmental determinants such as air pollution and extreme weather [27]. ...
... Previous work has demonstrated that built environments possessing PA-supportive features (e.g., gyms and green spaces) can facilitate more PA [70,71] than communities that lack these attributes. Other neighborhood and built environment factors, such as crime rate, neighborhood walkability, and the prevalence of inclement weather [24][25][26]72], have also been shown to impact PA participation. Within the context of organizational environments (i.e., workplaces, college campuses, etc.), these entities can support PA by facilitating spaces that encourage PA. ...
Physical activity (PA)’s benefits are well established, yet many U.S. adults fail to meet PA guidelines. This is especially true for minorities facing social inequities. This study explored PA’s barriers and facilitators among urban Midwestern minorities using a mixed-methods approach framed on the socio-ecological model. A cross-sectional survey was conducted between January and June 2024 among community-dwelling minorities. Participants were grouped as completing low (LLPA) or high (HLPA) weekly leisure-time PA for comparison. Quantitative analysis included MANOVA, follow-up ANOVAs, and calculation of effect sizes. Qualitative data were assessed using inductive thematic analysis. Twenty-nine adults (44.83% Black, 41.37% Latino) participated in the study. The HLPA group (n = 18) reported higher leisure-time PA (p = 0.001, d = 2.21) and total PA (p = 0.02, d = 1.00) compared to the LLPA group (n = 11). LLPA participants faced more personal barriers to PA (p = 0.02, d = −0.92). Common barriers identified in the interviews included a lack of time and financial costs. Facilitators included social support and available PA facilities. Both groups achieved the USPA guidelines through different PA domains. Increasing social support and lowering PA-related costs could enhance participation. Addressing barriers and leveraging existing facilitators are crucial to increasing PA among minorities.
... Meskipun studi lama ini menyampaikan terkait jumlah yang terbiasa hidup sehat lebih banyak, dibanding yang tidak, hal tersebut menggambarkan degradasi perilaku hidup sehat dan aktif selama sepuluh tahun terakhir. Apalagi diikuti dengan munculnya data terkait Masyarakat Indonesia menjadi yang termalas di dunia, dengan hanya menorehkan 3500 langkah per hari, jauh di bawah rekomendasi langkah yang seharusnya mencapai 10.000 langkah per hari (Althoff et al., 2017). Gap yang sangat besar diciptakan oleh perilaku kita sendiri, jika dibandingkan dengan negara-negara besar, maju dan berkembang lainnya. ...
... Hampir setengah dari subjek studi yang dilakukan di Indonesia, khususnya di kampus UI, memiliki tingkat perilaku gaya hidup tidak sehat, hal ini berkorelasi dengan hasil dari studi ini. Ditambah dengan kesamaan hasil data mengenai munculnya penurunan perilaku aktif berolahraga pada masyarakat (Mutohir et al., 2023) dan studi skala besar yang menyebutkan Indonesia menjadi negara termalas dengan capaian jumlah langkah kaki hanya 3500 langkah per hari (Althoff et al., 2017). Permasalahan anak remaja akhir, khususnya kelompok mahasiswa memang menjadi kompleks saat ini. ...
The aim of this study was to investigate the relationship between the active personality habits of student exchange in the Modul Nusantara movement toward their level of participation in sports and the risk of psychological impacts during the inbound activity. This study did not include a review of the Modul Nusantara activity process and lectures based on scientific areas. Methods: This study utilises a quantitative descriptive research design, combining two methods: survey and bibliometric analysis. The primary goal is to identify barriers in engaging in sports among specific students who participate in the Modul Nusantara activity. The participants were selected from a list of 99 student populations. These students were accepted by Universitas Negeri Yogyakarta to participate in student exchanges under the MBKM program, which is facilitated by the PMM scheme administered by the Ministry of Education, Culture, Research and Technology. Participants were selected using a population of 85 individuals, including 37 males and 48 females, who voluntarily participated part in generating the information. The study used questionnaires to assess motives engaging sports activity through exercise barrier questionnaires, as well as bibliometric measurements using digital learning media to verify the effects of obstacle activities on stress, depression, and anxiety levels. Data measurement analysis is conducted using descriptive statistical tests and calculation formulas that are based on the measurement category approach of each instrument, with a particular focus on the psychosocial element. Additional investigation into the association between variables related to barriers to physical activity, in connection to mental factors, including sub-indicators such as depression, anxiety, and stress. Results: Distinct findings from the distribution of perception to the exercise motive questionnaire revealed that most participants expressed disagreement with the statements in the instrument. However, the second highest response, particularly among inbound students, demonstrated agreement with the 12 indicators of challenge motivations in exercise. Psychosocial aspect data revealed a leftward trend in the distribution of responses. Each question item reflects the individual's condition, with answers indicating experiences at a certain level or throughout a certain period of time, some time, or experiences that do not apply to the subject at all. A thorough investigation of each item related to the correlation between barrier to exercise and psychosocial aspects revealed that variables such as lack of a preferred exercise location in close location, unfamiliarity with the nearest exercise facility, living at a considerable distance from colleagues, a far-off exercise location, preference for activities besides exercise, laziness, lack of motivation to exercise, and insufficient time or minimal time allocation for exercise exhibit a strong correlation (r0.05; p: 0.05) and a very powerful correlation (r0.05; p: 0.01) with dimensions of depression, anxiety, and stress. At the same time, weather conditions are strongly correlated with depression and stress. Additional variables, plenty of college assignments (r: 0.298 0.05; p: 0.006 0.05) and insufficient time for physical exercise (r: 0.355 0.05; p: 0.000 0.01) are strongly and significantly associated only with anxiety. In contrast to other variables, lack of exercise among friends and family members proved to be rather unsatisfactory in terms of depression, anxiety, and stress. In conclusion, Objectives xdriving physical activity and exercise among students participating in the Modul Nusantara strongly associated with the likelihood of developing depression, anxiety, and stress. This, in turn, can lead to a decrease in the willingness to engage in physical activity, therefore reducing the chance of impaired immunity associated with mental illnesses. More extensive study will be expected to explore in greater depth about the ratio of lectures load during inbound in each inbound students, possibilities for physical activity and recreation, and more thorough psychological factors.
... Proximity and access to natural spaces and sidewalks can lead to increased and regular physical activity, especially in urban areas (33)(34)(35)(36)(37). This association between walkable environments and physical activity has been noted across different geographical scales (9,38). For example, a recent study using data from smartphones to assess physical activity globally suggested that inequalities in physical activity were predictive of obesity prevalence and features of the physical environment such as, walkability, lowered the activity inequality (38). ...
... This association between walkable environments and physical activity has been noted across different geographical scales (9,38). For example, a recent study using data from smartphones to assess physical activity globally suggested that inequalities in physical activity were predictive of obesity prevalence and features of the physical environment such as, walkability, lowered the activity inequality (38). ...
More than one-third of the adult population in the United States is obese. Obesity has been linked to factors such as, genetics, diet, physical activity and the environment. However, evidence indicating associations between the built environment and obesity has varied across studies and geographical contexts. Here, we used deep learning and approximately 150,000 high resolution satellite images to extract features of the built environment. We then developed linear regression models to consistently quantify the association between the extracted features and obesity prevalence at the census tract level for six cities in the United States. The extracted features of the built environment explained 72% to 90% of the variation in obesity prevalence across cities. Outof-sample predictions were considerably high with correlations greater than 80% between predicted and true obesity prevalence across all census tracts. This study supports a strong association between the built environment and obesity prevalence. Additionally, it also illustrates that features of the built environment extracted from satellite images can be useful for studying health indicators, such as obesity. Understanding the association between specific features of the built environment and obesity prevalence can lead to structural changes that could encourage physical activity and decreases in obesity prevalence.
