Sedentary behaviors are linked to adverse health outcomes, but the total amount of time spent in these behaviors in the United States has not been objectively quantified. The authors evaluated participants from the 2003-2004 National Health and Nutrition Examination Survey aged >/=6 years who wore an activity monitor for up to 7 days. Among 6,329 participants with at least one 10-hour day of monitor wear, the average monitor-wearing time was 13.9 hours/day (standard deviation, 1.9). Overall, participants spent 54.9% of their monitored time, or 7.7 hours/day, in sedentary behaviors. The most sedentary groups in the United States were older adolescents and adults aged >/=60 years, and they spent about 60% of their waking time in sedentary pursuits. Females were more sedentary than males before age 30 years, but this pattern was reversed after age 60 years. Mexican-American adults were significantly less sedentary than other US adults, and White and Black females were similarly sedentary after age 12 years. These data provide the first objective measure of the amount of time spent in sedentary behavior in the US population and indicate that Americans spend the majority of their time in behaviors that expend very little energy.

Introduction The use of movement monitors (accelerometers) for measuring physical activity (PA) in intervention and population-based studies is becoming a standard methodology for the objective measurement of sedentary and active behaviors and for validation of subjective PA self-reports. A vital step in PA measurements is classification of daily time into accelerometer wear and nonwear intervals using its recordings (counts) and an accelerometer-specific algorithm. Purpose To validate and improve a commonly used algorithm for classifying accelerometer wear and nonwear time intervals using objective movement data obtained in the whole-room indirect calorimeter. Methods We conducted a validation study of a wear/nonwear automatic algorithm using data obtained from 49 adults and 76 youth wearing accelerometers during a strictly monitored 24-h stay in a room calorimeter. The accelerometer wear and nonwear time classified by the algorithm was compared with actual wearing time. Potential improvements to the algorithm were examined using the minimum classification error as an optimization target. Results The recommended elements in the new algorithm are: 1) zero-count threshold during a nonwear time interval, 2) 90-min time window for consecutive zero/nonzero counts, and 3) allowance of 2-min interval of nonzero counts with the up/downstream 30-min consecutive zero counts window for detection of artifactual movements. Compared to the true wearing status, improvements to the algorithm decreased nonwear time misclassification during the waking and the 24-h periods (all P < 0.001). Conclusions The accelerometer wear/nonwear time algorithm improvements may lead to more accurate estimation of time spent in sedentary and active behaviors.

Purpose To assess performance of existing wear/nonwear time classification algorithms for accelerometry data collected in the free-living environment using a wrist-worn triaxial accelerometer and a waist-worn uniaxial accelerometer in older adults. Methods Twenty-nine adults aged 76 to 96 years wore wrist accelerometers for ~24-h per day and waist accelerometers during waking for ~7 days of free-living. Wear and nonwear times were classified by existing algorithms (Alg[Actilife], Alg[Troiano] and Alg[Choi]) and compared with wear and nonwear times identified by data plots and diary records. Using bias and probability of correct classification, performance of the algorithms, two time-windows (60- and 90-min), and vector magnitude (VM) vs. vertical axis (V) counts from a triaxial accelerometer, were compared. Results Automated algorithms (Alg[Choi] and Alg[Troiano]) classified wear/nonwear time intervals more accurately from VM than V counts. The use of 90-min time window improved wear/nonwear classification accuracy when compared with the 60-min window. The Alg[Choi] and Alg[Troiano] performed better than the manufacturer-provided algorithm (Alg[Actilife]), and Alg[Choi] performed better than Alg[Troiano] for wear/nonwear time classification using data collected by both accelerometers. Conclusions Triaxial wrist-worn accelerometer can be used for an accurate wear/nonwear time classification in free-living older adults. The use of 90-min window and VM counts improves performance of commonly used algorithms for wear/nonwear classification for both uniaxial and triaxial accelerometers.

Total daily energy expenditure (“total expenditure”) reflects daily energy needs and is a critical variable in human health and physiology, but its trajectory over the life course is poorly studied. We analyzed a large, diverse database of total expenditure measured by the doubly labeled water method for males and females aged 8 days to 95 years. Total expenditure increased with fat-free mass in a power-law manner, with four distinct life stages. Fat-free mass–adjusted expenditure accelerates rapidly in neonates to ~50% above adult values at ~1 year; declines slowly to adult levels by ~20 years; remains stable in adulthood (20 to 60 years), even during pregnancy; then declines in older adults. These changes shed light on human development and aging and should help shape nutrition and health strategies across the life span.

BackgroundCannabis dependence is a significant public health problem. Because there are no approved medications for this condition, treatment must rely on behavioral approaches empirically complemented by such lifestyle change as exercise.AimsTo examine the effects of moderate aerobic exercise on cannabis craving and use in cannabis dependent adults under normal living conditions.DesignParticipants attended 10 supervised 30-min treadmill exercise sessions standardized using heart rate (HR) monitoring (60–70% HR reserve) over 2 weeks. Exercise sessions were conducted by exercise physiologists under medical oversight.ParticipantsSedentary or minimally active non-treatment seeking cannabis-dependent adults (n = 12, age 25±3 years, 8 females) met criteria for primary cannabis dependence using the Substance Abuse module of the Structured Clinical Interview for DSM-IV (SCID).MeasurementsSelf-reported drug use was assessed for 1-week before, during, and 2-weeks after the study. Participants viewed visual cannabis cues before and after exercise in conjunction with assessment of subjective cannabis craving using the Marijuana Craving Questionnaire (MCQ-SF).FindingsDaily cannabis use within the run-in period was 5.9 joints per day (SD = 3.1, range 1.8–10.9). Average cannabis use levels within the exercise (2.8 joints, SD = 1.6, range 0.9–5.4) and follow-up (4.1 joints, SD = 2.5, range 1.1–9.5) periods were lower than during the run-in period (both P<.005). Average MCQ factor scores for the pre- and post-exercise craving assessments were reduced for compulsivity (P  = .006), emotionality (P  = .002), expectancy (P  = .002), and purposefulness (P  = .002).ConclusionsThe findings of this pilot study warrant larger, adequately powered controlled trials to test the efficacy of prescribed moderate aerobic exercise as a component of cannabis dependence treatment. The neurobiological mechanisms that account for these beneficial effects on cannabis use may lead to understanding of the physical and emotional underpinnings of cannabis dependence and recovery from this disorder.Trial RegistrationClinicalTrials.gov NCT00838448]

Our findings suggest that major differences in diabetes prevalence between African Americans and Whites may simply reflect differences in established risk factors for the disease, such as SES, that typically vary according to race.

Sedentary time is increased in patients with PAH and may lead to increased risk for metabolic and cardiovascular morbidity. Quantitation of daily activity and sedentary time using accelerometry may be a novel end point for PAH management and clinical trials.

A recent alarming rise of neurodegenerative diseases in the developed world is one of the major medical issues affecting older adults. In this review, we provide information about the associations of physical activity (PA) with major age-related neurodegenerative diseases and syndromes, including Alzheimer’s disease, vascular dementia, and mild cognitive impairment. We also provide evidence of PA’s role in reducing the risks of these diseases and helping to improve cognitive outcomes in older adults. Finally, we describe some potential mechanisms by which this protective effect occurs, providing guidelines for future research.