4.1 Study Selection.
Upon removal of duplicates, 237 titles and abstracts were screened for eligibility. A total of 91 underwent full-text review. Of those, 39 studies met the pre-specified inclusion criteria. A PRISMA flow diagram detailing the database searches, the number of abstracts screened, and the full texts retrieved is illustrated in Figure 1. The study designs and methodologies of the included studies are cataloged in Figure 2. Of these studies, twenty-nine were interventional studies that focused on the efficacy of IF on weight, metabolism, and/or biological aging markers. Table 1 summarizes the key characteristics of the 29 interventional studies that included efficacy data, emphasizing intervention components, execution, feasibility measures, adherence monitoring, and primary and secondary outcomes. The remaining ten studies were observational studies focusing on evaluating the effects, acceptability, and implications of timing of eating within the context of weight management interventions, cardiometabolic risk factors, and eating behaviors in adolescents and young adults, without involving the experimental manipulation of timing of eating.
4.2 Participants.
Studies predominantly focused on adolescents and young adults, with mean ages between 15 to 25 years old [31,32,90–132]. Some included a majority of female participants (e.g., 64% in Vidmar et al. 2021) [31], while others only included male participants (e.g., 100% males in McAllister et al. 2020 and Harder-Lauridsen et al. 2017) [114,124]. A few studies reported an even sex distribution [116]. Participants' baseline weight status also varied widely, from those with a median or mean weight indicating overweight or obesity (e.g., median weight=101.4 kg in Vidmar et al. 2021) [31] to those with participants in the normal weight range (e.g., mean BMI = 22.7 kg/m² in Park et al. 2021) [112].
4.3 Study Design Characteristics.
Thirty-nine studies were included in this review. Ten studies did not involve interventional design, but rather examined the relationships between meal timing and weight management outcomes, cardiometabolic risk factors, and eating behaviors in adolescent and emerging adults [90–98,102,105]. Twenty-nine were interventional studies that focused on efficacy or effectiveness of IF interventions on weight, metabolism, and biological aging markers, as summarized in Table 1 [31,32,107–131]. The primary outcomes varied, but primarily involved assessing the efficacy of various intermittent fasting interventions on body weight, body composition, cardiometabolic health markers, energy balance, and specific physiological responses such as glycemic control and muscle damage indicators. Secondary outcomes were also diverse and included assessments of dietary intake quality, physical activity, sleep patterns, eating behaviors, quality of life, glycemic control, blood biomarkers, microbial diversity, muscular performance, hunger, craving, mood, cognitive function, appetite, and energy intake responses. Most interventional studies involved interventions with short duration, spanning 4 to 12 weeks, which limits conclusions about long-term efficacy and safety. Among these studies, twenty-one were RCTs [31,107,109–111,113–116,120,121,123,124,126], five were single-arm trials [32,108,112,122,125], and one was two-arm randomized trial [114]. Eighteen studies utilized an 8-hour time-restricted eating window [31,95,107,108,111,112,114,115,119,121–123,126], and two tested other forms of IF, including Alternate-Day Calorie Restriction [124] and Protein-Sparing Modified Fast [125]. Other studies investigated different TRE windows or other IF protocols. For instance, Zhang et al. 2022 compared early (7:00 a.m. - 1:00 p.m.) and late (12:00 p.m. - 6:00 p.m.) 6-hour TRE windows [113], and Bao et al. 2022 tested the efficacy of a 5.5-hour TRE window compared to an 11-hour eating control group [118].
In total, eight studies involved multicomponent interventions combining TRE, continuous glucose monitoring (CGM), resistance training (RT), energy restriction, low carbohydrate and added sugar diets, brisk walking, high-intensity exercise, antioxidant supplementation, and protein-sparing modified fasts (PSMF). Keenan et al. 2022 compared continuous energy restriction with 5:2 intermittent fasting, where the IF group consumed normal calories for 5 days and significantly reduced calories on 2 days of the week [116]. Only one study conducted a comparative analysis between early and late TRE, providing unique insights into how the timing of eating windows within IF regimens can affect metabolic health, weight loss, and potentially other well-being markers [113]. The caloric requirements varied across studies, with some implementing isocaloric conditions (maintaining the same caloric intake) [107,114], energy restriction (e.g., 25% calorie deficit, very low-calorie diets) [120], and intermittent fasting days such as alternate-day fasting (ADF) [32] and intermittent energy restriction (IER) [120]. Specific interventions like the protein-sparing modified fast (PSMF) had defined caloric intake ranges (1200–1800 calories with low carbohydrate and high protein) [125].
