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Breakfast, skipping, obesity, weight, meta-analysis, systematic review, randomized controlled trials
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Bohan Brown MM, Milanes JE, Allison DB and Brown AW. Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis [version 3; peer review: 2 approved, 1 approved with reservations]. F1000Research 2021, 9:140 (https://doi.org/10.12688/f1000research.22424.3)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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Research Article
Revised
Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis
[version 3; peer review: 2 approved, 1 approved with reservations]
PUBLISHED 30 Jul 2021
OPEN PEER REVIEW
REVIEWER STATUS
This article is included in the Agriculture, Food and Nutrition gateway.
Abstract
Corresponding authors:
Michelle M. Bohan Brown, Andrew W. Brown
Competing interests:
As of the initial submission, Dr. Allison has received personal payments or promises for same from the following: American Society for Nutrition; American Statistical Association; BioFortis; California Walnut Commission; Columbia University; Fish & Richardson, P.C.; Frontiers Publishing; Henry Stewart Talks; IKEA; Indiana University; Laura and John Arnold Foundation; Johns Hopkins University; Law Offices of Ronald Marron; MD Anderson Cancer Center; Medical College of Wisconsin; National Institutes of Health (NIH); Sage Publishing; The Obesity Society; Tomasik, Kotin & Kasserman LLC; University of Alabama at Birmingham; University of Miami; Nestle; WW (formerly Weight Watchers International, LLC). Donations to a foundation have been made on his behalf by the Northarvest Bean Growers Association. Dr. Allison is an unpaid member of the International Life Sciences Institute North America Board of Trustees. Dr. Allison’s institution, Indiana University, has received funds to support his research or educational activities from as follows: NIH; Alliance for Potato Research and Education; American Federation for Aging Research; Dairy Management Inc.; Herbalife; Laura and John Arnold Foundation; National Cattlemen’s Beef Association, Oxford University Press, the Sloan Foundation, The Gordon and Betty Moore Foundation, and numerous other for-profit and non-profit organizations to support the work of the School of Public Health and the university more broadly. Dr. Allison’s prior institution, the University of Alabama at Birmingham, received gifts, contracts, and grants from other organizations including the Coca-Cola Company, Pepsi, and Dr. Pepper/Snapple. Dr. Brown has received travel expenses from Academy of Nutrition and Dietetics, Alberta Milk, American Heart Association, Danisco, DC Metro Academy of Nutrition and Dietetics, Federation of American Societies for Experimental Biology, International Life Sciences Institute, and National Academy of Sciences; speaking fees from Academy of Nutrition and Dietetics, Alberta Milk, American Society for Nutrition, Birmingham District Dietetic Association, International Food Information Council and Foundation, Kentuckiana Health Collaborative, and Rippe Lifestyle Institute, Inc.; monetary awards from Alabama Public Health Association, American Society for Nutrition, Science Unbound Foundation, and University of Alabama at Birmingham Nutrition Obesity Research Center; consulting fees from CE Outcomes, Epigeum (Oxford University Press), Farmland Foods, and LA NORC; and grants through his institution from Dairy Management, Inc., National Cattlemen’s Beef Association, NIH/NIGMS-NIA-NINDS, and UAB NORC. He has been involved in research for which his institution or colleagues have received: contracts with Communiqué, and PepsiCo Inc.; unrestricted gifts from National Restaurant Association; and grants from Coca-Cola Foundation, Dairy Management, Inc., Gordon and Betty Moore Foundation, Indiana CTSI (Bloomington), National Cattlemen’s Beef Association, NIH/NHLBI, NIH/NIA, NIH/NIDDK, NIH/OD, and Sloan Foundation. The other authors declare no other conflicts of interest.
The opinions expressed are those of the authors and do not necessarily represent those of the NIH or any other organization.
Grant information:
This project received no specific funding. AWB and DBA were supported in part by NIH/NHLBI R25HL124208 and NIH/NIDDK R25DK099080.
Copyright:
© 2021 Bohan Brown MM et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Bohan Brown MM, Milanes JE, Allison DB and Brown AW. Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis [version 3; peer review: 2 approved, 1 approved with reservations]. F1000Research 2021, 9:140 (https://doi.org/10.12688/f1000research.22424.3)
First published: 24 Feb 2020, 9:140 (https://doi.org/10.12688/f1000research.22424.1)
Latest published: 30 Jul 2021, 9:140 (https://doi.org/10.12688/f1000research.22424.3)
Revised Amendments from Version 2
This version updates the discussion and includes a new cumulative meta-analysis in which studies were added sequentially by study duration from longest to shortest to investigate whether study duration was related to effects of breakfast versus skipping.
See the authors' detailed response to the review by Nerys Astbury
See the authors' detailed response to the review by Enhad A. Chowdhury
Introduction
Whether one should eat or skip breakfast for weight control or loss is a topic of continued interest in both the scientific and lay communities. In 20131, we documented how breakfast eating versus breakfast skipping served as an example of how beliefs about diet can go beyond the evidence within and beyond the scientific community. The evidence at the time was dominated by over 90 observational studies – most cross-sectional – leading us to conclude that eating versus skipping breakfast as a strategy for weight was a presumption: a belief “held to be true for which convincing evidence does not yet confirm or disprove their truth”2,3. The limited scientific evidence on the topic has been translated directly to the public. For instance, we noted in our prior paper that the website of the Dr. Oz Show included an article stating, “The fact is, when you’re trying to lose body fat, you can’t skip breakfast”4. More recently, Dr. Oz himself stated, "I think for 2020, the first thing I’m going to do is ban breakfast”5, and using the social media hashtag of #TeamNoBreakfast. Meanwhile, continued scientific interest in the topic is evidenced by many more cross-sectional observational and other studies having been published; more recent narrative review articles summarizing existing literature on the topic6,7; a meta-analysis evaluating breakfast eating versus skipping on weight8 that confirmed our prior registered preliminary analyses9,10; and another group registering an analysis similar to ours after our registration (PROSPERO CRD42018110858; subsequently published11.
With mixed messaging over time about the importance of eating or skipping breakfast for the ongoing obesity epidemic, and with continued interest in the topic both scientifically and generally, it is important to synthesize the causal evidence on the effect of breakfast eating versus skipping on obesity and related outcomes, rather than relying on weaker study designs or popular opinion.
Since our earlier summaries, additional RCTs have been conducted and published (as reviewed herein). Herein, we extend our prior work to synthesize causal evidence from RCTs on eating versus skipping breakfast in humans on all reported obesity-related anthropometric outcomes we were able to extract from relevant literature.
Methods
Registration
Our study was registered with the PROSPERO international prospective register of systematic reviews (CRD42016033290) on 21 JAN 2016. The initial registration limited papers up to the registration date; however, because of the time between initial registration and this manuscript, the search was updated to 02 JAN 2020 (see Search and review strategy, below). Earlier versions of this work were published as abstracts for the American Society for Nutrition’s Annual Meeting and Scientific Sessions9,10.
