Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities^1,2. This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity^3–6. Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017—and more than 80% in some low- and middle-income regions—was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing—and in some countries reversal—of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.

Physical inactivity is a global problem that increases the risk of many chronic diseases and shortens life expectancy. The built environment contributes to physical inactivity through accessibility of amenities and transportation patterns. With better urban planning, cities could be designed to enhance active transportation and population health on a permanent basis. We conducted a systematic review to identify determinants of the built environment associated with physical activity and to evaluate how changes in the built environment are associated with changes in physical activity. We searched six databases, from the inception of each until December 2015, for studies that were written in English, used longitudinal before-and-after design and assessed changes in both the built environment and physical activity. A total of 21 prospective cohort studies and 30 natural experiments were included in the review. The review showed that changes in the built environment and in physical activity were related. A higher objective accessibility and new infrastructure for walking, cycling and public transportation were associated with increased overall and transportation-related physical activity. Some evidence was found for perceived aesthetics and safety as determinants of physical activity. Improved objectively measured accessibility of different type of destinations and public transportation and land use mix were associated with increased physical activity. Creating new infrastructure for walking, cycling and public transportation could induce demand for walking and cycling. The results support the creation of compact and diverse residential areas and investments into infrastructure that encourage active modes of transportation.

Our aim was to identify factors predisposing athletes to multiple stress fractures, with the emphasis on biomechanical factors. Our hypothesis was that certain anatomic factors of the ankle are associated with risk of multiple stress fractures of the lower extremities in athletes. Thirty-one athletes (19 men and 12 women) with at least three separate stress fractures each, and a control group of 15 athletes without fractures completed a questionnaire focusing on putative risk factors for stress fractures, such as nutrition, training history, and hormonal history in women. Bone mineral density was measured by dual-energy x-ray absorptiometry in the lumbar spine and proximal femur. Biomechanical features such as foot structure, pronation and supination of the ankle, dorsiflexion of the ankle, forefoot varus and valgus, leg-length inequality, range of hip rotation, simple and choice reaction times, and balance in standing were measured. There was an average of 3.7 (range, 3 to 6) fractures in each athlete, totaling 114 fractures. The fracture site was the tibia or fibula in 70% of the fractures in men and the foot and ankle in 50% of the fractures in women. Most of the patients were runners (61%); the mean weekly running mileage was 117 km. Biomechanical factors associated with multiple stress fractures were high longitudinal arch of the foot, leg-length inequality, and excessive forefoot varus. Nearly half of the female patients (40%) reported menstrual irregularities. Runners with high weekly training mileage were found to be at risk of recurrent stress fractures of the lower extremities.

Cross-sectional comparison of self-reported low back pain (LBP) symptoms and Modic findings on magnetic resonance imaging (MRI). To investigate associations of frequency and intensity of LBP and sciatic pain with Modic changes in a sample of middle-aged male workers with or without whole-body vibration exposure. Vertebral endplate changes are bone marrow lesions visible on MRI and are assumed to be associated with degenerative intervertebral disc disease. Associations of these so-called Modic changes with clinical symptoms are controversial. Furthermore, most of these studies have been performed in selected series of patients. A total of 228 middle-aged male workers (159 train engineers and 69 sedentary controls) from northern Finland underwent sagittal T1 and T2-weighted MRI. Both endplates of 1140 lumbar interspaces were graded for type and extent of Modic changes. Logistic regression was used to analyze associations of pain variables with Modic changes. Train engineers had on the average higher sciatic pain scores than the sedentary controls, but the prevalence of Modic changes was similar in both occupational groups. Altogether, 178 Modic changes in 128 subjects were recorded: 30% were type I, 66% type II, and 4% both types I and II. Eighty percent of changes occurred at L4–L5 or L5–S1. Modic changes at L5–S1 showed significant association with pain symptoms with increased frequency of LBP (odds ratio [OR] 2.28; 95% confidence interval [CI] 1.44–3.15) and sciatica episodes (OR 1.44; 95% CI 1.01–1.89), and with higher LBP visual analog scores during the past week (OR 1.36; 95% CI 1.06–1.70). Type I lesions and extensive lesions in particular were closely associated with pain. Modic changes at L5–S1 and Modic type I lesions are more likely to be associated with pain symptoms than other types of Modic changes or changes located at other lumbar levels.

Objective measures using accelerometer-based activity monitors have been extensively used in physical activity (PA) and sedentary behavior (SB) research. To measure PA and SB precisely, the field is shifting towards machine learning-based (ML) approaches for calibration and validation of accelerometer-based activity monitors. Nevertheless, various parameters regarding the use and development of ML-based models, including data type (raw acceleration data versus activity counts), sampling frequency, window size, input features, ML technique, accelerometer placement, and free-living settings, affect the predictive ability of ML-based models. The effects of these parameters on ML-based models have remained elusive, and will be systematically reviewed here. The open challenges were identified and recommendations are made for future studies and directions. We conducted a systematic search of PubMed and Scopus databases to identify studies published before July 2017 that used ML-based techniques for calibration and validation of accelerometer-based activity monitors. Additional articles were manually identified from references in the identified articles. A total of 62 studies were eligible to be included in the review, comprising 48 studies that calibrated and validated ML-based models for predicting the type and intensity of activities, and 22 studies for predicting activity energy expenditure. It appears that various ML-based techniques together with raw acceleration data sampled at 20-30 Hz provide the opportunity of predicting the type and intensity of activities, as well as activity energy expenditure with comparable overall predictive accuracies regardless of accelerometer placement. However, the high predictive accuracy of laboratory-calibrated models is not reproducible in free-living settings, due to transitive and unseen activities together with differences in acceleration signals.