Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors.

Background. While increasing evidence links environments to health behavior, clinicians lack information about patients' physical activity levels and lifestyle environments. We present mobile health tools to collect and use spatio-behavioural lifestyle data for personalized physical activity plans in clinical settings. Methods. The Dyn@mo lifestyle intervention was developed at the Sainte-Justine University Hospital Center to promote physical activity and reduce sedentary time among children with cardiometabolic risk factors. Mobility, physical activity, and heart rate were measured in free-living environments during seven days. Algorithms processed data to generate spatio-behavioural indicators that fed a web-based interactive mapping application for personalised counseling. Proof of concept and tools are presented using data collected among the first 37 participants recruited in 2011. Results. Valid accelerometer data was available for 5.6 (SD = 1.62) days in average, heart rate data for 6.5 days, and GPS data was available for 6.1 (2.1) days. Spatio-behavioural indicators were shared between patients, parents, and practitioners to support counseling. Conclusion. Use of wearable sensors along with data treatment algorithms and visualisation tools allow to better measure and describe real-life environments, mobility, physical activity, and physiological responses. Increased specificity in lifestyle interventions opens new avenues for remote patient monitoring and intervention.

Impact of energy restriction with or without resistance training on energy metabolism in overweight and obese postmenopausal women: a Montreal Ottawa New Emerging Team group study.

OBJECTIVE: The present study measured the impact of adding resistance training to an energy-restricted diet on the components of energy expenditure in overweight or obese postmenopausal women. METHODS: Participants (n = 137) were randomly divided into two groups: (1) a diet and resistance training (DRT) group and (2) a diet-only (DO) group. Women followed a 6-month energy-restricted diet consisting of 2,100 to 3,360 kJ less than daily needs. The DRT group also followed a resistance training program (three times a week). Resting energy expenditure (REE) was measured by indirect calorimetry. Total energy expenditure was measured with doubly labeled water. Body composition was measured by dual-energy x-ray absorptiometry. RESULTS: Eighty nine women were included in the analyses for this study (DRT, n = 21; DO, n = 68). REE in both groups was significantly lower after the intervention (mean difference ± SD: DO, -0.26 ± 0.4 MJ d; DRT, -0.33 ± 0.4 MJ d; P ≤ 0.05). Relative REE, expressed per kilogram of lean body mass corrected for fat mass change, remained stable in both groups. Physical activity energy expenditure remained stable in both groups (mean difference ± SD: DO, 0.02 ± 1 MJ d, P = 0.91; DRT, -0.14 ± 1 MJ d, P = 0.64). CONCLUSIONS: Adding resistance training to an energy-restricted diet does not significantly alter any compartment of energy expenditure. REE is lower owing to reduction in body composition compartments, but relative REE is not significantly altered.

Variability of strength measurement in postmenopausal women who are overweight or obese: a Monet study.

The main objective of this study was to establish whether a stable measurement of strength could be obtained without prior exercise familiarization in postmenopausal women who were overweight or obese. A second objective was to evaluate the influence of physical activity on the variability of strength measurement. Thirty postmenopausal women (age: 57.9 yr; SD: 5 yr; body mass index: 31.0 kg/m2; SD: 4 kg/m2) underwent 3 strength testing sessions (48 hr apart) each including 3 exercises (leg press, chest press, and lat pull down). Energy expenditure was measured before the strength testing week with the doubly labelled water method over a 10-day period. Resting metabolic rate was measured by indirect calorimetry. Physical activity energy expenditure was calculated as follows: total energy expenditure x 0.9, minus the resting metabolic rate. Repeated analysis of variance and paired t-test were used to assess the difference and the reliability of the testing sequence. Results from leg press and chest press exercises indicated no significant difference among the 3 testing sessions. The lat pull down exercise was associated with a significant systematic bias between sessions 1 and 2 (mean difference: 1.4 kg; SD: 3 kg; 95% confidence intervals; 0.2-2.7 kg), but the difference disappeared at the third testing session (mean difference: 0.7 kg; SD: 3 kg; 95% confidence intervals; 0.5-2 kg). Physical activity did not influence the variability of the strength results. Overall, our results showed that a relatively stable strength measurement can be obtained within a maximum of 3 testing sessions without prior familiarization. In addition, physical activity did not influence strength testing in postmenopausal women who were overweight or obese.

Evaluation of a portable device to measure daily energy expenditure in free-living adults.