Inclusion and exclusion criteria
Inclusion criteria were:
the study had at least one breakfast skipping condition and one breakfast eating condition regardless of modality (e.g., whether recommended or provisioned);
the study was a randomized, controlled trial (RCT);
study length (i.e., time on intervention) was greater than 72 hr;
participants were normal weight or greater, as defined by original study authors, who did not have diseases that influence weight; and
the study reported weight or other anthropometric outcomes.
Studies were excluded if:
participants had diseases or conditions that affected weight except for obesity, diabetes, and CVD;
breakfast eating versus breakfast skipping were confounded with other intervention components (e.g., study designs that altered intake to ensure individual weight maintenance; multicomponent behavioral interventions including breakfast without matching those components in the skipping group; or enforcing time restricted feeding apart from breakfast skipping).
Search and review strategy
Our first search was completed on 20 JAN 2016, the search refreshed on 26 JAN 2017, and the search finalized on 02 JAN 2020, with results from prior searches being deduplicated from subsequent searches.
In all search phases, searches were executed by using the application programming interfaces (APIs) of AltHealthWatch, CINAHL, Proquest Theses and Dissertations Global, PsycInfo, and Scopus using R (version 3.5.2). The following was used to search Scopus, with analogous search strategies adapted for the other databases:
TITLE-ABS-KEY((Obesity OR obese OR adipose OR adiposity OR overweight* OR "over weight*" OR "weight gain*" OR "weight reduc*" OR "weight los*" OR "weight maint*" OR "weight decreas*" OR "weight control*" OR "weight restrict*" OR "BMI" OR "FMI" OR "BMIz" OR "zBMI" OR "weight percentile" OR "gestational weight" OR "weight for height" OR "waist circumference" OR "skinfold thickness" OR "body composition" OR "body size" OR "fat mass" OR "body fat" OR "body mass" OR "body weight" OR "bodyweight" OR "waist hip ratio") AND (breakfast OR "break fast" OR "morning fasting" OR "morning meal")) AND DOCTYPE(ar OR ip) AND SRCTYPE(j)
Search results across databases were compared for duplication, including by title, abstract, and PubMed ID. Studies with titles and abstracts addressing animals that did not also include words related to human subjects were excluded programmatically. Titles and abstracts were then coded independently by at least two authors for inclusion/exclusion criteria. If both authors excluded a study for violation of any inclusion or exclusion criterion, it was excluded; if at least one did not exclude it, the paper was passed on for full text review.
Meta-analysis
All data and code used to estimate effect sizes and meta-analyses are provided as Extended data at https://doi.org/10.5281/zenodo.497078112. Additional details are included as comments within the code, including exact approaches to estimating each effect size within a study.
Effect sizes comparing breakfast eating versus skipping on each outcome were calculated for each study. Each effect size was calculated as a difference-in-difference in the native units of the outcome (e.g., kg for weight). Only outcomes for which there was more than one effect size were meta-analyzed: body weight, BMI, body fat percentage, fat mass, lean mass, fat free mass, adipose tissue mass, waist circumference, waist:hip ratio, fat mass index, sagittal abdominal diameter, and lean tissue mass. Lean mass, fat-free mass, and lean tissue mass were meta-analyzed together as ‘lean mass’; fat mass and adipose tissue mass were meta-analyzed together as ‘fat mass’. Total body water percentage and muscle mass are both reported only in Ogata et al.13; although muscle mass as an outcome was excluded, Ogata et al. also reported lean mass, which is captured in the pooled lean mass analysis.
Farshchi et al.14 reported pre and post means and standard deviations separately for each treatment period in a two-arm cross-over design. Although the unbiased estimate of the difference-in-difference was calculable from the pre and post means in each condition, the lack of information on the correlation of change within or between conditions precluded us from directly calculating the variance of the effect. We requested summaries from the authors, but the authors informed us they no longer had the raw data given that the paper was published in 2005. Thus, within-condition and between-condition correlations had to be estimated. Sievert et al.8 used a correlation coefficient of 0.3 for post-only values. We chose to estimate within-period change scores based on the within-condition correlation coefficients we estimated from Geliebter et al.15 because Geliebter et al. had all values needed to estimate within-condition, pre-post correlation coefficients. All correlation coefficients from Geliebter were greater than 0.99. Effect sizes were estimated for each outcome. Because Farshchi et al. reported no statistically significant results for any outcome, any statistically significant estimates were recalculated using the largest within-condition correlation that resulted in non-significant effect sizes. This approach may underestimate the variance, which would provide the study more weight in the meta-analysis; however, the leave-one-out analysis described below gives Farshchi the lowest weight possible.
Geliebter et al.15 reported three conditions: skipping, corn flakes, and oat porridge. We used the recommended method of the Cochrane Handbook, which is to “combine multiple groups that are eligible as the experimental or comparator intervention to create a single pair-wise comparison”16. Because we were interested in breakfast eating versus breakfast skipping, the two breakfast conditions were pooled together.
Leidy et al.17 also reported three conditions: skipping, a normal protein breakfast, and a high protein breakfast. We requested summaries from Leidy et al., who graciously provided us with separate group means and standard deviations for the changes. We used the recommended method of the Cochrane Handbook to combine breakfast conditions as described above.
Neumann et al.18 reported three conditions: skipping, high carbohydrate breakfast, and high protein breakfast. Again, we used the method recommended by the Cochrane Handbook to combine breakfast conditions. Neumann et al. reported individual-level data in their supplementary table. While reviewing the values in their supplement, we found some results to be implausible. We reached out to the authors, who clarified several subjects’ data. For our analysis, we used the updated values, and include the final updated dataset in our supplement.
Schlundt et al.19 reported follow-up data at 6 months, but the methods descriptions were unclear as to whether the interventions to eat or skip breakfast were continued past the 12-week intervention. Authors were contacted about this detail and for additional outcomes data at 12 weeks that were either not directly reported or reported as no significant strata (i.e., habitual breakfast eaters or skippers) or treatment effects; the authors informed us they no longer had the raw data given the study was published in 1992. We therefore chose to only use the change in body weight data from 12-weeks. Independent effect sizes were estimated for habitual breakfast eaters and habitual breakfast skippers.
Dhurandhar et al.20 reported body weight for the completers-only analysis in their paper. Because they registered their study as also measuring BMI, and because of the mention of an intention to treat analysis, we contacted the authors (one of whom, DBA, is a coauthor on the present meta-analysis), who provided us with summary data. Note that they also had a third group, in which participants received no specific breakfast eating or breakfast skipping recommendations; we limited our analysis to the intention to treat analyses of the breakfast eating and breakfast skipping groups. Independent effect sizes were estimated for habitual breakfast eaters and habitual breakfast skippers.
