Abstract
Background
Intensive health behavior and lifestyle treatment is the foundation of pediatric obesity care, but sustained adherence and weight-loss maintenance remain difficult. Regular meal and sleep timing may complement standard care, although pediatric interventional evidence is limited. This prospective, single-arm pilot study aimed to evaluate longitudinal changes in weight, body composition, and metabolic markers during a ‘three meals and sleep routine’ (TMSR) intervention.
Methods
This prospective, single-center, single-arm pilot study consecutively included 119 children with obesity aged 5–18 years [body mass index (BMI)-for-age Z-score (BAZ) >2] from January 2022 to December 2023. Families received nutritionist-led written guidance for three structured meals per day, no caloric intake between meals, age-based energy targets of 1,500–2,300 kcal/day, a protocol-defined minimum of 7.5 hours of nighttime sleep with lights off by 22:30, and maintenance of habitual physical activity. Follow-up was planned at approximately 2-month intervals but occurred according to clinical availability. Anthropometry, bioelectrical-impedance body composition, and fasting metabolic markers were assessed. Adherence, dietary intake, sleep, and physical activity were not objectively monitored. Generalized linear mixed models used available repeated measurements to estimate average within-cohort change over time.
Results
The cohort had a mean age of 10.24±2.33 years; 82 participants (68.9%) were male and 37 (31.1%) were female. The fitted BMI trajectory decreased from approximately 26.0 kg/m2 at baseline to 24.5 kg/m2 at month 4, then increased to 25.4 kg/m2 by month 12. BAZ followed the same pattern, decreasing from 2.9 to 2.3 before increasing to 2.4. Visceral fat area and percentage body fat also declined through month 4 and subsequently rebounded. Fasting insulin and glucose showed early declines followed by partial rebound, whereas triglycerides and serum uric acid declined across follow-up. In linear-time models, average monthly coefficients were negative for BMI, BAZ, visceral fat area, percentage body fat, fasting insulin, glucose, triglycerides, and serum uric acid. These coefficients summarize within-cohort associations and do not capture the observed nonlinear trajectories.
Conclusions
In this uncontrolled cohort, several anthropometric and metabolic measures improved during the first 4–6 months, but BMI, BAZ, adiposity measures, insulin, and glucose later showed partial rebound. The findings are preliminary and hypothesis-generating; the absence of a comparator and objective adherence data precludes causal attribution to TMSR and limits assessment of durability and safety. Controlled, multicenter studies with age-appropriate sleep targets and objective behavioral monitoring are needed.
Keywords: Childhood obesity, circadian rhythm, meal timing, sleep, non-pharmacological intervention
Highlight box.
Key findings
• In 119 children with obesity, several within-cohort adiposity and metabolic measures improved during the first 4–6 months of a structured meal-and-sleep routine, but body mass index (BMI), BMI-for-age Z-score, percentage body fat, visceral fat area, fasting insulin, and glucose subsequently showed partial rebound. Without a comparator or objective adherence monitoring, these patterns cannot establish intervention efficacy or safety.
What is known and what is new?
• Intensive, family-centered behavioral treatment remains first-line care for pediatric obesity. Meal timing and sleep regularity are biologically plausible adjuncts, but pediatric intervention data remain limited.
• This single-arm study provides preliminary longitudinal observations and identifies a clinically important loss of early benefit over time.
What is the implication, and what should change now?
• ‘Three meals and sleep routine’ should be tested as an adjunct—not a replacement—for comprehensive pediatric obesity care. Practice-changing claims are premature; future trials require a standard-care comparator, age-specific sleep targets, predefined outcomes, objective dietary/sleep/activity monitoring, systematic participant-flow and adverse-event reporting, and strategies to sustain adherence beyond month 4.
Introduction
Childhood obesity has emerged as one of the most urgent global public health challenges (1). Over the past few decades, its prevalence has risen sharply across both developed and developing countries (2). This trend carries significant long-term implications for metabolic and cardiovascular health (3). The increase in childhood obesity has been particularly pronounced in developing countries. For example, national survey data from China revealed a dramatic increase in overweight prevalence among school-aged children—from 1.1% in 1985 to 20.4% by 2014 (4). Rapid economic growth in these regions has been accompanied by significant shifts in dietary patterns and lifestyle habits, including increased consumption of energy-dense, nutrient-poor foods and reduced levels of physical activity (5). These changes have contributed to a faster acceleration of childhood obesity than that observed in high-income countries, positioning obesity as a significant threat to child health in many developing regions (6-8). Childhood body size may also have long-term implications for cardiovascular disease risk later in life (9).