BACKGROUND: Increasing daily energy expenditure (EE) plays an important role in the prevention or treatment of several lifestyle-related diseases; however, its measurement remains problematic. OBJECTIVE: The objective was to evaluate a portable armband device for measuring daily and physical activity EE compared with doubly labeled water (DLW) in free-living individuals. DESIGN: Daily EE and physical activity EE were measured in 45 subjects over a 10-d period simultaneously with 2 techniques: a portable armband and DLW. Resting metabolic rate was measured by indirect calorimetry, and the thermic effect of a meal was estimated (10% of daily EE). Physical activity EE was obtained by subtracting the values for resting metabolic rate and thermic effect of a meal measured with DLW from those measured with the armband. Body composition was measured with dual-energy X-ray absorptiometry. Concordance between measures was evaluated by intraclass correlation, SEE, regression analysis, and Bland-Altman plots. RESULTS: Mean estimated daily EE measured with the armband was 117 kcal/d lower (2375 +/- 366 kcal/d) than that measured with DLW (2492 +/- 444 kcal/d; P < 0.01). Despite this group difference, individual comparisons between the armband and DLW were close, as evidenced by an intraclass correlation of 0.81 (P < 0.01). CONCLUSIONS: The portable armband shows reasonable concordance with DLW for measuring daily EE in free-living adults. The armband may therefore be useful to estimate daily EE.

Relationship between ghrelin and energy expenditure in healthy young women.

Ghrelin is a novel peptide that has been isolated from human and rat stomach tissues. Despite its known stimulatory effects on appetite and eating behavior, little information is available regarding its relationship with energy expenditure in normal-weight humans. To address this issue, we examined the relationship between serum ghrelin and resting metabolic rate (RMR), the thermic effect of food (TEF), fasting and postprandial respiratory quotient, physical activity level, peak aerobic capacity (VO(2 peak)), energy intake, and psychological measures of feeding behavior. We recruited 65 young healthy women and determined RMR and TEF by indirect calorimetry after a 12-h fast. Physical activity was determined by a leisure time physical activity questionnaire; VO(2 peak) was determined by bicycle ergometer test to exhaustion; energy intake was determined by a 24-h dietary recall; and food behavior was determined by a three-factor eating questionnaire. Our cohort showed a broad range of body mass index (range, 16.8-28.3 kg/m2), RMR (range, 820-1550 kcal/d), TEF (range, 74.4-136.5 kcal/d), and percent body fat (range, 14.0-37.7%). We noted significant inverse correlations between ghrelin and RMR (r = -0.350, P = 0.004) and TEF (r = -0.396, P = 0.001). These inverse correlations persisted after statistical control for both fat-free mass and fat mass (ghrelin vs. RMR partial, r = -0.284, P = 0.024; and ghrelin vs. TEF partial, r = -0.329, P = 0.01) and insulin levels (ghrelin vs. RMR partial, r = -0.255, P = 0.046; and ghrelin vs. TEF partial, r = -0.287, P = 0.024) using partial correlation analysis. We also observed a significant inverse correlation between ghrelin and daily caloric intake (r = -0.266, P = 0.032), but ghrelin levels were not significantly correlated with fasting (r = -0.002), postprandial respiratory quotient (r = -0.016), leisure time physical activity (r = 0.104), VO(2 peak) (r = 0.138), dietary disinhibition (r = -0.071), dietary restraint (r = 0.051), or feeling of general hunger (r = -0.028). These results suggest that higher levels of ghrelin are associated with low levels of resting and postprandial thermogenesis, which is independent of individual differences in fat-free mass and fat mass. Although speculative, serum ghrelin may play a role in the regulation of energy homeostasis by acting as a hormonal marker of increased energy efficiency.

Metabolic and behavioral characteristics of metabolically obese but normal-weight women.

A unique subset of individuals termed metabolically obese but normal weight (MONW) has been identified. These young women are potentially at increased risk for development of the metabolic syndrome despite their young age and normal body mass index. We seek to determine metabolic and behavioral factors that could potentially distinguish MONW women from young women with a normal metabolic profile.Ninety-six women were classified as MONW (n = 12) or non-MONW (n = 84) based on a cut point of insulin sensitivity (as estimated by the homeostasis model assessment). Potentially distinguishing phenotypes between groups measured included serum lipids, ghrelin, leptin, adiponectin, body composition and body fat distribution, resting and physical activity energy expenditure, peak oxygen uptake, dietary intake, dietary behavior, and family history and lifestyle variables. Despite a similar body mass index between groups, MONW women showed higher percent body fat, lower fat-free mass, lower physical activity energy expenditure, and lower peak oxygen uptake than non-MONW women. Plasma cholesterol level was higher in MONW women, whereas no differences were noted for other blood lipids, ghrelin, leptin, adiponectin, and resting energy expenditure. MONW women had lower dietary restraint scores than non-MONW women, but no differences were noted in disinhibition, hunger, and dietary intake. Stepwise regression analysis performed on all subjects showed that 33.5% of the unique variance of the homeostasis model assessment was explained with the variables of percentage of body fat (17.1%), level of dietary restraint (10.4%), and age (6%). Both metabolic and dietary behavioral variables contribute to the deleterious metabolic profile of MONW women. They display lower insulin sensitivity due potentially to a cluster of sedentary behavior patterns that contribute to their higher adiposity. Furthermore, cognitive attitudes toward food (i.e. dietary restraint) and concomitant lifestyle behaviors may play a role in regulating insulin sensitivity in MONW women.