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Wrist-worn accelerometers are increasingly being used for the assessment of physical activity in population studies, but little is known about their value for sleep assessment. We developed a novel method of assessing sleep duration using data from 4,094 Whitehall II Study (United Kingdom, 2012-2013) participants aged 60-83 who wore the accelerometer for 9 consecutive days, filled in a sleep log and reported sleep duration via questionnaire. Our sleep detection algorithm defined (nocturnal) sleep as a period of sustained inactivity, itself detected as the absence of change in arm angle greater than 5 degrees for 5 minutes or more, during a period recorded as sleep by the participant in their sleep log. The resulting estimate of sleep duration had a moderate (but similar to previous findings) agreement with questionnaire based measures for time in bed, defined as the difference between sleep onset and waking time (kappa = 0.32, 95%CI:0.29,0.34) and total sleep duration (kappa = 0.39, 0.36,0.42). This estimate was lower for time in bed for women, depressed participants, those reporting more insomnia symptoms, and on weekend days. No such group differences were found for total sleep duration. Our algorithm was validated against data from a polysomnography study on 28 persons which found a longer time window and lower angle threshold to have better sensitivity to wakefulness, while the reverse was true for sensitivity to sleep. The novelty of our method is the use of a generic algorithm that will allow comparison between studies rather than a \"count\" based, device specific method.

Normal timing and duration of sleep is vital for all physical and mental health. However, many sleep-related studies depend on self-reported sleep measurements, which have limitations. This study aims to investigate the association of physical activity and sociodemographic characteristics including age, gender, coffee intake and social status with objective sleep measurements. A cross-sectional analysis was carried out on 82995 participants within the UK Biobank cohort. Sociodemographic and lifestyle information were collected through touch-screen questionnaires in 2007-2010. Sleep and physical activity parameters were later measured objectively using wrist-worn accelerometers in 2013-2015 (participants were aged 43-79 years and wore watches for 7 days). Participants were divided into 5 groups based on their objective sleep duration per night (<5 hours, 5-6 hours, 6-7 hours, 7-8 hours and >8 hours). Binary logistic models were adjusted for age, gender and Townsend Deprivation Index. Participants who slept 6-7 hours/night were the most frequent (33.5%). Females had longer objective sleep duration than males. Short objective sleep duration (<6 hours) correlated with older age, social deprivation and high coffee intake. Finally, those who slept 6-7 hours/night were most physically active. Objectively determined short sleep duration was associated with male gender, older age, low social status and high coffee intake. An inverse 'U-shaped' relationship between sleep duration and physical activity was also established. Optimal sleep duration for health in those over 60 may therefore be shorter than younger groups.

Background An unhealthy lifestyle is one of the greatest contributors to obesity. A number of behaviours are linked with obesity, but are often measured separately. The UK Biobank cohort of >500,000 participants allows us to explore these behaviours simultaneously. We therefore aimed to compare physical activity, television (TV) viewing and sleep duration across body mass index (BMI) categories in a large sample of UK adults. Methods UK Biobank participants were recruited and baseline measures were taken between 2007 and 2010 and data analysis was performed in 2015. BMI was measured objectively using trained staff. Self-report questionnaires were used to measure lifestyle behaviours including the international physical activity questionnaire (IPAQ-short form) for physical activity. During data analysis, six groups were defined based on BMI; ‘Underweight’ ( n  = 2026), ‘Normal weight’ ( n  = 132,372), ‘Overweight ( n  = 171,030), ‘Obese I’ ( n  = 67,903), ‘Obese II’ ( n  = 18,653) and ‘Obese III’ ( n  = 7000). The odds of reporting unhealthy lifestyle behaviours (low physical activity, high TV viewing or poor sleep duration) were compared across BMI groups using logistic regression analysis. Results Overweight and obese adults were more likely to report low levels of physical activity (≤967.5 MET.mins/wk) (‘Overweight’-OR [95% CI]: 1.23 [1.20 to 1.26], ‘Obese I’ 1.66 [1.61–1.71], ‘Obese II’ 2.21 [2.12–2.30], and ‘Obese III’ 3.13 [2.95 to 3.23]) compared to ‘Normal weight’ adults. The odds of reporting high TV viewing (3 h/day) was greater in ‘Overweight’ (1.52 [1.48 to 1.55]) and obese adults (‘Obese I’ 2.06 [2.00–2.12], ‘Obese II’ 2.69 [2.58–2.80], ‘Obese III’ 3.26 [3.07 to 3.47]), and poor sleep duration (<7, >8 h/night) was higher in ‘Overweight’ (1.09 [1.07 to 1.12]) and obese adults (‘Obese I’ 1.31 [1.27–1.34], ‘Obese II’ 1.50 [1.44–1.56], ‘Obese III’ (1.78 [1.68 to 1.89]) compared to the ‘Normal weight’ group. These lifestyle behaviours were clustered, the odds of reporting simultaneous low physical activity, high TV viewing and poor sleep (unhealthy behavioural phenotype) was higher than reporting these behaviours independently, in overweight and obese groups. ‘Obese III’ adults were almost six times more likely (5.47 [4.96 to 6.05]) to report an unhealthy behavioural phenotype compared to the ‘Normal weight’ group. Conclusions Overweight and obese adults report low levels of physical activity, high TV viewing and poor sleep duration. These behaviours seem to cluster and collectively expose individuals to greater risk of obesity. Multiple lifestyle behaviours should be targeted in future interventions.