LeCheminant et al.21 were contacted for estimates of change over time for data in their Table 3. The authors graciously provided estimates of change within each group for each outcome. The data used herein, as shared by the authors, differs slightly from their publication because of increased precision and because of a reporting error in which percent body fat did, in fact, have a small but non-significant increase in the no breakfast group. This error does not change the results of their study, but the corrected values are used herein.
Ogata et al.13, Betts et al.22, and Chowdhury et al.23 effect sizes were calculated with routine equations.
Meta-analyses were calculated using the metafor package (version 2.1-0) in R. Each of 12 independent effects sizes (10 papers; 2 stratified by baseline habit) were included in each analysis as possible, depending on which outcomes were reported in which studies. Random effects analyses were calculated; no fixed effects analyses were calculated because design heterogeneity made the assumption of effect sizes being part of a homogenous distribution tenuous. The adjustment by Knapp and Hartung24 was used given the relatively small number of effect sizes. Two effect sizes were derived from separate papers of the Bath Breakfast Project (BBP; Betts et al. and Chowdhury et al.). Because these were independent samples (normal or with obesity) we treated them as independent even though they came from the same overarching study. Similarly, although there is plausibly some correlation amongst effect sizes calculated within the habit strata in Dhurandhar et al. and Schlundt et al. by nature of being part of the same overarching study, we treated the effect sizes as independent.
Leave-one-out analysis was used as a sensitivity analysis to investigate the influence of any single study for each outcome, in which each study was omitted from the dataset at a time, and then the meta-analysis was recalculated.
Effect estimates are displayed as mean differences with 95% confidence intervals in the native units of the outcome. I2 (%) and p-values for tests of heterogeneity are also reported. No multiple-comparison corrections are applied within or among outcomes. There are few effect sizes (k=12), there is substantial design heterogeneity (e.g., study length, types of breakfast, different populations), and there is statistical heterogeneity in several outcomes; therefore, funnel plot asymmetry is not presented because visual estimation of asymmetry is unreliable for small k25, the test is underpowered for small k26, and any association between effect size and variance may plausibly be explained by study design or other factors rather than just publication bias26.
We also calculated secondary analyses. First, we compared habitual breakfast eaters versus habitual breakfast skippers, as defined by the authors of the published papers. This subgroup analysis completed the preliminary analyses we initially published as abstracts9,10. If baseline habits were not reported, or if authors reported that the baseline habits of the participants were mixed, the study arms were excluded. Second, we conducted a cumulative meta-analysis, sequentially adding studies from the longest (16 weeks) to the shortest (6 days) to address concerns that breakfast effects may differ based on study duration. The secondary analyses were based on the same effect size estimates generated for the primary analysis. The same functions were used in R, except for modeling the outcome variable as a function of both assigned condition and baseline breakfast habits, or sequentially adding effect sizes, respectively. Additional methods can be found in the subgroup analysis document and code located in https://doi.org/10.5281/zenodo.4970781.
Risk of bias
Risk of bias was assessed independently by two investigators (MMBB/JEM for all but Ogata 2019 and MMBB/AWB for Ogata 2019) using Cochrane’s Risk of Bias Tool27. Given that the interventions are obvious to participants (eating versus skipping breakfast), we only coded blinding of personnel, and readers should be aware of the risk of non-blinded interventions. We do not use the approach of assigning a binary risk of bias to an entire study (e.g., if one criterion is high risk in a study, the entire study is considered high risk); however, we provide the individual ratings for each article and readers can apply such an approach if they wish.
Results
PRISMA diagram
The search results are shown in the PRISMA diagram in Figure 1. The results of each of the three phases of the search are shown.
Figure 1. PRISMA diagram.
Three searches were undertaken. For searches 2 and 3, the numbers in parentheses represent unique results to that search. *Several ‘papers from other sources’ were identified in prior searches, but those papers were captured by the third search.
Inclusion table
Ten papers were included with 12 effect sizes (see Table 1 for descriptions). Briefly, of the 10 studies included: six were conducted in the United States, three in the United Kingdom, and one in Japan; two were cross-over RCTs and eight were parallel arm RCTs; length ranged from 6 days to 16 weeks; five provisioned some or all foods and five were recommendations for dietary consumption; two stratified on baseline eating or skipping habits, two included only habitual breakfast eaters, three included only habitual breakfast skippers, two reported mixed baseline habits, and one did not specify baseline habits; four reported race/ethnicity of participants; four included females only, one included males only, and five included both females and males. For breakfast definitions, dietary compositions, and timing, see Table 1 and Figure 2. Breakfast definitions and timing of consumption varied amongst the studies included and ranged from highly controlled and prescribed to broad recommendations (Figure 2).
Table 1. Included studies.
| Study | Location | Population | Baseline BMI ± SD (*SEM) | Weight-related inclusion criteria and context | Age (Mean ± SD)1 | Race/Ethnicity2 | Intervention | Provision of Food | Baseline Breakfast Habits (Eaters vs Skippers) | Breakfast Eating and Breakfast Skipping Definitions3 | Dietary Composition3 | Weight-related anthropometric measures preregistered as primary or secondary outcome | Weight-related anthropometric measures reported4 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Betts 2014 | UK | Adults: n=33 64% Female 21 – 60 y | All: 22.4 ± 2.2 BF: 22.0 ± 2.2 Skip: 22.8 ± 2.3 | • Weight stable (1 kg past 6 mo) • DXA fat mass index ≤11 kg/m2 women; ≤7.5 kg/m2 men | All: 36 ± 11 y BF: 36 ± 11 y Skip: 36 ± 11 y | Not reported | 6 wk parallel arm RCT Recommendation to eat or skip breakfast | No | Mixed | Breakfast group: consume energy intake of ≥700 kcal before 1100h daily, with at least half consumed within 2 h of waking Fasting group (skip): Extend overnight fast by abstaining from ingestion of energy- providing nutrients (plain water only) until 1200 h each day. | No recommendation for the diet was given. | Yes: ISRCTN31521726 | BW, BF%, BMI, ATM, FMI, LTM, SAD, WC, WHR |
