Obesity is increasing at alarming rates worldwide( Reference Finucane, Stevens and Cowan 1 ). The media, health professionals and guidelines for health and weight management have postulated that higher eating frequency may be good for weight management( Reference Seagle, Strain and Makris 2 ), but such a recommendation lacks solid evidence to justify it.
Since the 1960s, some scientists have suggested an inverse association between the consumption of a greater number of small meals per day and body weight maintenance( Reference Fabry, Hejl and Fodor 3 ). According to them, the consumption of more meals per day might lead to greater thermogenesis, higher insulin sensitivity and lower total energy intake( Reference Jenkins, Wolever and Vuksan 4 , Reference Verboeket-van de Venne and Westerterp 5 ).
Since then, studies that have attempted to determine the effects of eating frequency on weight have reached different conclusions. Some experimental and observational studies of eating patterns and body weight status conducted in the 1960s and 1970s found an inverse relationship between eating frequency and adiposity, supporting the claim for an association between lower body weight and higher eating frequency( Reference Fabry, Hejl and Fodor 3 , Reference Metzner, Lamphiear and Wheeler 6 ). More recently, mainly in the 2000s, studies have shown mixed conclusions. A meta-analysis on meal frequency with respect to changes in fat and lean mass based on experimental research, published in 2015, found only a small potential benefit of increased feeding frequency for fat mass and body fat percentage( Reference Schoenfeld, Aragon and Krieger 7 ). Two observational studies showed results in the same direction( Reference Drummond, Crombie and Cursiter 8 , Reference Ma, Bertone and Stanek 9 ); while others have also reported a sex difference( Reference Holmback, Ericson and Gullberg 10 , Reference Smith, Blizzard and McNaughton 11 ). On the other hand, some studies found a positive association between eating frequency and body weight status( Reference Murakami and Livingstone 12 , Reference van der Heijden, Hu and Rimm 13 ) or did not find any relationship( Reference Kim, Park and Yang 14 , Reference Kant, Schatzkin and Graubard 15 ).
Overall energy intake also has a relevant role in the causal pathway that links meal frequency and weight maintenance, although the results of studies evaluating the effect of eating frequency on energy intake were inconclusive. Edelstein et al.( Reference Edelstein, Barrett-Connor and Wingard 16 ) and Howarth et al.( Reference Howarth, Huang and Roberts 17 ) showed that total energy intake increased with increasing frequency of meals or snacks, in both men and women. Meanwhile, Westerterp-Plantenga et al.( Reference Westerterp-Plantenga, Kovacs and Melanson 18 ) described that healthy young men with a high habitual meal frequency had lower total energy intake. Additionally, a study reported that meal frequency and a period of fasting have no major impact on energy intake( Reference Taylor and Garrow 19 ).
Considering the need to organize these divergent evidences, the aim of the present systematic literature review (SLR) was to examine the association between eating frequency and body weight and body composition in adults of both sexes.
Methods
An SLR was conducted aiming to find original articles on the association between eating frequency and body composition or body weight. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)( Reference Moher, Shamseer and Clarke 20 ) and MOOSE (Guidelines for Meta-Analyses and Systematic Reviews of Observational Studies)( Reference Stroup, Berlin and Morton 21 ) protocols were followed. Thus, the research protocol was identified using the PICO (patient, intervention, comparison, outcome) strategy. The articles retrieved from the literature met the following inclusion criteria: (i) the study design was observational; (ii) the article was derived from original research; (iii) the measured outcome included at least one of weight, change in body weight, overweight, obesity, BMI, adiposity, waist circumference, waist-to-hip ratio or abdominal obesity; (iv) the exposure measurement included eating frequency as number of meals per day; and (v) the samples comprised adults (aged >18 years).
PubMed, EMBASE and Scopus databases were searched. Articles published from 1960 to August 2016 were included. The search strategies are shown in Table 1. Terms relative to eating frequency and body weight were used. Additional papers were identified in the reference lists of selected articles that met the inclusion criteria. All records identified were uploaded or manually entered into EndNote X4. The searches were conducted by two independent investigators (R.C. and A.S.G.) and their results were compared.
Table 1 Search strategy for Pubmed, EMBASE and Scopus
MeSH, medical subject heading.
The articles that met all the established criteria were included. Two reviewers (R.C. and A.S.G.) independently read all titles and abstracts. At a second stage, the reviewers read in full all manuscripts that had consensus about their inclusion. If consensus between the two reviewers could not be reached, a third reviewer (M.T.A.O.) was called upon to make a final decision. In four instances the full text of the article was not available. In theses cases we contacted the authors by email up to three times.
