Chinese Version of the Nine Item ARFID Screen: Psychometric Properties and Cross-Cultural Measurement Invariance

Abstract

Objective: The current study aimed to provide initial psychometric evidence for a Chinese version of the Nine-Item ARFID Screen (C-NIAS), which measures the symptoms of avoidant/restrictive food intake disorder (ARFID), including picky eating, poor appetite/limited interest in eating, and fear of aversive consequences from eating. Method: The NIAS was translated into Chinese according to standard procedures. A total of 1,069 college students from mainland China responded to the C-NIAS. The factor structure was confirmed using confirmatory factor analysis. Convergent and divergent validity were assessed using regression analyses. Finally, measurement invariance was tested, and latent mean differences were compared, between Chinese and American college samples. Results: The measure’s original three-factor structure was confirmed. Strong measurement invariance between college samples of the United States and China was supported, and Chinese students had significantly higher latent mean scores on all three factors. The subscales showed the expected patterns of correlations with other validity constructs. Conclusions: The C-NIAS shows good psychometric characteristics and holds promise to facilitate much-needed research on subclinical symptoms of this understudied eating disorder in Chinese-speaking adult populations. Moreover, Chinese college students demonstrate greater potential ARFID symptomatology than American counterparts. More attention to ARFID should be called for in China.

Keywords

ARFID avoidant/restrictive food intake disorder Chinese validation invariance

Avoidant/restrictive food intake disorder (ARFID) is an eating/feeding disorder diagnosed in individuals with avoidant/restrictive eating behaviors that lead to one or more core symptoms: weight loss or failure to grow, nutritional deficiency, supplement dependence, and/or psychosocial impairment (American Psychiatric Association, 2013). In its diagnosis, the eating behaviors in ARFID must not be associated with distortions in how body shape and weight are experienced by the affected individual and cannot occur exclusively in the context of another restrictive eating disorder (e.g., anorexia or bulimia nervosa). Diagnostic and Statistical Manual of Mental Disorders–Fifth edition (DSM-5) lists three patterns of eating behavior that can lead to these symptoms: (a) avoidance of foods, often meats, vegetables, and/or fruits, based on aversion to their sensory properties (selective/neophobic, or ‘picky’ eating); (b) poor appetite and/or limited interest in eating; and (c) fear of aversive consequences from eating, such as choking, vomiting, and other forms of gastrointestinal (GI) distress (Strand et al., 2019).

When DSM-5 was introduced, there were no published data supporting the existence of the three distinct patterns of eating in ARFID or that they could lead to the core ARFID symptoms. However, chart review studies have since provided evidence that the three eating restrictions described in DSM-5 are consistent with those that cause weight loss/growth failure, nutritional problems, and psychosocial impairment in patients diagnosed with ARFID (Cooney et al., 2018; Lucarelli et al., 2018; Makhzoumi et al., 2019; Nicely et al., 2014; Norris et al., 2018; Reilly et al., 2019; Williams et al., 2015; Zickgraf, Lane-Loney, et al., 2019).

ARFID in the West and East

To date, most of the published chart reviews have been conducted in North America, Mexico, and Europe, with almost all patients being children, adolescents, and young adults. The only exception, to our knowledge, is data from Japan on adult inpatients with an ARFID diagnosis (Nakai et al., 2016; Nakai et al., 2017), which revealed some interesting differences from samples in the West. For example, whereas selective/neophobic eating appears to be a common cause of ARFID symptoms in pediatric/young adult samples from North America and Europe, Nakai et al. (2016) did not find any patients who attributed their eating restrictions to selective/neophobic eating in their Japanese sample. For another example, fear of aversive consequences is the most common presentation identified in adolescent medicine-focused chart reviews in Western samples, with the feared consequence almost invariably being choking or vomiting (Zickgraf, Lane-Loney, et al., 2019). In their Japanese adult sample, while Nakai et al. (2016) also found a high prevalence of fear-driven avoidance of eating, the most commonly articulated fear was abdominal pain.