| Chowdhury 2016 | UK | Adults: n=23 65% Female 21 – 60 y | All: 33.7 ± 4.9 BF: 35.4 ± 6.1 Skip: 31.9 ± 2.3 | • Weight stable (2% past 6 mo) • DXA fat mass index ≥13 kg/m2 women; ≥9 kg/m2 men | All: 44 ± 10 y BF: 44 ± 10 y Skip: 44 ± 10 y | Not reported | 6 wk parallel arm RCT Recommendation to eat or skip breakfast | No | Mixed | Breakfast group: consume energy intake of ≥700 kcal before 1100h daily, with at least half consumed within 2 h of waking Fasting group (skip): Extend overnight fast by abstaining from ingestion of energy- providing nutrients (plain water only) until 1200 h each day. | No recommendation for the diet was given. | Yes: ISRCTN31521726 | BW, BF%, BMI, ATM, FMI, LTM, SAD, WC, WHR |
| Dhurandhar 2014 | USA | Adults: n=185 76% Female 20 – 65 y | Not reported | • Interested in weight loss; no weight- reduction program past 3 mo; no weight loss/gain >= 5% bw past 6 mo • BMI > 25 but < 45 | BF: 40.6 ± 12.0 y Skip: 42.0 ± 12.4 y | Total: WHN: 93, BNH:74, WH:17, BH:8, O:12 Breakfast: WHN: 45, BNH:40, WH:5, BH:5, O:6 Skip: WHN: 48, BNH:34, WH:12, BH:3, O:6 | 16 wk parallel arm RCT Recommendation to eat breakfast, skip breakfast, or neither (control group); all three treatment groups were given a USDA pamphlet suggesting good nutrition habits in baseline skippers and eaters | No | Stratified | Breakfast Eating: meal before 1000h. Skipping: no eating or caloric consumption prior to 1100 h. | The breakfast group received the USDA pamphlet with a handout instructing participants to consume breakfast before 1000 h every day. The breakfast handout also provided suggestions of food items that might constitute a healthy breakfast; however, no specific restrictions were given on types of foods that could be consumed for the breakfast meal. The skipping group received the USDA pamphlet with a handout instructing participants not to consume any calories before 1100 h every day, and that only water or zero-calorie beverages could be consumed from the time of waking until 1100 h. No specific composition was recommended. | Yes: NCT01781780 | BW, BMI |
| Farshchi 2005 | UK | Adults: n=10 100% Female 19 – 38 y | All: 23.2 ± 1.6 | • Not dieting (score >30 on The Eating Inventory) • "Lean" | Total: 25.5 ± 5.7 y | Not reported | 2 wk per condition, cross- over RCT Intervention program to eat or skip breakfast | Breakfast and one snack | Habitual eaters | Breakfast between 0700h and 0800h. Skipping nothing prior to 1030 h. | Breakfast group consumed a pack (45 g) of whole-grain cereal with 200 mL 2% milk between 0700 h and 0800 h. and consumed a chocolate- covered cookie between 1030 h and 1100 h. Skippers had nothing prior to both groups consuming a 48-g chocolate- covered cookie between 1030 h and 1100 h. Skippers then had the cereal and 2%-fat milk between 1200 h and 1230 h. Both groups then consumed 2 additional meals and 2 snacks of content similar to usual during the times of 1330–1400, 1530–1600, 1800–1830, and 2030–2100. Subjects were asked to consume their main evening meal (dinner) between 1800 and 1830. | Not registered | BW, BF%, BMI, WC, WHR |
| Geliebter 2014 | USA | Adults: n=36 50% Female 8 – 65 y | All: 32.9 ± 4.7 | • Weight stable (<5% in 3 mo); not currently or intending weight-loss diet or exercise program • BMI > 25 | Total sample: 33.9 ± 7.5 y M:35.6 ± 6.1 y F: 32.3 ± 8.6 y | Total: W:16, B:10, H:6, A:3, O:3 Skip: W:4, B:3, H:3, A:1, O:1 C: W:6, Breakfast:3, H:2, A:2, O:1 P: W:6, B:4, H:1, A:0, O:1 | 4 wk parallel arm RCT Recommendation to skip breakfast compared to provision of high fiber (oat porridge) and non-fiber (cornflakes) breakfasts | Breakfast only | Unspecified | 0830 h arrival weekdays with 15 min given to consume breakfast or water for skip group. Breakfasts were given to take home for weekends with no time given on weekends | No recommendation for the remainder of the diet was given. | Registered after: NCT02035150 | BW, FFM, FM, WC, WHR |
| LeCheminant 2017 | USA | Adults: n=49 100% Female 18 – 55 y | BF: 22.6 ± 3.9 Skip: 21.5 ± 3.9 | • Weight stable (3 mo); not dieting or excessive activity • "Apparently healthy as indicated by a health history questionnaire" | BF: 23.7 ± 7.5 y Skip: 23.6 ± 5.0 y | Not reported | 4 wk parallel arm RCT Recommendation to eat or skip breakfast in habitual skippers | No | Habitual skippers | Breakfast group to eat within 1.5 h of awakening and consume 15% total energy intake for the day by 0830 h. Skippers were defined as not consuming a snack or meal (only noncaloric beverages) until after 1130 h. | No recommendation for the remainder of the diet was given. Both groups asked to wake up by 0800. | Not registered | BW, FM, LM, BF%, BMI |
| Leidy 2015 | USA | Adolescent: n=54 57% Female 19 y (mean) | Skip: 29.1 ± 6.3 Normal Protein: 29.3 ± 4.8 High Protein: 30.3 ± 3.7 | • Not currently/ previously on a weight loss diet • BMI 25-39.9 | Skip:19 ± 1 y Normal Protein BF: 18 ± 1 y High Protein BF: 19 ± 1 y | Total: W:33, B:19, O:2 Skip: W:6, B:3, O:0 Normal Protein: W:16, B:5, O:0 High Protein: W:11, B:11, O:2 | 12 wk parallel arm RCT Recommendation to skip breakfast compared to the provision of normal protein and high protein breakfasts in habitual skippers | Breakfast only | Habitual Skippers | Breakfast consuming groups were provided with specific breakfast meals with consumption of breakfast between 0600 h and 0945 h each day. The skipping group continued to skip breakfast (only water) before 1000. | The NP meals contained 15% protein, 65% carbohydrates, and 20% fat and consisted of ready-to-eat cereals with milk. The HP meals contained 40% protein, 40% carbohydrates, and 20% fat and consisted of egg-based pancakes and ham; egg-based waffles with pork-sausage; egg and pork scramble; and an egg and pork burrito. The breakfast meals were provided on a weekly basis with meal preparation instructions. Breakfasts were 18% of total dietary calories. No recommendation for the remainder of the diet was given. | Not registered | BW, FM, LM, BF%, BMI |
| Neumann 2016 | USA | Adults: n =24 100% Female 11 – 36 y | Skip: 27.8 ± 2.2* Carbohydrate: 26.0 ± 1.9* Protein: 26.6 ± 2.1* | • Not trying to lose weight/ restricting eating, not lost weight in 6 mo • Not reported | Skip: 27.1 ± 1.8 y Carbohydrate BF: 21.9 ± 0.9 y Protein BF: 23.3 ± 1.3 y | Skip: C:5, H:1, B:1, A:0, I:1 Carbohydrate: C:3, H:1, B:1, A:2, I:1 Protein: C:6, H:1, B:1, A:0, I:0 | 8 d parallel arm RCT Assignment to skip or eat breakfast with provision (breakfast or water) in habitual skippers | Breakfast only | Habitual skippers | Breakfast group: eat breakfast before or at the start of daily activities and within two hours of waking with consumption typically occurring no later than 1000 h. Skipping group: provided water with no other instructions given. | Breakfast: CHO breakfast consisted of 1 English muffin (57 g), yogurt (170 g), cream cheese (17g), and water (227 mL). The PRO breakfast consisted of a proprietary breakfast sandwich (145 g), Greek yogurt (150g), and water (227 mL). Both test breakfasts were similar in kilocalories and controlled for fat and fiber. Skipping group was provided water (227 mL). No recommendation for the remainder of the diet was given. | Not registered | BW, BMI |