The data were extracted and summarized according to the following variables: first author, date of publication, study design, sample size, subjects’ age, follow-up duration (prospective studies), outcomes and exposure assessment, statistical analysis including confounders and mediators used in the adjusted analysis, and numerical results.
Guidelines for SLR and meta-analysis have drawn attention to the importance of evaluating the possible bias in the key methodology domains of the primary studies( Reference Higgins and Green 22 ). In the present SLR, a validated checklist originally proposed by Downs and Black was used in order to assess the quality of the selected articles, especially regarding possible bias. This checklist, originally proposed to rate the quality of clinical trials, consists of twenty-seven items that evaluate the risk of bias in five domains: reporting, external validity, internal validity, confounding and power. Subsequently, this checklist was adapted for observational studies( Reference Monteiro and Victora 23 ), and items 8, 13, 23 and 24 were eliminated for longitudinal studies, while items 8, 9, 13, 17, 23 and 24 were excluded for the assessment of cross-sectional studies. In the present SLR, items 14 and 15 were also eliminated for both designs because they evaluate the blinding process and most observational studies do not take blinding into consideration. The final scale ranged from 0 (poorest quality) to 21 points (best quality) for longitudinal studies and 19 points (best quality) for cross-sectional studies. All items received scores of 0 or 1 (1 if the item was contemplated in the study and 0 if the item was not contemplated or was not able to be determined), with the exception of item 5. Item 5 evaluates if a list of main confounders was provided, ranging from 0 to 2 (0=no; 1=partially; 2=yes). In item 27, the score (0 or 1) depended on whether the statistical power of the survey was explicitly stated in the article.
A score of quality was created as follows: the number given by the total sum of the questions was then divided by the number of total applicable items in the study and finally multiplied by 100.
In the second stage of the quality assessment, in the same way, a general assessment of the quality of the articles was performed for each item of the evaluation instrument. The studies with questions that had scores of 1 or 2 were classified as having a ‘low risk of bias’, whereas scores of 0 reflected a ‘risk of bias’.
The two reviewers (R.C. and A.S.G.) independently made use of the checklist to assess the quality of the retrieved articles. When a consensus could not be reached between them, the third reviewer (M.T.A.O.) was called upon to make a final decision.
Results
The search strategies resulted in 6357 articles (2503 from PubMed; 2380 from EMBASE; 1474 from Scopus). After excluding duplications, 5789 titles and abstracts were examined; 209 full texts were selected for reading. One hundred and eighty-two articles were excluded for the following reasons: outcome and exposure measurements did not meet the inclusion criteria (n172); the study population was not adult (n1); and the article did not show the statistical results for the analysis of the relationship between exposure and outcome (n1). Twenty-seven articles met all of the inclusion criteria. The references of these articles were checked, resulting in four additional articles. As a result, a total of thirty-one articles were included in the present SLR (Fig. 1).
Fig. 1 The search and selection process in the present systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement
The studies had different sample characteristics. The majority of the studies were conducted in the USA( Reference Metzner, Lamphiear and Wheeler 6 , Reference Ma, Bertone and Stanek 9 , Reference Murakami and Livingstone 12 , Reference van der Heijden, Hu and Rimm 13 , Reference Kant, Schatzkin and Graubard 15 , Reference Howarth, Huang and Roberts 17 , Reference Aljuraiban, Chan and Oude Griep 24 – Reference Reicks, Degeneffe and Rendahl 28 ) and European countries( Reference Drummond, Crombie and Cursiter 8 , Reference Holmback, Ericson and Gullberg 10 , Reference Aljuraiban, Chan and Oude Griep 24 , Reference Karatzi, Yannakoulia and Psaltopoulou 29 – Reference Murakami and Livingstone 38 ). Two studies included only men( Reference van der Heijden, Hu and Rimm 13 , Reference Ruidavets, Bongard and Bataille 36 ), five included only women( Reference Yannakoulia, Melistas and Solomou 32 , Reference Bertéus-Forslund, Lindroos and Sjostrom 35 , Reference Amosa, Rush and Plank 37 , Reference Teichmann, Olinto and Costa 39 , Reference Mills, Perry and Reicks 40 ) and twenty-four included both sexes( Reference Metzner, Lamphiear and Wheeler 6 , Reference Drummond, Crombie and Cursiter 8 – Reference Murakami and Livingstone 12 , Reference Kim, Park and Yang 14 , Reference Kant, Schatzkin and Graubard 15 , Reference Howarth, Huang and Roberts 17 , Reference Aljuraiban, Chan and Oude Griep 24 – Reference Berg, Lappas and Wolk 31 , Reference Marín-Guerrero, Gutierrez-Fisac and Guallar-Castillon 33 , Reference Bertéus Forslund, Torgerson and Sjostrom 34 , Reference Amosa, Rush and Plank 37 , Reference Murakami and Livingstone 38 , Reference Al-Isa 41 – Reference Peixoto Mdo, Benicio and Jardim 43 ). The sample sizes of the studies ranged between eighty-two( Reference Amosa, Rush and Plank 37 ) and 34974 individuals( Reference Marín-Guerrero, Gutierrez-Fisac and Guallar-Castillon 33 ), and the age of the participants was between 18 and 90 years old. Two prospective( Reference van der Heijden, Hu and Rimm 13 , Reference Kant, Schatzkin and Graubard 15 ) and twenty-nine cross-sectional studies were retrieved. The follow-up of prospective studies was 8 and 10 years( Reference van der Heijden, Hu and Rimm 13 , Reference Kant, Schatzkin and Graubard 15 ) (Table 2).
