NIHR Leicester-Loughborough Diet, Lifestyle and Physical Activity Biomedical Research Unit

The aim of this meta-analysis was to quantify the effects of high-intensity interval training (HIIT) on markers of glucose regulation and insulin resistance compared with control conditions (CON) or continuous training (CT). Databases were searched for HIIT interventions based upon the inclusion criteria: training ≥2 weeks, adult participants and outcome measurements that included insulin resistance, fasting glucose, HbA1c or fasting insulin. Dual interventions and participants with type 1 diabetes were excluded. Fifty studies were included. There was a reduction in insulin resistance following HIIT compared with both CON and CT (HIIT vs. CON: standardized mean difference [SMD] = -0.49, confidence intervals [CIs] -0.87 to -0.12, P = 0.009; CT: SMD = -0.35, -0.68 to -0.02, P = 0.036). Compared with CON, HbA1c decreased by 0.19% (-0.36 to -0.03, P = 0.021) and body weight decreased by 1.3 kg (-1.9 to -0.7, P < 0.001). There were no statistically significant differences between groups in other outcomes overall. However, participants at risk of or with type 2 diabetes experienced reductions in fasting glucose (-0.92 mmol L(-1), -1.22 to -0.62, P < 0.001) compared with CON. HIIT appears effective at improving metabolic health, particularly in those at risk of or with type 2 diabetes. Larger randomized controlled trials of longer duration than those included in this meta-analysis are required to confirm these results.

Diabetes mellitus is a complex metabolic disorder associated with an increased risk of microvascular and macrovascular disease; its main clinical characteristic is hyperglycaemia. The last century has been characterised by remarkable advances in our understanding of the mechanisms leading to hyperglycaemia. The central role of insulin in glucose metabolism regulation was clearly demonstrated during the early 1920s, when Banting, Best, Collip and Macleod successfully reduced blood glucose levels and glycosuria in a patient treated with a substance purified from bovine pancreata. Later, during the mid-1930s, clinical observations suggested a possible distinction between 'insulin-sensitive' and 'insulin-insensitive' diabetes. Only during the 1950s, when a reliable measure of circulating insulin was available, was it possible to translate these clinical observations into pathophysiological and biochemical differences, and the terms 'insulin-dependent' (indicating undetectable insulin levels) and 'non-insulin-dependent' (normal or high insulin levels) started to emerge. The next 30 years were characterised by pivotal progress in the field of immunology that were instrumental in demonstrating an immune-mediated loss of insulin-secreting β-cells in subjects with 'insulin-dependent' diabetes. At the same time, new experimental techniques allowing measurement of insulin 'impedance' showed a reduced peripheral effect of insulin in subjects with 'non-insulin-dependent' diabetes (insulin resistance). The difference between the two types of diabetes emerging from decades of observations and experiments was further formally recognised in 1979, when the definitions 'type I' and 'type II' diabetes were introduced to replace the former 'insulin-dependent' and 'non-insulin-dependent' terms. In the following years, many studies elucidated the natural history and temporal contribution of insulin resistance and β-cell insulin secretion in 'type II' diabetes. Furthermore, a central role for insulin resistance in the development of a cluster of cardiometabolic alterations (dyslipidaemia, inflammation, high blood pressure) was suggested. Possibly as a consequence of the secular changes in diabetes risk factors, in the last 10 years the limitation of a simple distinction between 'type I' and 'type II' diabetes has been increasingly recognised, with subjects showing the coexistence of insulin resistance and immune activation against β-cells. With the advancement of our cellular and molecular understanding of diabetes, a more pathophysiological classification that overcomes the historical and simple 'glucocentric' view could result in a better patient phenotyping and therapeutic approach.

Sedentary behaviour - i.e., low energy-expending waking behaviour while seated or lying down - is a health risk factor, even when controlling for physical activity. This review sought to describe the behaviour change strategies used within interventions that have sought to reduce sedentary behaviour in adults. Studies were identified through existing literature reviews, a systematic database search, and hand-searches of eligible papers. Interventions were categorised as 'very promising', 'quite promising', or 'non-promising' according to observed behaviour changes. Intervention functions and behaviour change techniques were compared across promising and non-promising interventions. Twenty-six eligible studies reported thirty-eight interventions, of which twenty (53%) were worksite-based. Fifteen interventions (39%) were very promising, eight quite promising (21%), and fifteen non-promising (39%). Very or quite promising interventions tended to have targeted sedentary behaviour instead of physical activity. Interventions based on environmental restructuring, persuasion, or education were most promising. Self-monitoring, problem solving, and restructuring the social or physical environment were particularly promising behaviour change techniques. Future sedentary reduction interventions might most fruitfully incorporate environmental modification and self-regulatory skills training. The evidence base is, however, weakened by low-quality evaluation methods; more RCTs, employing no-treatment control groups, and collecting objective data are needed.