Current pediatric obesity care extends beyond simple advice to ‘eat less and exercise more’. Its foundation is structured, intensive, family-centered health behavior and lifestyle treatment that integrates nutrition, physical activity, behavior-change techniques, and longitudinal support (10). Access and sustained engagement remain difficult, and partial weight regain after an early response is common. Reductions in physical activity among children and adolescents were also widely reported during the coronavirus disease 2019 (COVID-19) pandemic (11-13). A routine focused on meal and sleep timing could therefore be evaluated as a potentially low-burden adjunct to comprehensive care, but it should not be presented as a substitute for established multicomponent treatment.
The ‘three meals and sleep routine’ (TMSR) intervention combines three structured, nutritionally complete meals per day, avoidance of caloric intake between meals, and a regular nighttime routine (Figure 1). The approach emphasizes behavioral regularity rather than pharmacotherapy or an added exercise prescription.
Figure 1.
“Three meals and sleep routine” lifestyle intervention (Adobe Illustrator).
The rationale is that meal timing and the light-sleep cycle act as time cues for central and peripheral circadian systems (14,15). Irregular eating, late caloric intake, and insufficient or irregular sleep may contribute to circadian misalignment, altered glucose regulation, appetite signaling, and lipid metabolism (16,17). Accordingly, regularizing these behaviors could plausibly influence the anthropometric and metabolic outcomes measured here. However, the mechanisms were not measured directly, and sustained benefit depends on sustained adherence. Real-world disruptions to routine care and lifestyle may also attenuate pediatric obesity treatment outcomes (18). We therefore aimed to describe feasibility and longitudinal within-cohort changes during TMSR, while treating the findings as hypothesis-generating rather than confirmatory. We present this article in accordance with the STROBE reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2026-0402/rc).
Methods
Study design and setting
This was a prospective, single-center, single-arm, pilot study embedded in routine care at the Department of Clinical Nutrition, Children’s Hospital, Zhejiang University School of Medicine. Children with obesity aged 5–18 years were consecutively included between January 2022 and December 2023 and received the TMSR behavioral regimen. The present analysis was subsequently performed using prospectively collected routine clinical data. There was no usual-care or untreated comparator. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of the Children’s Hospital, Zhejiang University School of Medicine (No. 2024-IRB-0011-P-01). Written informed consent was obtained from the legal guardians of all participants. The study was registered at the Chinese Clinical Trial Registry (ChiCTR2500097737).
Participants and eligibility criteria
Participants were eligible if they met all of the following criteria: aged 5–18 years; obesity is defined as body mass index (BMI)-for-age Z-score (BAZ) >2; child and guardian willing to receive the TMSR counseling protocol.
Participants were excluded for any of the following: presence of cardiovascular, pulmonary, renal, hepatic, or gastrointestinal diseases; presence of chronic infectious diseases; a psychiatric disorder or endocrine condition known to affect weight regulation, including diabetes mellitus or hyperthyroidism; current use of medications that affect body composition or metabolism; participation in other weight-loss programs within the past 3 months; concurrent participation in a structured exercise or weight-management program that would materially alter habitual activity or dietary exposure.
Baseline assessment and measurement procedures
At baseline and follow-up visits, trained research coordinators and clinical nutritionists used standardized procedures to measure height and weight. Body composition, including percentage body fat (PBF), visceral fat area (VFA), and skeletal muscle index (SMI), was assessed by bioelectrical impedance analysis (BIA). Fasting blood testing included glucose, insulin, total cholesterol, triglycerides, alanine aminotransferase, serum uric acid, and glycated hemoglobin. Staff received centralized training in measurement procedures and instrument calibration. Dietary intake, sleep duration, and physical activity were not measured with objective devices or validated repeated instruments.
Intervention procedures
Dietary and meal-routine counseling
At the initial outpatient consultation, a clinical nutritionist reviewed the protocol with the child and guardian and provided the written guidance shown in Figure 1. Families were instructed to provide three structured meals daily and to avoid caloric foods and drinks between meals. Daily energy guidance ranged from 1,500 to 2,300 kcal according to age, with tailored portions for major food groups. Sugar-sweetened beverages and highly processed snack foods were discouraged; staple packaged foods such as bread, steamed buns, and dumplings could be incorporated within meals. Exact clock times for the three meals were not mandated. Counseling was revisited during routine clinical follow-up, but the number and spacing of contacts varied with attendance.
Sleep and physical-activity components
The original protocol specified at least 7.5 hours of nighttime sleep and lights off no later than 22:30. This was a uniform protocol-defined minimum, not an age-specific recommendation for adequate sleep. It is lower than consensus recommendations for much of the 5–18-year age range and is addressed as a limitation.
No additional exercise intervention was prescribed. Participants were asked to maintain their habitual physical activity, but activity was not objectively tracked and stability could not be verified.