... Commercial mHealth devices (e.g., smartphones, wearables) passively record data from their sensors such as GPS (location coordinates i.e., longitudes, latitudes), microphone, and accelerometer (e.g., three-dimensional accelerations), without any user input. These "raw" data from the sensors are used to derive or infer behavioral features, such as physical activity level [2], the number of places visited [3], the amount of time spent in conversation [2], and the quality and duration of sleep [4,5]. They provide insight on users' physical activity, sleep patterns, and sociability. ...
... Commercial mHealth devices (e.g., smartphones, wearables) passively record data from their sensors such as GPS (location coordinates i.e., longitudes, latitudes), microphone, and accelerometer (e.g., three-dimensional accelerations), without any user input. These "raw" data from the sensors are used to derive or infer behavioral features, such as physical activity level [2], the number of places visited [3], the amount of time spent in conversation [2], and the quality and duration of sleep [4,5]. They provide insight on users' physical activity, sleep patterns, and sociability. ...
Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices. This article provides a series of simulation studies to show the utility of the proposed algorithm compared to existing methods. Application to a real clinical dataset is also illustrated.
... However, it aligns with the "optimal location" concept in urban planning, where a certain distance from facilities may encourage intentional physical activity. Althoff et al. (2017) demonstrated that the physical environment significantly influences physical activity levels using smartphone data [25]. However, this relationship is not always straightforward and may vary based on other factors. ...
... However, it aligns with the "optimal location" concept in urban planning, where a certain distance from facilities may encourage intentional physical activity. Althoff et al. (2017) demonstrated that the physical environment significantly influences physical activity levels using smartphone data [25]. However, this relationship is not always straightforward and may vary based on other factors. ...
Background
Lack of physical activity is a growing public health concern in Saudi Arabia, contributing to an increase in noncommunicable diseases. However, there is limited research on physical activity patterns and their associated factors in the Jazan region.
Objective
This study aimed to assess physical activity levels and identify sociodemographic, health-related, and environmental factors associated with physical activity among individuals in the Jazan region of Saudi Arabia.
Methods
This cross-sectional survey was conducted in Jazan. Physical activity was assessed using the abridged Arabic form of the International Physical Activity Questionnaire (IPAQ). Data on sociodemographic and health-related characteristics were collected using a self-administered questionnaire. Mann-Whitney U and Kruskal-Wallis tests were used to compare physical activity levels among groups, while Spearman's rank correlation was employed to investigate the relationships between continuous variables and physical activity.
Results
A total of 387 people completed the survey, 39.0% of whom were female and 61.0% were male. The participants' average age was 34.0 ± 10.1 years, and 98.4% were Saudi nationals. The study found that 42.1% of participants did not engage in high-intensity physical activity, while 39.8% did not engage in moderate-intensity activity. A strong association was found between income level, obesity, diabetes status, and high-intensity physical activity (p < 0.05). Proximity to recreational facilities was associated with moderate-intensity exercise (p = 0.05). Male participants reported walking more frequently than female participants (p = 0.033). Additionally, people with diabetes walked more often than those without (p = 0.018). There was a weak positive correlation between height and the frequency of high-intensity physical activity (r = 0.153, p = 0.022).
Conclusion
This study highlights the complex interactions between sociodemographic, health-related, and environmental factors that influence physical activity in Jazan. The findings underscore the need for targeted, culturally sensitive interventions to promote physical activity, particularly among women, individuals with obesity, and those with chronic conditions. Urban planning should consider the impact of the built environment on physical activity. Future research should use objective measurements and longitudinal methodologies to better understand these relationships and design effective public health interventions in line with Saudi Vision 2030.
... A prática regular de exercícios físicos contribui na diminuição em 31% na possibilidade de evoluir com síndrome metabólica (SM) (WU et al., 2017). No entanto, a crescente ociosidade desde os quarenta anos (ALTHOFF et al., 2017;OGUOMA et al., 2016) e a baixa aceitação a programas de encorajamento à prática de atividades físicas entre idosos (LEE; KIM, 2016;WU et al., 2016) colaboram para o incremento da prevalência da síndrome metabólica nesse grupo (BERGSTRÖM; BEHRE; SCHMIDT, 2012). Ocorre que, atualmente, houve uma mudança visível na forma como a população mundial se alimenta e se move. ...
Sarcopenia, conforme definição do Consenso Europeu de Diag-nóstico e Tratamento de Sarcopenia, é um distúrbio muscular generalizado e progressivo provocado pela perda de massa e perda de força muscular, sendo prevalente em pessoas idosas. Neste sentido, o presente trabalho buscou analisar a sarcopenia, fragilidades e fatores correlacionados entre idosos seguidos ambulatorialmente no Sudoeste do Brasil. Para isso, foi realizado um estudo transversal, com pacientes idosos do ambulatório de especialidade em Geriatria da Irmandade Santa Casa de Misericórdia de São Paulo. Ressalta que o presente estudo foi devidamente aprovado pelo Comitê de Ética em Pesquisa do ambulatório, tendo gerado o número do Certificado de Apresentação para Apreciação Ética (CAAE) na Plataforma Brasil: 37560720.4.0000.5479 e Número do Parecer Consubstanciado do CEP: 4.348.855. Para a análise de dados foi utilizado o coeficiente de cor-relação de Spearman considerando um nível de significância de 5% e uti-lização do software MINITAB 19 para análise estatística. Participaram do estudo 65 idosos com idade entre 60 e 70 anos. Desses, 18 eram do sexo masculino e 47 do sexo feminino. Os resultados indicam correlação entre o sexo masculino e a força de preensão palmar e níveis séricos de vitamina D. Também há correlação entre SARC-F com vitamina D e preensão palmar e circunferência da panturrilha com a osteoporose. Há associação negativa entre depressão e velocidade de marcha.
... MHC-Coach was developed by fine-tuning LLaMA 3-70B on a dataset of 3,268 human expert messages matched to a specific stage of change, 32 a domain-specific corpus on health and exercise to provide background information on the importance of physical activity in preventing cardiovascular disease 1,[35][36][37][38][39][40][41] , and detailed information about the TTM model and characteristics of individuals in each stage of change 10,42 (see Methods and Figure 1). The ...
Personalized, smartphone-based coaching improves physical activity but relies on static, human-crafted messages. We introduce My Heart Counts (MHC)-Coach, a large language model fine-tuned on the Transtheoretical Model of Change. MHC-Coach generates messages tailored to an individual's psychology (their "stage of change"), providing personalized support to foster long-term physical activity behavior change. To evaluate MHC-Coach's efficacy, 632 participants compared human-expert and MHC-Coach text-based interventions encouraging physical activity. Among messages matched to an individual's stage of change, 68.0% (N=430) preferred MHC-Coach-generated messages (P<0.001). Blinded behavioral science experts (N=2) rated MHC-Coach messages higher than human-expert messages for perceived effectiveness (4.4 vs. 2.8) and Transtheoretical Model alignment (4.1 vs. 3.5) on a 5-point Likert scale. This work demonstrates how language models can operationalize behavioral science frameworks for personalized health coaching, promoting long-term physical activity and potentially reducing cardiovascular disease risk at scale.