| Ogata 2019 | Japan | Adult: n=10 0% Female 20 – 30 y | BF to Skip: 23.1 ± 3.7 Skip to BF: 23.8 ± 1.5 | • Not reported • "Healthy males" | BF to Skip: 24.8 ± 2.9 y Skip to BF: 25.6 ± 3.0 y | Japanese:10 | 6 d per condition, cross-over RCT Intervention to eat or skip breakfast | All food | Habitual eaters | Breakfast eating group consumed breakfast at 0700 h, breakfast skipping group nothing prior to lunch at 1230 h. | Breakfast eating group had 33.3% of daily energy intake for each of the three meals of breakfast (0700 h), lunch (1230 h) and dinner (1800 h). The breakfast skipping group had 0% for breakfast, 50% of daily energy intake each for lunch (1230 h) and dinner (1800h). The 24-h energy intake was equal for both dietary conditions. The meals provided were individually adjusted (3042 ± 598 kcal/d, 14% protein, 25% fat, and 61% carbohydrates). | Yes: UMIN000032346 | BW, BF%, FM, FFM, MM, TBWP |
| Schlundt 1992 | United States | Adults: n= 45 100% Female 18 – 55 y | All: 30.6 ± 0.5 | • Interested in weight-loss research; no weight loss of > 4.5 kg past 1 mo or > 9 kg past 6 mo • 30–60% above ideal body weight from Metropolitan Life Tables | Only range stated | Not reported | 12 wk parallel arm RCT Baseline breakfast eaters and skippers were assigned to either eat or skip breakfast with total diet composition and caloric content same between groups | No | Stratified | Menus and instructions for 3 meals (breakfast, lunch and dinner) or 2 meals (lunch and dinner), timing not specified in the paper. | Total dietary composition: 50–55% of energy from carbohydrates, 15–20% from protein, and 25–30% from fats. No- breakfast diet consisted of two meals, lunch (1672 kJ) and supper (3344 kJ). Breakfast diet consisted of three meals, breakfast (1672 kJ), lunch (1254 kJ), and supper (2090 kJ). | Not registered | BW |
1BF, Breakfast.
2A, Asian; B, Black; BH, Black Hispanic; BNH, Black Non-Hispanic; C: Caucasian; H, Hispanic; I, Indian; O, Other; W, White; WH, White Hispanic; WNH, White Non-Hispanic.
3Definitions paraphrased from each study paper.
4ATM, adipose tissue mass; BF%, body fat percentage; BW, body weight; FFM, fat-free mass; FM, fat mass; FMI, fat mass index; LM, lean mass; LTM, lean tissue mass; MM, muscle mass; SAD, sagittal abdominal diameters; TBWP, total body water percentage; WC, waist circumference; WHR, waist:hip ratio. Some additional outcomes might have been mentioned in the paper, but quantitative results may not have been reported after the intervention.
Figure 2. Schematic of breakfast versus skipping timing and patterns.
The top section outlines the patterns for the included studies; the middle section shows a few examples of studies we did not classify as eating versus skipping breakfast that are explained further in the ‘Notable Exclusions’ section and in Table 3; and the bottom is a legend for the figure. ‘Inferred eating window’ represents the times we inferred that participants were permitted or recommended to consume food as reported in the papers; ‘specified eating window’, ‘breakfast eating window’, and ‘assigned eating times’ were reported by the authors in either absolute or relative times (e.g., number of hours since waking). For more details for the included studies, see Table 1.
Meta-analyses of anthropometric outcomes
Figure 3 shows a composite forest plot that includes all meta-analyzable, obesity-related, anthropometric outcomes. In all cases, the 95% confidence intervals included the null of no differences between skipping and eating breakfast (frequently interpreted as “not statistically significant”). Table 2 shows the numerical estimates of the values displayed in the forest plots. Therefore, no discernible effects of breakfast eating or breakfast skipping on body weight (kg), BMI (kg/m2), body fat percentage (%), fat mass (kg), lean mass (kg), waist (cm), waist:hip ratio, sagittal abdominal diameter (cm) and fat mass index (kg/m2) were found in these primary analyses.
In the secondary analysis comparing habitual breakfast eaters versus habitual breakfast skippers (as defined by the authors of the published papers), forest plots and a summary table can be found in the subgroup between group forest plots document and subgroup between group summary table located in https://doi.org/10.5281/zenodo.4970781. Briefly, there was no discernible effect of stratification of baseline habits on four of the outcomes: body weight (4 effect sizes for habitual breakfast eaters; 4 effect sizes for habitual breakfast skippers), body fat percentage (2 and 2 effect sizes, respectively), fat mass (1 and 2 effect sizes, respectively), or lean mass (1 and 2 effect sizes, respectively). Insufficient study arms existed to test differences for waist circumference, waist:hip ratio, sagittal abdominal diameter, and fat mass index. Only BMI was statistically significant (2 and 4 effect sizes, respectively). The negative estimate of –0.36[-0.65,-0.07] BMI units indicates that individuals assigned to conditions consistent with their baseline habits had lower values than those assigned opposite of their habits. That is, habitual breakfast skippers had lower BMI when skipping breakfast and habitual breakfast eaters had lower BMI when eating breakfast, compared to skippers eating and eaters skipping.
For the cumulative meta-analysis, in which studies were sequentially added from longest to shortest, a forest plot for body weight and BMI are included in the supplementary material at https://doi.org/10.5281/zenodo.4970781. Only when studies were 4 weeks and longer were results statistically significant in favor of skipping breakfast (0.57[0.08,1.07] kg and 0.16[0.03,0.30] kg/m2, respectively). Results were not statistically significant when including only studies longer than 4 weeks or adding studies shorter than 4 weeks, with point estimates spanning both sides of the null.
Figure 3. Composite forest plot of seven meta-analyzable anthropometric outcomes.
Sagittal abdominal diameter and fat mass index were only included in the two papers from the Bath Breakfast Project (Betts et al. and Chowdhury et al.), and are not plotted here; outcomes of muscle mass and total body water percent were only included in Ogata et al., and so no meta-analyzable estimate was possible. See Table 2 for the numerical values of these seven analyses, plus the sagittal abdominal diameter and fat mass index. Studies without point estimates and confidence intervals within an outcome indicates that the study did not report that outcome. 95% confidence intervals for individual studies and for the width of the diamond representing the summary estimate are presented. Horizontal dotted lines for the summary of the meta-analyses represents the 95% prediction interval. For the column ‘Habit’: e, habitual eaters; s, habitual skippers; u, unspecified or mixed. Values to the left of 0 indicate that the breakfast condition had a greater decrease in the outcome relative to the skipping condition. For all outcomes except lean mass, values to the left would therefore traditionally be considered 'favoring' the breakfast condition (e.g., lower body weight)
Table 2. Effect sizes for each study and meta-analyzable anthropometric outcome shown in Figure 3.