Follow-up, adherence assessment, and participant disposition
Follow-up was planned at approximately 2-month intervals for 12 months; in practice, visits occurred according to each family’s clinical availability. At attended visits, anthropometry, BIA-derived body composition, and available fasting metabolic measurements were collected. No standardized dietary diary, sleep log, phone-based reminder record, wearable device, or formal adherence score was used. Consequently, adherence and implementation fidelity could not be quantified or entered into the models. A contemporaneous screening log and standardized visit-disposition record were not available, preventing reliable retrospective reconstruction of numbers screened, excluded before enrollment, completing each planned time point, or lost to follow-up.
Outcomes
The primary anthropometric outcomes analyzed in this report were longitudinal changes in BMI and BAZ. Secondary exploratory outcomes were weight, height, PBF, VFA, SMI, fasting glucose, insulin, triglycerides, total cholesterol, alanine aminotransferase, serum uric acid. Because the pilot protocol did not designate a single confirmatory endpoint, this categorization is used for reporting clarity and the secondary analyses should not be interpreted as confirmatory.
Sample size
No formal a priori power calculation was performed. The sample comprised all 119 consecutive eligible participants enrolled during the predefined recruitment window. The study was exploratory and was not powered to establish clinical efficacy or to estimate uncommon adverse events.
Safety and adverse events
The source records did not include a prospectively defined, standardized adverse-event surveillance dataset. Height and SMI were evaluated as growth and body-composition outcomes, but their trajectories were not treated as substitutes for systematic safety assessment. Counts of adverse events, side effects, and unintended consequences therefore could not be reported reliably.
Statistical analysis
Continuous variables are summarized as mean ± standard deviation when approximately symmetric and as median (interquartile range) when visibly skewed; categorical variables are summarized as n (%). No formal normality test was prespecified. Longitudinal associations between study time and anthropometric, metabolic, and body-composition outcomes were assessed with generalized linear mixed models using all available repeated measurements; missing observations were not imputed. Study visit time was entered as the sole fixed predictor. Thus, the reported coefficient is an average monthly within-cohort association and does not model the nonlinear rebound apparent in several visit-specific trajectories. For graphical presentation, longitudinal trajectories were visualized using GAM smoothing, with the dashed lines representing 95% confidence intervals (CIs). These curves were used for descriptive visualization and were separate from the linear-time mixed-effects models. No separate paired t-tests, Wilcoxon signed-rank tests, or month-to-month pairwise comparisons were performed. All tests were two-tailed with α=0.05. No adjustment was made for multiple secondary outcomes; these P values are exploratory. Analyses were conducted in R version 4.2.0 and EmpowerStats (X&Y Solutions, Inc., Boston, MA, USA).
Results
Demographic and clinical characteristics of the study population
A total of 119 participants were included. Their mean age was 10.24±2.33 years (median, 10 years); 82 (68.91%) were male and 37 (31.09%) were female. Baseline anthropometric, body-composition, and metabolic characteristics are shown in Table 1.
Table 1. Demographic and clinical characteristics of the study population.
| Variable | Value |
|---|---|
| Age (years) | 10.24 (2.33) |
| Sex | |
| Male | 82 [68.91] |
| Female | 37 [31.09] |
| Weight (kg) | 57.76 (18.03) |
| BMI (kg/m2) | 25.86 (3.66) |
| BAZ | 2.87 (0.80) |
| Height-for-age Z-score | 1.05 (0.35, 1.73) |
| Percentage body fat (%) | 38.47 (5.95) |
| Visceral fat area (cm2) | 95.81 (36.10) |
| Skeletal muscle index (kg/m2) | 6.30 (1.15) |
| Glucose (mmol/L) | 5.45 (0.97) |
| Insulin (pmol/L) | 140.00 (79.58, 222.86) |
| Triglyceride (mmol/L) | 0.98 (0.76, 1.38) |
| Cholesterol (mmol/L) | 4.22 (0.78) |
| Alanine aminotransferase (U/L) | 28.00 (17.00, 57.00) |
| Serum uric acid (μmol/L) | 398.78 (84.16) |
| Glycated hemoglobin (%) | 5.49 (0.67) |
Data are presented as n [%], mean (SD) or median (interquartile range). BAZ, BMI-for-age Z-score; BMI, body mass index; SD, standard deviation.
Participant disposition and data completeness
The analytic cohort comprised 119 enrolled children. Because screening and visit-disposition logs were not available in a form that permitted reliable reconstruction, the numbers screened, excluded before enrollment, completing each nominal follow-up time point, and lost to follow-up could not be reported. We therefore did not create a flow diagram with inferred counts. Longitudinal models used the measurements available at attended visits without imputation.
Standardized adverse-event data were also unavailable; absence of a recorded adverse-event count should not be interpreted as evidence that no adverse events occurred.
