Weekly, seasonal and holiday body weight fluctuation patterns among individuals engaged in a European multi-centre behavioural weight loss maintenance intervention

Background: Technological advances in remote monitoring offer new opportunities to quantify body weight patterns in free-living populations. This paper describes body weight fluctuation patterns in response to weekly, holiday (Christmas) and seasonal time periods in a large group of individuals engaged in a weight loss maintenance intervention. Methods: Data was collected as part The NoHoW Project which was a pan-European weight loss maintenance trial. Three eligible groups were defined for weekly, holiday and seasonal analyses, resulting in inclusion of 1,421, 1,062 and 1,242 participants, respectively. Relative weight patterns were modelled on a time series following removal of trends and grouped by gender, country, BMI and age. Results: Within-week fluctuations of 0.35% were observed, characterised by weekend weight gain and weekday reduction which differed between all groups. Over the Christmas period, weight increased by a mean 1.35% and was not fully compensated for in following months, with some differences between countries observed. Seasonal patterns were primarily characterised by the effect of Christmas weight gain and generally not different between groups. Conclusions: This evidence may improve current understanding of regular body weight fluctuation patterns and help target future weight management interventions towards periods, and in groups, where weight gain is anticipated

Estimating physical activity and sedentary behaviour in a free-living environment: A comparative study between Fitbit Charge 2 and Actigraph GT3X

Background: Activity trackers such as the Fitbit Charge 2 enable users and researchers to monitor physical activity in daily life, which could be beneficial for changing behaviour. However, the accuracy of the Fitbit Charge 2 in a free-living environment is largely unknown. Objective: To investigate the agreement between Fitbit Charge 2 and ActiGraph GT3X for the estimation of steps, energy expenditure, time in sedentary behaviour, and light and moderate-to-vigorous physical activity under free-living conditions, and further examine to what extent placing the ActiGraph on the wrist as opposed to the hip would affect the findings. Methods: 41 adults (n = 10 males, n = 31 females) were asked to wear a Fitbit Charge 2 device and two ActiGraph GT3X devices (one on the hip and one on the wrist) for seven consecutive days and fill out a log of wear times. Agreement was assessed through Bland-Altman plots combined with multilevel analysis. Results: The Fitbit measured 1,492 steps/day more than the hip-worn ActiGraph (limits of agreement [LoA] = -2,250; 5,234), while for sedentary time, it measured 25 min/day less (LoA = -137; 87). Both Bland-Altman plots showed fixed bias. For time in light physical activity, the Fitbit measured 59 min/day more (LoA = -52;169). For time in moderate-to-vigorous physical activity, the Fitbit measured 31 min/day less (LoA = -132; 71) and for activity energy expenditure it measured 408 kcal/day more than the hip-worn ActiGraph (LoA = -385; 1,200). For the two latter outputs, the plots indicated proportional bias. Similar or more pronounced discrepancies, mostly in opposite direction, appeared when comparing to the wrist-worn ActiGraph. Conclusion: Moderate to substantial differences between devices were found for most outputs, which could be due to differences in algorithms. Caution should be taken if replacing one device with another and when comparing results

Data imputation and body weight variability calculation using linear and non-linear methods in data collected from digital smart scales: a simulation and validation study

Background: Body weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With advancements in technology allowing for the frequent collection of body weight data from electronic smart scales, new opportunities to analyze and identify patterns in body weight data are available. Objective: This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches Methods: In total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. We addressed the following aspects of data analysis: cleaning, imputation, detrending, and calculation of total and local BWV. To test imputation, missing data were simulated at random and using real patterns of missingness. A total of 10 imputation strategies were tested. Next, BWV was calculated using linear and nonlinear approaches, and the effects of missing data and data imputation on these estimates were investigated. Results: Body weight imputation using structural modeling with Kalman smoothing or an exponentially weighted moving average provided the best agreement with observed values (root mean square error range 0.62%-0.64%). Imputation performance decreased with missingness and was similar between random and nonrandom simulations. Errors in BWV estimations from missing simulated data sets were low (2%-7% with 80% missing data or a mean of 67, SD 40.1 available body weights) compared with that of imputation strategies where errors were significantly greater, varying by imputation method. Conclusions: The decision to impute body weight data depends on the purpose of the analysis. Directions for the best performing imputation methods are provided. For the purpose of estimating BWV, data imputation should not be conducted. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data

The validity of two widely used commercial and research-grade activity monitors, during resting, household and activity behaviours

Wearable devices are increasingly prevalent in research environments for the estimation of energy expenditure (EE) and heart rate (HR). The aim of this study was to validate the HR and EE estimates of the Fitbit charge 2 (FC2), and the EE estimates of the Sensewear armband mini (SWA). We recruited 59 healthy adults to participate in walking, running, cycling, sedentary and household tasks. Estimates of HR from the FC2 were compared to a HR chest strap (Polar) and EE to a stationary metabolic cart (Vyntus CPX). The SWA overestimated overall EE by 0.03 kcal/min−1 and was statistically equivalent to the criterion measure, with a mean absolute percentage error (MAPE) of 29%. In contrast, the FC2 was not equivalent overall (MAPE = 44%). In household tasks, MAPE values of 93% and 83% were observed for the FC2 and SWA, respectively. The FC2 HR estimates were equivalent to the criterion measure overall. The SWA is more accurate than the commercial-grade FC2. Neither device is consistently accurate across the range of activities used in this study. The HR data obtained from the FC2 is more accurate than its EE estimates and future research may focus more on this variable

