Food Addiction And Emotional Eating Are Associated With Intradaily Rest–Activity Rhythm Variability

doi:10.21203/rs.3.rs-1298306/v1

Purpose: The aim of the present study was to study the associations among parameters characterizing eating behavior and actimetry-derived indices of circadian rhythm of motor activity.

Methods: The study involved 81 participants (average age: 21.5 ± 9.6 y, women: 77.8%). Each study participant provided personal data, filled out the Yale Food Addiction Scale and the Dutch Eating Behavior Questionnaire, and wore a wrist actimeter for seven consecutive days to record motor activity. Using time series treatments, we obtained three cosinor-derived parametric—MESOR, amplitude, and acrophase—and four nonparametric indices—interdaily stability, intradaily variability (IV), most active 10-h period (M10), and least active 5-h period—characterizing the 24-h rhythm of motor activity. A multiple regression analysis adjusted for age, sex, and BMI was performed to assess the associations among the studied indicators.

Results: It was shown that IV is a predictor of symptoms of food addiction (β = 0.242, P = 0.037) and emotional eating (β = 0.390, P = 0.004), MESOR is a predictor of symptoms of food addiction (β = 0.342, P = 0.003), and M10 predicts restraint (β = 0.257, P = 0.015) and emotional eating (β = 0.464, P = 0.001).

Conclusion: It was shown for the first time that an increase in symptom counts of food addiction is associated with an increase in the average level and fragmentation of 24-h rhythm of motor activity.

Level of Evidence: Level V, cross-sectional descriptive study.

Food addiction

Emotional eating

Restraint eating

24-hour rest-activity rhythm

Intradaily variability

MESOR

Most active 10-h period.

Circadian misalignment is associated with unhealthy food preferences and increased risk of obesity. Previous studies have not found an association between subjective measure of circadian misalignment (social jetlag) and FA.

What your study adds?

This study shows for the first time that increase in intradaily variability – an objective indicator of 24-h RAR impairment – is positively associated with SC of FA. Preliminary data were also obtained indicating that the characteristics of the 24-h RAR in individuals with FA are different from those in individuals with BED.

Circadian misalignment is a risk factor for the development of impaired cognitive functions, psychoemotional states, and eating behavior, which has been repeatedly demonstrated in relation to such form of circadian misalignment as shift work [1–4]. Social jetlag (SJL) is a self-reported measure of circadian misalignment that arises due to mismatch between biological and social clocks [5]. Numerous studies have identified a number of negative consequences of SJL, including impaired academic performance [6], psychoemotional state [7], and sleep quality [8] of young people. Individuals with SJL have unhealthy dietary preferences [8, 9] and signs of obesity [10, 11], metabolic syndrome [12], and type 2 diabetes [13]. Given that eating disorders are one of the risk factors for obesity and metabolic disorders, it is to be expected that there is an association between eating disorders and the circadian misalignment. Recent data indicate an increased frequency of detection of food addiction (FA) in students with a late chronotype [14], indirectly confirming this position. However, in a direct study conducted on a large sample, it was not possible to identify a significant association between SJL and FA [15]. There was also no association of SJL with restraint, external, and emotional eating behaviors [16]. One of the possible reasons for the negative result is that the subjective method used in this study to assess the state of the circadian system was not sensitive enough to identify the association between eating behavior and circadian misalignment.

We hypothesize that actimetry-derived 24-h rest/activity rhythm (RAR) characteristics are sensitive indicators to identify the association between eating disorders and the circadian misalignment. Previously [17], actimetry was successfully used to assess 24-h RAR in individuals with binge eating disorders (BED). The authors showed that individuals with BED had a decrease in amplitude and Midline Estimating Statistics of Rhythm (MESOR) of 24-h RAR. Currently, there are no studies devoted to the study of 24-h RAR in individuals with FA. An association between FA and emotional eating has been shown in previous studies [18–20]. Given this fact, it can be assumed that FA and emotional eating will show similarities in the profiles of the daily rhythm of motor activity.

The aim of this study was to analyze the relationships between indicators characterizing eating behavior and objective indicators characterizing the daily rhythm of activity and rest.