Anthropometric changes
The fitted weight trajectory showed a U-shaped trajectory: it decreased from approximately 58 kg at baseline to 56 kg during the first 4 months and then increased to approximately 60 kg by month 12 (Figure 2). Because children were growing and there was no comparator, the increase in absolute weight cannot be separated from expected growth or other time-varying influences.
Figure 2.
Weight trend by month.
The fitted BMI trajectory decreased from approximately 26.0 kg/m2 at baseline to 24.5 kg/m2 at month 4, then rose to approximately 25.4 kg/m2 by month 12; BAZ followed the same pattern, decreasing from 2.9 to 2.3 before increasing to 2.4 (Figures 3,4). The fitted height trajectory increased from approximately 147 cm at baseline to 153 cm at month 12 (Figure 5). Height gain is expected during childhood and adolescence and should not be interpreted as an effect or safety proof of the intervention.
Figure 3.
BMI trend by month. BMI, body mass index.
Figure 4.
BAZ trend by month. BAZ, BMI-for-age Z-score; BMI, body mass index.
Figure 5.
Height trend by month.
Body-composition changes
VFA and PBF also showed U-shaped patterns. The fitted VFA trajectory decreased from approximately 98 cm2 at baseline to 80 cm2 at month 4 and then increased to approximately 91 cm2 at month 12. The fitted PBF trajectory decreased from approximately 38% to 34% and subsequently increased to approximately 35% (Figures 6,7). The fitted SMI trajectory changed from approximately 6.3 kg/m2 at baseline to 6.1 kg/m2 at month 4 and 6.5 kg/m2 at month 12 (Figure 8).
Figure 6.
PBF trend by month. PBF, percentage body fat.
Figure 7.
VFA trend by month. VFA, visceral fat area.
Figure 8.
SMI trend by month. SMI, skeletal muscle index.
Changes in metabolic parameters
Longitudinal trends in metabolic parameters were assessed over a 12-month follow-up period.
Glucose and insulin
The fitted fasting insulin trajectory decreased from approximately 160 pmol/L at baseline to a nadir of approximately 70 pmol/L around months 7–8 and then increased slightly to approximately 80 pmol/L by month 12 (Figure 9). The fitted fasting glucose trajectory decreased from approximately 5.4 mmol/L at baseline to 4.9 mmol/L at month 6 and then increased to approximately 5.2 mmol/L by month 12 (Figure 10).
Figure 9.
Insulin trend by month.
Figure 10.
Glucose trend by month.
Lipid profile
The fitted triglyceride trajectory decreased from approximately 1.1 mmol/L at baseline to 0.8 mmol/L by month 12 (Figure 11). The fitted total cholesterol trajectory decreased from approximately 4.2 mmol/L to 4.0 mmol/L at month 4 and then increased to approximately 4.2 mmol/L at month 12 (Figure 12).
Figure 11.
Triglyceride trend by month.
Figure 12.
Cholesterol trend by month.
Serum uric acid
The fitted serum uric acid trajectory decreased from approximately 400 μmol/L at baseline to approximately 350 μmol/L during later follow-up and remained at a similar level through month 12 (Figure 13).
Figure 13.
Serum uric acid trend by month.
Average longitudinal associations
With visit time modeled as a continuous predictor, statistically significant negative average monthly coefficients were observed for BMI (−0.1830 kg/m²; 95% CI: −0.2409 to −0.1251; P<0.001), BAZ (−0.0694; 95% CI: −0.0826 to −0.0562; P<0.001), VFA (−2.1685 cm²; 95% CI: −2.9352 to −1.4018; P<0.001), PBF (−0.6014 percentage points; 95% CI: −0.7584 to −0.4444; P<0.001), fasting insulin (−12.5080 pmol/L; 95% CI: −17.9417 to −7.0743; P<0.001), fasting glucose (−0.0527 mmol/L; 95% CI: −0.0935 to −0.0119; P=0.01), triglycerides (−0.0416 mmol/L; 95% CI: −0.0726 to −0.0105; P=0.01), serum uric acid (−6.0138 μmol/L; 95% CI: −9.3512 to −2.6763; P<0.001), and alanine aminotransferase (−4.3489 U/L; 95% CI: −6.4651 to −2.2327; P<0.001) (Table 2). The SMI coefficient was −0.0071 kg/m² (95% CI: −0.0230 to 0.0089; P=0.38). These estimates summarize average within-cohort change over time; they do not establish an intervention effect and can obscure the nonlinear rebound shown in Figures 2–10.