Whether these differences are due to the differing ages of the patients or to cross-cultural differences between American/European and Japanese ARFID patients is unclear. Even less is known about ARFID in the Chinese population, as there has been no research on ARFID patients in non-Western countries other than Japan, despite evidence for comparable prevalence, sociodemographic correlates, and descriptive psychopathology of other eating disorders and their subclinical manifestations between China and Western European and North American countries (Thomas et al., 2016; Tong et al., 2014). The lack of research on the prevalence and correlates of clinically diagnosed ARFID in China (Sun et al., 2020) is partly due to the lack of Chinese-language measures, particulary Chinese translations of existing measures of ARFID symptoms.

At the subclinical level, there has been limited research on ARFID-related eating restrictions in China. In both Western countries and China, picky eating in children not only is associated with parenting stress and nutritionally inadequate diets but also represents a risk factor for ARFID (Galloway et al., 2005; Xue, Zhao, et al., 2015). Departing from this cross-cultural commonality, a study found that individual differences in appetite and food motivation (e.g., enjoyment of eating, desire to eat in the presence of food, emotional overeating and undereating, awareness of satiety, slow eating) did not correlate with weight/growth in young children in China (Cao et al., 2012), in contrast to a large body of literature in Western countries showing that childhood appetitive traits are associated with body mass index (BMI; e.g., Jansen et al., 2012; Sleddens et al., 2008; Viana et al., 2008). To our knowledge, other than two studies on adult picky eating (He, Ellis, et al., 2019; He, Zickgraf, et al., 2020), no other research has been conducted on the subclinical manifestations of fearful avoidance of food, or related conditions such as vomiting/choking phobia, in any age group in China.

The Cultural Context of Food and Eating in China

Given the scarcity of empirical research on cultural differences in patterns of picky eating, low appetite, and food-related fear, the broad cultural context of food and eating seems important to consider first, as eating disorders are generally considered to be linked to culture (Lee, 2001).

Food and eating play a central role in Chinese life, as evidenced by the cultural function of food (e.g., the pervasive symbolic role of food, in which food, festivals/celebrations, and family reunion are intricately related to each other), by the social function of meals (e.g., the common practice to treat others with meals to enhance established relations or to facilitate new relations), by the time a typical family spends on purchasing food and then cooking (e.g., 2-3 hours every day) and the way food is served (e.g., dishes are usually for sharing), by the common way of everyday greeting (“Have you eaten yet?”), and by metaphors embedded in everyday language (e.g., to lose a job is referred to as to lose one’s rice bowl). The central role of food and eating in Chinese life has a long history, ingrained in philosophical thoughts, such as in Lao-tzu’s famous quote, “Govern a great nation as you would cook a small fish,” in which he implied not to overdo it.

While food and eating permeate the Chinese life, the desire for slimness is also widespread, particularly among Chinese females, perhaps owing to the adoption of Western attitudes regarding ideal feminine beauty. Throughout the Chinese history, slimness was not always the preferred body type; for example, plumpness was regarded as a sign of prestige and beauty in the Tang dynasty. Even today it is not uncommon for many parents to prefer their potential daughter-in-law to be not skinny. Nevertheless, among the younger generation, slimness is highly desirable. For instance, in a university student sample in Hong Kong, Lee (1993) reported that all female students above a BMI of 20.5 kg/m² were inclined to diet and weigh less. Relatedly, some Chinese, particularly the elderly, may not hesitate to comment directly in your face that you are being a little too fat (or too skinny, for that matter), and go on to give advice on how to become slimmer (or to put on weight).

Although the role of slimness and thin ideals in modern China has gained research attention, weight and slimness are not the only reason for eating difficulties, or restrictive eating disorders. In a social milieu where eating together, serving others, and being served is important, eating difficulties such as having a narrow diet because of aversions to the sensory properties of food or aversions to trying new foods, difficulty eating an appropriate volume of food, or fear of negative consequences of eating are also likely to cause distress and impairment. Because the biological mechanisms underlying food-related sensory perception, homeostatic appetite, and fear response might be compromised in ARFID (Thomas et al., 2017), it is likely that these eating restrictions may manifest cross-culturally, with the corresponding symptom manifestations and lived experiences of patients shaped by cultural context. Given the public nature and performative function of meals and eating in Chinese culture, the eating restrictions associated with ARFID are likely to be at least as problematic as they are in Western cultures.

