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BackgroundPhysical activity (PA) is a modifiable lifestyle factor that can be targeted to increase energy expenditure and promote weight loss. However, the amount of PA required for weight loss remains inconsistent. Wearable activity trackers constitute a valuable opportunity to obtain objective measurements of PA and study large populations in real-life settings. ObjectiveWe aim to study the associations of initial device-assessed PA characteristics (average step counts and step count variability) and their evolution with 6-month weight change. MethodsWe analyzed data from 26,935 Withings-connected device users (wearable activity trackers and digital scales). To assess the initial PA characteristics and their 6-month changes, we used data recorded during the first and sixth 30-day periods of activity tracker use. For each of these periods, we used the monthly mean of daily step values as a proxy for PA level and derived the monthly coefficient of variation (CV) of daily step values to estimate PA level variability. Associations between initial PA characteristics and 6-month weight change were assessed using multivariable linear regression analyses controlled for age, sex, blood pressure, heart rate, and the predominant season. Restricted cubic spline regression was performed to better characterize the continuous shape of the associations between PA characteristics and weight change. Secondary analyses were performed by analyzing the 6-month evolution of PA characteristics in relation to weight change. ResultsOur results revealed that both a greater PA level and lower PA level variability were associated with weight loss. Compared with individuals who were initially in the sedentary category (63%, users whose PA level CV ranged from 51% to 63%, 40% to 51%, and was ≤40%, had a 0.19-kg, a 0.23-kg, and a 0.33-kg greater decrease in weight, respectively (95% CI −0.38 to −0.01, −0.41 to −0.04, and −0.53 to −0.13, respectively). We also observed that each 1000 steps/day increase in PA level over the 6-month follow-up was associated with a 0.26-kg (95% CI −0.29 to −0.23) decrease in weight. No association was found between the 6-month changes in PA level variability and weight change. ConclusionsOur results add to the current body of knowledge that health benefits can be observed below the 10,000 steps/day threshold and suggest that not only increased mean PA level but also greater regularity of the PA level may play important roles in short-term weight loss.

Background: Monitoring population physical activity (PA) and sedentary behavior over time is important to guide public health actions. The objective of this study was to investigate the changes in PA and sedentary behavior of adult residents in Luxembourg over 10 years. We also investigated variations in change over time across sociodemographic subgroups. Methods: Two population-based cross-sectional studies of adults living in Luxembourg (Observation of Cardiovascular Risk Factors in Luxembourg [ORISCAV-LUX] [2007–2008] and ORISCAV-LUX 2 [2016–2018]) were considered. Multilevel mixed-effects models were used to investigate changes over time between the studies with regard to self-reported total PA (metabolic equivalent of task-min/week), PA levels (inactive/sufficiently active/highly active), total sitting time, recreational television viewing, and personal computer (PC) use outside of work (in minutes per day). Results: The ORISCAV-LUX study included 1318 participants and the ORISCAV-LUX 2 study involved 1477 participants; 573 adults took part in both studies. The proportion of participants categorized as highly active increased over time by 6.9%. Total PA (761 metabolic equivalent of task-min/wk), television viewing (12 min/d), and PC use outside of work (13 min/d) also increased, whereas the total sitting time decreased by 25 minutes per day. Variations in change over time were observed by sex, country of birth, education, employment status, and perceived financial difficulty. Conclusions: Over a 10-year period, PA increased and total sitting time decreased in adults living in Luxembourg. With regard to specific sedentary behaviors, television viewing, and PC use outside of work increased. Specific population subgroups will benefit the most from targeted efforts to increase PA and minimize sedentary behavior. [ABSTRACT FROM AUTHOR]

Purpose: Diet quality is a critical modifiable factor related to health, including the risk of cardiometabolic complications. Rather than assessing the intake of individual food items, it is more meaningful to examine overall dietary patterns. This study investigated the adherence to common dietary indices and their association with serum/metabolic parameters of disease risk. Methods: Dietary intakes of the general adult population (n = 1404, 25–79 years) were assessed by a validated food-frequency questionnaire (174 items). The French ANSES-Ciqual food composition database was used to compute nutrient intakes. Seven indicators were calculated to investigate participants' diet quality: the Alternative Healthy Eating Index (AHEI), Dietary Approaches to Stop Hypertension Score (DASH-S), Mediterranean Diet Score (MDS), Diet Quality Index-International (DQI-I), Dietary Inflammatory Index (DII), Dietary Antioxidant Index (DAI), and Naturally Nutrient-Rich Score (NNRS). Various serum/metabolic parameters were used in the validity and association analyses, including markers of inflammation, blood glucose, and blood lipid status. Results: Following linear regression models adjusted for confounders, the DASH-S was significantly associated with most metabolic parameters (14, e.g., inversely with blood pressure, triglycerides, urinary sodium, uric acid, and positively with serum vitamin D), followed by the DQI-I (13, e.g., total cholesterol, apo-A/B, uric acid, and blood pressure) and the AHEI (11, e.g., apo-A, uric acid, serum vitamin D, diastolic blood pressure and vascular age). Conclusion: Food-group-based indices, including DASH-S, DQI-I, and AHEI, were good predictors for serum/metabolic parameters, while nutrient-based indices, such as the DAI or NNRS, were less related to biological markers and, thus, less suitable to reflect diet quality in a general population. [ABSTRACT FROM AUTHOR]