Table 2. Average longitudinal associations for metabolic and body-composition parameters.
| Variable | Coefficient | Standard error | 95% CI | P |
|---|---|---|---|---|
| Weight | −0.0250 | 0.0675 | −0.1575 to 0.1073 | 0.71 |
| Height | 0.5213 | 0.0236 | 0.4750 to 0.5676 | <0.001 |
| BMI | −0.1830 | 0.0295 | −0.2409 to −0.1251 | <0.001 |
| BAZ | −0.0694 | 0.0067 | −0.0826 to −0.0562 | <0.001 |
| Height-for-age Z-score | 0.0035 | 0.0030 | −0.0024 to 0.0095 | 0.25 |
| Percentage body fat | −0.6014 | 0.0801 | −0.7584 to −0.4444 | <0.001 |
| Visceral fat area | −2.1685 | 0.3912 | −2.9352 to −1.4018 | <0.001 |
| Skeletal muscle index | −0.0071 | 0.0081 | −0.0230 to 0.0089 | 0.38 |
| Insulin | −12.5080 | 2.7723 | −17.9417 to −7.0743 | <0.001 |
| Glucose | −0.0527 | 0.0208 | −0.0935 to −0.0119 | 0.01 |
| Cholesterol | −0.0141 | 0.0123 | −0.0382 to 0.0099 | 0.25 |
| Triglycerides | −0.0416 | 0.0158 | −0.0726 to −0.0105 | 0.01 |
| Serum uric acid | −6.0138 | 1.7028 | −9.3512 to −2.6763 | <0.001 |
| Alanine aminotransferase | −4.3489 | 1.0797 | −6.4651 to −2.2327 | <0.001 |
Time was modeled as a continuous predictor. Coefficients represent average monthly within-cohort associations and do not capture nonlinear rebound. No multiplicity adjustment was applied. BAZ, BMI-for-age Z-score; BMI, body mass index; CI, confidence interval.
Discussion
This exploratory single-arm study identified early within-cohort decreases in BMI, BAZ, adiposity measures, and several metabolic markers during TMSR. The central finding is not a sustained monotonic response: BMI, BAZ, PBF, VFA, fasting insulin, glucose, and total cholesterol showed partial rebound after their early nadirs. In the absence of a comparator, the observed trajectories may reflect the intervention, natural growth and pubertal change, regression to the mean, seasonal variation, concurrent behaviors, selection and follow-up processes, or combinations of these factors. The data therefore support hypothesis generation rather than claims of efficacy.
The rebound after month 4 is clinically important and suggests limited durability of the initial changes. Plausible explanations include declining adherence, behavioral fatigue, reduced family support over time, compensatory appetite or eating, metabolic adaptation after early weight loss, and changes in school schedules or seasonal activity. Because food intake, sleep, physical activity, and adherence were not objectively recorded, these explanations cannot be distinguished in the present dataset. The pattern indicates that any future TMSR program should include maintenance contacts, objective feedback, and predefined strategies for relapse prevention.
Early decreases in fasting insulin, glucose, triglycerides, and alanine aminotransferase were consistent with improved metabolic status during the first months, but within-person change in an uncontrolled cohort cannot be attributed specifically to meal or sleep timing. The partial rebound in insulin and glucose further argues against describing the metabolic response as uniformly sustained. Lifestyle interventions have demonstrated benefits in the management of childhood overweight and obesity in China (19). Treatment response may vary with age, while broader disruptions in daily routines can also influence obesity-related behaviors and treatment outcomes in children and adolescents (20,21).
Meal timing, sleep, diet quality, and physical activity can each influence energy balance and metabolic regulation. Diet quality and sleep are also closely interrelated in adolescents with obesity (22). The early PBF and VFA patterns are broadly consistent with literature on time-restricted eating and chrononutrition (23-26), but TMSR combined meal structure, energy guidance, avoidance of between-meal calories, and a sleep routine. Without component-specific adherence measures, the contribution of each element cannot be isolated. Longitudinal evidence also suggests an interplay between sleep, eating behaviors, and anthropometric outcomes in adolescents (27).
Height increased and SMI did not show a significant average linear decline during follow-up. These observations are reassuring descriptive findings, but they do not establish safety: normal linear growth is expected in this age range, the study had no comparator, and adverse events were not systematically ascertained. Future studies should prespecify pediatric growth, nutritional adequacy, eating-disorder symptoms, compensatory behaviors, and other adverse outcomes.
TMSR may be reasonable to evaluate as an adjunct to comprehensive pediatric obesity treatment because its behavioral rules are simple. However, feasibility was not quantified, effectiveness was not established, and scalability cannot be inferred from a single-center cohort. Any clinical implementation should remain within age-appropriate, family-centered obesity care. It should not replace established nutrition, activity, behavioral, pharmacologic, or surgical options when indicated.
Meal timing has also been associated with sleep quality (28). Several mechanisms are biologically plausible but were not measured in this study:
Circadian synchronization: regular meal timing could act as a time cue for peripheral clocks involved in glucose and lipid metabolism. Experimental and observational evidence links circadian disruption with impaired insulin sensitivity, but the present study did not measure circadian phase, melatonin, clock-gene expression, or chronotype (29,30).