Data are presented as mean [95% CI] for each study and the summary estimate, expressed as mean difference. Positive values are higher during breakfast conditions. n represents the total number of individuals within a study; k is the number of effect sizes in a meta-analytic estimate; MD is mean difference; I2 represents heterogeneity, with the associated p-value representing the statistical test for significant heterogeneity. Outcomes of muscle mass and total body water percent were only included in Ogata et al., and so no meta-analyzable estimate was possible.
Table 3. Notable studies that were excluded with reasons.
| Study | Reason for exclusion* | Notes |
|---|---|---|
| Alwatter 201532 | No weight or anthropometry | Adolescent girls |
| Frape 199733 | No weight or anthropometry | Adults |
| Gwin 201834 | No weight or anthropometry | Adults |
| Halsey 201235 | No weight or anthropometry | Adults |
| Hoertel 201436 | No weight or anthropometry | Adolescent girls |
| Leidy 201337 | No weight or anthropometry | Adolescent girls |
| Reeves 201438 | No weight or anthropometry | Adults |
| Reeves 201539 | No weight or anthropometry | Adults |
| Rosi 201840 | Less than 72 hr | Adult men; no weight |
| Yoshimura 201741 | Less than 72 hr | Adult women; one-day study |
| Zakrewski-Frue 201742 | Less than 72 hr | Adolescent girls; only baseline weight |
| Carlson 200743 | Not about breakfast | Adults; did not include weight outcomes; compared 1 vs 3 meals per day with weight being deliberately maintained (see Figure 2) |
| Hirsch 197529 | Not about breakfast | Adults; dinner only versus breakfast only (see Figure 2) |
| Keim 199744 | Not about breakfast | Adult Women; distribution of calories as 70% morning versus 70% evening |
| Tinsley 201945 | Not about breakfast | Adult women; time-restricted feeding versus not (see Figure 2) |
| Wehrens 201728 | Not about breakfast | Adult men; non-randomized order; all meals (not just breakfast) shifted 5 hours (see Figure 2) |
| Ask 200646 | No skipping condition | Children; quasi-experiment |
| Crepinsek 200647 | No skipping condition | Children |
| Douglas 201948 | No skipping condition | Adolescent girls |
| Jakubowicz 201249 | No skipping condition | Adults |
| Powell 199831 | No skipping condition | Children |
| Rosado 200830 | No skipping condition | Children |
| St Onge 201550 | No skipping condition | Children |
| Versteeg 201751 | No skipping condition | Adult men |
| Zakrewski-Frue 201852 | No skipping condition | Adolescent girls; breakfast skipping was alternate day skipping; no weight beyond baseline |
| Chowdhury 201953 | Data published elsewhere | BBP: weight data in Chowdhury 2016 |
| Gonzalez 201854 | Data published elsewhere | BBP: weight data in Betts 2014 and Chowdhury 2016 |
| Tuttle 195455 | Confounded design | Boys, men, and women; non-counterbalanced cross- over; some participants were assigned to gain or lose weight |
Risk of bias
Risk of bias varied by study (Figure 4). Two studies had low risk of bias across all categories: Dhurandhar 2014 and Ogata 201913. Two studies, Betts 201422 and Chowdhury 201623, were coded as high risk of bias for the criterion of blinding participants and personnel because the authors clearly indicated that personnel were not blinded. Given that the interventions are obvious to participants (eating versus skipping breakfast), we only focus on blinding of personnel, and readers should be aware of the risk of non-blinded interventions. On the other hand, many of the categories in the risk of bias in each study were unclear, and it is therefore uncertain whether the risk was high or low.
Figure 4. Risk of bias assessment.
Each included paper was assessed for risk of bias using the Cochrane Risk of Bias tool. Given that the interventions are obvious to participants (eating versus skipping breakfast), we only coded blinding of personnel, and readers should be aware of the risk of non-blinded interventions.
Sensitivity analysis: Leave-one-out analysis
The leave-one-out analysis is shown in Figure 5. Little difference is noted among the analyses, with substantial overlap of confidence intervals in all cases. When considering statistical significance (i.e., confidence intervals that do not include 0), leaving Farshchi et al.14 out of the analysis results in significantly greater BMI in the breakfast conditions than the skipping conditions. When Leidy et al.17 is excluded, fat mass is greater in the breakfast than the skipping conditions. Waist:hip ratio is centered on zero with no estimable confidence interval when Chowdhury et al.23 is left out because the other three estimates are all 0.00. We reiterate that none of these summaries took multiple comparisons into account.
Figure 5. Leave-one-out analysis.
Within each column, the diamond represents the meta-analytic summary estimate when leaving out the study in a particular row. Row and column combinations without diamonds represent outcomes that are not reported for that particular study. *The waist:hip ratio had no estimable confidence interval because the three remaining estimates were all 0.00. Sagittal abdominal diameter and fat mass index were only included in the two papers from the Bath Breakfast Project (Betts et al. and Chowdhury et al.), and therefore a leave-one-out analysis would include only a single study; outcomes of muscle mass and total body water percent were only included in Ogata et al., and so a leave-one-out analysis is not possible.
Notable exclusions
Notable exclusions are located in Table 3. Broad areas to note are the lack of a skipping group for comparison to breakfast groups, intervention periods that were less than 72 hr in duration, studies that had the comparison of interest but did not measure body weight, and studies whose primary purpose did not isolate breakfast eating versus breakfast skipping, such as time restricted feeding and shift in consumption periods. Two examples of the latter include Wehrens et al.,28 who shifted all meals by 5 hours (as well as not being in a randomized order), to extreme time restriction of Halberg et al.29 who assigned only breakfast or dinner (Figure 2).
In this meta-analysis, our included studies were all conducted in adults/adolescents, but, as noted in Table 3, there have been several related studies conducted in children; however, none of the studies in children had a true skipping group. For instance, Rosado et al.30 had a control group with no intervention, which is not equivalent to assigning children to skip breakfast. Similarly, Powell et al.31 did have a group that was assigned to consume a slice of orange as an attention placebo control, but again the children were not assigned to otherwise skip breakfast.
Discussion
Summary
The causal effect of eating versus skipping breakfast on obesity-related anthropometric outcomes was non-significantly different from zero across body weight, BMI, body fat percentage, fat mass, lean mass, waist circumference, waist:hip ratio, sagittal abdominal diameter, and fat mass index. Our results largely match our prior analyses9,10, as well as the analysis of body weight conducted by Sievert et al.8.
The choices of inclusion/exclusion criteria, adjustments, and assumptions to use when meta-analyzing data can influence the interpretation of results, so we highlight some of our choices here. Our choice of including studies greater than 72 hours in duration (with the shortest actually included being 6 days) should avoid including experiments measuring only transient differences from hydration, glycogen, or other very acute physiological responses. Furthermore, this approach allowed us to collate all relevant experiments and enables readers to reanalyze our results with their preferred study duration cutoff should the reader so choose.