... In addition, our study was based on a convenience sample of participants, and the physical activity levels of these participants were generally high relative to the broader United States population, though not as high as in many non-industrial societies (Raichlen & Lieberman, 2022). Participants in this study took, on average, 10,998 ± 2916 (s.d.) steps per day during the study period, whereas the average daily step count for adults in the United States is only 4774 (Althoff et al., 2017). In contrast, Hadza hunter-gatherers have been shown to take an average of 15,047 ± 5377 steps per day (Sayre et al., 2023) and Rar amuri subsistence farmers have been reported to take an average of 18,800 ± 4500 steps per day (Shave et al., 2019). ...
The feet of people in non‐industrial societies often have higher, stiffer longitudinal arches (LAs) and larger intrinsic muscles than those of many people in post‐industrial societies. The prevailing explanation for this phenomenon is that people in post‐industrial societies commonly wear shoes that restrict foot mobility, while people in non‐industrial societies are often habitually barefoot or minimally shod. However, people in post‐industrial societies also tend to be less physically active than in non‐industrial societies, and it is possible that this, too, is a major determinant of their foot form and function. Here, we test the hypothesis that among people in post‐industrial societies, lower physical activity levels are associated with lower, less stiff LAs and smaller intrinsic muscles. In a cross‐sectional analysis of 40 adults in the United States, none of whom were habitually barefoot or minimally shod, we measured daily physical activity using accelerometry, LA height and static stiffness using photography, LA dynamic stiffness using kinematic and kinetic data, and intrinsic muscle size using ultrasound. Using Bayesian models, we found very low probabilities of positive associations between physical activity (step count, time spent in moderate‐to‐vigorous activity) and LA height, LA static stiffness, and muscle size. For LA dynamic stiffness, we found small to moderate probabilities of positive associations with physical activity variables. These findings suggest that physical activity is unlikely a major determinant of variation in LA and intrinsic muscle form and function among post‐industrial societies. It remains possible that physical activity affects LA and intrinsic muscle traits, but perhaps primarily among people who are habitually barefoot or minimally shod.
... We acknowledge that deconditioning confounds the conclusions related to the underlying cause of a lower exercise capacity, and that an age-, sex-, and activity-matched control group would be superior to age and sex alone. Althoff et al. 9 showed that the average daily steps in the United States is 4774 per day, roughly similar to our Long COVID group, but PEM is not experienced in the general US population. As can be appreciated from Figure 1A, there was considerable variability in exercise capacity, as some patients still possessed fitness levels >50th percentile for their sex and age (n = 5), while some healthy controls had very low fitness levels (<25th percentile, n = 7). ...
... Purposeful participants engage in recreational activities with specific, goal-oriented intentions, such as relaxation, skill development, or physical exercise. Their participation is deliberate and structured, reflecting a conscious effort to derive personal benefits from urban green spaces (Althoff et al., 2017). This highlights how individual motivations influence spatial engagement, reinforcing the role of green spaces as functional environments for self-improvement. ...
This study aims to evaluate visitors' assessments of urban green open spaces based on the Homo Urbanicus theory, encompassing the dimensions of people, opportunity, event, and space. Using data from 178 respondents visiting green open spaces (GOS) in Bandung, the analysis was conducted using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The findings indicate that both male and female visitors generally perceive the provision of primary and supporting facilities in GOS similarly. They equally recognize these facilities as essential without distinguishing between genders. However, concerning comfort, perceptions of primary and supporting facilities differ based on gender. Therefore, park management should design primary and supporting facilities to function generically for both men and women while differentiating comfort elements based on gender-specific preferences.
... Many research teams have employed the Gini coefficient to assess inequalities in green space exposure in urban areas [9,10]. This coefficient quantifies inequalities in green space exposure among residents within a region based on the Lorentz curve [11]. Some studies indicate that relatively equitable UGS provision is more prevalent in cities with favorable climatic and natural conditions [12,13]. ...
Given the critical role of urban green space (UGS) in fostering sustainable urban development, there is a growing focus on assessing levels and inequalities in urban green space exposure. However, the integration of UGS with urban development, particularly in the context of China’s unique national conditions, remains underexplored. This study evaluates the adequacy and equity of urban green space provision in 140 Chinese cities (with built-up areas exceeding 100 km²) using metrics such as the percentage of green space, green space exposure, and the Gini coefficient. Additionally, the study investigates the interplay between natural, economic, and social factors and UGS variables, particularly examining the interrelation of urbanization and green sustainable development within the framework of China’s distinctive land finance policy. The findings reveal that most large Chinese cities suffer from inadequate and inequitable green space provisions, with a clear connection between these deficiencies. The study highlights that factors such as favorable natural conditions, economic growth, urbanization, favorable living conditions, and the unique land finance and transfer system in China can enhance urban green space exposure and equality. This research offers valuable insights and evidence for Chinese central and local governments to devise effective and sustainable greening strategies, aiming to attain high-quality urban development.
... On the latter, a substantial science and public policy focus is on economic inequalities, i.e., how does the distribution of wages, income, or wealth vary across individuals and households, social groups, and neighborhoods within and across urban areas [14][15][16] . Beyond inequalities in economic outcomes, considerable disparities in the urban built environment exist and have been the focus of many recent studies [17][18][19][20][21] , with some emphasizing urban infrastructure gaps 22,23 . Other relevant studies highlight the inequality implications of infrastructure [24][25][26] . ...
Impending global urban population growth is expected to occur with considerable infrastructure expansion. However, our understanding of attendant infrastructure inequalities is limited, highlighting a critical knowledge gap in the sustainable development implications of urbanization. Using satellite data from 2000 to 2019, we examine country-level population-adjusted biases in infrastructure distribution within and between regions of varying urbanization levels and derive four key findings. First, we find long-run positive associations between infrastructure inequalities and both urbanization and economic development. Second, our estimates highlight increasing infrastructure inequalities across most of the countries examined. Third, we find greater future infrastructure inequality increases in the global south, where inequalities will rise more in countries with substantial urban primacy. Fourth, we find that infrastructure inequality may evolve differently than economic inequalities. Overall, advancing sustainable development vis-à-vis urbanization and economic development will require intentional infrastructure planning for spatial equity.
... Application notifications received when a friend completed a run caused peers to run more, even when they were physically distant 17 . Althoff et al. 18 analyzed the number of steps from over 700,000 people across 111 countries, and found that inequality in activity distribution within countries predicts obesity prevalence better than average activity levels. Pontin et al. 19 found that apps incentivizing physical activity are more likely to be used from populations in areas of lower socioeconomic status. ...
Analyzing the habits of exercisers is crucial for developing targeted interventions that can effectively promote long-term physical activity behavior. While much of existing literature has focused on individual-level factors, there is a growing recognition of the importance of examining how broader determinants impact physical activity. In this study, we analyze large-scale human mobility data from over 20 million individuals to investigate how visits to various locations, such as cafes and restaurants, influence visits to fitness centers. In particular, we (i) rank categories of locations that exercisers prefer to visit, (ii) compare visiting patterns between individuals who visit fitness centers and those who do not, (iii) investigate how exercisers replace fitness visits on non-exercise days, and (iv) identify location categories mainly visited before or after fitness sessions. We show that individuals engaging in physical exercise prefer to visit “Non-Alcoholic Beverage Bars” (e.g., Starbucks) in conjunction with their exercise sessions. On their rest days, they often substitute exercise with visits to full-service restaurants and parks. Moreover, they tend to visit grocery stores immediately after their exercise session. Our findings can help public health policy towards a more targeted promotion of exercise and well-being.