BACKGROUND: Research examining sedentary behaviour as a potentially independent risk factor for chronic disease morbidity and mortality has expanded rapidly in recent years. METHODS: We present a narrative overview of the sedentary behaviour measurement literature. Subjective and objective methods of measuring sedentary behaviour suitable for use in population-based research with children and adults are examined. The validity and reliability of each method is considered, gaps in the literature specific to each method identified and potential future directions discussed. RESULTS: To date, subjective approaches to sedentary behaviour measurement, e.g. questionnaires, have focused predominantly on TV viewing or other screen-based behaviours. Typically, such measures demonstrate moderate reliability but slight to moderate validity. Accelerometry is increasingly being used for sedentary behaviour assessments; this approach overcomes some of the limitations of subjective methods, but detection of specific postures and postural changes by this method is somewhat limited. Instruments developed specifically for the assessment of body posture have demonstrated good reliability and validity in the limited research conducted to date. Miniaturization of monitoring devices, interoperability between measurement and communication technologies and advanced analytical approaches are potential avenues for future developments in this field. CONCLUSIONS: High-quality measurement is essential in all elements of sedentary behaviour epidemiology, from determining associations with health outcomes to the development and evaluation of behaviour change interventions. Sedentary behaviour measurement remains relatively under-developed, although new instruments, both objective and subjective, show considerable promise and warrant further testing.

Research indicates that high levels of sedentary behavior (sitting or lying with low energy expenditure) are adversely associated with health. A key factor in improving our understanding of the impact of sedentary behavior (and patterns of sedentary time accumulation) on health is the use of objective measurement tools that collect date and time-stamped activity information. One such tool is the activPAL monitor. This thigh-worn device uses accelerometer-derived information about thigh position to determine the start and end of each period spent sitting/lying, standing, and stepping, as well as stepping speed, step counts, and postural transitions. The activPAL is increasingly being used within field-based research for its ability to measure sitting/lying via posture. We summarise key issues to consider when using the activPAL in physical activity and sedentary behavior field-based research with adult populations. It is intended that the findings and discussion points be informative for researchers who are currently using activPAL monitors or are intending to use them. Pre-data collection decisions, monitor preparation and distribution, data collection considerations, and manual and automated data processing possibilities are presented using examples from current literature and experiences from 2 research groups from the UK and Australia.

An international group of experts convened to provide guidance for employers to promote the avoidance of prolonged periods of sedentary work. The set of recommendations was developed from the totality of the current evidence, including long-term epidemiological studies and interventional studies of getting workers to stand and/or move more frequently. The evidence was ranked in quality using the four levels of the American College of Sports Medicine. The derived guidance is as follows: for those occupations which are predominantly desk based, workers should aim to initially progress towards accumulating 2 h/day of standing and light activity (light walking) during working hours, eventually progressing to a total accumulation of 4 h/day (prorated to part-time hours). To achieve this, seated-based work should be regularly broken up with standing-based work, the use of sit-stand desks, or the taking of short active standing breaks. Along with other health promotion goals (improved nutrition, reducing alcohol, smoking and stress), companies should also promote among their staff that prolonged sitting, aggregated from work and in leisure time, may significantly and independently increase the risk of cardiometabolic diseases and premature mortality. It is appreciated that these recommendations should be interpreted in relation to the evidence from which they were derived, largely observational and retrospective studies, or short-term interventional studies showing acute cardiometabolic changes. While longer term intervention studies are required, the level of consistent evidence accumulated to date, and the public health context of rising chronic diseases, suggest initial guidelines are justified. We hope these guidelines stimulate future research, and that greater precision will be possible within future iterations.

