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. Author manuscript; available in PMC: 2024 Aug 2.
Published in final edited form as: Eat Behav. 2023 Aug 2;50:101791. doi: 10.1016/j.eatbeh.2023.101791

Relations Between Forms of Dietary Restraint, Restriction, and Loss-of-Control Eating among Adolescents Seeking Weight Control: An Ecological Momentary Assessment Study

Laura D’Adamo 1,2,3, Ross M Sonnenblick 1,2, Adrienne S Juarascio 1,2, Stephanie M Manasse 1,2
PMCID: PMC10528310  NIHMSID: NIHMS1922924  PMID: 37544108

Abstract

Objective:

This study aimed to: 1) compare rates of dietary restraint and restriction between adolescents with and without loss-of-control (LOC) eating who were seeking weight control and 2) examine temporal relations between restraint/restriction and LOC eating.

Method:

37 adolescents seeking weight control (mean age: 15.4 ± 1.5; 62% White; 57% female; mean BMI percentile = 97.3 ± 3.1) completed a one-week ecological momentary assessment protocol and reported on dietary restraint/restriction and eating behavior prior to beginning a weight control intervention. Chi-square tests examined differences in frequency of restraint/restriction between participants with and without LOC eating. Multilevel models examined associations between dietary restraint/restriction and LOC eating at the next survey and on the next day.

Results:

Of 37 participants, 15 (41%) reported engaging in LOC eating. Participants with LOC eating more frequently endorsed several forms of restraint and restriction versus participants without LOC eating. Attempting to avoid enjoyable foods and attempting to limit eating at one survey predicted greater likelihood of LOC eating at the next survey.

Conclusions:

Findings suggest that attempted restraint, but not actual restriction, was associated with LOC eating. Research should explore additional factors that may influence these relationships, which could inform weight control treatments that address restraint/restriction.

Introduction

Loss-of-control (LOC) eating (i.e., the subjective experience of being unable to stop eating once started) is reported by one-third of adolescents with overweight/obesity (He et al., 2017). LOC eating during adolescence has been found to persist into adulthood (Goldschmidt et al., 2014) and is robustly associated with eating disorder (ED) symptoms, general psychopathology, and poor health outcomes (Goldschmidt et al., 2008; Goldschmidt et al., 2015; Radin et al., 2015; Schluter et al., 2016; Shomaker, 2011). Thus, identifying maintenance factors of LOC eating that can be addressed during this developmental period is critical. Dietary restraint, defined as attempts to restrict food intake to control one’s weight (regardless of whether actual restriction occurs), is considered a key risk and maintenance factor for LOC eating (Fairburn et al., 2003). Theoretical models of restraint posit that maintaining cognitive control over eating (e.g., by continuously attempting to adhere to strict dietary rules) increases psychological pressure to eat restricted foods, thereby increasing risk for LOC eating (Fairburn, 2008; Fairburn et al., 2003; Polivy & Herman, 1985). Dietary restriction (i.e., when one’s attempt to restrain eating results in actual restriction) has also been posited to maintain LOC eating, due to increased hunger and other physiological cues (Fairburn, 2013; Polivy, 1996; Stice et al., 2008). Notably, research has recognized distinct forms of dietary restraint/restriction, including (attempted/actual) delaying eating, avoiding enjoyable foods, and limiting food intake (Linardon et al., 2018; Manasse et al., 2023).

Dietary restraint and restriction have been empirically linked to increased LOC eating in some contexts. Self-directed dieting (i.e., outside of the context of structured lifestyle modification programs), which encompasses both attempted restraint and actual restriction, has longitudinally predicted binge eating among adolescents in numerous studies (Goldschmidt et al., 2012; Neumark-Sztainer et al., 2011; Neumark-Sztainer et al., 2009; Neumark-Sztainer et al., 2007). However, findings have not consistently supported this relationship. Several large studies of adolescents from the community have found no association between restraint and LOC eating (Sehm & Warschburger, 2018; Spoor et al., 2006), and behavioral weight control programs that prescribe moderate restriction have been found to reduce binge eating among adolescents (House et al., 2021; Moustafa et al., 2021). This discrepancy suggests that the relationship between restraint/restriction and LOC eating may depend on other factors, such as the type of restraint/restriction employed. In addition, the degree to which individuals’ attempts at restraint are successful (i.e., result in restriction) may be a key predictor of LOC eating (Schaumberg et al., 2016). For instance, degree of success with restraint may signal the degree of rigidity an individual employs toward restraining their eating, which may influence LOC eating. Examining differential effects of restraint and restriction may also suggest whether psychological (e.g., craving) or physiological (e.g., hunger) mechanisms more strongly drive LOC eating.