... Worldwide, walking is the most popular type of physical activity because it is accessible, requires no special skills or equipment, and is practically free [9]. Furthermore, the ubiquity of smartphones with built-in accelerometers has provided opportunities for physical activity monitoring and the evaluation of interventions [10,11]. ...
Background
Mobile technology offers great potential for physical activity promotion, especially by facilitating online communication, however, the impact of group communication norms on intervention effectiveness remains unclear. This study aimed to evaluate the effect on daily steps of a team-based social norms-related intervention using a mobile application.
Methods
The 13-week quasi-experimental study was conducted in Shanghai, China, from September to November 2019, involving 2,985 employees from 32 worksites. For the intervention group (n = 2,049), participants set a goal of 10,000 steps per day. The teams and individual members would receive points for meeting the daily goal, contributing to team-based rankings and financial rewards for the teams and their members. In addition, the intervention teams created dedicated WeChat groups to facilitate communication, which were also used to collect group chat messages. The communication type in these groups was classified into four types: (1) nudging – encouraging team members to be more active, (2) sharing – exchanging the completion of daily step goals, (3) feedback – providing responses or suggestions to team members, and (4) other -diverse topics that could not be classified otherwise. The control group only tracked their steps online.
Results
The weekly average steps of the intervention group increased by 2,523 steps, while the control group increased by 470 steps. In the first 3 weeks of follow-up, the frequency of nudging of 7–18 times/week had a positive cumulative effect on the step counts. Sharing more than 3 times/week had a positive cumulative effect. Over 6 and 13 weeks of follow-up, nudging 19 times/week or more had a positive cumulative effect while sharing and feedback at any frequency negatively affected average weekly steps.
Conclusions
Communication types within a team affected team-based step counts in a financial incentive intervention. The team-level social norms related to communications might have different cumulative effects on team-level physical activity. ‘nudging’ messages had a significant association with the change in individual-level step counts in the medium or long term.
Trial registration
Pilot Project of the application of walking incentive technology in occupational groups, 2019, ChiCTR1900023813. Registered 13 June 2019, https://www.chictr.org.cn/showproj.html?proj=39858.
... The prevailing mechanism between CVD health and greenspaces is an increase in physical activity levels (10), thereby enhancing cardio-respiratory fitness and reducing cardiometabolic predisposing factors (39), and thus preventing CVD events (10,40). Notably, although females tend to engage less in physical activities than males (41), this gender gap tends to be less pronounced when females live in urban settings with high walkability (42). However, females have a lower willingness to walk in empty streets that are perceived as unsafe in comparison to men, thus lowering their physical activity levels (43). ...
Background and aim
This study aims to investigate the role of the built environment in terms of urban-rural disparities in cardiovascular disease (CVD) epidemiology, focusing on middle- and long-term CVD risk assessment. Moreover, this study seeks to explore sex-specific differences in urban and rural settings.
Methods
The ATTICA Study is a prospective study conducted from 2002 onwards. At baseline, a random sample of 3,042 CVD-free adults (49.8% men) were randomly drawn from the population of the Attica region, in Greece, with 78% dwelling in urban and 22% in rural municipalities. Follow-up examinations were performed in 2006, 2012, and 2022. Of the total participants, 1,988 had complete data for CVD assessment in the 20-year follow-up.
Results
The 10-year and 20-year CVD incidence was 11.8%, 28.0% in rural municipalities and 16.8%, 38.7% in urban municipalities, respectively (ps < 0.05). Unadjusted data analyses revealed significant differences in clinical, laboratory, and lifestyle-related CVD risk factors between urban and rural residents (ps < 0.05). Additionally, sex-based discrepancies in clinical, anthropometric, circulating, and lifestyle risk factors were observed in stratified analyses of urban and rural settings. Multivariate analyses, including generalized structural equation modeling (GSEM), revealed that the impact of the urban built environment on the long-term (20-year) CVD risk is mediated by lifestyle-related risk factors.
Conclusion
Urban inhabitants exhibit a higher long-term CVD incidence compared to their rural counterparts, which was partially explained by their lifestyle behaviors. Targeted strategic city planning efforts promoting healthier lifestyle-related behaviors at the micro-environment level could potentially mitigate built-environment impacts on CVD health.
... Admittedly, conceptual representations of the physiology of energy metabolism regulation in humans are not likely adequate to explain phenomena occurring at a global level that have important psychosocial and sociocultural dimensions 51,52 . Accordingly, such factors may well be responsible for the epidemic of obesity; however, testing this hypothesis in a definitive manner will be a very difficult, if not an impossible task. ...
... Modern advancements in technology, combined with the ubiquity of smartphones and wearable devices, have made access to physical activity data increasingly straightforward. In addition, large-scale mobile health datasets have become much more accessible over the past two decades (Althoff et al., 2017), such as the UK Biobank (UKB) dataset, which includes data from over 500,000 individuals (Sudlow et al., 2015). ...
Physical activity is crucial for human health. With the increasing availability of large-scale mobile health data, strong associations have been found between physical activity and various diseases. However, accurately capturing this complex relationship is challenging, possibly because it varies across different subgroups of subjects, especially in large-scale datasets. To fill this gap, we propose a generalized heterogeneous functional method which simultaneously estimates functional effects and identifies subgroups within the generalized functional regression framework. The proposed method captures subgroup-specific functional relationships between physical activity and diseases, providing a more nuanced understanding of these associations. Additionally, we introduce a pre-clustering method that enhances computational efficiency for large-scale data through a finer partition of subjects compared to true subgroups. In the real data application, we examine the impact of physical activity on the risk of mental disorders and Parkinson's disease using the UK Biobank dataset, which includes over 79,000 participants. Our proposed method outperforms existing methods in future-day prediction accuracy, identifying four subgroups for mental disorder outcomes and three subgroups for Parkinson's disease diagnosis, with detailed scientific interpretations for each subgroup. We also demonstrate theoretical consistency of our methods. Supplementary materials are available online. Codes implementing the proposed method are available at: https://github.com/xiaojing777/GHFM.
... Step counts have been shown to vary greatly across countries and territories [12], as well as among individuals with different frailty statuses [13,14]. Therefore, appropriate step count goals that older adults with and without frailty can achieve daily are essential for maintaining health status. ...
The differences in the association of objectively measured physical activity with disability between frail and non-frail older adults remain unclear. We (1) evaluated the dose-dependent relationship between daily steps and disability in older adults with and without frailty and (2) examined the interaction between steps and frailty status in relation to the risk of disability. This prospective study used data from 4065 adults aged ≥ 65 years from the Kyoto-Kameoka Study, Japan. The mean daily step count obtained using triaxial accelerometers across ≥ 4 days was recorded. Frailty was evaluated using the validated Kihon Checklist. Disability was identified using the long-term care insurance system’s nationally unified database, with data collected until November 30, 2016. Overall, 385 disabilities were recorded during a median follow-up period of 3.32 years (12,855 person-years). After adjusting for confounders, an inverse association was observed between daily step count and disability risk. The disability risk plateaued at 5,000–7,000 steps/day in non-frail people, whereas step counts showed an almost linear inverse relationship with disability risk in frail people. Low step counts (< 5,000 steps) in frail people were more strongly associated with disability risk than were high step counts (≥ 5,000 steps) in non-frail people. The additive interaction between steps and frailty was associated with the relative excess risk of disability in frail people with low step counts (p for interaction = 0.015). The relationship between daily steps and disability differs between older adults with and without frailty, and the adverse effects of frailty on disability risk depend on physical activity.
