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The characterization of human behavior in real-world contexts is critical for developing a comprehensive model of human health. Recent technological advancements have enabled wearables and sensors to passively and unobtrusively record and presumably quantify human behavior. Better understanding human activities in unobtrusive and passive ways is an indispensable tool in understanding the relationship between behavioral determinants of health and diseases. Adult individuals (N = 60) emulated the behaviors of smoking, exercising, eating, and medication (pill) taking in a laboratory setting while equipped with smartwatches that captured accelerometer data. The collected data underwent expert annotation and was used to train a deep neural network integrating convolutional and long short-term memory architectures to effectively segment time series into discrete activities. An average macro-F1 score of at least 85.1 resulted from a rigorous leave-one-subject-out cross-validation procedure conducted across participants. The score indicates the method's high performance and potential for real-world applications, such as identifying health behaviors and informing strategies to influence health. Collectively, we demonstrated the potential of AI and its contributing role to healthcare during the early phases of diagnosis, prognosis, and/or intervention. From predictive analytics to personalized treatment plans, AI has the potential to assist healthcare professionals in making informed decisions, leading to more efficient and tailored patient care.
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Atividades Humanas , Redes Neurais de Computação , Dispositivos Eletrônicos Vestíveis , Humanos , Adulto , Masculino , Feminino , Acelerometria/métodos , Exercício Físico/fisiologiaRESUMO
BACKGROUND: PortionSize offers real-time feedback on dietary intake, including intake of MyPlate food groups but requires further evaluation on a larger sample in a laboratory-based setting. MyFitnessPal (MFP) is a commonly used commercial dietary assessment application, and to our knowledge, no known studies have evaluated MFP in a laboratory setting. OBJECTIVES: The overall objective was to test the validity of PortionSize and MFP to accurately measure intake compared with that of weighed food (WB) and to compare error between applications. A secondary objective was to test usability, satisfaction, and user preference between applications. METHODS: This randomized crossover study was completed between February and October 2021. Participants (N = 43) used both applications to estimate intake in a laboratory setting. Participants were provided with a preweighed plated meal and plated leftovers. Two 1-sided t tests assessed equivalence (±21% bounds) between simulated intake from PortionSize and WB, and MFP and WB. The primary outcome was energy intake, and secondary outcome measures were portion size (in grams), food groups, and other nutrients. Differences in relative absolute error, usability, satisfaction, and user preference between applications were evaluated using dependent samples t tests. Cohen d assessed effect size. RESULTS: For PortionSize, energy and portion size were underestimated by 13.3% and 14.0%, respectively, and were not equivalent to WB. For MFP, energy was overestimated by 7.0%, and equivalent to WB (P = 0.04). Relative absolute error for energy did not differ between applications. For PortionSize, Cohen d was small (<0.2) for fruits, grains, protein foods, and specific nutrients. No differences were seen with usability, and the only difference for satisfaction was that participants found it easier to use MFP to find foods consumed (P = 0.019), and participants preferred using MFP (P = 0.014). CONCLUSIONS: PortionSize requires further updates to improve energy estimates and usability but demonstrates clinical utility for tracking food group and nutrient intake. PortionSize did not outperform MFP for measuring energy intake. CLINICAL TRIAL REGISTRY: This trial was registered at clinicaltrials.gov as NCT04700904 (https://classic. CLINICALTRIALS: gov/ct2/show/NCT04700904).
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Estudos Cross-Over , Ingestão de Energia , Humanos , Masculino , Feminino , Adulto , Tamanho da Porção , Adulto Jovem , Avaliação Nutricional , Reprodutibilidade dos Testes , Pessoa de Meia-Idade , DietaRESUMO
OBJECTIVE: Evaluate the validity of the PortionSize application. METHODS: In this pilot study, 14 adults used PortionSize to record their free-living food intake over 3 consecutive days. Digital photography was the criterion measure, and the main outcomes were estimated intake of food (grams), energy (kilocalories), and food groups. Equivalence tests with ±25% equivalence bounds and Bland-Altman analysis were performed. RESULTS: Estimated gram intake from PortionSize was equivalent (P < 0.001) to digital photography estimates. PortionSize and digital photography estimated energy intake, however, were not equivalent (P = 0.08), with larger estimates from PortionSize. In addition, PortionSize and digital photography were equivalent for vegetable intake (P = 0.01), but PortionSize had larger estimates of fruits, grains, dairy, and protein intake (P >0.07; error range 11% to 23%). CONCLUSIONS AND IMPLICATIONS: Compared with digital photography, PortionSize accurately estimated food intake and had reasonable error rates for other nutrients; however, it overestimated energy intake, indicating further application improvements are needed for free-living conditions.