Table 2 Summary of population and design characteristics of the studies sorted according to quality scores
NA, not available; INTERMAP, International Study on Macro/Micronutrients and Blood Pressure; NHANES, National Health and Nutrition Examination Survey; NHEFS, NHANES Epidemiologic Follow-up Study; EPIC, European Prospective Investigation into Cancer and Nutrition; HPSF, Health Professionals Follow-up Study.
In the reporting items, most articles were classified as having a ‘low risk of bias’. On the other hand, in the external validity domain, several of the articles were not clear about how their participants were selected (42 %) or/and did not rely on representative samples (70 %). As regards internal validity, 34·8 % of the studies did not use an accurate method (valid and reliable) to measure the outcomes, using self-reported measures. In the confounding domain, 54·8 % of the articles did not describe characteristics of participants lost between the initial selection process and the final sample, and 29·0 % of the studies did not perform any adjustment for confounding in the analysis. Finally, almost all studies (96·7 %) did not report a power calculation for their sample size and were classified as having a ‘risk of bias’ in the power domain (Fig. 2).
Fig. 2 Summary of quality assessment (
, low risk of bias; 
, risk of bias) of the studies (n 31) included in the present systematic literature review. *Items ‘different lengths of follow-up’ and ‘losses to follow-up’ were evaluated only in prospective studies
Considering the fourteen studies that found inverse associations between eating frequency and the outcomes, the following was observed: all studies were cross-sectional; eleven had scores of quality above 70 %( Reference Drummond, Crombie and Cursiter 8 – Reference Smith, Blizzard and McNaughton 11 , Reference Aljuraiban, Chan and Oude Griep 24 , Reference Mohindra, Nicklas and O’Neil 25 , Reference Titan, Bingham and Welch 30 , Reference Marín-Guerrero, Gutierrez-Fisac and Guallar-Castillon 33 , Reference Ruidavets, Bongard and Bataille 36 , Reference Peixoto Mdo, Benicio and Jardim 43 , Reference Oliveira, Assis and Silva Mda 44 ); and eight studies found an association between eating frequency and outcomes measured as body weight, BMI, overweight or obesity( Reference Drummond, Crombie and Cursiter 8 – Reference Holmback, Ericson and Gullberg 10 , Reference Aljuraiban, Chan and Oude Griep 24 , Reference Mohindra, Nicklas and O’Neil 25 , Reference Titan, Bingham and Welch 30 ), five as waist circumference or waist-to-hip ratio( Reference Holmback, Ericson and Gullberg 10 , Reference Smith, Blizzard and McNaughton 11 , Reference Mohindra, Nicklas and O’Neil 25 , Reference Ruidavets, Bongard and Bataille 36 , Reference Oliveira, Assis and Silva Mda 44 ) and one as adiposity index( Reference Metzner, Lamphiear and Wheeler 6 ). Only one study used self-reported measurements( Reference Reicks, Degeneffe and Rendahl 28 ). In most studies (n 11) multiple recalls or meal pattern questionnaires were used in order to assess the eating frequency( Reference Metzner, Lamphiear and Wheeler 6 , Reference Drummond, Crombie and Cursiter 8 – Reference Smith, Blizzard and McNaughton 11 , Reference Aljuraiban, Chan and Oude Griep 24 – Reference Bachman, Phelan and Wing 26 , Reference Reicks, Degeneffe and Rendahl 28 , Reference Marín-Guerrero, Gutierrez-Fisac and Guallar-Castillon 33 , Reference Ruidavets, Bongard and Bataille 36 ). Five studies classified the exposure as a continuous variable( Reference Drummond, Crombie and Cursiter 8 , Reference Smith, Blizzard and McNaughton 11 , Reference Mohindra, Nicklas and O’Neil 25 , Reference Bachman, Phelan and Wing 26 , Reference Titan, Bingham and Welch 30 ); seven according to the three major meals (breakfast, lunch and dinner) and compared the intake of three meals with a greater or lower number of meals( Reference Ma, Bertone and Stanek 9 , Reference Holmback, Ericson and Gullberg 10 , Reference Aljuraiban, Chan and Oude Griep 24 , Reference Marín-Guerrero, Gutierrez-Fisac and Guallar-Castillon 33 , Reference Ruidavets, Bongard and Bataille 36 , Reference Peixoto Mdo, Benicio and Jardim 43 , Reference Oliveira, Assis and Silva Mda 44 ). Most studies (n 10) performed multiple analyses or other statistical methods to adjust for possible confounding, including sociodemographic variables such as sex( Reference Drummond, Crombie and Cursiter 8 – Reference Holmback, Ericson and Gullberg 10 , Reference Aljuraiban, Chan and Oude Griep 24 , Reference Mohindra, Nicklas and O’Neil 25 , Reference Titan, Bingham and Welch 30 , Reference Marín-Guerrero, Gutierrez-Fisac and Guallar-Castillon 33 , Reference Ruidavets, Bongard and Bataille 36 , Reference Peixoto Mdo, Benicio and Jardim 43 , Reference Oliveira, Assis and Silva Mda 44 ), age( Reference Ma, Bertone and Stanek 9 – Reference Smith, Blizzard and McNaughton 11 , Reference Aljuraiban, Chan and Oude Griep 24 , Reference Mohindra, Nicklas and O’Neil 25 , Reference Titan, Bingham and Welch 30 , Reference Marín-Guerrero, Gutierrez-Fisac and Guallar-Castillon 33 , Reference Ruidavets, Bongard and Bataille 36 , Reference Peixoto Mdo, Benicio and Jardim 43 , Reference Oliveira, Assis and Silva Mda 44 ), education( Reference Holmback, Ericson and Gullberg 10 , Reference Smith, Blizzard and McNaughton 11 , Reference Aljuraiban, Chan and Oude Griep 24 , Reference Marín-Guerrero, Gutierrez-Fisac and Guallar-Castillon 33 , Reference Ruidavets, Bongard and Bataille 36 , Reference Oliveira, Assis and Silva Mda 44 , Reference Blumberg, Heaney and Huncharek 45 ), income( Reference Holmback, Ericson and Gullberg 10 , Reference Peixoto Mdo, Benicio and Jardim 43 , Reference Oliveira, Assis and Silva Mda 44 ) and race/ethnicity( Reference Mohindra, Nicklas and O’Neil 25 , Reference Oliveira, Assis and Silva Mda 44 ); and behavioural variables such as smoking( Reference Aljuraiban, Chan and Oude Griep 24 , Reference Mohindra, Nicklas and O’Neil 25 , Reference Titan, Bingham and Welch 30 , Reference Marín-Guerrero, Gutierrez-Fisac and Guallar-Castillon 33 , Reference Ruidavets, Bongard and Bataille 36 , Reference Peixoto Mdo, Benicio and Jardim 43 , Reference Oliveira, Assis and Silva Mda 44 ), physical activity( Reference Ma, Bertone and Stanek 9 – Reference Smith, Blizzard and McNaughton 11 , Reference Edelstein, Barrett-Connor and Wingard 16 , Reference Aljuraiban, Chan and Oude Griep 24 , Reference Titan, Bingham and Welch 30 , Reference Marín-Guerrero, Gutierrez-Fisac and Guallar-Castillon 33 , Reference Ruidavets, Bongard and Bataille 36 , Reference Peixoto Mdo, Benicio and Jardim 43 , Reference Oliveira, Assis and Silva Mda 44 ), alcohol intake( Reference Holmback, Ericson and Gullberg 10 , Reference Smith, Blizzard and McNaughton 11 , Reference Titan, Bingham and Welch 30 , Reference Marín-Guerrero, Gutierrez-Fisac and Guallar-Castillon 33 , Reference Peixoto Mdo, Benicio and Jardim 43 , Reference Oliveira, Assis and Silva Mda 44 ) and dietary characteristics( Reference Ma, Bertone and Stanek 9 – Reference Smith, Blizzard and McNaughton 11 , Reference Aljuraiban, Chan and Oude Griep 24 , Reference Mohindra, Nicklas and O’Neil 25 , Reference Titan, Bingham and Welch 30 , Reference Ruidavets, Bongard and Bataille 36 , Reference Peixoto Mdo, Benicio and Jardim 43 , Reference Oliveira, Assis and Silva Mda 44 ). Seven studies took into account energy intake as a confounder( Reference Metzner, Lamphiear and Wheeler 6 , Reference Ma, Bertone and Stanek 9 , Reference Holmback, Ericson and Gullberg 10 , Reference Aljuraiban, Chan and Oude Griep 24 , Reference Mohindra, Nicklas and O’Neil 25 , Reference Titan, Bingham and Welch 30 , Reference Ruidavets, Bongard and Bataille 36 ) and in three of them misreporters of energy were excluded( Reference Holmback, Ericson and Gullberg 10 , Reference Aljuraiban, Chan and Oude Griep 24 , Reference Ruidavets, Bongard and Bataille 36 ) (Table 3).