In a recent randomised trial investigating running shoe cushioning, injury risk was greater in recreational runners who trained in the shoe version with greater cushioning stiffness (Stiff) compared to those using the Soft version. However, vertical impact peak force (VIPF) was lower in the Stiff version. To investigate further the mechanisms involved in the protective effect of greater cushioning, the present study used an intra-subject design and analysed the differences in running kinematics and kinetics between the Stiff and Soft shoe versions on a subsample of 41 runners from the previous trial. Data were recorded in the two shoe conditions using an instrumented treadmill at 10 km.h−1. VIPF was confirmed to be lower in the Stiff version compared to the Soft version (1.39 ± 0.25 vs. 1.50 ± 0.25 BW, respectively; p = 0.009, d = 0.42), but not difference was observed in vertical loading rate (p = 0.255 and 0.897 for vertical average and instantaneous loading rate, respectively). Ankle eversion maximal velocity was not different (p = 0.099), but the Stiff version induced greater ankle negative work (−0.55 ± 0.09 vs. −0.52 ± 0.10 J.kg−1; p = 0.009, d = 0.32), maximal ankle negative power (−7.21 ± 1.90 vs. −6.96 ± 1.92 W.kg−1; p = 0.037, d = 0.13) and maximal hip extension moment (1.25 ± 0.32 vs.1.18 ± 0.30 N.m.kg−1; p = 0.009, d = 0.22). Our results suggest that the Stiff shoe version is related to increased mechanical burden for the musculoskeletal system, especially around the ankle joint. Trial registration: ClinicalTrials.gov identifier: NCT03115437. [ABSTRACT FROM AUTHOR]

Background Frail elderly people experience elevated mortality. However, no consensus exists on the definition of frailty, and many frailty scores have been developed. The main aim of this study was to compare the association between 35 frailty scores and incident cardiovascular disease (CVD), incident cancer, and all-cause mortality. Also, we aimed to assess whether frailty scores added predictive value to basic and adjusted models for these outcomes. Methods and findings Through a structured literature search, we identified 35 frailty scores that could be calculated at wave 2 of the English Longitudinal Study of Ageing (ELSA), an observational cohort study. We analysed data from 5,294 participants, 44.9% men, aged 60 years and over. We studied the association between each of the scores and the incidence of CVD, cancer, and all-cause mortality during a 7-year follow-up using Cox proportional hazard models at progressive levels of adjustment. We also examined the added predictive performance of each score on top of basic models using Harrell's C statistic. Using age of the participant as a timescale, in sex-adjusted models, hazard ratios (HRs) (95% confidence intervals) for all-cause mortality ranged from 2.4 (95% CI: 1.7-3.3) to 26.2 (95% CI: 15.4-44.5). In further adjusted models including smoking status and alcohol consumption, HR ranged from 2.3 (95% CI: 1.6-3.1) to 20.2 (95% CI: 11.8-34.5). In fully adjusted models including lifestyle and comorbidity, HR ranged from 0.9 (95% CI: 0.5-1.7) to 8.4 (95% CI: 4.9-14.4). HRs for CVD and cancer incidence in sex-adjusted models ranged from 1.2 (95% CI: 0.5-3.2) to 16.5 (95% CI: 7.8-35.0) and from 0.7 (95% CI: 0.4-1.2) to 2.4 (95% CI: 1.0-5.7), respectively. In sex- and age-adjusted models, all frailty scores showed significant added predictive performance for all-cause mortality, increasing the C statistic by up to 3%. None of the scores significantly improved basic prediction models for CVD or cancer. A source of bias could be the differences in mortality follow-up time compared to CVD/cancer, because the existence of informative censoring cannot be excluded. Conclusion There is high variability in the strength of the association between frailty scores and 7-year all-cause mortality, incident CVD, and cancer. With regard to all-cause mortality, some scores give a modest improvement to the predictive ability. Our results show that certain scores clearly outperform others with regard to three important health outcomes in later life. Finally, we think that despite their limitations, the use of frailty scores to identify the elderly population at risk is still a useful measure, and the choice of a frailty score should balance feasibility with performance., Author(s): Gloria A. Aguayo 1,*, Michel T. Vaillant 2, Anne-Françoise Donneau 3, Anna Schritz 2, Saverio Stranges 4, Laurent Malisoux 1, Anna Chioti 1, Michèle Guillaume 3, Majon Muller 5, [...]