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Background: The amount and type of food consumed impacts the glycemic response and insulin needs of people with type 1 diabetes mellitus (T1DM). Daily variability in consumption, reflected in diet quality, may acutely impact glycemic levels and insulin needs. Objective: Type 1 Diabetes Exercise Initiative (T1DEXI) data were examined to evaluate the impact of daily diet quality on near-term glycemic control and interaction with exercise. Methods: Using the Remote Food Photography Method, ≤8 d of dietary intake data were analyzed per participant. Diet quality was quantified with the Healthy Eating Index-2015 (HEI), where a score of 100 indicates the highest-quality diet. Each participant day was classified as low HEI (≤57) or high HEI (>57) based on the mean of nationally reported HEI data. Within participants, the relationship between diet quality and subsequent glycemia measured by continuous glucose monitoring (CGM) and total insulin dose usage was evaluated using a paired t-test and robust regression models. Results: Two hundred twenty-three adults (76% female) with mean ± SD age, HbA1c, and body mass index (BMI) of 37 ± 14 y, 6.6% ± 0.7%, and 25.1 ± 3.6 kg/m2, respectively, were included in these analyses. The mean HEI score was 56 across all participant days. On high HEI days (mean, 66 ± 4) compared with low HEI days (mean, 47 ± 5), total time in range (70-180 mg/dL) was greater (77.2% ± 14% compared with 75.7% ± 14%, respectively, P = 0.01), whereas time above 180 mg/dL (19% ± 14% compared with 21% ± 15%, respectively, P = 0.004), mean glucose (143 ± 22 compared with 145 ± 22 mg/dL, respectively, P = 0.02), and total daily insulin dose (0.52 ± 0.18 compared with 0.54 ± 0.18 U/kg/d, respectively, P = 0.009) were lower. The interaction between diet quality and exercise on glycemia was not significant. Conclusions: Higher HEI scores correlated with improved glycemia and lower insulin needs, although the impact of diet quality was modest and smaller than the previously reported impact of exercise.
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AIMS: To evaluate factors affecting within-participant reproducibility in glycemic response to different forms of exercise. METHODS: Structured exercise sessions ~30 minutes in length from the Type 1 Diabetes Exercise Initiative (T1DEXI) study were used to assess within-participant glycemic variability during and after exercise. The effect of several pre-exercise factors on the within-participant glycemic variability was evaluated. RESULTS: Data from 476 adults with type 1 diabetes were analyzed. A participant's change in glucose during exercise was reproducible within 15 mg/dL of the participant's other exercise sessions only 32% of the time. Participants who exercised with lower and more consistent glucose level, insulin on board (IOB), and carbohydrate intake at exercise start had less variability in glycemic change during exercise. Participants with lower mean glucose (P < .001), lower glucose coefficient of variation (CV) (P < .001), and lower % time <70 mg/dL (P = .005) on sedentary days had less variable 24-hour post-exercise mean glucose. CONCLUSIONS: Reproducibility of change in glucose during exercise was low in this cohort of adults with T1D, but more consistency in pre-exercise glucose levels, IOB, and carbohydrates may increase this reproducibility. Mean glucose variability in the 24 hours after exercise is influenced more by the participant's overall glycemic control than other modifiable factors.
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AIMS/HYPOTHESIS: Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics. METHODS: In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l). RESULTS: A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%). CONCLUSIONS/INTERPRETATION: Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes. DATA AVAILABILITY: The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).