Adolescents with and without LOC eating may engage in dietary restraint/restriction at different rates, yet no study has compared the prevalence of these behaviors between adolescents with and without LOC eating. It is also unknown whether adolescents with and without LOC eating have different rates of success with restraint. Furthermore, no study has explored whether specific forms of restraint/restriction prospectively predict LOC eating among adolescents. Given that dietary restraint, restriction, and LOC eating are prevalent among adolescents seeking weight control (Hayes et al., 2018; He et al., 2017), examining these relations in a weight control-seeking sample could inform the forms of restraint/restriction that do not promote LOC eating during treatment. Ecological momentary assessment (EMA) is a useful design for investigating these relations because it minimizes retrospective recall bias and allows for closer scrutiny of temporal relationships (Shiffman, Stone, & Hufford, 2008; Engel et al., 2016; Haedt-Matt & Keel, 2011). One prior study used EMA to examine momentary relations between dietary restraint and LOC eating among youth (aged 8–14) with overweight/obesity from the community and found that among children of parents with low parental self-efficacy, restraint was associated with greater LOC eating. However, this study did not explore the effects of restriction or specific forms of restraint/restriction (Smith et al., 2020).

In a secondary analysis of data collected for a pilot study evaluating the relation of aberrant decision-making to problematic eating behavior and weight in adolescents, this exploratory study used an EMA design to explore relations between forms of dietary restraint/restriction and LOC eating over a 7-day period prior to the start of treatment. We aimed to: 1) compare rates of dietary restraint (i.e., attempted limiting eating, avoiding enjoyable foods, delaying eating, and any attempted restraint), dietary restriction (i.e., actually limiting eating, actually avoiding enjoyable foods, actually delaying eating, and any actual restriction), and conversion from restraint to restriction (i.e., the proportion of restraint attempts that resulted in actual restriction) between adolescents with and without LOC eating; and 2) examine prospective relations between dietary restraint/restriction and LOC eating at the next survey and on the next day. Examining these relationships at the next survey and on the next day served to clarify the time scale on which restraint/restriction predict LOC eating (Zunker et al., 2011).

Method

Participants and Procedures

Participants were adolescents with higher weight enrolled in a pilot study evaluating predictors of outcome from a lifestyle modification program (Clinicaltrials.gov identifier NCT04848532). Eligible participants were between ages 14 and 18, had a BMI above the 85th percentile for sex and age, currently lived in the U.S. with a parent or guardian who was also willing to participate, had a smartphone, and completed a 3-day food diary to ensure willingness to adhere to lifestyle modification recommendations. Exclusion criteria included imminent suicide risk, inability to partake in physical activity, current diabetes or bariatric surgery history, current medical condition that might pose a risk during treatment or impede treatment adherence, pregnancy or plans to become pregnant, past 6-month weight loss of ≥6%, plans to start another weight loss treatment in the next 16 months, presence of any severe compensatory weight control behaviors (e.g., vomiting to compensate for LOC eating) or ≥12 episodes of any compensatory weight control behaviors (e.g., laxative/diuretic use, vomiting, driven/compelled exercise, or fasting to compensate for LOC eating) in the past 3 months, and currently taking weight loss medication.

Eligible participants completed an initial visit via videoconference during which informed assent/consent was obtained and height and weight were measured. Enrolled participants subsequently completed a self-report demographics survey and received training on the EMA protocol. Prior to beginning lifestyle modification, participants completed the one-week EMA protocol during which they reported momentary LOC eating, dietary restraint, and restriction. On each weekday of the recording period, participants were texted prompts to complete surveys at 5 random times per day during non-school hours. On weekends and during the summer, participants received prompts to complete 5 surveys between 9am and 9pm. Adjustments to the timing of morning prompts were made for individual waking times on weekdays, weekends, during the school year, and during the summer. Participants were given 45 minutes to complete a survey after receiving a prompt. Study procedures were approved by the Drexel University Institutional Review Board.