Table 3 Summary of the main results of studies that found an inverse association between eating frequency and body weight or body composition (n 14)
WC, waist circumference; WHR, waist-to-hip ratio; PA, physical activity; EI, energy intake; EER, estimated energy requirement; SES, socio-economic status; β, linear regression coefficient; NS, not statistically significant and P value not available; r, correlation coefficient.
Eating frequency was positively associated with the outcomes in seven studies and one of them presented a prospective design( Reference van der Heijden, Hu and Rimm 13 ). Five studies received quality scores above 70 %( Reference Murakami and Livingstone 12 , Reference van der Heijden, Hu and Rimm 13 , Reference Howarth, Huang and Roberts 17 , Reference Yannakoulia, Melistas and Solomou 32 , Reference Murakami and Livingstone 38 ). The outcomes were measured as weight, BMI, overweight/obesity in six studies( Reference Murakami and Livingstone 12 , Reference van der Heijden, Hu and Rimm 13 , Reference Howarth, Huang and Roberts 17 , Reference Bertéus Forslund, Torgerson and Sjostrom 34 , Reference Bertéus-Forslund, Lindroos and Sjostrom 35 , Reference Murakami and Livingstone 38 ); two studies used self-reported measurements( Reference van der Heijden, Hu and Rimm 13 , Reference Howarth, Huang and Roberts 17 ), including the prospective study. In the majority of studies (n 5), eating frequency was assessed through multiple recalls or meal pattern questionnaires. In four studies, the exposure was classified as a continuous variable( Reference Yannakoulia, Melistas and Solomou 32 , Reference Bertéus Forslund, Torgerson and Sjostrom 34 , Reference Bertéus-Forslund, Lindroos and Sjostrom 35 , Reference Murakami and Livingstone 38 ), and in two according to the three major meals and compared with a greater number of meals( Reference Murakami and Livingstone 12 , Reference Howarth, Huang and Roberts 17 ). Four studies that provided a complete list of sociodemographic (age, race/ethnicity, education and income) and behavioural (smoking, physical activity and dietary characteristics) confounders were included in the analysis( Reference Murakami and Livingstone 12 , Reference van der Heijden, Hu and Rimm 13 , Reference Howarth, Huang and Roberts 17 , Reference Murakami and Livingstone 38 ). In five studies, energy intake was investigated as a confounder( Reference Murakami and Livingstone 12 , Reference Howarth, Huang and Roberts 17 , Reference Yannakoulia, Melistas and Solomou 32 , Reference Bertéus Forslund, Torgerson and Sjostrom 34 , Reference Murakami and Livingstone 38 ), and one investigated the mediation effect of energy intake( Reference Howarth, Huang and Roberts 17 ). In three of them its measurement took into account misreporting( Reference Murakami and Livingstone 12 , Reference Yannakoulia, Melistas and Solomou 32 , Reference Murakami and Livingstone 38 ) (Table 4).
Table 4 Summary of the main results of studies that found a positive association between eating frequency and body weight or body composition (n 6)
WC, waist circumference; WHR, waist-to-hip ratio; PA, physical activity; EI, energy intake; EER, estimated energy requirement; TV, television; ref., reference category; β, linear regression coefficient.