Physical activity and sedentary behaviour are associated with metabolic and mental health during childhood and adolescence. Understanding the inter-relationships between these behaviours will help to inform intervention design. This systematic review and meta-analysis synthesized evidence from observational studies describing the association between sedentary behaviour and physical activity in young people (<18 years). English-language publications up to August 2013 were located through electronic and manual searches. Included studies presented statistical associations between at least one measure of sedentary behaviour and one measure of physical activity. One hundred sixty-three papers were included in the meta-analysis, from which data on 254 independent samples was extracted. In the summary meta-analytic model (k = 230), a small, but significant, negative association between sedentary behaviour and physical activity was observed (r = -0.108, 95% confidence interval [CI] = -0.128, -0.087). In moderator analyses, studies that recruited smaller samples (n < 100, r = -0.193, 95% CI = -0.276, -0.109) employed objective methods of measurement (objectively measured physical activity; r = -0.233, 95% CI = -0.330, -0.137) or were assessed to be of higher methodological quality (r = -0.176, 95% CI = -0.215, -0.138) reported stronger associations, although effect sizes remained small. The association between sedentary behaviour and physical activity in young people is negative, but small, suggesting that these behaviours do not directly displace one another.

BACKGROUND: Active video games (AVGs) have gained interest as a way to increase physical activity in children and youth. The effect of AVGs on acute energy expenditure (EE) has previously been reported; however, the influence of AVGs on other health-related lifestyle indicators remains unclear. OBJECTIVE: This systematic review aimed to explain the relationship between AVGs and nine health and behavioural indicators in the pediatric population (aged 0-17 years). DATA SOURCES: Online databases (MEDLINE, EMBASE, psycINFO, SPORTDiscus and Cochrane Central Database) and personal libraries were searched and content experts were consulted for additional material. DATA SELECTION: Included articles were required to have a measure of AVG and at least one relevant health or behaviour indicator: EE (both habitual and acute), adherence and appeal (i.e., participation and enjoyment), opportunity cost (both time and financial considerations, and adverse events), adiposity, cardiometabolic health, energy intake, adaptation (effects of continued play), learning and rehabilitation, and video game evolution (i.e., sustainability of AVG technology). RESULTS: 51 unique studies, represented in 52 articles were included in the review. Data were available from 1992 participants, aged 3-17 years, from 8 countries, and published from 2006-2012. Overall, AVGs are associated with acute increases in EE, but effects on habitual physical activity are not clear. Further, AVGs show promise when used for learning and rehabilitation within special populations. Evidence related to other indicators was limited and inconclusive. CONCLUSIONS: Controlled studies show that AVGs acutely increase light- to moderate-intensity physical activity; however, the findings about if or how AVG lead to increases in habitual physical activity or decreases in sedentary behaviour are less clear. Although AVGs may elicit some health benefits in special populations, there is not sufficient evidence to recommend AVGs as a means of increasing daily physical activity.

PURPOSE: This study aimed to 1) explore children's compliance to wearing wrist- and hip-mounted accelerometers, 2) compare children's physical activity (PA) derived from raw accelerations of wrist and hip, and 3) examine differences in raw and counts PA measured by hip-worn accelerometry. METHODS: One hundred and twenty-nine 9- to 10-yr-old children wore a wrist-mounted GENEActiv accelerometer (GAwrist) and a hip-mounted ActiGraph GT3X+ accelerometer (AGhip) for 7 d. Both devices measured raw accelerations, and the AGhip also provided count-based data. RESULTS: More children wore the GAwrist than those from the AGhip regardless of wear time criteria applied (P < 0.001-0.035). Raw data signal vector magnitude (r = 0.68), moderate PA (MPA) (r = 0.81), vigorous PA (VPA) (r = 0.85), and moderate-to-vigorous PA (MVPA) (r = 0.83) were strongly associated between devices (P < 0.001). GAwrist signal vector magnitude (P = 0.001), MPA (P = 0.037), VPA (P = 0.002), and MVPA (P = 0.016) were significantly greater than those from the AGhip. According to GAwrist raw data, 86.9% of children engaged in at least 60 min · d(-1) of MVPA, compared with 19% for AGhip. ActiGraph MPA (raw) was 42.00 ± 1.61 min · d(-1) compared with 35.05 ± 0.99 min · d(-1) (counts) (P = 0.02). ActiGraph VPA was 7.59 ± 0.46 min · d(-1) (raw) and 37.06 ± 1.85 min · d(-1) (counts; P = 0.19). CONCLUSIONS: In children, accelerometer wrist placement promotes superior compliance than the hip. Raw accelerations were significantly higher for GAwrist compared with those for AGhip possibly because of placement location and technical differences between devices. AGhip PA calculated from raw accelerations and counts differed substantially, demonstrating that PA outcomes derived from cut points for raw output and counts cannot be directly compared.