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Automonitorização da Glicemia , Glicemia , Diabetes Mellitus Tipo 1 , Exercício Físico , Humanos , Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 1/terapia , Diabetes Mellitus Tipo 1/tratamento farmacológico , Adulto , Feminino , Masculino , Automonitorização da Glicemia/métodos , Glicemia/metabolismo , Glicemia/análise , Pessoa de Meia-Idade , Exercício Físico/fisiologia , Hemoglobinas Glicadas/metabolismo , Hemoglobinas Glicadas/análise , Insulina/uso terapêutico , Insulina/administração & dosagem , Estudos de Coortes , Monitoramento Contínuo da GlicoseRESUMO
Caloric restriction (CR) modifies lifespan and aging biology in animal models. The Comprehensive Assessment of Long-Term Effects of Reducing Intake of Energy (CALERIE™) 2 trial tested translation of these findings to humans. CALERIE™ randomized healthy, nonobese men and premenopausal women (age 21-50y; BMI 22.0-27.9 kg/m2), to 25% CR or ad-libitum (AL) control (2:1) for 2 years. Prior analyses of CALERIE™ participants' blood chemistries, immunology, and epigenetic data suggest the 2-year CR intervention slowed biological aging. Here, we extend these analyses to test effects of CR on telomere length (TL) attrition. TL was quantified in blood samples collected at baseline, 12-, and 24-months by quantitative PCR (absolute TL; aTL) and a published DNA-methylation algorithm (DNAmTL). Intent-to-treat analysis found no significant differences in TL attrition across the first year, although there were trends toward increased attrition in the CR group for both aTL and DNAmTL measurements. When accounting for adherence heterogeneity with an Effect-of-Treatment-on-the-Treated analysis, greater CR dose was associated with increased DNAmTL attrition during the baseline to 12-month weight-loss period. By contrast, both CR group status and increased CR were associated with reduced aTL attrition over the month 12 to month 24 weight maintenance period. No differences were observed when considering TL change across the study duration from baseline to 24-months, leaving it unclear whether CR-related effects reflect long-term detriments to telomere fidelity, a hormesis-like adaptation to decreased energy availability, or measurement error and insufficient statistical power. Unraveling these trends will be a focus of future CALERIE™ analyses and trials.
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Restrição Calórica , Telômero , Humanos , Restrição Calórica/métodos , Adulto , Masculino , Feminino , Pessoa de Meia-Idade , Telômero/metabolismo , Adulto Jovem , Homeostase do Telômero , Envelhecimento/genética , Metilação de DNARESUMO
CONTEXT: Adults with type 1 diabetes (T1D) face the necessity of balancing the benefits of exercise with the potential hazards of hypoglycemia. OBJECTIVE: This work aimed to assess whether impaired awareness of hypoglycemia (IAH) affects exercise-associated hypoglycemia in adults with T1D. METHODS: We compared continuous glucose monitoring (CGM)-measured glucose during exercise and for 24 hours following exercise from 95 adults with T1D and IAH (Clarke score ≥4 or ≥1 severe hypoglycemic event within the past year) to 95 "aware" adults (Clarke score ≤2 and no severe hypoglycemic event within the past year) matched on sex, age, insulin delivery modality, and glycated hemoglobin A1c. A total of 4236 exercise sessions, and 1794 exercise days and 839 sedentary days, defined as 24 hours following exercise or a day without exercise, respectively, were available for analysis. RESULTS: Participants with IAH exhibited a nonsignificant trend toward greater decline in glucose during exercise compared to "aware" (-21 ± 44 vs -19 ± 43â mg/dL [-1.17 ± 2.44 vs -1.05 ± 2.39 mmol/L], adjusted group difference of -4.2 [95% CI, -8.4 to 0.05] mg/dL [-0.23 95% CI, -.47 to 0.003â mmol/L]; P = .051). Individuals with IAH had a higher proportion of days with hypoglycemic events below 70â mg/dL [3.89â mmol/L] (≥15â minutes <70 mg/dL [<3.89 mmol/L]) both on exercise days (51% vs 43%; P = .006) and sedentary days (48% vs 30%; P = .001). The increased odds of experiencing a hypoglycemic event below 70 mg/dL (<3.89â mmol/L) for individuals with IAH compared to "aware" did not differ significantly between exercise and sedentary days (interaction P = .36). CONCLUSION: Individuals with IAH have a higher underlying risk of hypoglycemia than "aware" individuals. Exercise does not appear to differentially increase risk for hypoglycemia during the activity, or in the subsequent 24 hours for IAH compared to aware individuals with T1D.