The Current Study

Given the relatively poor understanding of ARFID symptoms, diagnosis, and correlates in China, and given the potentially important cultural differences in ARFID, more empirical research in ARFID in China is needed. To date, this research has been hindered by a lack of valid Chinese-language measurement tools. Therefore, the aim of the current study was to validate a Chinese-language measure of adult ARFID symptoms. The Nine-Item ARFID Screen (NIAS) is a brief instrument, currently validated for use as a measure of dimensional symptoms of the three predominant ARFID presentations in English-speaking samples in the United States. The NIAS is the only measure designed for adult self-report to capture all three ARFID-related eating restrictions. We expected that the three-factor structure of the NIAS would be replicated, and that there would be cross-cultural measurement invariance between United States and Chinese college samples. We also explored 4-week test–retest reliability in a subsample of participants.

Furthermore, we explored convergent validity of C-NIAS by using a measure of appetitive traits (the Adult Eating Behavior Questionnaire [AEBQ]), expecting that (a) scales measuring low appetite and undereating (satiety responsiveness, slow eating, emotional undereating) would be positively related to the Appetite scale of NIAS (hereafter referred to as Appetite); (b) scales measuring food motivation and overeating (enjoyment of food, food responsiveness, emotional overeating) would be moderately negatively related to Appetite; and (c) the scale measuring selective/neophobic eating (food fussiness) would be strongly related to the NIAS scale measuring the same construct (picky eating). We also explored predictive validity with proxy measures of ARFID symptoms: weight loss/low weight (BMI based on self-reported weight and height) and psychosocial impairment (the Kessler Psychological Distress Scale, a measure of psychological distress; and the Satisfaction with Food-related Life Scale [SWFL], a measure of eating-specific quality of life). Again, we expected to replicate findings from the original development paper that only Appetite would be associated with BMI. Furthermore, we predicted that all three NIAS scales would be positively related to psychological distress and negatively related to satisfaction with food life, unattenuated by controlling for symptoms of weight/shape-related disordered eating symptoms (Zickgraf & Ellis, 2018).

Method

Participants and Procedure

The study protocol was approved by the Research and Development Administration Office of the first author’s previous affiliation, and participants provided informed consent. Participants were 1,069 college students (52.6% women) from two colleges (one in Liaoning province, the other in Zhejiang province), aged 17 to 24 years (M = 20.11, SD = 1.01). BMI, derived from self-reported height and weight, ranged from 14.30 to 36.89 kg/m² (M = 21.11 kg/m², SD = 3.10). Based on the recommended cutoff points of BMI for Chinese adults (i.e., <18.5 kg/m² = underweight, 18.5 ~ 23.9 kg/m² = healthy weight, 24.0 ~ 27.9 kg/m² = overweight, and >28 kg/m² = obese; Zhou, 2002), 209 (19.6%) had a BMI in the underweight range, 676 (63.2%) in the healthy weight range, and 172 (16.1%) in the overweight/obese range. BMI was missing for 12 (1.1%) participants who did not report height and/or weight. To evaluate test–retest reliability, a subsample of 101 participants retook the survey 4 weeks after the initial administration. All information was obtained using paper-and-pencil surveys. The current study is part of an ongoing project, and several papers have been published based on the same sample (e.g., He, Ellis, et al., 2019; He, Ma, et al., 2019; He, Sun, & Fan, 2020; He, Sun, Zickgraf, et al., 2019; He, Zickgraf, et al., 2020).

To test cross-cultural measurement invariance, two college samples from two different states in the United States were obtained from previous publications. One sample was from Ellis et al. (2018) and included 1,219 college students, of whom 63.3% were women, 74.4% were White, and the mean (SD) age was 18.57 (1.52) years. The other sample was from the original NIAS validation study (Zickgraf & Ellis, 2018) and included 314 college students, of whom 68.6% were women, 56.6% were White, and the mean (SD) age was 19.90 (2.50) years.

Measures

The Nine-Item ARFID Screen

The NIAS (Zickgraf & Ellis, 2018) has nine items rated on a 6-point Likert-type scale from 0 (strongly disagree) to 5 (strongly agree). The NIAS has three subscales: Picky eating, Appetite, and Fear. The original validation study showed that it has high internal consistency, high test–retest reliability, and satisfactory convergent/discriminant validity with other measures of picky eating, appetite, fear of negative consequences, and psychopathology (Zickgraf & Ellis, 2018).