Overnight fasting and reduced snacking: avoiding calories between three meals may extend the overnight fasting interval and reduce opportunities for unplanned intake. These pathways could influence fat oxidation and energy intake, but daily intake and eating windows were not recorded, so an intermittent-fasting mechanism cannot be confirmed (31).
Sleep and appetite regulation: adequate, regular sleep may influence leptin, ghrelin, appetite, and insulin sensitivity. However, the protocol’s 7.5-hour minimum was below recommended duration for many participants, and neither sleep duration nor sleep quality was measured objectively; improved sleep-hormone regulation therefore remains a hypothesis rather than an observed mediator (32,33).
Contemporary guidance identifies intensive health behavior and lifestyle treatment as foundational pediatric obesity care (34). Consensus sleep guidance recommends 10–13 hours per 24 hours for 5-year-olds, 9–12 hours for children aged 6–12 years, and 8–10 hours for adolescents aged 13–18 years (35). Recent pediatric data also associate later meal timing or a longer eating window with less favorable diet quality and metabolic markers (36), while a recent controlled pediatric time-restricted-eating protocol illustrates the value of a comparator, prespecified outcomes, regular follow-up, food diaries, adherence measures, and adverse-event surveillance (37). These developments support further testing of circadian-informed routines but also underscore the design features required before causal or practice-changing claims can be made.
Limitations
First, the absence of a standard-care comparator is the central limitation. Natural growth, puberty, regression to the mean, seasonal variation, maturation, and secular or concurrent behavioral changes could account for part or all of the observed trajectories. The study therefore cannot estimate a treatment effect or support causal claims about TMSR.
Second, implementation fidelity and key time-varying confounders were not measured adequately. Dietary intake, meal times, sleep duration and quality, physical activity, and adherence were not captured with diaries, validated questionnaires, reminders, wearables, or other objective tools. Self-report was not available in a standardized longitudinal form. Consequently, we could not verify exposure, adjust for behavior changes, perform dose-response analyses, or determine whether declining adherence explained the rebound.
Third, the uniform 7.5-hour sleep minimum was below consensus recommendations for most of the age range and may have introduced exposure heterogeneity. Fourth, contemporaneous screening, visit-completion, dropout, and standardized adverse-event records were insufficient to reconstruct a reliable participant flow diagram or characterize attrition and safety. Height gain and a stable average SMI coefficient do not substitute for systematic adverse-event assessment.
Fifth, the single-center convenience cohort limits generalizability. No formal sample size calculation or single confirmatory endpoint was specified, multiple exploratory outcomes were tested without multiplicity adjustment, and linear-time models summarized average slopes despite visibly nonlinear trajectories. Missing data were not imputed, and the assumptions underlying available-case mixed modeling could not be verified. Mechanistic outcomes, pubertal stage, socioeconomic factors, and seasonal exposures were not assessed.
Future research directions
Future research should use prospective multicenter controlled designs, preferably randomized when feasible, with a standard-care comparator; prespecified primary outcomes and sample-size calculations; age-specific sleep targets; fixed follow-up windows; complete screening and retention logs; objective or validated measurement of food intake, meal timing, sleep, and physical activity; repeated adherence assessment; and systematic adverse-event surveillance. Maintenance support should be intensified before and after month 4, and analyses should model nonlinear trajectories and examine whether adherence mediates early response and later rebound.
Conclusions
In this prospective, single-center, single-arm pilot study, several anthropometric and metabolic measures improved during the first 4–6 months of TMSR, followed by partial rebound in BMI, BAZ, adiposity, insulin, and glucose. These preliminary within-cohort associations do not establish efficacy, durability, scalability, or safety because the study lacked a comparator and objective monitoring of diet, sleep, activity, adherence, attrition, and adverse events. Larger controlled multicenter trials with age-appropriate sleep targets and robust implementation and safety measurement are required.
Supplementary
The article’s supplementary files as
Acknowledgments
We would like to thank Editage (www.editage.cn) for English-language editing. During the preparation of this work, the authors used ChatGPT to assist with language and readability improvements. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the publication’s content.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of the Children’s Hospital, Zhejiang University School of Medicine (No. 2024-IRB-0011-P-01). Written informed consent was obtained from the legal guardians of all participants.
Footnotes
Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2026-0402/rc
Funding: This work was supported by The Key R&D Program of Zhejiang (No. 2023C03047) and the ZJU-GENSCI Children’s Health Research & Development Center (No. ZJU-GENSCI2024YY010).
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2026-0402/coif). The authors have no conflicts of interest to declare.