Measures

Demographics

Participants self-reported their age, sex, gender identity, and race on a survey after the virtual visit.

BMI percentile

During the virtual visit, participants’ height was measured by their parents using a wall-mounted measuring tape, and weight was measured using household digital scales. Research staff trained participants and parents on measurement procedures before the visit. These data, date of birth, and sex were used to calculate BMI percentile per CDC growth charts (Kuczmarski et al., 2002; Ogden et al., 2002).

Momentary LOC eating

At each EMA survey, participants were asked whether they had eaten since the last survey. If this was endorsed, presence of LOC eating was assessed with the following two questions: (1) “While you were eating, did you feel a sense of loss of control?”, and (2) “While you were eating, did you feel like a car without brakes, you just kept eating and eating?” Responses were rated on a 5-point Likert-type scale (range: 1 = “No, not at all” to 5 = “Yes, extremely”). These items have been used in numerous EMA studies of adolescents (Goldschmidt et al., 2020; Goldschmidt et al., 2018; Manasse et al., 2022) and were developed based on conceptualizations of LOC in previous EMA research (Goldschmidt et al., 2014; Ranzenhofer et al., 2014). Consistent with previous EMA studies, participants were counted as having endorsed LOC eating if they reported a “3” or higher on either question at least once during the recording period (Berg et al., 2015; Smith et al., 2018; Wonderlich et al., 2015).

Momentary dietary restraint and restriction

Participants were asked the following question at each EMA survey: “Since the last survey, which of the following have you attempted, even if you were unsuccessful?” Response options included: (1) Tried to limit the amount you ate, (2) Tried to avoid eating certain foods that you like, (3) Tried to delay eating, (4) None of the above (Manasse et al., 2023). Participants were able to select all forms of dietary restraint that they had engaged in since the last survey.

If participants endorsed a form of dietary restraint, the corresponding question was asked to assess dietary restriction: (1) “Since the last survey, were you successful in actually limiting the amount that you ate in order to influence your shape and/or weight?”, (2) “Since the last survey, were you successful in actually avoiding eating certain foods that you like in order to influence your shape or weight?”, and (3) “Since the last survey, were you successful in actually delaying your eating in order to influence your shape or weight?” Response options were binary (Yes/No) (Manasse et al., 2023).

Statistical Analyses

Analyses were conducted using R version 7.1. Participants were grouped based on whether they endorsed any LOC eating during the recording period. Descriptive statistics on demographics (i.e., age, sex, gender identity, race) and BMI percentile in the two groups were calculated, and preliminary analyses tested for significant differences between groups.

The number and percent of surveys for which participants with and without LOC eating endorsed each form of dietary restraint (i.e., attempts to avoid foods that one enjoys, attempts to delay eating, attempts to limit eating, any attempted restraint) and restriction (i.e., actually avoiding foods that one enjoys, actually delaying eating, actually limiting eating, any actual restriction) were calculated. Holm-corrected Chi-square tests and Fisher’s exact tests examined differences in frequencies of each form of restraint and restriction between participants with and without LOC eating. The percentage of restraint attempts that were successful (i.e., resulted in actual restriction) in each group was calculated and compared.

Given the nested nature of our data (repeated observations within person), we used multilevel models using a binomial distribution with a logit link function to test whether dietary restraint/restriction on one day was associated with likelihood of engaging in LOC eating at the next survey and on the following day among participants with LOC eating. Separate models were conducted for each type of dietary restraint and restriction (i.e., 8 models in total). Models included restraint/restriction variables as fixed predictors and a random intercept per subject.

Results

Participant Characteristics

37 adolescents (M age: 15.4 ± 1.5; 57.0% female; M BMI percentile = 97.3 ± 3.1) completed the EMA protocol. The racial breakdown of the sample was 62.1% White, 16.2% Black/African American, 16.2% multiracial, 2.7% American Indian, and 2.7% unknown. Of the total sample, 15 participants (40.5%) reported ≥ 1 LOC eating episode during the recording period; this subsample reported 62 episodes of LOC eating in total with an average of 0.59 LOC episodes per day (range = 0–4) and 4.43 episodes over the EMA period (range = 1–11). LOC eating was reported on 3.07 days on average. Participants with LOC eating completed 454 surveys; participants without LOC eating completed 624 surveys. Descriptive statistics for each group are reported in Table 1. Given that all participants but one reported a gender identity that was the same as their reported sex, we only reported sex. The two groups did not significantly differ by demographics or BMI percentile. Participants with LOC eating reported presence of LOC at 26.7% of eating episodes.