. In free-living data from Australian adults, a simple algorithm developed in a different population showed 'almost perfect' agreement with the diary method for most individuals (88%). For several purposes (e.g. with wear standardisation), adopting a low burden, automated approach would be expected to have little impact on data quality. The accuracy for total waking wear time was less and algorithm thresholds may require adjustments for older populations.

BACKGROUND AND OBJECTIVE: Infants born preterm are significantly lighter and shorter on reaching term equivalent age (TEA) than are those born at term, but the relation with body composition is less clear. We conducted a systematic review to assess the body composition at TEA of infants born preterm. METHODS: The databases MEDLINE, Embase, CINAHL, HMIC, "Web of Science," and "CSA Conference Papers Index" were searched between 1947 and June 2011, with selective citation and reference searching. Included studies had to have directly compared measures of body composition at TEA in preterm infants and infants born full-term. Data on body composition, anthropometry, and birth details were extracted from each article. RESULTS: Eight studies (733 infants) fulfilled the inclusion criteria. Mean gestational age and weight at birth were 30.0 weeks and 1.18 kg in the preterm group and 39.6 weeks and 3.41 kg in the term group, respectively. Meta-analysis showed that the preterm infants had a greater percentage total body fat at TEA than those born full-term (mean difference, 3%; P = .03), less fat mass (mean difference, 50 g; P = .03), and much less fat-free mass (mean difference, 460 g; P < .0001). CONCLUSIONS: The body composition at TEA of infants born preterm is different than that of infants born at term. Preterm infants have less lean tissue but more similar fat mass. There is a need to determine whether improved nutritional management can enhance lean tissue acquisition, which indicates a need for measures of body composition in addition to routine anthropometry.

OBJECTIVE: To determine whether breaking up prolonged sitting with short bouts of standing or walking improves postprandial markers of cardiometabolic health in women at high risk of type 2 diabetes. RESEARCH DESIGN AND METHODS: Twenty-two overweight/obese, dysglycemic, postmenopausal women (mean ± SD age 66.6 ± 4.7 years) each participated in two of the following treatments: prolonged, unbroken sitting (7.5 h) or prolonged sitting broken up with either standing or walking at a self-perceived light intensity (for 5 min every 30 min). Both allocation and treatment order were randomized. The incremental area under the curves (iAUCs) for glucose, insulin, nonesterified fatty acids (NEFA), and triglycerides were calculated for each treatment condition (mean ± SEM). The following day, all participants underwent the 7.5-h sitting protocol. RESULTS: Compared with a prolonged bout of sitting (iAUC 5.3 ± 0.8 mmol/L ⋅ h), both standing (3.5 ± 0.8 mmol/L ⋅ h) and walking (3.8 ± 0.7 mmol/L ⋅ h) significantly reduced the glucose iAUC (both P < 0.05). When compared with prolonged sitting (548.2 ± 71.8 mU/L ⋅ h), insulin was also reduced for both activity conditions (standing, 437.2 ± 73.5 mU/L ⋅ h; walking, 347.9 ± 78.7 mU/L ⋅ h; both P < 0.05). Both standing (-1.0 ± 0.2 mmol/L ⋅ h) and walking (-0.8 ± 0.2 mmol/L ⋅ h) attenuated the suppression of NEFA compared with prolonged sitting (-1.5 ± 0.2 mmol/L ⋅ h) (both P < 0.05). There was no significant effect on triglyceride iAUC. The effects on glucose (standing and walking) and insulin (walking only) persisted into the following day. CONCLUSIONS: Breaking up prolonged sitting with 5-min bouts of standing or walking at a self-perceived light intensity reduced postprandial glucose, insulin, and NEFA responses in women at high risk of type 2 diabetes. This simple, behavioral approach could inform future public health interventions aimed at improving the metabolic profile of postmenopausal, dysglycemic women.

