Comparing caloric restriction regimens for effective weight management in adults: a systematic review and network meta-analysis.

Randomized controlled trials have confirmed the effectiveness of four prevalent caloric restriction regimens in reducing obesity-related health risks. However, there is no consensus on the optimal regimen for weight management in adults. We systematically searched PubMed, Embase, Web of Science, and Cochrane CENTRAL up to January 15, 2024, for randomized controlled trials (RCT) involving adults, evaluating the weight-loss effects of alternate day fasting (ADF), short-term fasting (STF), time-restricted eating (TRE), and continuous energy restriction (CER). The primary outcome was body weight, with secondary outcomes including BMI, fat mass, lean mass, waist circumference, fasting glucose, HOMA-IR, and adverse events. Bayesian network meta-analysis was conducted, ranking regimens using the surface under the cumulative ranking curve and the probability of being the best. Study quality was assessed using the Confidence in Network Meta-Analysis tool. Data from 47 RCTs (representing 3363 participants) were included. ADF showed the most significant body weight loss (Mean difference (MD): -3.42; 95% Confidence interval (CI): -4.28 to -2.55), followed by TRE (MD: -2.25; 95% CI: -2.92 to -1.59). STF (MD: -1.87; 95% CI: -3.32 to -0.56) and CER (MD: -1.59; 95% CI: -2.42 to -0.79) rank third and fourth, respectively. STF lead to decline in lean mass (MD: -1.26; 95% CI: -2.16, -0.47). TRE showed benefits on fasting glucose (MD: -2.98; 95% CI: -4.7, -1.26). Subgroup analysis revealed all four caloric restriction regimens likely lead to modest weight loss after 1-3 months, with ADF ranked highest, but by 4-6 months, varying degrees of weight regain occur, particularly with CER, while interventions lasting 7-12 months may result in effective weight loss, with TRE potentially ranking first during both the 4-6 months and 7-12 months periods. ADF showing fewer and shorter-lasting physical symptoms. All four included regiments were effective in reducing body weight, with ADF likely having the most significant impact. Each regimen likely leads to modest weight loss after 1-3 months, followed by weight regain by 4-6 months. However, interventions lasting 7-12 months achieve greater weight loss overall. PROSPERO: CRD42022382478.

被引量：- 发表：1970

Publisher Correction: Impact of physical activity on caloric and macronutrient intake in children and adolescents: a systematic review and meta-analysis of randomized controlled trials.

被引量：- 发表：1970

Associations of daily eating frequency and nighttime fasting duration with biological aging in National Health and Nutrition Examination Survey (NHANES) 2003-2010 and 2015-2018.

被引量：- 发表：1970

Twelve-month outcomes of a community-based, father-daughter physical activity program delivered by trained facilitators.

被引量：- 发表：1970

Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry.

Accurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling. We first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure. The activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively. The integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings.

被引量：- 发表：1970