We chose to define breakfast eating versus breakfast skipping according to the intentions of the original study authors where possible. This was primarily done because a common recommendation is to eat breakfast to lose weight without any other qualifications. Similarly, skipping breakfast is sometimes suggested to be an antecedent to weight gain, and thus regular consumption of breakfast has been recommended in lay discussions as a form of primary prevention of obesity. We therefore included studies that 1) may or may not have had a focus on weight loss or active prevention of weight gain, 2) included a range of baseline participant weight characteristics, and 3) included a variety of breakfast compositions, timings, and definitions. Indeed, as one reviewer noted, “Many of the studies included in this and other SR [systematic reviews] are small, and are often conducted in participants with a mixed usual breakfast habit who are not overweight or attempting to limit their energy intake or reduce their body weight.” We therefore cannot rule out the possibility that breakfast eating versus skipping may result in different effects in a weight-gain-prevention versus weight-loss context, or for people in different weight classes. The data from included studies is insufficient for us to test these additional hypotheses.
Furthermore, we cannot rule out that there may be some statistically significant combination of studies, subgroups, splitting-versus-pooling of different breakfasts, or different imputation strategies. However, we note that the results are fairly consistently centered near zero. We also note that Sievert et al.8 and Bonnet et al.11 both concluded small but statistically significant differences in favor of breakfast skipping. Each review included a different subset of studies, predominantly driven by duration of studies included. For one specific point of comparison, Sievert et al. used a different imputation strategy than we did for Farshchi et al. We estimated the correlations based on the estimates from Geliebter et al., while constraining the interval to the statistically non-significant results. Bonnet et al. did not include Farshchi et al. because it was too short (2 weeks) for their cutoff of 4 weeks. Our cumulative analysis similarly concluded a statistically significant difference only when studies of 4 weeks and longer were included for body weight and BMI (but not when shorter studies were included, or only studies longer than 4 weeks were included).
Our leave-one-out analyses, produced only two values that became statistically significantly different in favor of skipping breakfast: BMI when Farshchi et al. was excluded, and fat mass when Leidy et al. was excluded.
The subgroup analysis of baseline breakfast habits included few effect sizes to estimate interaction effects between baseline breakfast habits and breakfast assignment. We could test for differences in 5 of the outcomes. Of these, BMI showed a significant interaction between baseline habits and assigned condition. The statistical significance of these secondary estimates should be considered within the following contexts. In the leave-one-out analyses, the 95% confidence intervals did not differ substantially from other leave-one-out analyses. In the subgroup analyses, few effect sizes were available for comparison. There were 4 treatment effect estimates each for habitual breakfast eaters and habitual breakfast skippers for body weight, while there were only 2 and 4, respectively, for BMI. Some outcomes had only 1 and 2 effect estimates for eaters and skippers, respectively. In all cases, we did not adjust for multiple comparisons because of their exploratory nature. Given the small departure of the confidence intervals from 0, it is likely these would no longer be statistically significant if multiple comparisons were considered. Even if effects turned out to be non-zero, the 95% confidence and prediction intervals of the outcomes include effect sizes of low clinical significance, and thus further work would be needed to determine if non-zero effects are actually clinically meaningful.
Despite this relative consistency in summary effect sizes, we note that there was substantial design heterogeneity. The length of studies, for instance, varied substantially. To be confident in effects of obesity-related interventions, longer term studies are desired. However, the need for longer-term studies is often to guard against concluding that early effects (weeks to months) will result in sustained weight loss over months to years. Given the overall null findings herein, suggesting a need for longer studies would serve to test whether these relatively acute null findings reflect long-term adaptations to establishing breakfast habits. In addition, some have argued that it is not merely eating versus skipping breakfast that is important, but rather that the type of breakfast matters (c.f., Leidy et al. 20167). Such an argument does not invalidate the question asked or the findings of this meta-analysis, however. If, for instance, a breakfast of a particular characteristic is what influences weight – be it fiber content, protein, energetic load, timing from waking, or others – then the question would not be whether eating versus skipping breakfast matters; rather, research would need to test the effects of that particular breakfast versus comparator groups, whether those comparator groups be different breakfasts or no breakfast at all. Similarly, whether effects exist that depend on baseline anthropometry, or in different contexts (e.g., weight loss versus prevention of weight gain), also may warrant further study.
We clarify that our results are limited to obesity-related anthropometric outcomes. As stated previously, “Just because breakfast consumption may not have a statistically significant effect on weight does not make breakfast a bad recommendation”56, nor does it necessarily make it a good recommendation. Our results do not inform whether eating versus skipping breakfast is of value for blood glucose control (c.f.,57, cardiometabolic risk factors (c.f.,11,15), school performance (c.f.,58, or other outcomes; nor do our results inform the effects of eating versus skipping breakfast as part of a multicomponent behavioral/intensive lifestyle intervention or time restriction paradigm (e.g., early vs late time-restricted feeding). The inference of the results of this study are limited to the broad and simplistic recommendation to eat or skip breakfast to affect anthropometric outcomes.
Conclusion
There was no discernible effect of eating or skipping breakfast on obesity-related anthropometric measures when pooling studies with substantial design heterogeneity and sometimes statistical heterogeneity in our primary analyses.
Data availability
Underlying data
All data underlying the results are available as part of the article and no additional source data are required.
Extended data
Zenodo: Supplemental files for "Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis.". https://doi.org/10.5281/zenodo.497078112.
This project contains the following extended data:
calculations.R (calculates individual effect sizes for each study)
metaanalysis with subgroup.R (reproduces the composite forest plot, leave-one-out plot, the summary table, and the subgroup analyses)
neumann2016.csv (contains the raw data from Neumann 2016 with authors’ corrections)
rho estimates for farshchi.R (uses data from Geliebter et al. to estimate within-condition pre-post correlations)
Subgroup analysis - methods and results.pdf (provides the methods, results, summary table, and forest plots for the subgroup interaction estimates between baseline habits and assigned breakfast conditions)
Reporting guidelines
Zenodo: PRISMA checklist for "Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis". https://doi.org/10.5281/zenodo.497078112.
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
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CITE
how to cite this article
Bohan Brown MM, Milanes JE, Allison DB and Brown AW. Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis [version 3; peer review: 2 approved, 1 approved with reservations]. F1000Research 2021, 9:140 (https://doi.org/10.12688/f1000research.22424.3)
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A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Version 3
VERSION 3
PUBLISHED 30 Jul 2021
Revised
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How to cite this report:
Bailey B. Reviewer Report For: Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis [version 3; peer review: 2 approved, 1 approved with reservations]. F1000Research 2021, 9:140 (https://doi.org/10.5256/f1000research.57931.r122860)
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NOTE: it is important to ensure the information in square brackets after the title is included in this citation.