4.4 Catalog of Feasibility Measures and Adherence Monitoring
To evaluate the acceptability and feasibility of healthcare interventions, a generic, theoretically grounded questionnaire was previously developed around the constructs of the Theoretical Framework of Acceptability (TFA) [133]. This tool was designed to measure seven specific elements related to feasibility: affective attitude, burden, ethicality, intervention coherence, opportunity costs, perceived effectiveness, and self-efficacy. This versatile questionnaire can be customized to analyze the acceptability of various healthcare interventions across diverse settings. Studies were evaluated on whether they measure acceptability and feasibility consistent with TFA. Table 2 presents an overview of the interventional studies, cataloged by the results reported. There was great heterogeneity in how feasibility was defined across various study designs. None of the studies utilized the seven TFA components. A few of the individual components of the framework were captured: 7/29 affective attitude, 4/29 burden, 0/29 ethicality, 0/29 intervention coherence, 0/29 opportunity costs, 1/29 perceived effectiveness, and 0/29 self-efficacy.
4.5 Summary of Clinical Trials
Body Weight
There was a variety of weight status and body composition measures utilized across reports making comparison of the effect of IF on body weight challenging. Most studies highlighted significant weight loss among participants adhering to IF protocols, though with varying degrees of weight reduction across studies and samples [31,107,108,112–115]. Vidmar et al. (2021) examined the efficacy of late TRE in adolescents with obesity. All groups experienced weight loss, with 31% of the participants of the TRE plus continuous glucose monitoring (CGM) group, 26% of the TRE with blinded CGM, and 13% of the control group [31]. Hegedus et al. (2023) reported a significant decrease BMI at the 95th percentile (%BMIp95) at week 12, with a 46% reduction observed in the late TRE (lTRE) group compared to 21% in the control group with an extended eating window [107]. Zhang et al. (2022) observed decreases in weight and BMI in both early and late TRE groups compared to controls [113].
In Moro et al. (2020) study, the TRE group experienced a 2% weight change from baseline, while this was not the case for participants assigned to the control group [115].
Park et al. (2021) documented significant weight loss among female participants, while no significant weight loss was observed among male participants [112]. In contrast, research examining the combination of TRE with resistance training (RT) offers a different perspective [109,110]. Tinsley et al. (2019) investigated the effects of an 8-hour TRE combined with β-hydroxy β-methylbutyrate (HMB) supplementation and RT in active females, only to find an increase in body weight across all groups [110]. Similarly, a study by Tinsley et al. (2017) on a 4-hour TRE regimen coupled with RT in men reported no significant change in body weight [109], indicating that the efficacy of TRE on weight loss might be influenced by factors such as biological sex, baseline weight, and exercise regimens.
Cardiometabolic Risk Factors
Several studies reported improvements in markers of glucose metabolism [107,112,113]. For instance, Hegedus et al. (2023) found reductions in hemoglobin A1c (HbA1c) and alterations in C-peptide levels in late TRE groups [107]. Kim & Song (2023) observed reductions in fasting blood glucose and improvements in HOMA-IR, indicating better glucose regulation and insulin sensitivity [122]. Zhang et al. (2022) highlighted a decrease in insulin resistance [113]. One study also reported reductions in systolic and diastolic blood pressure [114]. Conversely, two studies [32,109] observed no metabolic changes compared to baseline. Another study reported significant reductions in fasting insulin, acyl ghrelin, and leptin concentrations during energy deprivation compared to energy balance. Postprandial hormone responses, including insulin, GLP-1, and PP, were elevated after energy deprivation, while acyl ghrelin was suppressed, indicating that altered sensitivity to appetite-mediating hormones may contribute to the adaptive response to negative energy balance [105].
McAllister et al. (2020) and Zhang et al. (2022) noted decreases in body mass and fat mass (FM) in participants adhering to TRE, while preserving lean mass [113,114]. Additionally, IF was associated with decreased liver enzymes aspartate aminotransferase (AST) and alanine transaminase (ALT) in two studies [107,111]. McAllister et al. (2020) reported increases in high-density lipoprotein (HDL) and variations in low-density lipoprotein (LDL) and total cholesterol depending on the type of TRE (ad libitum vs. isocaloric) [114]. Zeb et al. (2020) found decreased total cholesterol (TC) and triglycerides (TAG), and an increase in HDL post-TRE [111]. However, divergent effects on lipid profiles were observed as well, with increases in HDL [111,114] as well as in LDL [112,113].