Ten studies did not show associations between eating frequency and outcomes, one being prospective( Reference Kant, Schatzkin and Graubard 15 ). Seven received quality scores above 70 %( Reference Kim, Park and Yang 14 , Reference Kant, Schatzkin and Graubard 15 , Reference Karatzi, Yannakoulia and Psaltopoulou 29 , Reference Berg, Lappas and Wolk 31 , Reference Teichmann, Olinto and Costa 39 , Reference Mills, Perry and Reicks 40 , Reference Gigante, Barros and Post 42 ). Six studies used a simple question or meal pattern questionnaire to access eating frequency( Reference Kim, Park and Yang 14 , Reference Kant, Schatzkin and Graubard 15 , Reference Berg, Lappas and Wolk 31 , Reference Teichmann, Olinto and Costa 39 , Reference Al-Isa 41 , Reference Gigante, Barros and Post 42 ) and three used multiple recalls( Reference Pearcey and de Castro 27 , Reference Karatzi, Yannakoulia and Psaltopoulou 29 , Reference Amosa, Rush and Plank 37 ). Five studies provided a complete list of possible sociodemographic confounders (age, race/ethnicity, education and income)( Reference Kant, Schatzkin and Graubard 15 , Reference Teichmann, Olinto and Costa 39 – Reference Gigante, Barros and Post 42 ) and four of behavioural confounders (smoking, physical activity and alcohol intake)( Reference Kant, Schatzkin and Graubard 15 , Reference Teichmann, Olinto and Costa 39 , Reference Al-Isa 41 , Reference Gigante, Barros and Post 42 ). Investigation of energy intake as confounder was included in the analyses of only two studies( Reference Kant, Schatzkin and Graubard 15 , Reference Mills, Perry and Reicks 40 ) (Table 5).
Table 5 Summary of the main results of studies that did not find an association between eating frequency and body weight or body composition (n 10)
WC, waist circumference; PA, physical activity; EI, energy intake; β, linear regression coefficient; PR: prevalence ratio; ref., reference category.
Among the ten studies that showed analyses in men separately, five found inverse associations between high eating frequency and waist circumference( Reference Holmback, Ericson and Gullberg 10 , Reference Smith, Blizzard and McNaughton 11 , Reference Oliveira, Assis and Silva Mda 44 ) or waist-to-hip ratio( Reference Titan, Bingham and Welch 30 , Reference Ruidavets, Bongard and Bataille 36 ) and seven found inverse associations for body weight( Reference Drummond, Crombie and Cursiter 8 , Reference Holmback, Ericson and Gullberg 10 , Reference Smith, Blizzard and McNaughton 11 , Reference Titan, Bingham and Welch 30 , Reference Marín-Guerrero, Gutierrez-Fisac and Guallar-Castillon 33 , Reference Ruidavets, Bongard and Bataille 36 , Reference Peixoto Mdo, Benicio and Jardim 43 , Reference Leidy and Campbell 46 ). All of these studies adjusted for physical activity and dietary characteristics in multiple analyses, but only two of them took into account dietary intake misreporting( Reference Holmback, Ericson and Gullberg 10 , Reference Ruidavets, Bongard and Bataille 36 ). On the other hand, when the results were analysed only in women, no pattern was observed in the results.
Finally, when the results were analysed according to exposure and outcomes, no association pattern was observed.
Discussion
Our SLR focused on the association of eating frequency with body composition or body weight. We concluded that, to date, there is not sufficient evidence for establishing a clear association between eating frequency and body composition or body weight. However, among men, a potential protective effect of high eating frequency on these outcomes was observed.
The findings should be interpreted in light of the methodological characteristics of the articles included. First, the outcome and exposure measurement might not be accurate in some studies. Moreover, the outcome measurement varied among the studies, thereby limiting the comparability among them. For example, the role of eating frequency on body weight might be different from the one it has on central adiposity. With respect to exposure, different methods were used for data collection. Some studies used methods such as multiple recalls or food diaries and meal pattern questionnaires, and may be more accurate than others, such as simple questions, for eating frequency assessment. Although dietary records and 24 h dietary recalls are subject to misreporting, particularly under-reporting( Reference Summerbell, Moody and Shanks 47 ), there is no information about the validity of most of the meal pattern questionnaires and simple questions used in these studies. Furthermore, different cut-offs were used to determine high or low eating frequency. Only six authors classified eating frequency according to the three major meals (breakfast, lunch and dinner) and compared intake of three meals with intake of a greater number of meals. Other studies assessed the exposure as a continuous variable or compared the extremes of eating frequency (e.g. two v. six meals per day).
Second, the results are based mostly on cross-sectional studies, with only two studies selected having a longitudinal design, which should be considered a limitation in the current available literature. Longitudinal studies are well known for being a better study design to investigate the temporal relationship between the exposure and change in outcome status. The issue of reverse causality is especially important in this case, because people skip meals, thus reducing eating frequency, when they become overweight in an attempt to lose weight or to prevent further gain( Reference Summerbell, Moody and Shanks 47 ).