Lack of physical activity leads to detrimental changes in body composition and metabolism, functional decline, and increased risk of disease in old age. The potential of Web-assisted interventions for increasing physical activity and improving metabolism in older individuals holds great promise but to our knowledge it has not been studied. The goal of our study was to assess whether a Web-based intervention increases physical activity and improves metabolic health in inactive older adults. We conducted a 3-month randomized, waitlist-controlled trial in a volunteer sample of 235 inactive adults aged 60-70 years without diabetes. The intervention group received the Internet program Philips DirectLife, which was directed at increasing physical activity using monitoring and feedback by accelerometer and digital coaching. The primary outcome was relative increase in physical activity measured objectively using ankle- and wrist-worn accelerometers. Secondary outcomes of metabolic health included anthropometric measures and parameters of glucose metabolism. In total, 226 participants (97%) completed the study. At the ankle, activity counts increased by 46% (standard error [SE] 7%) in the intervention group, compared to 12% (SE 3%) in the control group (P(difference)<.001). Measured at the wrist, activity counts increased by 11% (SE 3%) in the intervention group and 5% (SE 2%) in the control group (P(difference)=.11). After processing of the data, this corresponded to a daily increase of 11 minutes in moderate-to-vigorous activity in the intervention group versus 0 minutes in the control group (P(difference)=.001). Weight decreased significantly more in the intervention group compared to controls (-1.5 kg vs -0.8 kg respectively, P=.046), as did waist circumference (-2.3 cm vs -1.3 cm respectively, P=.036) and fat mass (-0.6% vs 0.07% respectively, P=.025). Furthermore, insulin and HbA1c levels were significantly more reduced in the intervention group compared to controls (both P<.05). This was the first study to show that in inactive older adults, a 3-month Web-based physical activity intervention was effective in increasing objectively measured daily physical activity and improving metabolic health. Such Web-based interventions provide novel opportunities for large scale prevention of metabolic deregulation in our rapidly aging population.

The desire of many to look young for their age has led to the establishment of a large cosmetics industry. However, the features of appearance that primarily determine how old women look for their age and whether genetic or environmental factors predominately influence such features are largely unknown. We studied the facial appearance of 102 pairs of female Danish twins aged 59 to 81 as well as 162 British females aged 45 to 75. Skin wrinkling, hair graying and lip height were significantly and independently associated with how old the women looked for their age. The appearance of facial sun-damage was also found to be significantly correlated to how old women look for their age and was primarily due to its commonality with the appearance of skin wrinkles. There was also considerable variation in the perceived age data that was unaccounted for. Composite facial images created from women who looked young or old for their age indicated that the structure of subcutaneous tissue was partly responsible. Heritability analyses of the appearance features revealed that perceived age, pigmented age spots, skin wrinkles and the appearance of sun-damage were influenced more or less equally by genetic and environmental factors. Hair graying, recession of hair from the forehead and lip height were influenced mainly by genetic factors whereas environmental factors influenced hair thinning. These findings indicate that women who look young for their age have large lips, avoid sun-exposure and possess genetic factors that protect against the development of gray hair and skin wrinkles. The findings also demonstrate that perceived age is a better biomarker of skin, hair and facial aging than chronological age.

Few studies have compared the validity of objective measures of physical activity energy expenditure (PAEE) in pregnant and non-pregnant women. PAEE is commonly estimated with accelerometers attached to the hip or waist, but little is known about the validity and participant acceptability of wrist attachment. The objectives of the current study were to assess the validity of a simple summary measure derived from a wrist-worn accelerometer (GENEA, Unilever Discover, UK) to estimate PAEE in pregnant and non-pregnant women, and to evaluate participant acceptability. Non-pregnant (N = 73) and pregnant (N = 35) Swedish women (aged 20-35 yrs) wore the accelerometer on their wrist for 10 days during which total energy expenditure (TEE) was assessed using doubly-labelled water. PAEE was calculated as 0.9×TEE-REE. British participants (N = 99; aged 22-65 yrs) wore accelerometers on their non-dominant wrist and hip for seven days and were asked to score the acceptability of monitor placement (scored 1 [least] through 10 [most] acceptable). There was no significant correlation between body weight and PAEE. In non-pregnant women, acceleration explained 24% of the variation in PAEE, which decreased to 19% in leave-one-out cross-validation. In pregnant women, acceleration explained 11% of the variation in PAEE, which was not significant in leave-one-out cross-validation. Median (IQR) acceptability of wrist and hip placement was 9(8-10) and 9(7-10), respectively; there was a within-individual difference of 0.47 (p<.001). A simple summary measure derived from a wrist-worn tri-axial accelerometer adds significantly to the prediction of energy expenditure in non-pregnant women and is scored acceptable by participants.