Table 1.

Participant characteristics.

No LOC (n = 22) LOC (n = 15)
N % N % X2 P Cramer’s V
Sex 0.47 .49 0.2
Male 8 36.4% 8 53.3%
Female 14 63.6% 7 46.7%
Race - .09 0.3
White 11 50.0% 12 80.0%
Non-White 11 50.0% 3 20.0%
No LOC (n = 22) LOC (n = 15)
M SD M SD t P Cohen’s d
Age 15.4 1.7 15.3 1.2 0.16 .88 −0.05
BMI Percentile 97.3 2.8 97.3 3.3 0.04 .97 −0.02
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†

Bolded statistics indicate significance at p < .05. Cramer’s V interpretation: small ≤ 0.2; medium = 0.3–0.6; large > 0.6. Cohen’s d interpretation: small = 0.2; medium = 0.5; large = 0.8.

‡

Racial categories were collapsed into White and Non-White due to small cell sizes.

§

A Fisher’s exact test was used to examine frequencies of race due to small cell sizes; thus, no test statistic was reported.

Comparing Frequency of Dietary Restraint and Restriction Between Participants with and without LOC Eating

As shown in Table 2, adolescents who endorsed LOC eating during the recording period more frequently attempted to avoid enjoyable foods, attempted to limit their eating, and attempted any form of dietary restraint relative to those without LOC eating, with small effect sizes. There was no significant difference in frequency of attempts to delay eating between groups, and the effect size was small.

Table 2.

Evaluating differences in frequency of dietary restraint and restriction forms between participants with and without LOC eating.

No LOC (n = 22) LOC (n = 15)
N % N % X 2 P Cramer’s V
Dietary restraint
Attempted avoidance of enjoyable foods 5 1.0% 15 3.9% 7.2 .007 0.1
Attempted delaying eating 93 18.3% 75 19.5% 0.1 .720 0.0
Attempted limiting eating 6 1.2% 41 10.6% 37.5 .000 0.2
Any attempted restraint 100 19.7% 120 31.2% 14.9 .000 0.1
Dietary restriction
Actual avoidance of enjoyable foods 3 0.6% 7 1.8% .110 0.1
Actual delaying eating 19 3.7% 25 6.5% 1.98 .197 0.1
Actual limiting eating 6 1.2% 25 6.5% 16.9 .000 0.1
Any actual restriction 23 4.5% 52 13.5% 21.8 .000 0.2
Open in a new tab
†

Bolded statistics indicate significance at p < .05. Cramer’s V interpretation: small ≤ 0.2; medium = 0.3–0.6; large > 0.6.

‡

A Fisher’s exact test was used to examine frequencies of actual avoidance of enjoyable foods due to small cell sizes; thus, no test statistic was reported.

Participants with LOC eating more frequently reported actually limiting their eating and engaging in any actual restriction relative to those without LOC eating, with small effect sizes. There were no significant differences in actual avoidance of foods or actual delaying of eating between groups, and effect sizes were small.

Comparing Rates of Successful Dietary Restraint Between Participants with and without

LOC Eating

34% of participants’ attempts at restraint were successful (i.e., resulted in actual restriction). As shown in Table 3, participants with LOC eating had a significantly greater proportion of any dietary restraint attempts that were successful relative to those without LOC eating, with a small effect size. Rates of successful attempts to avoid enjoyable foods, delay eating, and limit eating did not significantly differ between groups, and effect sizes were small to medium.

Table 3.

Evaluating differences in percentages of dietary restraint attempts that were successful between participants with and without LOC eating.

No LOC (n = 22) LOC (n = 15)
N % N % X 2 P Cramer’s V
Avoidance 3/5 60.0% 7/15 46.7% - 1.0 0.1
Delaying eating 19/93 20.4% 25/75 33.3% 2.89 .089 0.1
Limiting eating 6/6 100% 25/41 61.0% 2.02 .238 0.3
Total 23/100 23.0% 52/120 43.3% 9.15 .006 0.2
Open in a new tab
†

Bolded statistics indicate significance at p < .05. Cramer’s V interpretation: small ≤ 0.2; medium = 0.3–0.6; large > 0.6.