Finally, obesity is a multifactorial disorder arising from genetic, environmental, socio-economic and behavioural factors. These differ in their respective contributions to the obesity epidemic( Reference Cohen 48 ). In this sense, another methodological issue that is very important in this type of epidemiological investigation is the inclusion of main confounders and mediators in the analysis. A confounding variable is an extraneous variable in a statistical model that correlates (positively or negatively) with both the exposure and the outcome variable; meanwhile, a mediator factor is a variable that occurs in the causal pathway between the exposure and the outcome( Reference Rothman and Lash 49 ). The majority of studies compiled in the present SLR applied multiple analyses adjusted for potential confounders, such as sex, age, education, income, smoking, physical activity and alcohol intake.
The effect of dietary characteristics (energy intake and quality of diet) in the causal pathway linking eating frequency to body weight and body composition was adjusted for in the multiple analyses of most studies. However, in order to understand the role of a variable in a causal chain, sometimes it is more informative to stratify the analyses according to this variable, rather than adjusting for it in multiple analyses. In this sense, only one study investigated the mediation effect of energy intake by stratifying the analysis according to it( Reference Howarth, Huang and Roberts 17 ). However, several studies included in the present SLR showed that higher eating frequency is positively associated with energy intake( Reference Holmback, Ericson and Gullberg 10 , Reference Howarth, Huang and Roberts 17 , Reference Titan, Bingham and Welch 30 , Reference Bertéus Forslund, Torgerson and Sjostrom 34 , Reference Mills, Perry and Reicks 40 ). In Holmback et al.( Reference Holmback, Ericson and Gullberg 10 ), eating frequency and carbohydrate energy percentage, as well as relative fibre intake, increased with higher eating frequency; while the energy percentage from fat, protein and alcohol decreased. Bertéus Forslund et al.( Reference Bertéus Forslund, Torgerson and Sjostrom 34 ) found that sweet and fatty food groups were associated with snacking and contributed considerably to energy intake. Regarding quality of diet, Mills et al. ( Reference Mills, Perry and Reicks 40 ) showed that intakes of fruit and vegetables, whole grains, dietary fibre, dairy and added sugars also increased as eating frequency increased. Other aspects of diet, such as meal timing, are also important in the control and reduction of body weight, total body fat and visceral fat( Reference Jakubowicz, Froy and Wainstein 50 ).
In our SLR, fourteen studies reported an inverse association between eating frequency and body weight or body composition. Murakami and Livingstone and Bellisle et al.( Reference Murakami and Livingstone 12 , Reference Bellisle, McDevitt and Prentice 51 ) have called attention to this apparent inverse relationship between eating frequency and adiposity measures, suggesting it is an artifact that in part can be attributed to the under-reporting of eating frequency concomitant with the under-reporting of energy intake by overweight or obese subjects. In this regard, Murakami and Livingstone’s( Reference Murakami and Livingstone 12 ) study showed the importance of evaluating energy intake misreporting when examining the association between eating frequency and overweight/obesity and central obesity. In their study, energy intake misreporting was evaluated based on ratio of energy intake to estimated energy requirement (EI:EER). In the multiple analyses, without taking into account energy intake or EI:EER, eating frequency showed an inverse or null association with the outcomes. However, after full adjustment including EI:EER, a completely different picture emerged: eating frequency was positively associated with overweight/obesity and central obesity. Only three studies that reported an inverse association between eating frequency and body weight or body composition in the present SLR took into account under-reporting of energy intake and/or eating frequency; and three of the six studies that found a positive association between eating frequency and outcomes took into account misreporting of energy intake.
Physical activity also plays a role in the association that links eating frequency with body weight and body composition( Reference Cohen 52 ). Physical activity might be a potential confounder in this association, since physical activity practice may improve diet quality( Reference Holmes, Chen and Hankinson 53 , Reference Pearson and Biddle 54 ); although physical activity has been well described in the literature as an independent factor in the control and reduction of body weight, total body fat and visceral fat( Reference Ross and Janssen 55 , Reference Slentz, Houmard and Kraus 56 ).