BACKGROUND: Sedentary behavior is defined as any waking behavior characterized by an energy expenditure of 1.5 METS or less while in a sitting or reclining posture. This study examines this definition by assessing the energy cost (METs) of common sitting, standing and walking tasks. METHODS: Fifty one adults spent 10 min during each activity in a variety of sitting tasks (watching TV, Playing on the Wii, Playing on the PlayStation Portable (PSP) and typing) and non-sedentary tasks (standing still, walking at 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, and 1.6 mph). Activities were completed on the same day in a random order following an assessment of resting metabolic rate (RMR). A portable gas analyzer was used to measure oxygen uptake, and data were converted to units of energy expenditure (METs). RESULTS: Average of standardized MET values for screen-based sitting tasks were: 1.33 (SD: 0.24) METS (TV), 1.41 (SD: 0.28) (PSP), and 1.45 (SD: 0.32) (Typing). The more active, yet still seated, games on the Wii yielded an average of 2.06 (SD: 0.5) METS. Standing still yielded an average of 1.59 (SD: 0.37) METs. Walking MET values increased incrementally with speed from 2.17 to 2.99 (SD: 0.5 - 0.69) METs. CONCLUSIONS: The suggested 1.5 MET threshold for sedentary behaviors seems reasonable however some sitting based activities may be classified as non-sedentary. The effect of this on the definition of sedentary behavior and associations with metabolic health needs further investigation.

BACKGROUND: Sedentary behaviour has been identified as a distinct risk factor for several health outcomes. Nevertheless, little research has been conducted into the underlying mechanisms driving these observations. This study aimed to investigate the association of objectively measured sedentary time and breaks in sedentary time with markers of chronic low-grade inflammation and adiposity in a population at a high risk of type 2 diabetes mellitus. METHODS: This study reports data from an ongoing diabetes prevention programme conducted in Leicestershire, UK. High risk individuals were recruited from 10 primary care practices. Sedentary time (<25 counts per 15 s) was measured using Actigraph GT3X accelerometers (15 s epochs). A break was considered as any interruption in sedentary time (≥25 counts per 15 s). Biochemical outcomes included interleukin-6 (IL-6), C-reactive protein (CRP), leptin, adiponectin and leptin:adiponectin ratio (LAR). A sensitivity analysis investigated whether results were affected by removing participants with a CRP level >10 mg/L, as this can be indicative of acute inflammation. RESULTS: 558 participants (age = 63.6±7.7 years; male = 64.7%) had complete adipokine and accelerometer data. Following adjustment for various confounders, sedentary time was detrimentally associated with CRP (β = 0.176±0.057, p = 0.002), IL-6 (β = 0.242±0.056, p = <0.001), leptin (β = 0.146±0.043, p = <0.001) and LAR (β = 0.208±0.052, p = <0.001). Associations were attenuated after further adjustment for moderate-to-vigorous physical activity (MVPA) with only IL-6 (β = 0.231±0.073, p = 0.002) remaining significant; this result was unaffected after further adjustment for body mass index and glycosylated haemoglobin (HbA1c). Similarly, breaks in sedentary time were significantly inversely associated with IL-6 (β = -0.094±0.047, p = 0.045) and leptin (β = -0.075±0.037, p = 0.039); however, these associations were attenuated after adjustment for accelerometer derived variables. Excluding individuals with a CRP level >10 mg/L consistently attenuated the significant associations across all markers of inflammation. CONCLUSION: These novel findings from a high risk population recruited through primary care suggest that sedentary behaviour may influence markers associated with inflammation, independent of MVPA, glycaemia and adiposity.

BACKGROUND: Sedentary behaviour and its association with dietary intake in young people and adults are important topics and were systematically reviewed in 2011. There is a need to update this evidence given the changing nature of sedentary behaviour and continued interest in this field. This review aims to assist researchers in better interpreting the diversity of findings concerning sedentary behaviour and weight status. OBJECTIVE: To provide an update of the associations between sedentary behaviour and dietary intake across the lifespan. METHODS: Electronic databases searched were MEDLINE, PsychInfo, Cochrane Library, Web of Science and Science Direct for publications between January 2010 and October 2013, thus updating a previous review. Included were observational studies assessing an association between at least one sedentary behaviour and at least one aspect of dietary intake in preschool children (<5 years), school-aged children (6-11 years), adolescents (12-18 years) and adults (>18 years). RESULTS: 27 papers met inclusion criteria (preschool k=3, school-aged children k=9, adolescents k=15, adults k=3). For all three groups of young people, trends were evident for higher levels of sedentary behaviour, especially TV viewing, to be associated with a less healthful diet, such as less fruit and vegetable and greater consumption of energy-dense snacks and sugar sweetened beverages. Data for the three studies with adults were less conclusive. CONCLUSIONS: Sedentary behaviour continues to be associated with unhealthy diet in young people in mostly cross-sectional studies. More studies utilising a prospective design are needed to corroborate findings and more studies are needed with adults.