Subjects and Data Collection

The study was conducted from February 2019 to March 2020 upon 81 voluntary and anonymous participants aged 18 to 62 y. The research program was approved by the Ethics Committee of the Institute of Physiology of the Komi Scientific Center, Ural Branch of the Russian Academy of Sciences. Each study participant signed an informed consent form. Persons working on night shifts, as well as those with sleep disorders, were excluded from the study. Each study participant indicated their sex, age, height, and weight, and completed the Yale Food Addiction Scale (YFAS) [21] and the Dutch Eating Behavior Questionnaire (DEBQ) [22]. Weight and height were used to calculate body mass index (BMI) as weight in kilograms divided by height in meters squared. Sex- and age-specific BMI percentiles were calculated using BMI growth charts [23]. Table 1 shows mean values of the studied indicators, standard deviations, skewness, and kurtosis. Almost all indicators, with the exception of L5, have normal distribution, so they can be used in the analyses without any modifications. A modified indicator (2+lnL5), which has a normal distribution was used in the analyses.

Table 1

Quantitative characteristics of study participants
Parameter	Abbreviations	Instrument	Mean	SD	S	K
Age, years		Self-report	21.51	9.64	0.79	0.86
Body mass index, percentiles	BMI	Self-report	47.51	25.33	0.26	-0.63
Symptom counts, scores	SC	YFAS	2.31	1.75	0.65	0.42
Restraint behavior, scores	DEBQ_restr	DEBQ	2.19	1.00	0.68	-0.28
External behavior, scores	DEBQ_extern	DEBQ	2.92	0.58	-0.15	-0.15
Emotional behavior, scores	DEBQ_emo	DEBQ	1.94	0.85	0.99	0.03
Medline estimating statistics of rhythm, a.u.	MESOR	Actimetry	10.36	1.97	0.09	-0.27
Amplitude, a.u.	A	Actimetry	7.49	2.07	0.17	-0.47
Acrophase, hh : min	φ	Actimetry	14:52	01:07	0.17	-0.20
Most active 10-h period, a.u.	M10	Actimetry	17.28	3.29	0.21	-0.45
Least active 5-h period, a.u.	L5	Actimetry	0.60	0.24	2.25	9.95
Ln(Least active 5-h period), a.u.	2+lnL5	-	1.42	0.36	0.29	0.76
Inter-daily stability, a.u.	IS	Actimetry	14.84	2.61	-0.14	-0.76
Intra-daily variability, a.u.	IV	Actimetry	1.65	1.01	0.40	-0.16
a.u. – arbitrary units; SD – standard deviation; S – skewness; K – kurtosis.

Instruments

YFAS. The scale consists of 25 questions and describes seven diagnostic criteria for drug addiction (such as tolerance, withdrawal, and loss of control) and clinically significant eating disorders. The results of the treatment are presented in the form of (a) a quantitative indicator equal to the sum of confirmed symptoms (SC, varying within the range of 0 to 7), and (b) a qualitative indicator corresponding to a clinically significant eating disorder in the presence of three or more symptoms of FA. The Cronbach’s α for this sample was 0.87.

DEBQ. The test consists of 33 questions divided into three subscales evaluating three types of eating behavior: restraint (DEBQ_restr, 10 points), external (DEBQ_extern, 10 points), and emotional (DEBQ_emo, 13 points). A Likert-type scale ranging from 1 to 5 is used in response to all items. To evaluate the results, the sum of points for each subscale is calculated separately and divided by the number of points. The Cronbach’s α values for DEBQ, DEBQ_restr, DEBQ_extern, and DEBQ_emo were 0.88, 0.92, 0.52, and 0.94, respectively.

Wrist Actimetry. Each participant wore a Daqtometer v2.4 actimeter (Daqtix, Germany) on the wrist of their nondominant hand continuously for 1 week. The activity was measured at 1 Hz and the values were summed up once a minute and expressed in arbitrary units (a.u.). The study participants kept a diary in which they indicated the times that they removed the actimeter to engage in activities involving water, as well as the times that they went to bed in the evening and got out of bed in the morning. The Chronos-Fit software package (Zuther P. and Lemmer B., Germany) was used to calculate the following parametric indices: Midline Estimating Statistic of Rhythm (MESOR; Figure 1), amplitude (A; Figure 1), and acrophase (φ; Figure 1) [24, 25]. The whole dataset from each person was approximated by a sine function with a fixed period (tau=24 hours). In addition, nonparametric indices were also calculated: most active 10-h period (M10; Figure 1), and least active 5-h period (L5; Figure 1) [26], interdaily stability (IS), intradaily variability (IV) [27].