‡

A Fisher’s exact test was used to examine frequencies of avoidance of enjoyable foods due to small cell sizes; thus, no test statistic was reported.

Temporal Relations Between Dietary Restraint/Restriction and LOC Eating

As shown in Table 4, attempted avoidance of enjoyable foods at one survey predicted greater likelihood of LOC eating at the next survey and attempted limiting eating at one survey predicted greater likelihood of LOC eating at the next survey. There were no significant effects of attempted delaying eating, any attempted restraint, or any form of successful restriction on next-survey LOC eating. There were no significant associations between dietary restraint/restriction on one day and LOC eating on the the next day.

Table 4.

Multilevel models examining relations between dietary restraint/restriction and LOC eating at the next survey.

Independent variable Odds ratio Statistic SE P
Dietary restraint
Attempted avoidance 6.55 1.88 .895 .036
Attempted delaying eating 0.67 −0.40 .704 .570
Attempted limiting eating 5.38 1.68 .778 .031
Any attempted restraint 1.57 0.45 .510 .378
Dietary restriction
Actual avoidance 0.07 −2.65 1.28 .974
Actual delaying eating 0.42 −.868 .962 .998
Actual limiting eating 0.43 −0.83 1.28 .516
Any actual restriction 0.25 −1.37 .813 .092
Open in a new tab
†

Bolded statistics indicate significance at p < .05.

Discussion

This study naturalistically examined the frequency of engagement in forms of dietary restraint and restriction among adolescents with and without LOC eating who were seeking weight control. We also examined whether dietary restraint/restriction predicted likelihood of LOC eating at the next survey and on the next day.

Prevalence of Forms of Dietary Restraint/Restriction

Overall, participants reported engaging in some form of dietary restraint at 25% of surveys and reported engaging in some form of dietary restriction at 9% of surveys. Participants with LOC eating attempted avoidance of enjoyable foods, attempted limiting eating, and attempted any form of dietary restraint more frequently than those without LOC eating. Participants with LOC eating also actually limited their eating and engaged in any form of restriction more often than those without LOC eating. Both findings align with theoretical models of dietary restraint (Fairburn et al., 2003; Polivy & Herman, 1985). Results suggest that adolescents with LOC eating may be particularly likely to engage in some forms of both restraint and restriction.

Across the sample, about one-third of participants’ attempts at dietary restraint were successful (i.e., resulted in actual restriction). Relative to participants without LOC eating, those with LOC eating had a significantly greater proportion of successful attempts at restraint (43% vs. 23% of attempts). This finding further reinforces theoretical models in which dietary restriction leads to LOC eating via increased physiological cues to binge. For example, it is possible that participants whose restraint attempts resulted in successful restriction more frequently experienced feelings of hunger and thus more frequently engaged in LOC eating.

Prospective Relations Between Restraint/Restriction and LOC Eating

Among participants with LOC eating, attempting to avoid enjoyable foods at one survey predicted greater likelihood of LOC eating at the next survey. Attempting to limit eating at one survey also predicted greater likelihood of LOC eating at the next survey. These findings provided support for the theory that specific forms of dietary restraint may differentially relate to LOC eating on a momentary level. No effects of restraint/restriction on one day in relation to LOC eating on the next day were observed, suggesting that these prospective relationships occur on a shorter time scale. These findings also align with the only previous EMA study to examine the effects of specific forms of restraint/restriction in relation to LOC eating, which found that attempted avoidance of enjoyable foods predicted greater LOC eating at the next survey among adults with binge-spectrum EDs (Manasse et al., 2023).

No relationships between attempted delaying eating or composite restraint and LOC eating were observed, adding to the mixed literature on the relation between restraint and LOC eating among youth (Sehm & Warschburger, 2018; Smith et al., 2020; Spoor et al., 2006; Stice et al., 2011). The effects of attempted avoidance and attempted limiting eating may suggest that hedonic hunger or craving, which may be promoted by efforts to avoid specific foods or limit intake, is a particularly powerful mechanism of LOC eating among adolescents seeking weight control. Additional factors may have also played a role in the relation between restraint at one survey and LOC eating at the next survey. For instance, the relation between momentary negative affect and LOC eating is well-documented in EMA literature (Engel et al., 2016; Schaefer et al., 2020). It is plausible that attempts to avoid and limit eating increase momentary negative affect, thus increasing likelihood of LOC eating. Finally, it is also possible that the timing of the EMA surveys did not adequately capture possible effects of attempted delaying eating or composite restraint.