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Automonitorização da Glicemia , Glicemia , Diabetes Mellitus Tipo 1 , Exercício Físico , Hipoglicemia , Humanos , Hipoglicemia/sangue , Masculino , Feminino , Exercício Físico/fisiologia , Adulto , Glicemia/análise , Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 1/complicações , Diabetes Mellitus Tipo 1/tratamento farmacológico , Pessoa de Meia-Idade , Automonitorização da Glicemia/métodos , Hipoglicemiantes/uso terapêutico , Hipoglicemiantes/administração & dosagem , Conscientização , Hemoglobinas Glicadas/análise , Insulina/administração & dosagemRESUMO
Background: Managing exercise in type 1 diabetes is challenging, in part, because different types of exercises can have diverging effects on glycemia. The aim of this work was to develop a classification model that can classify an exercise event (structured or unstructured) as aerobic, interval, or resistance for the purpose of incorporation into an automated insulin delivery (AID) system. Methods: A long short-term memory network model was developed with real-world data from 30-min structured sessions of at-home exercise (aerobic, resistance, or mixed) using triaxial accelerometer, heart rate, and activity duration information. The detection algorithm was used to classify 15 common free-living and unstructured activities and relate each to exercise-associated change in glucose. Results: A total of 1610 structured exercise sessions were used to train, validate, and test the model. The accuracy for the structured exercise sessions in the testing set was 72% for aerobic, 65% for interval, and 77% for resistance. In addition, we tested the classifier on 3328 unstructured sessions. We validated the session-associated change in glucose against the expected change during exercise for each type. Mean and standard deviation of the change in glucose of -20.8 (40.3) mg/dL were achieved for sessions classified as aerobic, -16.2 (39.0) mg/dL for sessions classified as interval, and -11.6 (38.8) mg/dL for sessions classified as resistance. Conclusions: The proposed algorithm reliably identified physical activity associated with expected change in glucose, which could be integrated into an AID system to manage the exercise disturbance in glycemia according to the predicted class.
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Previous work has aimed to disentangle the acute effects of nicotine and smoking on appetite with mixed findings. Electronic nicotine delivery systems (ENDS) have yet to be examined in this regard despite evidence of use for weight control. The present study tested the influence of an ENDS on acute energy intake and associated subjective effects. Participants (n = 34; 18-65 years) with current ENDS use completed two randomly ordered clinical lab sessions after overnight abstinence from tobacco/nicotine/food/drinks (other than water). Sessions differed by the product administered over 20 min: active (20 puffs of a JUUL ENDS device; 5% nicotine tobacco-flavored pod) or control (access to an uncharged JUUL with an empty pod). About 40 min after product administration, participants were provided an ad lib buffet-style meal with 21 food/drink items. Subjective ratings were assessed at baseline, after product use, and before/after the meal. Energy intake (kcal) was calculated using pre-post buffet item weights. Repeated measures analyses of variance and pairwise comparisons were used to detect differences by condition and time (α < .05). Mean ± standard error of the mean energy intake did not differ significantly between active (1011.9 ± 98.8 kcal) and control (939.8 ± 88.4 kcal; p = .108) conditions. Nicotine abstinence symptoms significantly decreased after the active condition, while satiety significantly increased. Following the control condition, satiety remained constant while hunger significantly increased relative to baseline. Findings indicate that acute ENDS use did not significantly impact energy intake, but there was an ENDS-associated subjective increase in satiety and relative decrease in hunger. Results support further investigation of ENDS on appetite. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Sistemas Eletrônicos de Liberação de Nicotina , Ingestão de Energia , Nicotina , Humanos , Adulto , Masculino , Feminino , Adulto Jovem , Adolescente , Pessoa de Meia-Idade , Nicotina/administração & dosagem , Apetite/efeitos dos fármacos , Idoso , Fome/efeitos dos fármacosRESUMO