Statistical analyses

We used SPSS version 20 (SPSS, Inc., Chicago, IL, USA) software package for the statistical analysis. Descriptive statistics (mean, standard deviation) of variables were calculated. Multiple regression analyses were performed, in which SC, DEBQ_restr, DEBQ_extern, and DEBQ_emo were used as dependent variables, and sex, age, BMI, Mesor, A, φ, M10, 2+lnL5, IS, and IV were used as independent variables (predictors). A stepwise inclusion procedure was used to determine the final set of predictors used in the model. To assess multicollinearity, the variance inflation factor (VIF) was assessed. A predictor was excluded from the analysis if VIF was ≥5.

The results of the study showed that 13.6% of the individuals we examined were overweight or obese (Table 2). The incidence of FA in study participants was 12.3% (Table 2), and the mean SC of FA was 2.31±1.73 (Table 1). SC of FA were higher in women, in persons with overweight/obesity, and in young adults (Table 3, Model 1). DEBQ_restr were higher in individuals with overweight/obesity, and in females (Table 3, Model 2), whereas DEBQ_extern was higher in females (Table 3, Model 3).

Table 2

Qualitative characteristics of study participants
Parameter	Gradations	%
Sex	Females	63	77.8
BMI	Ov/ob	11	13.6
Food addiction	FA	10	12.3

Table 3

Associations of eating behavior and depression symptoms with rest-activity rhythm characteristics
Model #	Dependent variable	Independent variables	B	β	P	R²	ΔR²	VIF
1	SC	Sex	-1.464	-0.345	0.001	0.066	0.066	1.116
		Age	-0.068	-0.323	0.007	0.178	0.112	1.567
		BMI	1.415	0.482	0.000	0.249	0.071	1.482
		MESOR	0.308	0.342	0.003	0.322	0.073	1.407
		IV	2.161	0.242	0.037	0.360	0.038	1.524
2	DEBQ_restr	BMI	0.014	0.340	0.001	0.080	0.080	1.088
		M10	0.076	0.257	0.015	0.240	0.160	1.119
		Sex	-0.770	-0.318	0.002	0.335	0.095	1.060
3	DEBQ_extern	Sex	-0.339	-0.237	0.042	0.056	0.056	1.000
4	DEBQ_emo	M10	0.118	0.464	0.001	0.059	0.059	1.472
		IV	1.712	0.390	0.004	0.162	0.103	1.472
Note. B: non-standardized regression coefficient; β: standardized regression coefficient; p: significance of B; ΔR²: portion of the variance accounted for by separate predictors in the model; VIF: variation inflation factor; in each model all parameters presented in the Table 1 were used as predictors; only significant variables were included in the final model.

There was a positive association between MESOR and SC (Table 3, Model 1), indicating that individuals with pronounced signs of FA had a higher average daily level of motor activity.

A positive association of IV with SC and DEBQ_emo was noted (Table 3, Models 1&4). These data indicate that the fragmentation of the circadian rhythm of motor activity is characteristic of persons with SC of FA and emotional eating.

A positive associations of M10 with DEBQ_restr and DEBQ_emo were noted (Table 3, Model 2&4). These data indicate that persons with restraint and emotional types of eating behavior are characterized by an increased level of motor activity in the daytime.