... Previous work has explored the relationship between circadian cycles and external factors, linking prolonged disruption of rhythms to pathological conditions, including cancer [18,21]. Nowadays, "the alternation of sleep and walking and all the bodily cycles attendant on those states" [13] can be measured based on the use of social media [1,6,25], of augmented-reality games [3,9], and, more reliably, of activity trackers [2,19]. ...
Our internal experience of time reflects what is going in the world around us. Our body's natural rhythms get disrupted for a variety of external factors, including exposure to collective events. We collect readings of steps, sleep, and heart rates from 11K users of health tracking devices in London and San Francisco. We introduce measures to quantify changes in not only volume of these three bio-signals (as previous research has done) but also synchronicity and periodicity, and we empirically assess how strong those variations are, compared to random expectation, during four major events: Christmas, New Year's Eve, Brexit, and the US presidential election of 2016 (Donald Trump's election). While Christmas and New Year's eve are associated with short-term effects, Brexit and Trump's election are associated with longer-term disruptions. Our results promise to inform the design of new ways of monitoring population health at scale.
... Over 60% of adults worldwide own a smartphone, with worldwide penetration rates highest in the United States (where >80% of the population uses a smartphone) [20]. In addition to being able to deliver interventions through wireless internet and messaging connectivity, smartphones have in-built tools like global positioning systems, inertial measurement units, and cameras that can objectively measure several exercise parameters [21][22][23]. Smartphones also have powerful computation and communication capabilities that enable the use of machine learning (ML) and artificial intelligence to individualize each user's exercise program. ...
Background
The increasing prevalence of sedentary lifestyles has prompted the development of innovative public health interventions, such as smartphone apps that deliver personalized exercise programs. The widespread availability of mobile technologies (eg, smartphone apps and wearable activity trackers) provides a cost-effective, scalable way to remotely deliver personalized exercise programs to users. Using machine learning (ML), specifically reinforcement learning (RL), may enhance user engagement and effectiveness of these programs by tailoring them to individual preferences and needs.
Objective
The primary aim was to investigate the impact of the Samsung-developed i80 BPM app, implementing ML for exercise prescription, on user satisfaction and exercise intensity among the general population. The secondary objective was to assess the effectiveness of ML-generated exercise programs for remote prescription of exercise to members of the public.
Methods
Participants were randomized to complete 3 exercise sessions per week for 12 weeks using the i80 BPM mobile app, crossing over weekly between intervention and control conditions. The intervention condition involved individualizing exercise sessions using RL, based on user preferences such as exercise difficulty, selection, and intensity, whereas under the control condition, exercise sessions were not individualized. Exercise intensity (measured by the 10-item Borg scale) and user satisfaction (measured by the 8-item version of the Physical Activity Enjoyment Scale) were recorded after the session.
Results
In total, 62 participants (27 male and 42 female participants; mean age 43, SD 13 years) completed 559 exercise sessions over 12 weeks (9 sessions per participant). Generalized estimating equations showed that participants were more likely to exercise at a higher intensity (intervention: mean intensity 5.82, 95% CI 5.59‐6.05 and control: mean intensity 5.19, 95% CI 4.97‐5.41) and report higher satisfaction (RL: mean satisfaction 4, 95% CI 3.9-4.1 and baseline: mean satisfaction 3.73, 95% CI 3.6-3.8) in the RL model condition.
Conclusions
The findings suggest that RL can effectively increase both the intensity with which people exercise and their enjoyment of the sessions, highlighting the potential of ML to enhance remote exercise interventions. This study underscores the benefits of personalized exercise prescriptions in increasing adherence and satisfaction, which are crucial for the long-term effectiveness of fitness programs. Further research is warranted to explore the long-term impacts and potential scalability of RL-enhanced exercise apps in diverse populations. This study contributes to the understanding of digital health interventions in exercise science, suggesting that personalized, app-based exercise prescriptions may be more effective than traditional, nonpersonalized methods. The integration of RL into exercise apps could significantly impact public health, particularly in enhancing engagement and reducing the global burden of physical inactivity.
... Such associations have also been reported in familial hypercholesterolemia patients, for example, patients from traditionally underserved groups or with inadequate health literacy report lower adherence to familial hypercholesterolemia therapy [47,48]. There is also research reporting lower participation in health behaviors in certain demographic groupsfor example women and older adults tend to be less physically active [49,50]. ...
Purpose of review
Patients with familial hypercholesterolemia have an elevated risk of premature atherosclerotic cardiovascular disease. Risks can be minimized through pharmacological and ‘lifestyle’ behavioral (low fat diet, physical activity) therapies, although therapeutic adherence is sub-optimal. Behavioral interventions to promote familial hypercholesterolemia therapy adherence should be informed by theory-based psychological determinants for maximal efficacy. The current review summarizes research on determinants of familial hypercholesterolemia therapy adherence and behavior change interventions, identifies limitations of the extant research, and sets future research agenda.
Recent findings
A recent meta-analysis identified attitudes, subjective norms, self-efficacy, and risk perceptions as key determinants of familial hypercholesterolemia therapy adherence intentions, with intentions identified as a key correlate of concurrent behavior. Studies have specified techniques targeting key theory-based determinants that may be efficacious in interventions. Research is limited by overuse of cross-sectional correlational study designs, use of self-report behavioral measures, few theory-based intervention tests, and limited consideration of nonconscious processes and effects of socio-structural variables.
Summary
Researchers should adopt study designs permitting better directional and causal inferences in determinant effects, provide tests of interventions targeting determinants and their mechanisms of action, consider determinants representing nonconscious processes (habits, implicit attitudes), and test determinants as mediators of socio-structural variables on familial hypercholesterolemia therapy adherence.
... We further performed Cox proportional hazards regression models to explore the association between physical activity metrics and selected chronic diseases. These chronic diseases were specifically chosen based on associations reported in previous studies, allowing us to focus on conditions with established links to physical activity [22][23][24][25][26][27][28][29] . The primary predictor variables were derived from Fitbit activity data, which included activity metrics such as activity calories, elevation, minutes of activity at different intensities (e.g., fairly active, lightly active, very active), and steps. ...
Background: Physical activity is widely recognized as a key modifiable factor for reducing the risk of chronic diseases. Wearable devices such as Fitbit offer a unique opportunity to objectively measure physical activity metrics, providing insights into the association between different types of physical activity and chronic disease risk.
Objective: This study aims to examine the association between physical activity metrics derived from Fitbit devices and the incidence of various chronic diseases among participants from the All of Us Research Program.
Methods: Physical activity metrics included daily steps, elevation gain, and activity durations at different intensities (e.g., very active, lightly active, and sedentary). Cox proportional hazards models and multiple regression models were used to assess the relationship between these metrics and the incidence of chronic diseases represented by Phecodes. Age, sex, and body mass index (BMI) were included as covariates.
Results: A total of 15,538 participants provided Fitbit activity data, of which 9,320 also had electronic health records (EHR). Increased daily step count, elevation gain, and very active minutes were significantly associated with a reduced risk of several chronic conditions, including obesity, Type 2 diabetes, and major depressive disorder. Conversely, increased sedentary time was linked to higher risks for conditions such as obesity, Type 2 diabetes, and essential hypertension. Multiple regression analyses confirmed these associations.
Conclusion: Our findings highlight the beneficial effects of increased physical activity, particularly daily steps and elevation gain, on reducing the risk of chronic diseases. Conversely, sedentary behavior remains a significant risk factor for the development of several conditions. These insights may inform personalized activity recommendations aimed at reducing disease burden and improving population health outcomes.