Given prior literature focused on the Developmental Origins of Health and Disease framework, there is strong rationale to hypothesize that reducing depression in the prenatal period will cause improvements in offspring cardiometabolic health. The current review outlines evidence that prenatal depression is associated with offspring cardiometabolic risk and health behaviors. We review evidence of these associations in humans and in nonhuman animals at multiple developmental periods, from the prenatal period (maternal preeclampsia, gestational diabetes), neonatal period (preterm birth, small size at birth), infancy (rapid weight gain), childhood and adolescence (high blood pressure, impaired glucose-insulin homeostasis, unfavorable lipid profiles, abdominal obesity), and into adulthood (diabetes, cardiovascular disease). In addition to these cardiometabolic outcomes, we focus on health behaviors associated with cardiometabolic risk, such as child eating behaviors, diet, physical activity, and sleep health. Our review focuses on child behaviors (e.g., emotional eating, preference for highly palatable foods, short sleep duration) and parenting behaviors (e.g., pressuring child to eat, modeling of health behaviors). These changes in health behaviors may be detected before changes to cardiometabolic outcomes, which may allow for early identification of and prevention for children at risk for poor adult cardiometabolic outcomes. We also discuss the methods of the ongoing Care Project, which is a randomized clinical trial to test whether reducing prenatal maternal depression improves offspring's cardiometabolic health and health behaviors in preschool. The goal of this review and the Care Project are to inform future research, interventions, and policies that support prenatal mental health and offspring cardiometabolic health. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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We explored the association between macronutrient intake and postprandial glucose variability in a large sample of youth living with T1D and consuming free-living meals. In the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study, youth took photographs before and after their meals on 3 days during a 10 day observation period. We used the remote food photograph method to obtain the macronutrient content of youth's meals. We also collected physical activity, continuous glucose monitoring, and insulin use data. We measured glycemic variability using standard deviation (SD) and coefficient of variation (CV) of glucose for up to 3 h after meals. Our sample included 208 youth with T1D (mean age: 14 ± 2 years, mean HbA1c: 54 ± 14.2 mmol/mol [7.1 ± 1.3%]; 40% female). We observed greater postprandial glycemic variability (SD and CV) following meals with more carbohydrates. In contrast, we observed less postprandial variability following meals with more fat (SD and CV) and protein (SD only) after adjusting for carbohydrates. Insulin modality, exercise after meals, and exercise intensity did not influence associations between macronutrients and postprandial glycemic variability. To reduce postprandial glycemic variability in youth with T1D, clinicians should encourage diversified macronutrient meal content, with a goal to approximate dietary guidelines for suggested carbohydrate intake.
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Diabetes Mellitus Tipo 1 , Glucose , Adolescente , Feminino , Humanos , Criança , Masculino , Automonitorização da Glicemia , Glicemia , Refeições , InsulinaRESUMO
OBJECTIVE: This study's objective was to develop models predicting the relative reduction in skeletal muscle (SM) mass during periods of voluntary calorie restriction (CR) and to validate model predictions in longitudinally monitored samples. METHODS: The model development group included healthy nonexercising adults (n = 897) who had whole-body SM mass measured with magnetic resonance imaging. Model predictions of relative SM changes with CR were evaluated in two longitudinal studies, one 12 to 14 weeks in duration (n = 74) and the other 12 months in duration (n = 26). RESULTS: A series of SM prediction models were developed in a sample of 415 males and 482 females. Model-predicted changes in SM mass relative to changes in body weight (i.e., ΔSM/Δbody weight) with a representative model were (mean ± SE) 0.26 ± 0.013 in males and 0.14 ± 0.007 in females (sex difference, p < 0.001). The actual mean proportions of weight loss as SM in the longitudinal studies were 0.23 ± 0.02/0.20 ± 0.06 in males and 0.10 ± 0.02/0.17 ± 0.03 in females, similar to model-predicted values. CONCLUSIONS: Nonelderly males and females with overweight and obesity experience respective reductions in SM mass with voluntary CR in the absence of a structured exercise program of about 2 to 2.5 kg and 1 to 1.5 kg per 10-kg weight loss, respectively. These estimates are predicted to be influenced by interactions between age and body mass index in males, a hypothesis that needs future testing.
