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

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Kestens Yan ◽  
Barnett Tracie ◽  
Mathieu Marie-Ève ◽  
Henderson Mélanie ◽  
Bigras Jean-Luc ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Heart Rate ◽  
Cardiometabolic Risk ◽  
Wearable Sensors ◽  
Real Life ◽  
Cardiometabolic Risk Factors ◽  
University Hospital ◽  
Activity Levels ◽  
Accelerometer Data

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.

scholarly journals Assessment of Cardiometabolic Risk Factors, Physical Activity Levels, and Quality of Life in Stratified Groups up to 10 Years after Bariatric Surgery

2019 ◽  
Vol 16 (11) ◽  
pp. 1975 ◽  
Author(s):  
Larissa Monteiro Costa Pereira ◽  
Felipe J. Aidar ◽  
Dihogo Gama de Matos ◽  
Jader Pereira de Farias Neto ◽  
Raphael Fabrício de Souza ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Risk Factors ◽  
Physical Activity Level ◽  
Cardiometabolic Risk ◽  
Activity Level ◽  
Cardiometabolic Risk Factors ◽  
Activity Levels ◽  
Physical Activity Levels

Obesity is a highly prevalent chronic metabolic disease, with an increasing incidence, and is currently approaching epidemic proportions in developing countries. Ouraim was to evaluate the activity levels, quality of life (QoL), clinical parameters, laboratory parameters, and cardiometabolic risk factors afterbariatric surgery (BS). We classified78 patients who underwentBS into four groups, as follows: Those evaluated 1–2 years after BS (BS2), 2–4 years after BS (BS4), 4–6 years after BS (BS6), and 6–10 years after BS (BS+6). Body weight (BW), body mass index (BMI), comorbidities associated with obesity (ACRO), physical activity level, and QoL were evaluated. Patients exhibited improvements in BW, BMI, cardiometabolic risk, hypertension, dyslipidemia, and diabetes and significant changes in lipid profiles in the first postoperative yearafter BS.The physical activity level inthe BS2, BS4, and BS6 groups was increased, compared with that in the first postoperative year, with a decrease in International Physical Activity Questionnaire scores at 1 year in the BS2 (207.50 ± 30.79), BS4 (210.67 ± 33.69), and BS6 (220.00 ± 42.78) groups. The QoL of patients in theBS2 and BS4 groups was excellent and that of patients in the BS4 and BS+6 groupswas very good. These findings suggest that BS promoted improved physical activity levels and QoL and reduced comorbidities in patients with morbid obesity.

scholarly journals Comparison between recent and long-term physical activity levels as predictors of cardiometabolic risk: a cohort study

BMJ Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. e033797 ◽  
Author(s):  
Tuija Leskinen ◽  
Sari Stenholm ◽  
Anna Pulakka ◽  
Jaana Pentti ◽  
Mika Kivimäki ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Cohort Study ◽  
Cardiometabolic Risk ◽  
Multinomial Logistic Regression ◽  
Activity Level ◽  
Cardiometabolic Risk Factors ◽  
Activity Levels ◽  
Physical Activity Levels