Based on the standard procedures for translating a measurement instrument (Brislin, 1970), the English version of the NIAS was translated into Chinese by three Chinese doctoral students in psychology with high levels of English proficiency. Specifically, the three students translated the instrument independently, which resulted in three draft Chinese translations; then, based on the three drafts, a group discussion was conducted among the three translators and the first author of this article to obtain a preliminary consensual Chinese version of the instrument. Back-translation was done by a fourth bilingual Chinese doctoral student in psychology who had no previous knowledge about the NIAS. The back-translated version was then reviewed by the developer of the NIAS and some slight wording modifications were made (e.g., ‘我吃很少的食物，因为我害怕肠胃不适、被噎或呕吐 [I eat a little food because I am afraid of GI discomfort, choking, or vomiting] was changed to ‘我吃小份的食物，因为我害怕肠胃不适、被噎或呕吐 [I eat small portions of food because I’m afraid of gastrointestinal discomfort, choking, or vomiting]). Next, the modified version of the Chinese NIAS (C-NIAS) was administered to 10 Chinese college students. Based on their responses and postsurvey interviews, all students could correctly interpret each item of the C-NIAS; this version was finalized and administered to the full study sample.

Eating Attitude Test–26

The Eating Attitude Test–26 (EAT-26; Garner et al., 1982) is a 26-item self-report measure assessing eating disturbance. Each item is scored on a 6-point Likert-type scale from never to always, which, after a recoding procedure suggested by Garner et al. (1982), yields a response score ranging from 0 (never) to 3 (always). Higher total scores of EAT-26 reflect greater levels of eating disturbance. In the current study, the Chinese version of EAT-26 (Wang et al., 2015) was used, with Cronbach’s α = .92 in the current sample.

Satisfaction With Food-Related Life Scale

The SWFL (Grunert et al., 2007) is a five-item self-report measure assessing life satisfaction with food-related life. Each item is scored on a 6-point Likert-type scale from 1 (strongly disagree) to 6 (strongly agree), with higher scores reflecting higher levels of satisfaction with food-related life. In the current study, the Chinese version of the SWFL was used (He, Ma, et al., 2019), with Cronbach’s α = .90 in the current sample.

Kessler Psychological Distress Scale

The Kessler Psychological Distress Scale (K10; Andrews & Slade, 2001) is a 10-item self-report measure assessing psychological distress. Each item is scored on a 5-point Likert-type scale from 1 (none of the time) to 5 (all of the time), with higher scores reflecting greater levels of psychological distress. In the current study, the Chinese version of K10 (Xu et al., 2013) was used, with Cronbach’s α = .92 in the current sample.

Adult Eating Behavior Questionnaire

The AEBQ (Hunot et al., 2016) is a 35-item self-report measure for a comprehensive assessment of appetitive traits in young adults. The AEBQ assesses eight appetitive traits, including four food-approach traits (i.e., Hunger, Food Responsiveness, Emotional Overeating, and Enjoyment of Food), and four food-avoidance traits (Satiety Responsiveness, Emotional Undereating, Food Fussiness, and Slowness in Eating). Each item is scored on a 5-point Likert-type scale from 1 (strongly disagree) to 5 (strongly agree). In the current study, the Chinese version of AEBQ was used (He, Sun, et al., 2019), with Cronbach’s α = .72 for Food Responsiveness, 0.94 for Emotional Overeating, 0.87 for Enjoyment of Food, 0.75 for Satiety Responsiveness, 0.96 for Emotional Undereating, 0.73 for Food Fussiness, and 0.83 for Slowness in Eating.

Data Analysis

Data analyses were carried out using R version 3.5.2 (R Core Team, 2018). The percentage of values missing for the nine items of the C-NIAS ranged from 1.2% to 3.8%. As the amount of missing data was less than the recommended cutoff value of 5% (Schafer, 1999), we did not impute values for the missing data.

Using the lavaan package (Rosseel, 2012), we conducted confirmatory factor analysis with the “maximum likelihood with robust standard errors” estimator. Model fit was evaluated using the comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR); cutoffs for acceptable fit were evaluated based on Hu and Bentler’s (1999) recommendations. Additionally, tests of measurement invariance between the Chinese and U.S. college student samples were conducted. According to previous literature (Chen, 2007; Cheung & Rensvold, 2002), ΔCFI < 0.010 and ΔRMSEA < 0.015 indicate measurement invariance across different groups.