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Restrição Calórica , Redução de Peso , Adulto , Humanos , Masculino , Feminino , Redução de Peso/fisiologia , Obesidade/metabolismo , Sobrepeso/metabolismo , Músculo Esquelético/metabolismo , Índice de Massa Corporal , Composição CorporalRESUMO
Calorie restriction (CR) with adequate nutrient intake is a potential geroprotective intervention. To advance this concept in humans, we tested the hypothesis that moderate CR in healthy young-to-middle-aged individuals would reduce circulating biomarkers of cellular senescence, a fundamental mechanism of aging and aging-related conditions. Using plasma specimens from the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE™) phase 2 study, we found that CR significantly reduced the concentrations of several senescence biomarkers at 12 and 24 months compared to an ad libitum diet. Using machine learning, changes in biomarker concentrations emerged as important predictors of the change in HOMA-IR and insulin sensitivity index at 12 and 24 months, and the change in resting metabolic rate residual at 12 months. Finally, using adipose tissue RNA-sequencing data from a subset of participants, we observed a significant reduction in a senescence-focused gene set in response to CR at both 12 and 24 months compared to baseline. Our results advance the understanding of the effects of CR in humans and further support a link between cellular senescence and metabolic health.
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Envelhecimento , Restrição Calórica , Pessoa de Meia-Idade , Humanos , Senescência Celular/genética , Ingestão de Energia , BiomarcadoresRESUMO
CONTEXT: Exercise can decrease central adiposity, but the effect of exercise dose and the relationship between central adiposity and exercise-induced compensation is unclear. OBJECTIVE: Test the effect of exercise dose on central adiposity change and the association between central adiposity and exercise-induced weight compensation. METHODS: In this ancillary analysis of a 6-month randomized controlled trial, 170 participants with overweight or obesity (mean ± SD body mass index: 31.5 ± 4.7â kg/m2) were randomized to a control group or exercise groups that reflected exercise recommendations for health (8 kcal/kg/week [KKW]) or weight loss and weight maintenance (20 KKW). Waist circumference was measured, and dual-energy X-ray absorptiometry assessed central adiposity. Predicted weight change was estimated and weight compensation (weight change - predicted weight change) was calculated. RESULTS: Between-group change in waist circumference (control: .0â cm [95% CI, -1.0 to 1.0], 8 KKW: -.7â cm [95% CI, -1.7 to .4], 20 KKW: -1.3â cm [95% CI, -2.4 to -.2]) and visceral adipose tissue (VAT; control: -.02â kg [95% CI, -.07 to .04], 8 KKW: -.01â kg [95% CI, -.07 to .04], 20 KKW: -.04â kg [95% CI, -.10 to .02]) was similar (P ≥ .23). Most exercisers (82.6%) compensated (weight loss less than expected). Exercisers who compensated exhibited a 2.5-cm (95% CI, .8 to 4.2) and .23-kg (95% CI, .14 to .31) increase in waist circumference and VAT, respectively, vs those who did not (P < .01). Desire to eat predicted VAT change during exercise (ß = .21; P = .03). CONCLUSION: In the presence of significant weight compensation, exercise at doses recommended for health and weight loss and weight maintenance leads to negligible changes in central adiposity.