Data Sharing Statement
Available at https://tp.amegroups.com/article/view/10.21037/tp-2026-0402/dss
References
- 1. Zhang X, Liu J, Ni Y, et al. Global Prevalence of Overweight and Obesity in Children and Adolescents: A Systematic Review and Meta-Analysis. JAMA Pediatr 2024;178:800-13.. 10.1001/jamapediatrics.2024.1576 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet 2024;403:1027-50.. 10.1016/S0140-6736(23)02750-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Kibret KT, Strugnell C, Backholer K, et al. Life-course trajectories of body mass index and cardiovascular disease risks and health outcomes in adulthood: Systematic review and meta-analysis. Obes Rev 2024;25:e13695.. 10.1111/obr.13695 [DOI] [PubMed] [Google Scholar]
- 4. Song Y, Agardh A, Ma J, et al. National trends in stunting, thinness and overweight among Chinese school-aged children, 1985-2014. Int J Obes (Lond) 2019;43:402-11.. 10.1038/s41366-018-0129-7 [DOI] [PubMed] [Google Scholar]
- 5. Lafia AT, Ketounou TR, Honfoga JNB, et al. Dietary habits, prevalence of obesity and overweight in developed and developing countries. Res Soc Dev 2022;11:e2929601. [Google Scholar]
- 6. Lister NB, Baur LA, Felix JF, et al. Child and adolescent obesity. Nat Rev Dis Primers 2023;9:24.. 10.1038/s41572-023-00435-4 [DOI] [PubMed] [Google Scholar]
- 7. Jayasinghe S, Hills AP. Strategies to Improve Physical Activity and Nutrition Behaviours in Children and Adolescents: A Review. Nutrients 2023;15:3370.. 10.3390/nu15153370 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Olufadewa I, Adesina M, Oladele R, et al. Interventions to reduce and prevent childhood obesity in low-income and middle-income countries: a systematic review and meta-analysis. Lancet Glob Health 2023;11 Suppl 1:S16.. 10.1016/S2214-109X(23)00099-2 [DOI] [PubMed] [Google Scholar]
- 9. Power GM, Tyrrell J, Frayling TM, et al. Mendelian Randomization Analyses Suggest Childhood Body Size Indirectly Influences End Points From Across the Cardiovascular Disease Spectrum Through Adult Body Size. J Am Heart Assoc 2021;10:e021503.. 10.1161/JAHA.121.021503 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Smith JD, Fu E, Kobayashi MA. Prevention and Management of Childhood Obesity and Its Psychological and Health Comorbidities. Annu Rev Clin Psychol 2020;16:351-78.. 10.1146/annurev-clinpsy-100219-060201 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Moore SA, Faulkner G, Rhodes RE, et al. Impact of the COVID-19 virus outbreak on movement and play behaviours of Canadian children and youth: a national survey. Int J Behav Nutr Phys Act 2020;17:85.. 10.1186/s12966-020-00987-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Neville RD, Lakes KD, Hopkins WG, et al. Global Changes in Child and Adolescent Physical Activity During the COVID-19 Pandemic: A Systematic Review and Meta-analysis. JAMA Pediatr 2022;176:886-94.. 10.1001/jamapediatrics.2022.2313 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Rossi L, Behme N, Breuer C. Physical Activity of Children and Adolescents during the COVID-19 Pandemic-A Scoping Review. Int J Environ Res Public Health 2021;18:11440.. 10.3390/ijerph182111440 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Fuad SA, Ginting RP, Lee MW. Chrononutrition: Potential, Challenges, and Application in Managing Obesity. Int J Mol Sci 2025;26:5116.. 10.3390/ijms26115116 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Regmi P, Heilbronn LK. Time-Restricted Eating: Benefits, Mechanisms, and Challenges in Translation. iScience 2020;23:101161.. 10.1016/j.isci.2020.101161 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Kim HM, Kang HJ, Lee DH, et al. Association between breakfast frequency and metabolic syndrome among young adults in South Korea. Sci Rep 2023;13:16826.. 10.1038/s41598-023-43957-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Jung J, Kim AS, Ko HJ, et al. Association between Breakfast Skipping and the Metabolic Syndrome: The Korea National Health and Nutrition Examination Survey, 2017. Medicina (Kaunas) 2020;56:396.. 10.3390/medicina56080396 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Appelhans BM, French SA, Martin MA, et al. Attenuated efficacy of pediatric obesity treatment during the COVID-19 pandemic. Obesity (Silver Spring) 2022;30:45-9.. 10.1002/oby.23313 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Li B, Gao S, Bao W, et al. Effectiveness of lifestyle interventions for treatment of overweight/obesity among children in China: A systematic review and meta-analysis. Front Endocrinol (Lausanne) 2022;13:972954.. 