Furthermore, to develop comparable instruments in international studies, it is essential to examine differential item functioning (DIF) between different demographic groups—especially between different cultural and language groups (Le, 2006). DIF can reveal the extent to which an item assesses different constructs for members of separate subgroups. That is, in the current study, if there is no significant DIF across Chinese and U.S. samples, participants of different countries with the same level of a given ARFID eating behavior (e.g., picky eating) should exhibit the same likelihood of endorsing an item (e.g., “I am a picky eater”). Thus, DIF across countries was examined in the current study, as assessed with ordinal logistic regression models in a stepwise approach. Using the lordif package (Choi et al., 2011), for each item, we estimated three nested models that included the trait score for the NIAS (Step 1), country (China vs. United States; Step 2), and the interaction between trait score and country (Step 3). If the model at Step 3 explains significantly more variance than the model at Step 1, this would suggest that the item displays significant DIF; if the model at Step 2 explains significantly more variance than the model at Step 1, the item exhibits uniform DIF (i.e., one group consistently shows a higher or lower probability than the other group to give a certain response when responding to the item); and if the model at Step 3 explains significantly more variance than the model at Step 2, the item exhibits nonuniform DIF (i.e., one group shows a higher probability to give a certain response level, but a lower probability to give another response level). Traditionally, DIF is indicated by a significant chi-square difference test. However, when sample sizes are large, chi-square difference tests are oversensitive and may detect negligible effects that bear no clinical significance (Schaefer et al., 2019). Thus, given the large sample size used in the current study, and as recommended by Meade et al. (2008), we relied on the change in McFadden’s pseudo-R². As in Kisala et al. (2019), McFadden’s pseudo-R² was used to assess the significance of DIF, which is considered negligible when the change in R² is less than .02 (Choi et al, 2011; Jodoin & Gierl, 2001).

Internal consistency of the C-NIAS was assessed by ordinal Cronbach’s α. The correlation between original and follow-up scores was used to assess test–retest reliability. Moreover, using analysis of variance, we conducted group comparisons of the subscale scores and total scores of the C-NIAS by BMI category and by gender. Finally, we further evaluated the convergent/divergent validity of the C-NIAS subscales via separate regressions, controlling for (a) the other two scales, and (b) EAT-26.

Results

Confirmatory Factor Analysis

Based on the factor structure proposed in the development study of this measure (Zickgraf & Ellis, 2018), a three-factor correlated model was evaluated. Results showed that the model fit for the three-factor correlated model was good: χ² = 110.82 (degrees of freedom = 24, p < .01), RMSEA = 0.07, 90% confidence interval [0.06, 0.08], CFI = 0.97, TLI = 0.95, and SRMR = 0.04. Loadings and interfactor correlations are presented in Table 1.

Table 1.

Factor Loadings and Interfactor Correlations for CFA.

	F1. Picky eating	F2. Appetite	F3. Fear
1. I am a picky eater.	.58
2. I dislike most of the foods that other people eat.	.84
3. The list of foods that I like and will eat is shorter than the list of foods I won’t eat.	.71
4. I am not very interested in eating; I seem to have a smaller appetite than other people.		.72
5. I have to push myself to eat regular meals throughout the day, or to eat a large enough amount of food at meals.		.62
6. Even when I am eating a food I really like, it is hard for me to eat a large enough volume at meals.		.72
7. I avoid or put off eating because I am afraid of GI discomfort, choking, or vomiting.			.77
8. I restrict myself to certain foods because I am afraid that other foods will cause gastrointestinal discomfort, choking, or vomiting.			.85
9. I eat small portions because I am afraid of GI discomfort, choking, or vomiting.			.82
F1. Picky eating	1
F2. Appetite	.76	1
F3. Fear	.46	.78	1

Note. CFA = confirmatory factor analysis; GI = gastrointestinal.

Reliability

Cronbach’s α for the total scale of the C-NIAS was 0.86, with 0.74 for Picky eating, 0.73 for Appetite, and 0.86 for Fear. Four-week test–retest reliability was 0.72 for the total scale, 0.69 for Picky eating, 0.62 for Appetite, and 0.65 for Fear.