Reviewer Report 15 Mar 2022
Approved
VIEWS 8
I am reviewing the manuscript “Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis.” The paper reviews the randomized studies that examine the impact breakfast skipping vs. breakfast eating on weight and
... Continue reading
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HOW TO CITE THIS REPORT
Bailey B. Reviewer Report For: Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis [version 3; peer review: 2 approved, 1 approved with reservations]. F1000Research 2021, 9:140 (https://doi.org/10.5256/f1000research.57931.r122860)
The direct URL for this report is:
https://f1000research.com/articles/9-140/v3#referee-response-122860
https://f1000research.com/articles/9-140/v3#referee-response-122860
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Version 2
VERSION 2
PUBLISHED 15 Oct 2020
Revised
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Astbury N. Reviewer Report For: Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis [version 3; peer review: 2 approved, 1 approved with reservations]. F1000Research 2021, 9:140 (https://doi.org/10.5256/f1000research.29951.r73123)
The direct URL for this report is:
https://f1000research.com/articles/9-140/v2#referee-response-73123
https://f1000research.com/articles/9-140/v2#referee-response-73123
NOTE: it is important to ensure the information in square brackets after the title is included in this citation.
Reviewer Report 03 Dec 2020
Approved with Reservations
VIEWS 29
Although some changes have been made to this manuscript, I do not believe that some of the authors arguments are valid.
If the purpose of the review was to review the literature to determine whether breakfast has ... Continue reading
If the purpose of the review was to review the literature to determine whether breakfast has ... Continue reading
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HOW TO CITE THIS REPORT
Astbury N. Reviewer Report For: Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis [version 3; peer review: 2 approved, 1 approved with reservations]. F1000Research 2021, 9:140 (https://doi.org/10.5256/f1000research.29951.r73123)
The direct URL for this report is:
https://f1000research.com/articles/9-140/v2#referee-response-73123
https://f1000research.com/articles/9-140/v2#referee-response-73123
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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Author Response 30 Jul 2021Michelle M Brown, Department of Applied Health Science, Indiana University School of Public Health - Bloomington, Bloomington, 47405, USA30 Jul 2021Author ResponseWe appreciate Dr. Astbury’s thoughtful comments. The comments reinforce that there are many details about recommendations to eat or skip breakfast that need to be considered beyond simplistic advice, including ... Continue reading
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Author Response 30 Jul 2021Michelle M Brown, Department of Applied Health Science, Indiana University School of Public Health - Bloomington, Bloomington, 47405, USA30 Jul 2021Author ResponseWe appreciate Dr. Astbury’s thoughtful comments. The comments reinforce that there are many details about recommendations to eat or skip breakfast that need to be considered beyond simplistic advice, including ... Continue reading
Version 1
VERSION 1
PUBLISHED 24 Feb 2020
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Chowdhury EA. Reviewer Report For: Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis [version 3; peer review: 2 approved, 1 approved with reservations]. F1000Research 2021, 9:140 (https://doi.org/10.5256/f1000research.24742.r62393)
The direct URL for this report is:
https://f1000research.com/articles/9-140/v1#referee-response-62393
https://f1000research.com/articles/9-140/v1#referee-response-62393
NOTE: it is important to ensure the information in square brackets after the title is included in this citation.
Reviewer Report 29 May 2020
Approved
VIEWS 34
Overview
The work of Bohan Brown et al examines the impact of breakfast skipping versus consumption on obesity related anthropometric measures using meta-analytical methods. The authors have selected a focused question and I found the description of ... Continue reading
The work of Bohan Brown et al examines the impact of breakfast skipping versus consumption on obesity related anthropometric measures using meta-analytical methods. The authors have selected a focused question and I found the description of ... Continue reading
CITE
HOW TO CITE THIS REPORT
Chowdhury EA. Reviewer Report For: Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis [version 3; peer review: 2 approved, 1 approved with reservations]. F1000Research 2021, 9:140 (https://doi.org/10.5256/f1000research.24742.r62393)
The direct URL for this report is:
https://f1000research.com/articles/9-140/v1#referee-response-62393
https://f1000research.com/articles/9-140/v1#referee-response-62393
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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Author Response 15 Oct 2020Michelle M Brown, Department of Applied Health Science, Indiana University School of Public Health - Bloomington, Bloomington, 47405, USA15 Oct 2020Author ResponseWe thank Dr. Chowdhury for the feedback. We address the critiques below:
Regarding Bonnet et al, we have now added the reference. It was published between our first submission ... Continue reading
COMMENTS ON THIS REPORT
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Author Response 15 Oct 2020Michelle M Brown, Department of Applied Health Science, Indiana University School of Public Health - Bloomington, Bloomington, 47405, USA15 Oct 2020Author ResponseWe thank Dr. Chowdhury for the feedback. We address the critiques below:
Regarding Bonnet et al, we have now added the reference. It was published between our first submission ... Continue reading
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38
How to cite this report:
Astbury N. Reviewer Report For: Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis [version 3; peer review: 2 approved, 1 approved with reservations]. F1000Research 2021, 9:140 (https://doi.org/10.5256/f1000research.24742.r62392)
The direct URL for this report is:
https://f1000research.com/articles/9-140/v1#referee-response-62392
https://f1000research.com/articles/9-140/v1#referee-response-62392
NOTE: it is important to ensure the information in square brackets after the title is included in this citation.
Reviewer Report 27 May 2020
Approved with Reservations
VIEWS 38
This is a systematic review of randomised controlled trials comparing the consumption of breakfast with skipping breakfast and follows other similar SR on breakfast eating/skipping.
There has been a long held debate regarding the potential effect of ... Continue reading
There has been a long held debate regarding the potential effect of ... Continue reading
CITE
HOW TO CITE THIS REPORT
Astbury N. Reviewer Report For: Eating versus skipping breakfast has no discernible effect on obesity-related anthropometric outcomes: a systematic review and meta-analysis [version 3; peer review: 2 approved, 1 approved with reservations]. F1000Research 2021, 9:140 (https://doi.org/10.5256/f1000research.24742.r62392)
The direct URL for this report is:
https://f1000research.com/articles/9-140/v1#referee-response-62392
https://f1000research.com/articles/9-140/v1#referee-response-62392
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
-
Author Response 15 Oct 2020Michelle M Brown, Department of Applied Health Science, Indiana University School of Public Health - Bloomington, Bloomington, 47405, USA15 Oct 2020Author ResponseWe thank Dr. Astbury for the feedback. We address the critiques below:
We have added to our discussion to outline more completely some of the limitations and choices we ... Continue reading
COMMENTS ON THIS REPORT
-
Author Response 15 Oct 2020Michelle M Brown, Department of Applied Health Science, Indiana University School of Public Health - Bloomington, Bloomington, 47405, USA15 Oct 2020Author ResponseWe thank Dr. Astbury for the feedback. We address the critiques below:
We have added to our discussion to outline more completely some of the limitations and choices we ... Continue reading
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