... This does not consider that approximately 70% of the participants in our sample (1703/2394, 71.13%) were nurses who may not have always had a smartphone with them during their daily activities. In addition, in this case, the extrapolated number was closer to the lower end of the average number of steps taken by American adults, as the latest study of smartphone-based physical activity data shows [53]. Despite the aforementioned limitations, the method we used to quantify walking activity was found to be a reliable approach for identifying walking bouts and deriving step counts and walking minutes [32]. ...
Background
Previous studies investigating environmental and behavioral drivers of chronic disease have often had limited temporal and spatial data coverage. Smartphone-based digital phenotyping mitigates the limitations of these studies by using intensive data collection schemes that take advantage of the widespread use of smartphones while allowing for less burdensome data collection and longer follow-up periods. In addition, smartphone apps can be programmed to conduct daily or intraday surveys on health behaviors and psychological well-being.
Objective
The aim of this study was to investigate the feasibility and scalability of embedding smartphone-based digital phenotyping in large epidemiological cohorts by examining participant adherence to a smartphone-based data collection protocol in 2 ongoing nationwide prospective cohort studies.
Methods
Participants (N=2394) of the Beiwe Substudy of the Nurses’ Health Study 3 and Growing Up Today Study were followed over 1 year. During this time, they completed questionnaires every 10 days delivered via the Beiwe smartphone app covering topics such as emotions, stress and enjoyment, physical activity, access to green spaces, pets, diet (vegetables, meats, beverages, nuts and dairy, and fruits), sleep, and sitting. These questionnaires aimed to measure participants’ key health behaviors to combine them with objectively assessed high-resolution GPS and accelerometer data provided by participants during the same period.
Results
Between July 2021 and June 2023, we received 11.1 TB of GPS and accelerometer data from 2394 participants and 23,682 survey responses. The average follow-up time for each participant was 214 (SD 148) days. During this period, participants provided an average of 14.8 (SD 5.9) valid hours of GPS data and 13.2 (SD 4.8) valid hours of accelerometer data. Using a 10-hour cutoff, we found that 51.46% (1232/2394) and 53.23% (1274/2394) of participants had >50% of valid data collection days for GPS and accelerometer data, respectively. In addition, each participant submitted an average of 10 (SD 11) surveys during the same period, with a mean response rate of 36% across all surveys (SD 17%; median 41%). After initial processing of GPS and accelerometer data, we also found that participants spent an average of 14.6 (SD 7.5) hours per day at home and 1.6 (SD 1.6) hours per day on trips. We also recorded an average of 1046 (SD 1029) steps per day.
Conclusions
In this study, smartphone-based digital phenotyping was used to collect intensive longitudinal data on lifestyle and behavioral factors in 2 well-established prospective cohorts. Our assessment of adherence to smartphone-based data collection protocols over 1 year suggests that adherence in our study was either higher or similar to most previous studies with shorter follow-up periods and smaller sample sizes. Our efforts resulted in a large dataset on health behaviors that can be linked to spatial datasets to examine environmental and behavioral drivers of chronic disease.
... For comparison: an average participant of a large-scale physical activity study recorded 4,961 steps per day (Althoff et al., 2017) which implies that not later than 2016, at least 10% of people executed more "taps" than "steps" -and with ongoing increase of smartphone penetration the number is most probably increasing. ...
This file is part B2 of project proposal "Digital Habits and Acquisition of Reading" unsuccessfully submitted to European Research Council's 2022 Starting Grant. DHAR's main objective is to assess strength and intensity of smartphone-induced habits among elementary school pupils and potentially falsify the zero hypothesis H0: "Strength of pupil's digital habits does not correlate with level of pupil's reading competence". DHAR consists of 130 distinct sessions integrated into 45-minute elementary school courses; during each session, all pupils present in the classroom will be assessed in parallel by means of a specially modified smartphone transformed into passive sensing experimental device (ED). H0 is to be falsified or non-falsified by correlating haptic interaction data collected during the "free play" phase with reading competence data collected by means of an innovative, audio-textual "digital reading probe" (DRP).
... These studies suggest that genetic, cultural, and socioeconomic factors are related to the prevalence of obesity and underweight and the contribution of these factors varies widely depending on population. Judging from the results of a previous study on physical activity among adults (Althoff et al., 2017), Japan had a relatively homogeneous and favorable environment, which may result in a low prevalence of obesity. On the other hand, sleeping time was very short (OECD, 2011). ...
Urban green space is associated with cognitive functions, but the underlying mechanisms remain unclear due to limited research. Given the diverse forms of green space, which lead to distinct health effects, it is essential to differentiate between types of green space. In this review, we propose a novel conceptual framework categorizing three primary effects of green space on cognitive function: functional, spatial, and perceptual. We then conduct a scoping review using the Web of Science, identifying 37 relevant studies. Among them, 20 studies employ modeling to explore potential mechanisms, while 17 studies infer pathways indirectly. Most studies examine reduced air pollution and increased physical activity as mediating factors, with stronger support for air pollution reduction as a protective mediator. However, evidence on physical activities as a mediator remains mixed. Some studies suggest that merely perceiving green space enhances brain activity, and exposure to nature is linked to improved test performance. Other potential pathways, such as heat reduction and social interaction, remain underexplored. We highlight the limitations of current methods in distinguishing various forms of green space and emphasize the need for advanced methods, such as local climate zones and street view imagery, for more precise assessment.
Background/Objectives : Rural populations face an elevated risk of Alzheimer’s disease and related dementias partially attributed to modifiable risk factors such as physical inactivity. The study gathered key community partners’ perspectives about (a) feasibility of implementing the telerehabilitation physical activity behavioral (TPAB) intervention, a virtual program aimed at increasing daily stepping with sensor-based monitoring, and (b) necessary adaptions for implementation for rural-dwelling people with cognitive impairment and care partners (dyads). Methods : Eleven rural-dwelling individuals (three medical providers, four older adults, and four care partners of individuals with mild cognitive impairment) in Nebraska were recruited for a focus group via fliers and word of mouth. The person-based approach was used to gain the perspectives of participants during a 1-hr group discussion regarding six dimensions of feasibility. Session transcriptions were analyzed using thematic analysis. Results : Participants highlighted the need to improve physical activity in the community and supported care partners receiving TPAB alongside individuals with mild cognitive impairment. Suggested modifications to TPAB included, establishing a local champion, integrating local resources, involving primary care providers, setting recruitment deadlines, using participant testimonies for recruitment, and emphasizing program individualization. Conclusion : Overall, participants perceived TPAB was practical, feasible, and necessary for rural-dwelling participants with cognitive impairment and their care partners; however, several adaptations and the creation of the Cognitive Impairment Rural Community—TPAB are required to optimize rural implementation. Significance/Implications : There is an identified need for the TPAB intervention with outlined redesign approaches to facilitate intervention development and rural implementation.
Introduction: Walkability is crucial for sustainable development and promotes environmental, economic, and health benefits. Based on the importance of walkability in the built environment (WBE) during the sustainability era, this study offers a critical review of WBE using a bibliometric approach to showcase the following: a) progress and main areas, b) research gaps and trends, and c) the proposal of a comprehensive framework for future studies.
Method: To better understand the WBE, 2150 documents covering 2000 to November 1, 2024, were extracted from the Scopus database. VOSviewer and the Bibliometrix package in the R statistical programming language were used to analyze and visualize the data. In addition, desk studies and gray literature, including movements, conferences, reports, and concepts, were investigated.