Convergent and Divergent Validity

As Table 2 shows, the total score of C-NIAS significantly and negatively predicted BMI, significantly and positively predicted psychological distress with a moderate to large effect, and showed a moderate negative relationship with satisfaction with food-related life. These associations were not attenuated by controlling for the EAT-26 score.

Table 2.

Convergent and Divergent Validity: Total Scores of the C-NIAS and/or the EAT-26 as Predictors.

Dependent variables	Independent variables	Step 1 (NIAS only)	Step 2 (controlling for EAT-26)
Dependent variables	Independent variables	β	β
BMI	C-NIAS Total	−0.15**	−0.18**
	EAT-26	—	0.09**
K10	C-NIAS Total	0.39**	0.35**
	EAT-26	—	0.23**
SWFL	C-NIAS Total	−0.28**	−0.26**
	EAT-26	—	0.05

Note. C-NIAS = Chinese version of the Nine-Item ARFID Scale; EAT-26 = Eating Attitude Test 26-Item version; SWFL = Satisfaction with Food-Related Life Scale; K10 = Psychological Distress Scale; BMI = body mass index.

p < .01.

Table 3 shows the results when the three NIAS subscales were included as independent variables in the regression models predicting each validity variable. Overall, Appetite traits showed the expected pattern of relationships with the AEBQ subscales, with positive and independent relationships with the four food avoidance subscales (including slow eating and emotional undereating), and negative relationships with the three food approach subscales. Food fussiness had a positive and moderate to large association with the Picky eating scale. Furthermore, the addition of the total scores of the EAT-26 to the models generally did not change the magnitude, direction, or significance of the effects in predicting the AEBQ subscales.

Table 3.

Convergent and Divergent Validity: The Three Subscales of the C-NIAS and/or the EAT-26 as Predictors.

Dependent variables	Independent variables	Step 1 (NIAS only)	Step 2 (controlling for EAT-26)
Dependent variables	Independent variables	β	β
EAT-26	Picky eating	0.02	—
	Appetite	0.08	—
	Fear	0.06	—
	EAT-26	—	—
BMI	Picky eating	−0.09*	−0.12**
	Appetite	−0.11*	−0.10*
	Fear	0.02	0.01
	EAT-26	—	0.10**
SWFL	Picky eating	−0.02	−0.02
	Appetite	−0.18**	−0.19**
	Fear	−0.13**	−0.11**
	EAT-26	—	0.05
K10	Picky eating	0.04	0.05
	Appetite	0.23**	0.22**
	Fear	0.20**	0.15**
	EAT-26	—	0.23**
AEBQ
Food fussiness	Picky eating	0.40**	0.42**
	Appetite	−0.02	−0.02
	Fear	0.10*	0.08*
	EAT-26	—	−0.06
Enjoyment of food	Picky eating	0.01	−0.01
	Appetite	−0.24**	−0.24**
	Fear	−0.11**	−0.11**
	EAT-26	—	0.12**
Food responsiveness	Picky eating	0.08*	0.07
	Appetite	−0.10*	−0.12**
	Fear	−0.01	−0.04
	EAT-26	—	0.29**
Slow eating	Picky eating	0.02	−0.002
	Appetite	0.11*	0.07
	Fear	0.05	0.04
	EAT-26	—	0.02
Satiety responsiveness	Picky eating	0.12**	0.15**
	Appetite	0.24**	0.22**
	Fear	0.07	0.08*
	EAT-26	—	−0.01
Emotional overeating	Picky eating	−0.02	−0.02
	Appetite	−0.10*	−0.10*
	Fear	0.07	0.05
	EAT-26	—	0.08*
Emotional undereating	Picky eating	0.001	−0.002
	Appetite	0.11*	0.12*
	Fear	0.01	0.02
	EAT-26	—	−0.002

p < .05. **p < .01.

When other eating symptoms/behaviors were controlled for, Appetite was negatively related to BMI, as was Picky eating. Picky eating was not related to satisfaction with food-related life and psychological distress; however, the Appetite scale and the Fear scale were significantly related to these proxy measures of ARFID psychosocial impairment, both with small to moderate effects. Their relationships were not attenuated when the EAT-26 score was controlled for as a covariate (see Table 3).