Biological Aging Markers
Only a few studies measured markers associated with biological aging [111,113–115]. McAllister et al. (2020) and Moro et al. (2020) both reported an increase in adiponectin levels in participants following an 8-hour TRE regimen, whether combined with an ad libitum diet or an isocaloric diet. Elevated adiponectin levels are inversely associated with obesity and oxidative stress and correspond to improved metabolism and resting energy expenditure [114,115]. Additionally, Moro et al. (2020) observed a significant decrease in the neutrophil-to-lymphocyte ratio, an inflammatory marker, within the TRE groups compared to controls, indicating reduced inflammation [115]. Zeb et al. (2020) observed reductions in serum IL-1B and TNF-a levels post-TRE, though these changes were not statistically significant, suggesting a potential trend towards reduced inflammation that warrants further investigation [111]. Zhang et al. (2022) reported that superoxide dismutase (SOD), a crucial antioxidant defense in nearly all living cells exposed to oxygen, significantly increased in participants who engaged in early TRE compared to those in late TRE and control groups [113].
4.6 Summary of Observational Studies
Observational studies varied in their focus. Some addressed parental interest in time-restricted eating (TRE), while others looked into nutritional adequacy, concerns, and the efficacy of TRE [90–94,96]. Tucker et al. (2022) found that two-thirds of parents with children in pediatric weight management programs showed interest in time-limited eating (TLE) for ≤12 hours per day, with interest waning for stricter limits of ≤10 or ≤8 hours [90]. Lister et al. (2020) challenged the notion that continuous energy restriction (CER) is the sole method for weight management in metabolically unhealthy adolescents, proposing intermittent energy restriction (IER) as a viable alternative in tertiary settings [91]. Similarly, Lister (2017) emphasized the need for careful consideration of nutritional adequacy in energy-restricted diets, highlighting that various eating patterns can achieve both nutritional adequacy and energy restriction, which is crucial when prescribing diet interventions for adolescent weight loss [92]. Nevertheless, skipping breakfast was associated with increased cardiometabolic risk factors in adolescence, as observed is a cross-sectional survey study by de Souza et al. (2021) [93]. One study reported that diets low in carbohydrates and those involving intermittent fasting were linked to increased disordered eating behaviors, including binge eating and food cravings. These findings suggest that such restrictive diets may heighten cognitive restraint, leading to an upsurge in food cravings. However, this study's reliance on a cross-sectional design and a web-recruited university sample, predominantly female, introduces potential biases [102].
One review study evaluated the impact of the timing and composition of food intake, physical activity, sedentary time, and sleep on health outcomes, suggesting that these factors independently predict health trajectories and disease risks. This underscores the need for a unifying framework that integrates time-based recommendations into current health guidelines for children and adolescents [94]. However, the practical implications of IER, such as the risk of fostering restricted eating patterns and inhibiting growth in adolescent girls on a 600-700 kcal diet, raise concerns. Vanderwall et al. (2020) pointed out that physical activity, an essential strategy for preventing obesity and metabolic syndrome, was not adequately measured in some studies, despite its likely contributory impact. These findings collectively highlight the potential benefits and challenges of dietary interventions like TLE, CER, and IER, emphasizing the importance of ensuring nutritional adequacy and integrating physical activity for effective adolescent weight management [96].
Observational research examining the relationships between the timing of eating, weight management outcomes, and cardiometabolic risk factors suggests there is no meaningful impact on body composition. However, there may be benefits to cardiometabolic health from adopting earlier and shorter eating windows [97,98,132]. These findings are consistent with studies in adults indicating that aligning meal consumption with circadian rhythms can enhance metabolic outcomes [134,135]. One possible explanation for the disparate findings across clinical trials and observational studies is that existing observational studies have failed to consider how eating timing interacts with eating window duration to influence health. Studies in adults have reported that eating late in the day, even with shorter eating window, can worsen postprandial glucose levels and b-cell responsiveness or confers no health benefit [136]. More studies are needed to better characterize the joint influence of eating timing, eating frequency, and daily eating duration on health outcomes.