10.3389/fendo.2022.972954 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Jørgensen RM, Bjørn A, Bjørn V, et al. The younger the better: importance of age in treatment of childhood obesity. Eur J Pediatr 2023;182:5417-25.. 10.1007/s00431-023-05218-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Stavridou A, Kapsali E, Panagouli E, et al. Obesity in Children and Adolescents during COVID-19 Pandemic. Children (Basel) 2021;8:135.. 10.3390/children8020135 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Saidi O, Rochette E, Del Sordo G, et al. Eucaloric Balanced Diet Improved Objective Sleep in Adolescents with Obesity. Nutrients 2021;13:3550.. 10.3390/nu13103550 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Wilkinson MJ, Manoogian ENC, Zadourian A, et al. Ten-Hour Time-Restricted Eating Reduces Weight, Blood Pressure, and Atherogenic Lipids in Patients with Metabolic Syndrome. Cell Metab 2020;31:92-104.e5.. 10.1016/j.cmet.2019.11.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Kawasaki Y, Kitamura E, Kasai T. Impact of Body Composition on Sleep and Its Relationship with Sleep Disorders: Current Insights. Nat Sci Sleep 2023;15:375-88.. 10.2147/NSS.S340946 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Kohanmoo A, Kazemi A, Zare M, et al. Gender-specific link between sleep quality and body composition components: a cross-sectional study on the elderly. Sci Rep 2024;14:8113.. 10.1038/s41598-024-58801-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Song Y, Gong L, Lou X, et al. Sleep-Body Composition Relationship: Roles of Sleep Behaviors in General and Abdominal Obesity in Chinese Adolescents Aged 17-22 Years. Nutrients 2023;15:4130.. 10.3390/nu15194130 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Grimaldi M, Bacaro V, Natale V, et al. The Longitudinal Interplay between Sleep, Anthropometric Indices, Eating Behaviors, and Nutritional Aspects: A Systematic Review and Meta-Analysis. Nutrients 2023;15:3179.. 10.3390/nu15143179 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Yan LM, Li HJ, Fan Q, et al. Chronobiological perspectives: Association between meal timing and sleep quality. PLoS One 2024;19:e0308172.. 10.1371/journal.pone.0308172 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. BaHammam AS, Pirzada A. Timing Matters: The Interplay between Early Mealtime, Circadian Rhythms, Gene Expression, Circadian Hormones, and Metabolism-A Narrative Review. Clocks Sleep 2023;5:507-35.. 10.3390/clockssleep5030034 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Ruddick-Collins LC, Morgan PJ, Johnstone AM. Mealtime: A circadian disruptor and determinant of energy balance?. J Neuroendocrinol 2020;32:e12886.. 10.1111/jne.12886 [DOI] [PubMed] [Google Scholar]
- 31. Velingkaar N, Mezhnina V, Poe A, et al. Two-meal caloric restriction induces 12-hour rhythms and improves glucose homeostasis. FASEB J 2021;35:e21342.. 10.1096/fj.202002470R [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Krietsch KN, Chardon ML, Beebe DW, et al. Sleep and weight-related factors in youth: A systematic review of recent studies. Sleep Med Rev 2019;46:87-96.. 10.1016/j.smrv.2019.04.010 [DOI] [PubMed] [Google Scholar]
- 33. van Egmond LT, Meth EMS, Engström J, et al. Effects of acute sleep loss on leptin, ghrelin, and adiponectin in adults with healthy weight and obesity: A laboratory study. Obesity (Silver Spring) 2023;31:635-41.. 10.1002/oby.23616 [DOI] [PubMed] [Google Scholar]
- 34. Hampl SE, Hassink SG, Skinner AC, et al. Clinical Practice Guideline for the Evaluation and Treatment of Children and Adolescents With Obesity. Pediatrics 2023;151:e2022060640. Erratum in: Pediatrics. 2024 Jan 1;153(1):e2023064612.. 10.1542/peds.2022-060640 [DOI] [PubMed] [Google Scholar]
- 35. Paruthi S, Brooks LJ, D'Ambrosio C, et al. Recommended Amount of Sleep for Pediatric Populations: A Consensus Statement of the American Academy of Sleep Medicine. J Clin Sleep Med 2016;12:785-6.. 10.5664/jcsm.5866 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Salas-González MD, Loria-Kohen V, Aparicio A, et al. Meal timing, nutritional status and diet quality in a group of Spanish children. Pediatr Obes 2025;20:e70006.. 10.1111/ijpo.70006 [DOI] [PubMed] [Google Scholar]
- 37. Shi X, Jiang P, Wang P, et al. Study protocol for a randomized clinical trial to reduce weight among youth with obesity using time-restricted eating: the TRECO study. BMC Pediatr 2025;26:21.. 10.1186/s12887-025-06298-w [DOI] [PMC free article] [PubMed] [Google Scholar]