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Adiposidade , Obesidade , Humanos , Obesidade/terapia , Obesidade Abdominal , Exercício Físico , Redução de Peso , Índice de Massa Corporal , Circunferência da CinturaRESUMO
OBJECTIVE: Studies have consistently shown that African American individuals lose less weight in response to behavioral interventions, but the mechanisms leading to this result have been understudied. METHODS: Data were derived from the PROmoting Successful Weight Loss in Primary CarE in Louisiana (PROPEL) study, which was a cluster-randomized, two-arm trial conducted in primary care clinics. In the PROPEL trial, African American individuals lost less weight compared with patients who belonged to other racial groups after 24 months. In the current study, counterfactual mediation analyses among 445 patients in the intervention arm of PROPEL were used to determine which variables mediated the relationship between race and weight loss. The mediators included treatment engagement, psychosocial, and lifestyle factors. RESULTS: At 6 months, daily weighing mediated 33% (p = 0.008) of the racial differences in weight loss. At 24 months, session attendance and daily weighing mediated 35% (p = 0.027) and 66% (p = 0.005) of the racial differences in weight loss, respectively. None of the psychosocial or lifestyle variables mediated the race-weight loss association. CONCLUSIONS: Strategies specifically targeting engagement, such as improving session attendance and self-weighing behaviors, among African American individuals are needed to support more equitable weight losses over extended time periods.
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Estilo de Vida , Redução de Peso , Humanos , Negro ou Afro-Americano , Fatores Raciais , Grupos Raciais , Redução de Peso/fisiologiaRESUMO
Understanding and intervening on eating behavior often necessitates measurement of energy intake (EI); however, commonly utilized and widely accepted methods vary in accuracy and place significant burden on users (e.g., food diaries), or are costly to implement (e.g., doubly labeled water). Thus, researchers have sought to leverage inexpensive and low-burden technologies such as wearable sensors for EI estimation. Paradoxically, one such methodology that estimates EI via smartwatch-based bite counting has demonstrated high accuracy in laboratory and free-living studies, despite only measuring the amount, not the composition, of food consumed. This secondary analysis sought to further explore this phenomenon by evaluating the degree to which EI can be explained by a sensor-based estimate of the amount consumed versus the energy density (ED) of the food consumed. Data were collected from 82 adults in free-living conditions (51.2% female, 31.7% racial and/or ethnic minority; Mage = 33.5, SD = 14.7) who wore a bite counter device on their wrist and used smartphone app to implement the Remote Food Photography Method (RFPM) to assess EI and ED for two weeks. Bite-based estimates of EI were generated via a previously validated algorithm. At a per-meal level, linear mixed effect models indicated that bite-based EI estimates accounted for 23.4% of the variance in RFPM-measured EI, while ED and presence of a beverage accounted for only 0.2% and 0.1% of the variance, respectively. For full days of intake, bite-based EI estimates and ED accounted for 41.5% and 0.2% of the variance, respectively. These results help to explain the viability of sensor-based EI estimation even in the absence of information about dietary composition.
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Etnicidade , Grupos Minoritários , Adulto , Humanos , Feminino , Masculino , Dieta , Ingestão de Energia , RefeiçõesRESUMO
Background: The commercial application Openfit allows for automatic identification and quantification of food intake through short video capture without a physical reference marker. There are no known peer-reviewed publications on the validity of this Nutrition Artificial Intelligence (AI). Objectives: To test the validity of Openfit to identify food automatically and semiautomatically (with user correction), test the validity of Openfit at quantifying energy intake (kcal) automatically and semiautomatically, and assess satisfaction and usability of Openfit. Methods: During a laboratory-based visit, adults (7 male and 17 female), used Openfit to automatically and semiautomatically record provided meals, which were covertly weighed. Foods logged were identified as an "exact match," "far match," or an "intrusion" using Food and Nutrient Database for Dietary Studies (FNDDS) codes. Descriptive data were stratified by meal, food item, and FNDDS group, and presented with or without beverages. Bland-Altman analyses assessed errors over levels of energy intake. Participants completed a User Satisfaction Survey (USS) and the Computer Systems Usability Questionnaire (CSUQ). Open-ended questions were assessed with qualitative methods. Results: Exact matches, far matches, and intrusions were 46%, 41%, and 13% for automated identification, and 87%, 23%, and 0% for semiautomated identification, respectively. Error for automated and semiautomated energy estimates were 43% and 33% with beverages, and 16% and 42% without beverages. Bland-Altman analyses indicated larger error for higher energy meals. Overall mean scores were 2.4 for the CSUQ and subscale means scores ranged from 4.1 to 5.5. for the USS. Participants recommended improvements to Openfit's Nutrition AI, manual estimation, and overall app. Conclusion: Openfit worked relatively well for automatically and semiautomatically identifying foods. Error in automated energy estimates was relatively high; however, after excluding beverages, error was relatively low (16%). For semiautomated energy estimates, error was comparable to previous studies. Improvements to the Nutrition AI, manual estimation and overall application may increase Openfit's usability and validity.This trial was registered at clinicaltrials.gov as NCT05343585.