To our knowledge this is a first report on the association between FA and actimetry-derived 24-h RAR characteristics. Studies of 24-h RAR characteristics in BED patients have been previously conducted [17, 28]. Individuals with BED have been shown to have a decrease in amplitude and MESOR [17, 28]. Women with BED during multidisciplinary therapy [28] had higher IS values than the control group, lower L5 and M10 values, and no statistically significant differences in IV and acrophase of 24-h RAR. The authors concluded that multidisciplinary therapy leads to improved sleep-wake rhythm characteristics in patients with BED, primarily IS and L5. In this study, we have shown that the nature of the association between FA and 24-h RAR characteristics differs significantly from those in BED patients. We noted a positive association between SC of FA and MESOR and did not detect an association between SC and the amplitude and acrophase of 24-h RAR. In addition, we have shown that of all the studied nonparametric indicators of 24-h RAR, only the IV index was significantly associated with SC. It has been repeatedly shown that BED and FA have similarities [29–31]. Both eating disorders are more common in women, as well as in overweight individuals. Both types of eating disorders are characterized by a close association with impulsivity and depression [29]. At the same time, the difference between these two types of eating disorders, mainly related to behavior, was noted; there was the least close correlation between the behavioral component of BED and SC of FA [31]. It can be assumed that actimetry-derived 24-h RAR is a sensitive method for detecting differences between these two types of eating disorders. To test the validity of this assumption, it is necessary to conduct comparative studies of 24-h RAR characteristics in BED and FA.

The present study showed that an objective assessment of motor activity rhythm is a convenient and sensitive method for studying the associations between the states of the circadian system and eating behavior. Two features of motor activity rhythm characteristic of persons with FA and emotional eating were identified. Firstly, it was shown that individuals with symptoms of FA and emotional eating had a more pronounced fragmentation of the 24-h rhythm of motor activity. Previously, it was shown that FA is more often detected in young people with a late chronotype [14]. At the same time, there was no association between SJL and FA [15]. This negative result may be due to the fact that MCTQ is not sensitive enough to study the association between the states of the circadian system and eating behavior. SJL is primarily a measure of the instability of the phase of circadian rhythm during a calendar week. Judging by the data we obtained, fragmentation of the circadian rhythms of motor activity is a characteristic trait of persons with FA and emotional eating. The relationship between FA and emotional eating has been repeatedly noted in the past [18, 19], indicating similarities between these two characteristics of eating behavior.

Secondly, we showed that persons with high SC of FA have a higher average daily level of motor activity (MESOR), and that the profile of the daily rhythm of motor activity of people with restraint and emotional eating behaviors is characterized by an increased level of motor activity during the daytime (M10). These results were unexpected, since it is well known that FA is a risk factor for obesity [20, 21]. Obesity, in turn, is associated with a decrease in the level of motor activity [32, 33]. However, there is quite often a weak or nonlinear association between SC of FA and the morphological signs of obesity [34–36]. In addition, FA and emotional eating are just some of the many risk factors for obesity. A distinctive character trait of people with FA and emotional eating is impulsivity of behavior [37, 38]. A positive association between impulsivity and the level of motor activity in the daytime has been repeatedly noted in previous research [39, 40].

The data obtained indicate the presence of a significant association between FA and sociodemographic indicators: higher SC of FA was more often detected in women and in overweight people, and there was also a decrease in the SC of FA with age. These data are consistent with the results of previous studies [41, 42] and indicate that the study was conducted on a representative sample.

Strengths And Limitations

The study has a number of limitations. A significant bias towards women among the study participants could have affected the results of the analysis. The cross-sectional design of the study did not allow us to judge the causal associations between the studied indicators. The study involved practically healthy people whose anthropometric data and eating behavior were evaluated by self-assessment, so the results obtained cannot be used in clinical practice.

Overall, the current study shows that actimetry is a sensitive tool for studying the relationship between 24-h rhythms and eating behavior. There was an increase in fragmentation of the rhythm of motor activity in individuals with symptoms of FA and emotional eating. There was also an increased level of motor activity in people with FA and restraint and emotional eating.

Acknowledgements

The YFAS scale and scoring instruction were kindly provided by Dr. Ashley Gearhardt.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

This study was approved by the ethics committee of the Institute of Physiology of Komi Science Center, Ural Branch of RAS, and was conducted in accordance with the ethical standards of the Helsinki declaration.

Funding

No grant was obtained for this research.

Informed consent

Informed consent was obtained from all participants and parents of minors included in the study.

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Food Addiction And Emotional Eating Are Associated With Intradaily Rest–Activity Rhythm Variability

Status:

Version 1

Abstract

Figures

What Is Already Known On This Subject?

Introduction

Materials And Methods

Subjects and Data Collection

Instruments

Statistical analyses

Results

Discussion

Strengths And Limitations

Conclusions

Declarations

References

Status:

Version 1