ObjectiveTo compare recent and long-term physical activity levels as predictors of cardiometabolic risk in a risk factor-free adult population.DesignA 12-year prospective cohort study.SettingThe Finnish Public Sector study with surveys conducted in four waves at 4-year intervals.Participants19 230 participants (mean age 50.2 (SD 9.1) years, 84% women) with no prevalent cardiometabolic risk factors at wave 3 were included. Physical activity was assessed at waves 1, 2 and 3. The long-term physical activity level was determined as the mean of activity from wave 1 to 3 (over 8 years).Outcome measure4-year incidence of cardiometabolic risk factors (obesity, hypertension, dyslipidaemia and diabetes) after wave 3, measured individually and as a sum (accumulation of two or more risk factors vs none). Logistic and multinomial logistic regression analyses were used for the analysis.ResultsGraded associations between higher physical activity levels and lower odds of all risk factors were observed (p for trend <0.01). In comparison with the persistently vigorously active participants (≥60 metabolic equivalent (MET)-hours/week), those who were persistently inactive (<7 MET-hours/week) were about four times more likely to develop obesity (OR=4.24, 95% CI=2.83 to 6.36), two times more likely to develop hypertension (OR=2.02, 95% CI=1.45 to 2.82) and dyslipidaemia (OR=1.82, 95% CI=1.03 to 3.22) and eight times more likely to develop diabetes (OR=7.84, 95% CI=1.78 to 34.6). The corresponding OR for accumulating two or more risk factors was 5.24-fold (95% CI=2.39 to 11.47). For recently inactive versus recently vigorously active, the estimates were weaker (OR=2.36, 95% CI=1.71 to 3.25 for obesity; 1.78, 95% CI=1.35 to 2.35 for hypertension; 1.71, 95% CI=1.04 to 2.82 for dyslipidaemia; 3.56, 95% CI=1.06 to 11.96 for diabetes and 2.66, 95% CI=1.48 to 4.78 for ≥2 risk factors).ConclusionCardiometabolic risk associated with physical inactivity is better captured by repeated measurements of physical activity than by a single measurement of the most recent activity level.

Sedentary behavior moderates the relationship between physical activity and cardiometabolic risk in young Latino children

2021 ◽  
Author(s):  
Jamil A Malik ◽  
Jennifer Coto ◽  
Elizabeth R Pulgaron ◽  
Amber Daigre ◽  
Janine E Sanchez ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Sedentary Behavior ◽  
Low Income ◽  
Latent Variables ◽  
Cardiometabolic Risk ◽  
Vigorous Physical Activity ◽  
Cardiometabolic Risk Factors ◽  
Metabolic Risk ◽  
Latino Children

Abstract This study investigated the role of objectively measured moderate–vigorous physical activity (MVPA) and sedentary behavior on cardiometabolic risk factors of young Latino children. We hypothesized that MVPA would be associated with lower cardiometabolic risk when sedentary behavior is low. We studied 86 primarily low-income, Latino children using a cross-sectional study design. The study sample consisted of 51 girls and 35 boys, with mean age 5.6 (SD = .53) years. Physical activity was measured by accelerometry, anthropometric measures obtained, and fasting blood samples were used to measure cardiometabolic risk factors. Greater levels of sedentary behavior were associated with increased waist circumference (rs = .24, p &lt; .05) and metabolic risks. MVPA, however, had significant beneficial associations with all cardiometabolic risk factors (rs-range = −.20 to −.45, p &lt; .05) with the exception of plasma insulin. MVPA predicted latent variables representing anthropometric risk (β = −.57, p &lt; .01), cardiac risk (β = −.74, p &lt; .01), and metabolic risk (β = −.88, p &lt; .01). Sedentary behavior significantly moderated the effect of MVPA on anthropometric (β-interaction = .49, p &lt; .01), cardiac (β-interaction = .45, p &lt; .01), and metabolic risk (β-interaction = .77, p &lt; .01), such that more MVPA was associated with better health outcomes under conditions of lower sedentary behavior. The model explained 13%, 22%, and 45% variance in anthropometric, cardiac, and metabolic risk factors, respectively. Increased MVPA is associated with decreased cardiometabolic risk in young Latino children, particularly when sedentary behavior is low.

scholarly journals Relationship Between Meeting 24-Hour Movement Guidelines and Cardiometabolic Risk Factors in Children

2017 ◽  
Vol 14 (10) ◽  
pp. 779-784 ◽  
Author(s):  
Peter T. Katzmarzyk ◽  
Amanda E. Staiano
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Blood Pressure ◽  
Adipose Tissue ◽  
Subcutaneous Adipose Tissue ◽  
Cardiometabolic Risk ◽  
Vigorous Physical Activity ◽  
Density Lipoprotein ◽  
Cardiometabolic Risk Factors ◽  
Subcutaneous Adipose