OBJECTIVES: (1) To develop and internally-validate Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) thresholds for separating sedentary behaviours from common light-intensity physical activities using raw acceleration data collected from both hip- and wrist-worn tri-axial accelerometers; and (2) to compare and evaluate the performances between the ENMO and MAD metrics. METHODS: Thirty-three adults [mean age (standard deviation (SD)) = 27.4 (5.9) years; mean BMI (SD) = 23.9 (3.7) kg/m2; 20 females (60.6%)] wore four accelerometers; an ActiGraph GT3X+ and a GENEActiv on the right hip; and an ActiGraph GT3X+ and a GENEActiv on the non-dominant wrist. Under laboratory-conditions, participants performed 16 different activities (11 sedentary behaviours and 5 light-intensity physical activities) for 5 minutes each. ENMO and MAD were computed from the raw acceleration data, and logistic regression and receiver-operating-characteristic (ROC) analyses were implemented to derive thresholds for activity discrimination. Areas under ROC curves (AUROC) were calculated to summarise performances and thresholds were assessed via executing leave-one-out-cross-validations. RESULTS: For both hip and wrist monitor placements, in comparison to the ActiGraph GT3X+ monitors, the ENMO and MAD values derived from the GENEActiv devices were observed to be slightly higher, particularly for the lower-intensity activities. Monitor-specific hip and wrist ENMO and MAD thresholds showed excellent ability for separating sedentary behaviours from motion-based light-intensity physical activities (in general, AUROCs >0.95), with validation indicating robustness. However, poor classification was experienced when attempting to isolate standing still from sedentary behaviours (in general, AUROCs <0.65). The ENMO and MAD metrics tended to perform similarly across activities and accelerometer brands. CONCLUSIONS: Researchers can utilise these robust monitor-specific hip and wrist ENMO and MAD thresholds, in order to accurately separate sedentary behaviours from common motion-based light-intensity physical activities. However, caution should be taken if isolating sedentary behaviours from standing is of particular interest.

Although physical activity performed after bariatric surgery is associated with enhanced weight loss outcomes, there is limited information on patients' physical activity behaviour in this context. This systematic review and meta-analysis assessed pre-operative to post-operative changes in physical activity and physical function outcomes among obese adults undergoing bariatric surgery. A total of 50 studies met inclusion criteria with 26 papers reporting data for meta-analysis. Increases in both objectively recorded and self-reported physical activity at 12 months were demonstrated. Studies indicated that there was a shift towards a greater amount of active time, but of a lower intensity within the first 6 months of bariatric surgery, suggested by a reduction in moderate to vigorous physical activity but an increase in step count. A standardized mean difference (SMD) of 1.53 (95% CI: 1.02-2.04) based on nine studies indicated improved walking performance at 12 months. Similarly, analysis of five studies demonstrated increased musculoskeletal function at 3-6 months (SMD: 1.51; 95% CI: 0.60-2.42). No relationship was identified between changes in weight and walking performance post-surgery. More studies assessing physical activity, physical function and weight loss would help understand the role of physical activity in optimizing post-operative weight and functional outcomes.

BACKGROUND: It is well documented that meeting the guideline levels (150 minutes per week) of moderate-to-vigorous physical activity (PA) is protective against chronic disease. Conversely, emerging evidence indicates the deleterious effects of prolonged sitting. Therefore, there is a need to change both behaviors. Self-monitoring of behavior is one of the most robust behavior-change techniques available. The growing number of technologies in the consumer electronics sector provides a unique opportunity for individuals to self-monitor their behavior. OBJECTIVE: The aim of this study is to review the characteristics and measurement properties of currently available self-monitoring devices for sedentary time and/or PA. METHODS: To identify technologies, four scientific databases were systematically searched using key terms related to behavior, measurement, and population. Articles published through October 2015 were identified. To identify technologies from the consumer electronic sector, systematic searches of three Internet search engines were also performed through to October 1, 2015. RESULTS: The initial database searches identified 46 devices and the Internet search engines identified 100 devices yielding a total of 146 technologies. Of these, 64 were further removed because they were currently unavailable for purchase or there was no evidence that they were designed for, had been used in, or could readily be modified for self-monitoring purposes. The remaining 82 technologies were included in this review (73 devices self-monitored PA, 9 devices self-monitored sedentary time). Of the 82 devices included, this review identified no published articles in which these devices were used for the purpose of self-monitoring PA and/or sedentary behavior; however, a number of technologies were found via Internet searches that matched the criteria for self-monitoring and provided immediate feedback on PA (ActiGraph Link, Microsoft Band, and Garmin Vivofit) and sedentary time (activPAL VT, the Lumo Back, and Darma). CONCLUSIONS: There are a large number of devices that self-monitor PA; however, there is a greater need for the development of tools to self-monitor sedentary time. The novelty of these devices means they have yet to be used in behavior change interventions, although the growing field of wearable technology may facilitate this to change.