Even though it was not the objective of our SLR to investigate differences between sexes, when analysing the results of the articles included, a potential protective effect of high eating frequency on the outcomes was observed among men. In general, increased eating frequency has been postulated to increase metabolism( Reference Jenkins, Wolever and Vuksan 4 ), appetite control and food intake( Reference Leidy and Campbell 46 , Reference Leidy, Armstrong and Tang 57 ), and to improve glucose and insulin control( Reference Schwarz, Rigby and La Bounty 58 , Reference Farshchi, Taylor and Macdonald 59 ). However, this difference could be due to the fact that men who have high eating frequency also have a healthier lifestyle, including practice of physical activity and healthier eating habits, which results in reduced body fat and waist circumference. In Holmback et al.( Reference Holmback, Ericson and Gullberg 10 ), a high fibre intake was the clearest diet-quality indicator associated with a high eating frequency among men. In two other studies, men were more physically active than women, which may help to explain the protective effect found only in men( Reference Smith, Blizzard and McNaughton 11 , Reference Peixoto Mdo, Benicio and Jardim 43 ). However, only in Smith et al.’s study( Reference Smith, Blizzard and McNaughton 11 ) was physical activity measured by a direct method (pedometer). In the other two studies( Reference Holmback, Ericson and Gullberg 10 , Reference Peixoto Mdo, Benicio and Jardim 43 ), self-reported measurements were used, which can be inaccurate. In addition, there is evidence that energy intake compensation is poor in women, a factor normally associated with obesity( Reference Lissner, Levitsky and Strupp 60 ). Drummond et al. ( Reference Drummond, Crombie and Cursiter 8 ) showed that men compensated for extra eating occasions by reducing the mean energy per eating episode. Although all studies adjusted for physical activity and dietary characteristics in the multiple analyses, it is possible that residual confounding of both lifestyle factors could contribute to the results.
The present review is the first SLR of observational studies that examines specifically the association of eating frequency with body weight and body composition in adults with a systematic approach. Several narrative reviews have been conducted( Reference Seagle, Strain and Makris 2 , Reference Cohen 52 , Reference Kulovitz, Kravitz and Mermier 61 – Reference Hutfless, Gudzune and Maruthur 63 ) and these concluded that the evidence available to suggest the presence of an association between eating frequency and weight, BMI and body fatness is limited. In addition, a meta-analysis evaluating experimental research suggested that eating frequency is positively associated with reductions in fat mass and body fat percentage, as well as an increase in fat-free mass; however, the positive findings were the product of a single study and need to be interpreted with circumspection( Reference Schoenfeld, Aragon and Krieger 7 ).
Considering there is contradictory evidence about the association between eating frequency and body weight, it is important to assess the whole body of evidence about this topic and in particular to do so systematically. In addition, researchers have suggested that nutrition policy decisions will have to be made using the totality of the available evidence( Reference Mitchell, Aggett and Richardson 64 ). It is almost inevitable that causal chains in nutrition outcomes involve long periods of latency, complex individual variability in the biological response, and cultural, economic and geographic influences; aspects that observational studies may help to understand, since observational studies for food habits indicate what happens over a lifetime of consumption( Reference Blumberg, Heaney and Huncharek 45 , Reference Mitchell, Aggett and Richardson 64 , Reference Mann 65 ).
In this respect, at the same time as we encourage the conduct of more clinical trials to help to examine and potentially determine this causal relationship, future high-quality observational studies are needed to understand the role of eating frequency on loss and maintenance of weight and body composition and to guide clinical recommendations. However, evaluating eating behaviour is also a complex task and we demonstrated substantial heterogeneity in the methodological quality of studies. Thus, in Table 6, we call attention to some important methodological issues that should be considered in future observational studies. It is necessary to conduct studies with long-term longitudinal design and representative samples. Outcome and exposure should be measured with accurate methods and classified based on clinically relevant aspects. Moreover, it is important that statistical analyses should be stratified by sex, if the sample size allows, and to include the potentially relevant confounders and mediators, with special attention to nutrients and energy intake, and these should be appropriately measured.
Table 6 Methodological recommendations for future observational studies
Acknowledgements
Financial support: This study was supported by the Foundation for Research Support of the State of Rio Grande do Sul (grant number 1220-2551/13-3). M.T.A.O. and G.K. received research productivity grants from the Brazilian National Council for Scientific and Technological Development (grant numbers 307257/2013-4 and 304182/2013-3). Conflict of interest: none. Authorship: R.C. and M.T.A.O. conceptualized the study. R.C. and A.S.G. completed the searches, abstract/title screening, data extraction and quality assessment. R.C. and M.T.A.O. drafted the manuscript. G.K. and P.I.C.L. assisted in drafting and revision of the manuscript. All authors read and approved the final manuscript. Ethics of human subject participation: Not applicable.