Surprisingly, there were no significant effects of actual restriction on subsequent LOC eating in this study. This finding suggests that attempting to restrain eating is a stronger driver of feelings of LOC, which has been supported in a previous study (Manasse et al., 2023). This also aligns with the theory that failed restraint attempts lead to negative self-evaluation due to perceived lack of control, which triggers LOC eating (Schaumberg et al., 2016). Another possibility is that the restriction employed by participants in this study was moderate in nature and thus did not trigger sufficient physiological pressure to promote LOC eating. Prior research has also suggested that the relation between dieting and risk for disordered eating may depend on the extent of dietary restriction and type of psychological approach to dieting (e.g., rigid versus flexible) (Haynos et al., 2015). For example, an adolescent who successfully restricts their eating and displays psychological rigidity toward dieting (which might be evidenced by, for instance, experiencing intense distress after breaking a dietary rule) may have greater risk for LOC eating than one who successfully restricts and has a flexible approach to dieting. This relationship may also be influenced by the nature of individuals’ restraint intentions. Perhaps adolescents with goals to employ moderate dietary restraint are more likely to successfully restrict and then evaluate themselves positively, which could protect against subsequent LOC eating. In contrast, adolescents with intentions for extreme restraint may be less likely to achieve restriction, triggering poor self-evaluation and LOC eating. Thus, additional research is needed to understand the interplay between attempted versus successful restraint, the degree of rigidity toward dieting, and the nature of intentions of restraint.

Strengths and Limitations

A strength of this study was the use of an EMA design, which allowed us to collect ecologically valid data. By exploring the restraint-LOC eating relationship in a weight control-seeking sample, we were able to acquire results with clinical relevance for adolescents seeking weight control. Limitations included that participants may not have recorded all episodes of restraint/restriction and LOC eating, which may have hindered our ability to detect temporal relationships. Another limitation is that, due to the binary nature of our restriction items, we lacked information about the degree to which restriction was endorsed (e.g., moderate versus extreme restriction) or the psychological approach used to employ restraint/restriction (e.g., rigid versus flexible). Additionally, relations between restraint and LOC eating among adolescents seeking weight control may be different from those relations in other samples of adolescents. Finally, the study sample was predominantly White, which hindered our ability to evaluate presence of LOC eating by race/ethnicity and may limit generalizability of findings.

Conclusions and Future Directions

Findings suggest that adolescents seeking weight control who present with LOC eating are more likely to display forms of dietary restraint and restriction, and more likely to be successful in their attempts to restrict their eating, versus those without LOC eating. Results also indicate a nuanced relationship between restraint/restriction and subsequent LOC eating in this sample. This study provides rationale for assessment and monitoring of restraint/restriction and LOC eating among adolescents seeking weight control. Given that reducing LOC eating aids weight loss (Ariel & Perri, 2016) and may reduce subsequent dietary restriction (Fairburn, 2008), targeting restraint/restriction in adolescent weight control treatments may improve both disordered eating and weight outcomes.

Future research might consider using passively collected data to better assess the duration of restraint attempts in relation to LOC eating. To further explore our finding that attempted avoidance predicted LOC eating at the next survey, it may be worthwhile to investigate the nature of attempted avoidance (e.g., whether avoidance of specific foods versus enjoyable food in general is more predictive of LOC eating) and whether subsequent LOC eating episodes include previously avoided foods. Future studies should also explore adolescents’ restraint intentions (e.g., moderate versus extreme) and psychological approach to restraint/restriction (e.g., flexible versus rigid), which may help to clarify circumstances under which restraint/restriction predict LOC eating.

Highlights.

  • Adolescents with LOC eating more commonly engaged in dietary restraint/restriction.

  • Attempting to avoid enjoyable foods predicted LOC eating at the next survey.

  • Attempting to limit eating predicted LOC eating at the next survey.

Acknowledgments

Role of funding sources:

This research was supported by NIH grants K23 DK124514 and T32 HL130357.

Footnotes

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Conflicts of interest: The authors have no conflicts to declare.

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