Results: The document publication process and results of co-occurrence and factorial analyses revealed that the concept of WBE evolved over four periods. The main areas of evolution include social aspects, health, and the built environment, all of which improved in each period. Economics and technology were integrated into the other areas during the final period. This provides a comprehensive framework for a better understanding of the WBE concept. Additionally, the strong correlation (r = 0.99, p < 0.05) between the number of citations and annual publications underscores the significance of WBE studies in the scientific community. Nevertheless, developing countries account for only 10% of scientific studies in this field.
Conclusion: These findings and emerging trends can offer valuable insights to urban planners, designers, managers, and researchers from diverse disciplines to enhance the WBE, particularly in developing countries.
Objectives:
(i) To develop sex-specific reference equations to predict distance walked (6MWD) in the 6-minute walk test (6MWT), in healthy subjects aged 45-85 years, from different geographic areas of Spain; and (ii) to compare developed equations with previously published in a large sample of COPD patients.
Methods:
First, a cross-sectional multicentre sample of randomly selected healthy subjects from 17 Spanish hospitals and universities performed two 6MWT. Linear regression and fractional polynomial modelling were used to develop the equations. Second, the developed equations were applied to 715 COPD patients from Spanish primary care centres and hospitals, and the % predicted 6MWD obtained was compared with previously published equations using Dunnett's multiple comparisons test.
Results:
568 healthy subjects were included (51% females, mean (SD) age 62 (11) years), walked a 6MWD of 615 (113) and 557 (93)m in males and females, respectively. The developed equations included age, weight and height, and explained 43% and 51% of the 6MWD variance for males and females, respectively. In the COPD sample (n=715, 14% females, 68 (9) years, FEV1 61 (18) % predicted, 6MWD 464 (97)m), only 1 out of 9 previously published equations for males, and 6 out of 9 for females predicted 6MWD values similar to those of the newly developed Spanish reference equations.
Conclusions:
The newly developed reference equations provide a more valid prediction of 6MWD in Spanish adults with COPD compared to previously published equations. We suggest their use in future research and clinical practice for the Spanish adult population.
Dementia affects over 55 million people worldwide, with much support provided by unpaid carers, who struggle to access leisure-time physical activities. This review investigates gender differences in engagement in group-based physical activity for people with dementia and their unpaid carers. A systematic review from inception until 1 August 2024 resulted in the inclusion of 15 studies. The review found that slightly more males than females with dementia attended the group physical activity sessions, with most carers attending being female. Further research is required to inform interventions to promote physical activity in male and female unpaid carers for people living with dementia.
This indispensable collection provides extensive, yet accessible, coverage of conceptual and practical issues in research design in personality and social psychology. Using numerous examples and clear guidelines, especially for conducting complex statistical analysis, leading experts address specific methods and areas of research to capture a definitive overview of contemporary practice. Updated and expanded, this third edition engages with the most important methodological innovations over the past decade, offering a timely perspective on research practice in the field. To reflect such rapid advances, this volume includes commentary on particularly timely areas of development such as social neuroscience, mobile sensing methods, and innovative statistical applications. Seasoned and early-career researchers alike will find a range of tools, methods, and practices that will help improve their research and develop new conceptual and methodological possibilities. Supplementary online materials are available on Cambridge Core.
The Child Opportunity Index (COI) is a validated measurement that uses a composite index of 29 indicators of social determinants of health linked to the US Census. Patients post-Fontan palliation for single ventricle physiology often have reduced exercise capacity compared to the general population. Our hypothesis is that COI levels are directly associated with exercise capacity and inversely with late outcomes. A retrospective, single-center study was performed, including 99 patients post-Fontan procedure who had cardiac magnetic resonance imaging at our institution from January 2010 to July 2023, of which 78 had undergone an exercise test. Univariate analysis was performed with Pearson correlational testing and multivariable linear regression was then used to evaluate independent predictors of % predicted VO2. The mean age and sex were not different between the low and high COI groups (24.1 ± 8.5 y vs 22.5 ± 9.7 y; 34.5% vs 29.3% female). Patients with low COI had lower peak VO2 (25.7 vs 31.0 L/min/kg, p = 0.002) and % predicted peak VO2 (61.9 vs 71.4%, p = 0.003). At follow-up post-Fontan (mean of 17.9 ± 7.4 y) there was one mortality and two heart transplants. There were more interventions in the low COI group (1.5 vs 0.9 intervention occurrence/patient, p = 0.038). There was no difference in hospital admissions or significant comorbidities between COI groups. Lower COI was associated with worse exercise capacity in Fontan patients and may negatively impact the need for late interventions. This highlights the need for efforts to provide community resources to promote equity in cardiac outcomes.Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.Confirmed as correct. Thank you!
Objective
Exercise plays a crucial role in maintaining and improving human health. However, the precise molecular mechanisms that govern the body’s response to exercise or/compared to periods of inactivity remain elusive. Current evidence appears to suggest that exercise exerts a seemingly dual influence on macrophage polarization states, inducing both pro-immune response M1 activation and cell-repair-focused M2 activation. To reconcile this apparent paradox, we leveraged a comprehensive meta-analysis of 75 diverse exercise and immobilization published datasets (7000+ samples), encompassing various exercise modalities, sampling techniques, and species.
Methods
75 exercise and immobilization expression datasets were identified and processed for analysis. The data was analyzed using boolean relationships which uses binary gene expression relationships in order to increase the signal to noise achieved from the data, allowing for the use of comparison across such a diverse set of datasets. We utilized a boolean relationship-aided macrophage gene model [1], to model the macrophage polarization state in pre and post exercise samples in both immediate exercise and long term training.
Results
Our modeling uncovered a key temporal dynamic: exercise triggers an immediate M1 surge, while long term training transitions to sustained M2 activation. These patterns were consistent across different species (human vs mouse), sampling methods (blood vs muscle biopsy), and exercise type (resistance vs endurance), and routinely showed statistically significant results. Immobilization was shown to have the opposite effect of exercise by triggering an immediate M2 activation. Individual characteristics like gender, exercise intensity and age were found to impact the degree of polarization without changing the overall patterns. To model macrophages within the specific context of muscle tissue, we identified a focused gene set signature of muscle resident macrophage polarization, allowing for the precise measurement of macrophage activity in response to exercise within the muscle.
Conclusions
These consistent patterns across all 75 examined studies suggest that the long term health benefits of exercise stem from its ability to orchestrate a balanced and temporally-regulated interplay between pro-immune response (M1) and reparative macrophage activity (M2). Similarly, it suggests that an imbalance between pro-immune and cell repair responses could facilitate disease development. Our findings shed light on the intricate molecular choreography behind exercise-induced health benefits with a particular insight on its effect on the macrophages within the muscle.
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
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)
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.
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.
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.
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.
Predicting unmeasurable wealth
In developing countries, collecting data on basic economic quantities, such as wealth and income, is costly, time-consuming, and unreliable. Taking advantage of the ubiquity of mobile phones in Rwanda, Blumenstock et al. mapped mobile phone metadata inputs to individual phone subscriber wealth. They applied the model to predict wealth throughout Rwanda and show that the predictions matched well with those from detailed boots-on-the-ground surveys of the population.
Science , this issue p. 1073
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.
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.
None.
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 day. Steps per day were positively related to other self-reported measures of physical activity and negatively related to self-reported measures on physical inactivity. Living environment (urban, suburban, or rural) and eating habits were not associated with steps per day.
In the current study, men and women living in the United States took fewer steps per day than those living in Switzerland, Australia, and Japan. We conclude that low levels of ambulatory physical activity are contributing to the high prevalence of adult obesity in the United States.