Changes in Waist Circumference and the Incidence of Acute Myocardial Infarction in Middle-Aged Men and Women

BACKGROUND: Waist circumference (WC) measured at one point in time is positively associated with the risk of acute myocardial infarction (MI), but the association with changes in WC (DWC) is not clear. We investigated the association between DWC and the risk of MI in middle-aged men and women, and evaluated the influence from concurrent changes in BMI (DBMI). METHODOLOGY/PRINCIPAL FINDINGS: Data on 38,593 participants from the Danish Diet, Cancer and Health study was analysed. Anthropometry was assessed in 1993-97 and 1999-02. Information on fatal and non-fatal MI was obtained from National Registers. Cases were validated by review of the medical records. Hazard ratios (HR) were calculated from Cox proportional hazard models with individuals considered at risk from 1999-02 until December 30 2009. During 8.4 years of follow-up, 1,041 incident cases of MI occurred. WC was positively associated with the risk of MI, but weakly after adjustment for BMI. DWC was not associated with the risk of MI (HR per 5 cm change = 1.01 (0.95, 1.09) with adjustment for covariates, baseline WC, BMI and DBMI). Associations with DWC were not notably different in sub-groups stratified according to baseline WC or DBMI, or when individuals with MI occurring within the first years of follow-up were excluded. CONCLUSIONS/SIGNIFICANCE: WC was positively associated with the risk of MI in middle-aged men and women, but changes in WC were not. These findings suggest that a reduction in WC may be an insufficient target for prevention of MI in middle-aged men and women

Relationship between tooth loss and mortality in 80-year-old Japanese community-dwelling subjects

<p>Abstract</p> <p>Background</p> <p>Findings from several studies suggest associations between tooth loss and health outcomes, including malnutrition, poor quality of life, and mortality, in older individuals. However, limited information is available regarding whether those associations remain true in very elderly subjects after adequately considering confounding factors such as sex and smoking status. Herein, we determined whether the number of teeth in 80-year-old subjects is an independent predictor of mortality.</p> <p>Methods</p> <p>We initially contacted 1282 80-year-old community-dwelling individuals born in 1917, of whom 697 responded and participated in a baseline study, with follow-up examinations conducted 4 and 5.5 years later. Data from interviews and medical and oral examinations were obtained, and oral health was determined according to the number of teeth remaining in the oral cavity.</p> <p>Results</p> <p>A total of 108 and 157 subjects died in 4 years and 5.5 years, respectively, after the baseline study. Tooth loss was significantly associated with mortality at age 85.5, but not at age 84, after adjusting for potential confounders. When the analysis was stratified by sex, we found a stronger association in females in follow-up examinations conducted at both 4- and 5.5 years. On the other hand, the effect of tooth loss on mortality was not significantly different between smokers and non-smokers.</p> <p>Conclusion</p> <p>Tooth loss is a significant predictor of mortality independent of health factors, socio-economic status, and lifestyle in octogenarians, with a stronger association in females.</p

Energy Density, Portion Size, and Eating Occasions: Contributions to Increased Energy Intake in the United States, 1977–2006

Using data from three surveys, Kiyah Duffey and Barry Popkin found that changes in eating/drinking occasions and portion size consistently account for most of the change in daily total energy intake over a 30-year period

A Prospective study of the association between weight changes and self-rated health

<p>Abstract</p> <p>Background</p> <p>Obesity and self-rated health (SRH) are strong predictors of morbidity and mortality but their interrelation is sparsely studied. The aim of this study was to analyse the association between weight changes and changes in SRH among women. We also examined if poor SRH at baseline was associated with later weight gain.</p> <p>Methods</p> <p>The Danish Nurse Cohort Study is a prospective population study (1993–1999) and comprises 13,684 female nurses aged 44 to 69 years. Logistic regression analyses were used to examine the association between weight changes and changes in SRH.</p> <p>Results</p> <p>Women who gained weight during the study period had higher odds of reporting poorer self-rated health (Odds Ratio (OR): 1.18, 95% CI: 1.04–1.35). Weight loss among overweight women, did not result in an increase in self-rated health ratings, in fully adjusted analyses (0.96 (95% CI: 0.76–1.23). Poor self-rated health combined with normal weight at first examination was associated with higher odds of later weight gain (OR: 1.29, 95% CI: 1.10–1.51).</p> <p>Conclusion</p> <p>Weight changes may result in lower SRH. Further, poor self-rated health at baseline seems to predict an increase in weight, among women without any longstanding chronic diseases. Future obesity prevention may focus on normal weight individuals with poor SRH.</p

Obesity prevalence from a European perspective: a systematic review

<p>Abstract</p> <p>Background</p> <p>Obesity has been recognised as an important contributing factor in the development of various diseases, but comparative data on this condition are limited. We therefore aimed to identify and discuss current epidemiological data on the prevalence of obesity in European countries.</p> <p>Methods</p> <p>We identified relevant published studies by means of a MEDLINE search (1990–2008) supplemented by information obtained from regulatory agencies. We only included surveys that used direct measures of weight and height and were representative of each country's overall population.</p> <p>Results</p> <p>In Europe, the prevalence of obesity (body mass index ≥ 30 kg/m²) in men ranged from 4.0% to 28.3% and in women from 6.2% to 36.5%. We observed considerable geographic variation, with prevalence rates in Central, Eastern, and Southern Europe being higher than those in Western and Northern Europe.</p> <p>Conclusion</p> <p>In Europe, obesity has reached epidemic proportions. The data presented in our review emphasise the need for effective therapeutic and preventive strategies.</p