Descriptive Analysis and Group Comparisons by BMI Category and by Gender

Table 4 shows the descriptive and inferential statistics for the comparisons by BMI category and by gender on the full scale of the C-NIAS, as well as on its three separate factors. Specifically, for BMI, underweight participants showed significantly higher scores in the full scale of C-NIAS and in two of its three factors (Picky eating and Appetite) than those in healthy weight and overweight/obese range (all p < .01). For gender, except for the Appetite factor of the C-NIAS (p < .05), no significant gender differences were found in the scores of the full scale of the C-NIAS or the other two factors.

Table 4.

Group Comparisons by BMI Category and by Gender on the Full Scale of the C-NIAS and its Three Subscales (n = 1,069).

	Underweight (n = 209)	Healthy weight (n = 676)	Overweight/obese (n = 172)	F	Post hoc
	M (SD)	M (SD)	M (SD)	F	Post hoc
Picky eating	5.96 (3.28)	5.01 (3.17)	4.46 (3.13)	11.17**	Underweight > healthy weight > overweight/obese
Appetite	5.17 (3.19)	4.14 (2.83)	3.80 (2.61)	12.63**	Underweight > healthy weight, overweight/obese
Fear	4.16 (3.33)	3.92 (3.20)	3.58 (3.17)	1.50	—
C-NIAS total	15.27 (8.00)	12.94 (7.49)	11.75 (7.36)	10.69**	Underweight > healthy weight, overweight/obese
	Men (n = 507)	Women (n = 562)		F
	M (SD)	M (SD)		F
Picky eating	5.25 (3.20)	5.01 (3.24)		1.46
Appetite	4.51 (2.77)	4.09 (3.00)		5.33*
Fear	3.76 (3.00)	4.07 (3.42)		2.42
C-NIAS total	13.48 (7.48)	13.00 (7.82)		0.98

Note. C-NIAS = Chinese version of the Nine-Item ARFID Scale; BMI = body mass index.

p < .05. **p < .01.

Cross-Cultural Measurement Invariance and Latent Mean Difference

Tests of measurement invariance between samples from the two countries were conducted progressively (i.e., configural invariance, metric invariance, and scalar invariance), with each subsequent model representing a more stringent invariance condition than the prior model. As Table 5 shows, findings supported the most stringent scalar measurement invariance across the Chinese and U.S. samples.

Table 5.

Fit Indices for Invariance Tests Across the Two Countries (Chinese Sample, n = 1,069; American sample, n = 1,533).

Model	M (SD)			χ²	df	CFI	TLI	RMSEA	SRMR	ΔCFI	ΔRMSEA
Model	Picky eating	Appetite	Fear	χ²	df	CFI	TLI	[90% CI]	SRMR	ΔCFI	ΔRMSEA
Chinese sample	5.13 (3.23)	4.29 (2.90)	3.92 (3.23)	110.823**	24	0.967	0.950	0.071 [0.058, 0.085]	0.040	—	—
American sample	3.43 (3.26)	1.94 (2.89)	0.78 (1.86)	49.44**	24	0.995	0.992	0.032 [0.019, 0.045]	0.023	—	—
Configural invariance	—	—	—	158.496**	48	0.985	0.978	0.052 [0.043, 0.061]	0.027	—	—
Metric invariance	—	—	—	199.925**	54	0.980	0.974	0.056 [0.048, 0.065]	0.039	0.005	0.004
Scalar invariance	—	—	—	277.042**	60	0.971	0.965	0.065 [0.057, 0.073]	0.044	0.009	0.009

Note. df = degrees of freedom; CI = confidence interval; CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual; ΔCFI = change in CFI relative to the preceding model; ΔRMSEA = change in RMSEA relative to the preceding model.

p < .01.

Given the support for scalar invariance, latent mean differences were compared between the two samples. Latent mean values for the three factors were constrained to zero in the Chinese sample, and freely estimated for the American sample. Results showed that American college students have significantly lower latent scores on the NIAS subscales than their Chinese peers, with moderate to large effect sizes: Picky eating (Z = −11.95, p < .01, d = −0.52), Appetite (Z = −18.95, p < .01, d = −0.91), and Fear (Z = −26.15, p < .01, d = −1.34).

Differential Item Functioning

Results from the DIF analyses are shown in Table 6. The changes of McFadden’s pseudo-R² from Step 1 to Step 3 ranged from 0.0001 (Item 8) to 0.0140 (Item 1), suggesting no significant DIF across the two samples for any of the items.