The 2005-2006 National Health and Nutrition Examination Survey (NHANES) released, for the first time, the ActiGraph AM-7164 accelerometer-defined step data in addition to time in intensity (defined by activity counts). This accelerometer is known to have a lower sensitivity threshold than accepted pedometers, leading to relatively higher step estimates.
The purpose of this analysis is to provide the population- and sex-specific epidemiology of accelerometer-determined steps per day with and without censoring steps detected at the lowest end of the activity spectrum (i.e., inactivity).
The analysis sample represents 3744 participants > or =20 yr who had at least one valid day (i.e., at least 10 h) of monitoring. Means (SE) for steps per day were computed using all steps (i.e., uncensored) and again after censoring those steps taken at an intensity <500 activity counts per minute (indicative of inactivity intensity).
On average, US adults took 9676 +/- 107 uncensored steps per day or 6540 +/- 106 censored steps per day.
The results of this nationally representative survey indicate that US adults take approximately 10,000 uncensored accelerometer-determined steps per day, which is improbable considering other studies of free-living physical activity in the United States. Until a more valid conversion factor is ascertained for translating accelerometer and pedometer-determined steps, our use of the <500 activity count per minute threshold holds considerable merit.
Ecologic studies in the U.S. and elsewhere in the world have demonstrated that income inequality is strongly related to mortality and life expectancy: the greater the dispersion of income within a given society, the lower the life expectancy. However, these empirical studies have been criticized on the grounds that the choice of indicator may have influenced positive findings. Using a cross-sectional, ecologic design, we tested the relationships of six different income inequality indicators to total mortality rates in the 50 U.S. states. The following summary measures of income distribution were examined: the Gini coefficient; the decile ratio; the proportions of total income earned by the bottom 50%, 60%, and 70% of households; the Robin Hood Index; the Atkinson Index; and Theil's entropy measure. All were highly correlated with each other (Pearson r > or = 0.94), and all were strongly associated with mortality (Pearson r ranging from 0.50 to 0.66), even after adjustment for median income and poverty. Thus, the choice of income distribution measure does not appear to alter the conclusion that income inequality is linked to higher mortality. Furthermore, adjustment for taxes and transfers, as well as household size (using equivalence scales), made no difference to the income inequality/mortality association. From a policy perspective, the alternative income distribution measures perform differently under varying types of income transfers, so that theoretical considerations should guide the selection of an indicator to assess the impact of social and economic policies that address income inequality.
This paper reviews the large and growing body of literature on the apparently negative effects of income inequality on population health. Various hypotheses are identified and described that explain the empirically observed association between measures of income inequality and population health. We have concluded that data from aggregate-level studies of the effect of income inequality on health, i.e. studies at the population and community (e.g. state) levels, are largely insufficient to discriminate between competing hypotheses. Only individual-level studies have the potential to discriminate between most of the advanced hypotheses. The relevant individual-level studies to date, all on U.S. population data, provide strong support for the "absolute-income hypothesis," no support for the "relative-income hypothesis," and little or no support for the "income-inequality hypothesis." Results that provide some support for the income-inequality hypothesis suggest that income inequality at the state level affects mainly the health of the poor. There is only indirect evidence for the "deprivation hypothesis," and no evidence supports the "relative-position hypothesis." Overall, the absolute-income hypothesis, although > 20 years old, is still the most likely to explain the frequently observed strong association between population health and income inequality levels.
The Gini coefficient has been the most popular method for operationalising income inequality in the public health literature. However, a number of alternative methods exist, and they offer researchers the means to develop a more nuanced understanding of the distribution of income. Income inequality measures such as the generalised entropy index and the Atkinson index offer the ability to examine the effects of inequalities in different areas of the income spectrum, enabling more meaningful quantitative assessments of qualitatively different inequalities. This glossary provides a conceptual introduction to these and other income inequality measures.
To describe physical activity levels of children (6-11 yr), adolescents (12-19 yr), and adults (20+ yr), using objective data obtained with accelerometers from a representative sample of the U.S. population.
These results were obtained from the 2003-2004 National Health and Nutritional Examination Survey (NHANES), a cross-sectional study of a complex, multistage probability sample of the civilian, noninstitutionalized U.S. population in the United States. Data are described from 6329 participants who provided at least 1 d of accelerometer data and from 4867 participants who provided four or more days of accelerometer data.
Males are more physically active than females. Physical activity declines dramatically across age groups between childhood and adolescence and continues to decline with age. For example, 42% of children ages 6-11 yr obtain the recommended 60 min x d(-1) of physical activity, whereas only 8% of adolescents achieve this goal. Among adults, adherence to the recommendation to obtain 30 min x d(-1) of physical activity is less than 5%.
Objective and subjective measures of physical activity give qualitatively similar results regarding gender and age patterns of activity. However, adherence to physical activity recommendations according to accelerometer-measured activity is substantially lower than according to self-report. Great care must be taken when interpreting self-reported physical activity in clinical practice, public health program design and evaluation, and epidemiological research.
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.
There is little evidence on unemployment duration and its determinants in developing countries. This study is on the duration aspect of unemployment in a developing country, Turkey. We analyze the determinants of the probability of leaving unemployment for employment or the hazard rate. The effects of the personal and household characteristics and the local labor market conditions are examined. The analyses are carried out for men and women separately. The results indicate that the nature of unemployment in Turkey exhibits similarities to the unemployment in both the developed and the developing countries.
In most developing countries, income inequality tends to worsen during initial stages of growth, especially in urban areas. The People’s Republic of China (PRC) provides a sharp contrast where income inequality among urban households is lower than that among rural households. In terms of inclusive growth, the existence of income mobility over a longer period of time may mitigate the impacts of widening income inequality measured using crosssectional data. We explore several ways of measuring income mobility and found considerable income mobility in the PRC, with income mobility lower among rural households than among urban households. When incomes are averaged over 3 years and when adjustments are made for the size and composition of households, income inequality decreases. Social welfare functions are posited that allow for a trade-off between increases in income and increases in income inequality. These suggest strong increases in well-being for urban households in the PRC. In comparison, the corresponding changes in rural households are much smaller.
Prevention and control of non-communicable diseases. http://www.who.int/nmh/publications/2011-report-of-SG-to-UNGA.pdf (Regional Office for South-East Asia, World Health Organisation
- Un
Prevalence of Insufficient Physical Activity among Adults: Data by Country. http://apps.who.int/gho/data/node.main.A893?lang=en (Global Health Observatory data repository, WHO
- Health World
- Organization
Female (% of Total) http://data.worldbank.org/indicator
- World Bank
Field Listing: Median Age https://www.cia.gov/library/publications/the-world-factbook
- Factbook Cia World
World Bank Country and Lending Groups. https://datahelpdesk.worldbank.org/ knowledgebase/articles/906519-world-bank-country-and-lending-groups
- World Bank
World Bank. World Bank Country and Lending Groups. https://datahelpdesk.worldbank.org/
knowledgebase/articles/906519-world-bank-country-and-lending-groups. Accessed October 5,
2016.
Prevention and control of non-communicable diseases
- Un Secretary General
UN Secretary General. Prevention and control of non-communicable diseases. 2011. http://
www.who.int/nmh/publications/2011-report-of-SG-to-UNGA.pdfAccessed April 21, 2016.
CDC vital signs: more people walk to better health
Centers for Disease Control and Prevention. CDC vital signs: more people walk to better health.
2012. http://www.cdc.gov/vitalsigns/walking/Accessed November 3, 2016.