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The lifespan extension induced by 40% caloric restriction (CR) in rodents is accompanied by postponement of disease, preservation of function, and increased stress resistance. Whether CR elicits the same physiological and molecular responses in humans remains mostly unexplored. In the CALERIE study, 12% CR for 2 years in healthy humans induced minor losses of muscle mass (leg lean mass) without changes of muscle strength, but mechanisms for muscle quality preservation remained unclear. We performed high-depth RNA-Seq (387-618 million paired reads) on human vastus lateralis muscle biopsies collected from the CALERIE participants at baseline, 12- and 24-month follow-up from the 90 CALERIE participants randomized to CR and "ad libitum" control. Using linear mixed effect model, we identified protein-coding genes and splicing variants whose expression was significantly changed in the CR group compared to controls, including genes related to proteostasis, circadian rhythm regulation, DNA repair, mitochondrial biogenesis, mRNA processing/splicing, FOXO3 metabolism, apoptosis, and inflammation. Changes in some of these biological pathways mediated part of the positive effect of CR on muscle quality. Differentially expressed splicing variants were associated with change in pathways shown to be affected by CR in model organisms. Two years of sustained CR in humans positively affected skeletal muscle quality, and impacted gene expression and splicing profiles of biological pathways affected by CR in model organisms, suggesting that attainable levels of CR in a lifestyle intervention can benefit muscle health in humans.
Assuntos
Restrição Calórica , Longevidade , Humanos , Longevidade/genética , Músculo Esquelético/metabolismo , Força MuscularRESUMO
OBJECTIVE: Nocturnal hypoglycemia is a known challenge for people with type 1 diabetes, especially for physically active individuals or those on multiple daily injections. We developed an evidential neural network (ENN) to predict at bedtime the probability and timing of nocturnal hypoglycemia (0-4 vs 4-8 h after bedtime) based on several glucose metrics and physical activity patterns. We utilized these predictions in silico to prescribe bedtime carbohydrates with a Smart Snack intervention specific to the predicted minimum nocturnal glucose and timing of nocturnal hypoglycemia. MATERIALS AND METHODS: We leveraged free-living datasets collected from 366 individuals from the T1DEXI Study and Glooko. Inputs to the ENN used to model nocturnal hypoglycemia were derived from demographic information, continuous glucose monitoring, and physical activity data. We assessed the accuracy of the ENN using area under the receiver operating curve, and the clinical impact of the Smart Snack intervention through simulations. RESULTS: The ENN achieved an area under the receiver operating curve of 0.80 and 0.71 to predict nocturnal hypoglycemic events during 0-4 and 4-8 h after bedtime, respectively, outperforming all evaluated baseline methods. Use of the Smart Snack intervention reduced probability of nocturnal hypoglycemia from 23.9 ± 14.1% to 14.0 ± 13.3% and duration from 7.4 ± 7.0% to 2.4 ± 3.3% in silico. DISCUSSION: Our findings indicate that the ENN-based Smart Snack intervention has the potential to significantly reduce the frequency and duration of nocturnal hypoglycemic events. CONCLUSION: A decision support system that combines prediction of minimum nocturnal glucose and proactive recommendations for bedtime carbohydrate intake might effectively prevent nocturnal hypoglycemia and reduce the burden of glycemic self-management.