PURPOSE: The objective of this study is to compare the accuracy of the activPAL and ActiGraph GT3X+ (waist and thigh) proprietary postural allocation algorithms and an open-source postural allocation algorithm applied to GENEActiv (thigh) and ActiGraph GT3X+ (thigh) data. METHODS: Thirty-four adults (≥18 yr) wore the activPAL3, GENEActiv, and ActiGraph GT3X+ on the right thigh and an ActiGraph on the right hip while performing four lying, seven sitting, and five upright activities in the laboratory. Lying and sitting tasks incorporated a range of leg angles (e.g., lying with legs bent and sitting with legs crossed). Each activity was performed for 5 min while being directly observed. The percentage of the time the posture was correctly classified was calculated. RESULTS: Participants consisted of 14 males and 20 females (mean age, 27.2 ± 5.9 yr; mean body mass index, 23.8 ± 3.7 kg·m). All postural allocation algorithms applied to monitors worn on the thigh correctly classified ≥93% of the time lying, ≥91% of the time sitting, and ≥93% of the time upright. The ActiGraph waist proprietary algorithm correctly classified 72% of the time lying, 58% of the time sitting, and 74% of the time upright. Both the activPAL and ActiGraph thigh proprietary algorithms misclassified sitting on a chair with legs stretched out (58% and 5% classified incorrectly, respectively). The ActiGraph thigh proprietary and open-source algorithm applied to the thigh-worn ActiGraph misclassified participants lying on their back with their legs bent 27% and 9% of the time, respectively. CONCLUSION: All postural allocation algorithms when applied to devices worn on the thigh were highly accurate in identifying lying, sitting, and upright postures. Given the poor accuracy of the waist algorithm for detecting sitting, caution should be taken if inferring sitting time from a waist-worn device.

PURPOSE: This study aimed to determine the agreement between outputs from contemporaneous measures of acceleration from wrist-worn GENEActiv and ActiGraph accelerometers when processed using the GGIR open source package. METHODS: Thirty-four participants wore a GENEActiv and an ActiGraph GT3X+ on their nondominant wrist continuously for 2 d to ensure the capture of one 24-h day and one nocturnal sleep. GENEActiv.bin files and ActiGraph .csv files were analyzed with R-package GGIR version 1.2-0. Key outcome variables were as follows: wear time, average magnitude of dynamic wrist acceleration (Euclidean norm minus one [ENMO]), percentile distribution of accelerations, time spent across acceleration levels in a 40-mg resolution, time in moderate-to-vigorous physical activity (MVPA: total, 10-min bouts), and duration of nocturnal sleep. RESULTS: There was a high agreement between accelerometer brands for all derived outcomes (wear time, MVPA, and sleep; intraclass correlation coefficient [ICC] > 0.96), ENMO (ICC = 0.99), time spent across acceleration levels (ICC > 0.93), and accelerations ≥50th percentile of the distribution (ICC > 0.82). ENMO (mean ± SD, GENEActiv = 29.9 ± 20.7 mg, ActiGraph = 27.8 ± 21.4 mg) and accelerations between the 5th and the 75th percentile of the distribution measured by the GENEActiv were significantly higher than those measured by the ActiGraph. Correspondingly, the number of minutes recorded between 0 and 40 mg was significantly greater for the ActiGraph (745 min cf. 734 min), and the number of minutes recorded between 40 and 80 mg was significantly greater for the GENEActiv (110 min cf. 105 min). CONCLUSION: Derived outcomes (wear time, MVPA, and sleep) were similar between brands. Brands compared well for acceleration magnitudes >50-80 mg but not lower magnitudes indicative of sedentary time. Caution is advised when comparing the magnitude of ENMO between brands, but there was a high consistency between brands for the ranking of individuals for activity and sleep outcomes.