Background:The purpose of this study was to evaluate the relationship between adherence to pediatric 24-hour movement guidelines (moderate to vigorous physical activity, sedentary behavior, and sleep) and cardiometabolic risk factors.Methods:The sample included 357 white and African American children aged 5–18 years. Physical activity, television viewing, and sleep duration were measured using questionnaires, and the 24-hour movement guidelines were defined as ≥60 minutes per day of moderate to vigorous physical activity on ≥5 days per week, ≤ 2 hours per day of television, and sleeping 9–11 hours per night (ages 5–13 y) or 8–10 hours per night (ages 14–18 y). Waist circumference, body fat, abdominal visceral and subcutaneous adipose tissue, blood pressure, fasting triglycerides, high-density lipoprotein cholesterol, and glucose were measured in a clinical setting.Results:A total of 26.9% of the sample met none of the guidelines, whereas 36.4%, 28.3%, and 8.4% of the sample met 1, 2, or all 3 guidelines, respectively. There were significant associations between the number of guidelines met and body mass index, visceral and subcutaneous adipose tissue, triglycerides, and glucose. There were no associations with blood pressure or high-density lipoprotein cholesterol.Conclusions:Meeting more components of the 24-hour movement guidelines was associated with lower levels of obesity and several cardiometabolic risk factors. Future efforts should consider novel strategies to simultaneously improve physical activity, sedentary time, and sleep in children.

scholarly journals Late eating is associated with cardiometabolic risk traits, obesogenic behaviors, and impaired weight loss

2020 ◽  
Vol 113 (1) ◽  
pp. 154-161 ◽  
Author(s):  
Hassan S Dashti ◽  
Puri Gómez-Abellán ◽  
Jingyi Qian ◽  
Alberto Esteban ◽  
Eva Morales ◽  
...  
Keyword(s):  
Risk Factors ◽  
Weight Loss ◽  
Cardiometabolic Risk ◽  
Cardiometabolic Risk Factors ◽  
Cardiometabolic Health ◽  
Activity Levels ◽  
Weight Loss Intervention ◽  
Meal Intake ◽  
Intervention Protocol ◽  
Obesogenic Behaviors

ABSTRACT Background There is a paucity of evidence regarding the role of food timing on cardiometabolic health and weight loss in adults. Objectives To determine whether late eating is cross-sectionally associated with obesity and cardiometabolic risk factors at baseline; and whether late eating is associated with weight loss rate and success following a weight loss intervention protocol. Also, to identify obesogenic behaviors and weight loss barriers associated with late eating. Methods Participants were recruited from a weight-loss program in Spain. Upon recruitment, the midpoint of meal intake was determined by calculating the midway point between breakfast and dinner times, and dietary composition was determined from diet recall. Population median for the midpoint of meal intake was used to stratify participants into early (before 14:54) and late (after 14:54) eaters. Cardiometabolic and satiety hormonal profiles were determined from fasting blood samples collected prior to intervention. Weekly weight loss and barriers were evaluated during the ∼19-wk program. Linear and logistic regression models were used to assess differences between late and early eaters in cardiometabolic traits, satiety hormones, obesogenic behaviors, and weight loss, adjusted for age, sex, clinic site, year of recruitment, and baseline BMI. Results A total of 3362 adults [mean (SD): age: 41 (14) y; 79.2% women, BMI: 31.05 (5.58) kg/m2] were enrolled. At baseline, no differences were observed in energy intake or physical activity levels between early and late eaters (P &gt;0.05). Late eaters had higher BMI, higher concentrations of triglycerides, and lower insulin sensitivity compared with early eaters (all P &lt;0.05) prior to intervention. In addition, late eaters had higher concentrations of the satiety hormone leptin in the morning (P = 0.001). On average, late eaters had an average 80 g lower weekly rate of weight loss [early, 585 (667) g/wk; late, 505 (467) g/wk; P = 0.008], higher odds of having weight-loss barriers [OR (95% CI): 1.22 (1.03, 1.46); P = 0.025], and lower odds of motivation for weight loss [0.81 (0.66, 0.99); P = 0.044] compared with early eaters. Conclusion Our results suggest that late eating is associated with cardiometabolic risk factors and reduced efficacy of a weight-loss intervention. Insights into the characteristics and behaviors related to late eating may be useful in the development of future interventions aimed at advancing the timing of food intake.

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