The Internet has potential as a medium for health behavior change programs, but no controlled studies have yet evaluated the impact of a fully automated physical activity intervention over several months with real-time objective feedback from a monitor. The aim was to evaluate the impact of a physical activity program based on the Internet and mobile phone technology provided to individuals for 9 weeks. A single-center, randomized, stratified controlled trial was conducted from September to December 2005 in Bedfordshire, United Kingdom, with 77 healthy adults whose mean age was 40.4 years (SD = 7.6) and mean body mass index was 26.3 (SD = 3.4). Participants were randomized to a test group that had access to an Internet and mobile phone-based physical activity program (n = 47) or to a control group (n = 30) that received no support. The test group received tailored solutions for perceived barriers, a schedule to plan weekly exercise sessions with mobile phone and email reminders, a message board to share their experiences with others, and feedback on their level of physical activity. Both groups were issued a wrist-worn accelerometer to monitor their level of physical activity; only the test group received real-time feedback via the Internet. The main outcome measures were accelerometer data and self-report of physical activity. At the end of the study period, the test group reported a significantly greater increase over baseline than did the control group for perceived control (P < .001) and intention/expectation to exercise (P < .001). Intent-to-treat analyses of both the accelerometer data (P = .02) and leisure time self-report data (P = .03) found a higher level of moderate physical activity in the test group. The average increase (over the control group) in accelerometer-measured moderate physical activity was 2 h 18 min per week. The test group also lost more percent body fat than the control group (test group: -2.18, SD = 0.59; control group: -0.17, SD = 0.81; P = .04). A fully automated Internet and mobile phone-based motivation and action support system can significantly increase and maintain the level of physical activity in healthy adults.

Short and long sleep durations have adverse effects on physical and mental health. However, most studies are based on self-reported sleep duration and health status. Therefore, this longitudinal study aims to investigate objectively measured sleep duration and subsequent primary health care records in older adults to investigate the impact of sleep duration and fragmentation on physical and mental health. Data on objective sleep duration were measured using accelerometry. Primary care health records were then obtained from the UK Biobank (n=84,404). Participants (mean age, 62.4 years) were divided into five groups according to their sleep duration derived from the accelerometry data: <5 hours, 5-6 hours, 6-7 hours, 7-8 hours and >8 hours. ICD-10 codes were used for the analysis of primary care data. Wake after sleep onset, activity level during the least active 5 hours and episodes of movement during sleep were analysed as an indication for sleep fragmentation. Binary regression models were adjusted for age, gender and Townsend deprivation score. A \"U-shaped\" relationship was found between sleep duration and diseases including diabetes, hypertension and heart disease and depression. Short and long sleep durations and fragmented sleep were associated with increased odds of disease. Six to eight hours of sleep, as well as less fragmented sleep, predicted better long-term metabolic and mental health.

The clinical diagnosis and treatment of Alzheimer’s disease (AD) represent a challenge to clinicians due to the variability of clinical symptomatology as well as the unavailability of reliable diagnostic tests. In this study, the development of a novel electrochemical assay and its potential to detect peripheral blood biomarkers to diagnose AD using plasma immunoglobulins is investigated. The immunosensor employs a gold electrode as the immobilizing substrate, albumin depleted plasma immunoglobulin as the biomarker, and polyclonal rabbit Anti-human immunoglobulin (against IgA, IgG, IgM) as the receptor for plasma conjugation. The assay showed good response, sensitivity and reproducibility in differentiating plasma immunoglobulin from AD and control subjects down to 10−9 dilutions of plasma immunoglobulin representing plasma content concentrations in the pg mL−1 range. The newly developed assay is highly sensitive, less time consuming, easy to handle, can be easily modified to detect other dementia-related biomarkers in blood samples, and can be easily integrated into portable devices.

Perceived facial age has been proposed as a biomarker of ageing with 'looking young for one's age' linked to physical and cognitive functioning and to increased survival for Caucasians. We have investigated the environmental and lifestyle factors associated with perceived facial ageing in Chinese women. Facial photographs were collected from 250 Chinese women, aged 25-70 years in Shanghai, China. Perceived facial age was determined and related to chronological age for each participant. Lifestyle and health information was collected by questionnaire. Bivariate analyses (controlling for chronological age) identified and quantified lifestyle variables associated with perceived facial age. Independent predictors of perceived age were identified by multivariate modelling. Factors which significantly associated with looking younger for one's chronological age included greater years of education (p<0.001), fewer household members (p=0.027), menopausal status (p=0.020), frequency of visiting one's doctor (p=0.013), working indoors (p<0.001), spending less time in the sun (p=0.015), moderate levels of physical activity (p=0.004), higher frequency of teeth cleaning (p<0.001) and more frequent use of facial care products: cleanser (p<0.001); moisturiser (p=0.016) or night cream (p=0.016). Overall, 36.5% of the variation in the difference between perceived and chronological age could be explained by a combination of chronological age and 6 independent lifestyle variables. We have thus identified and quantified a number of factors associated with younger appearance in Chinese women. Presentation of these factors in the context of facial appearance could provide significant motivation for the adoption of a range of healthy behaviours at the level of both individuals and populations.