Table 6.

Differential Item Functioning (DIF) Results (Chinese Sample, n = 1,069; American Sample, n = 1,533).

	Chinese sample	American sample	DIF	Uniform DIF	Nonuniform DIF
	M (SD)	M (SD)	ΔR²: Step 1 vs. Step 3	ΔR²: Step 1 vs. Step 2	ΔR²: Step 2 vs. Step 3
1. I am a picky eater.	1.90 (1.43)	1.45 (1.45)	0.0140	0.0090	0.0050
2. I dislike most of the foods that other people eat.	1.55 (1.17)	1.03 (1.26)	0.0084	0.0064	0.0020
3. The list of foods that I like and will eat is shorter than the list of foods I won’t eat.	1.67 (1.36)	0.94 (1.33)	0.0029	0.0018	0.0011
4. I am not very interested in eating; I seem to have a smaller appetite than other people.	1.49 (1.25)	0.59 (1.01)	0.0032	0.0005	0.0027
5. I have to push myself to eat regular meals throughout the day, or to eat a large enough amount of food at meals.	1.42 (1.17)	0.68 (1.10)	0.0085	0.0046	0.0040
6. Even when I am eating a food I really like, it is hard for me to eat a large enough volume at meals.	1.41 (1.18)	0.62 (1.07)	0.0061	0.0054	0.0007
7. I avoid or put off eating because I am afraid of gastrointestinal discomfort, choking, or vomiting.	1.38 (1.24)	0.21 (0.64)	0.0123	0.0101	0.0022
8. I restrict myself to certain foods because I am afraid that other foods will cause GI discomfort, choking, or vomiting.	1.30 (1.21)	0.32 (0.83)	0.0001	0.0000	0.0001
9. I eat small portions because I am afraid of GI discomfort, choking, or vomiting.	1.24 (1.20)	0.24 (0.66)	0.0004	0.0002	0.0002

Discussion

The current study provided empirical support for the validity of the translated Chinese version of the NIAS (C-NIAS) in 1,069 college students from two colleges in China. The three-factor structure of the NIAS—Picky eating, Appetite, and Fear—was replicated in the C-NIAS, with measurement invariance across Chinese and U.S. college samples. The C-NIAS subscales showed expected convergent validity with AEBQ scales measuring related appetitive traits; these relationships were generally not attenuated when the EAT-26 score was covaried, supporting divergent validity from other restrictive disordered eating symptoms.

The C-NIAS subscales were also related to proxy ARFID measures, including BMI, psychological distress (K10), and satisfaction with food-related life. As in the U.S. validation sample, the C-NIAS subscales showed relationships of varying magnitudes with the proxy ARFID measures, but some specific relationships were slightly different from those previously observed in the literature. In particular, of the two factors—Appetite and Picky eating—that were found to be associated with BMI, the relationship between Picky eating and BMI was not expected based on prior work, because in samples from English-speaking Western nations, such as the United States (e.g., Ellis et al., 2017; Zickgraf & Ellis, 2018), United Kingdom (e.g., Hunot et al., 2016), and Australia (e.g., Mallan et al., 2017), adult picky eating has been found to be unrelated to BMI. This unexpected finding may be accounted for by the much lower prevalence of overweight/obesity in China compared with the United States. That is, if adult picky eating is relatively weakly protective against overweight/obesity, the protective effect might be more visible in a cultural context with fewer strong environmental contributors to overweight and obesity (e.g., in China).

As expected, Appetite and Fear were each independently related to measures of psychological distress and satisfaction with food-related life after controlling for the EAT-26 score; Picky eating showed significant zero-order correlations, but no independent relationships, with these measures. This was consistent with the pattern observed in the original NIAS validation paper where Picky eating was not independently related to a measure of impairment from disordered eating when controlling for the other two NIAS subscales (Zickgraf & Ellis, 2018). However, in clinical samples of patients diagnosed with ARFID, selective/neophobic (picky) eating is a common cause of symptoms, including, and in many cases limited to, psychosocial impairment (e.g., Norris et al., 2018; Reilly et al., 2019; Zickgraf, Lane-Loney, et al., 2019; Zickgraf, Murray, et al., 2019). In addition, self-reported picky eating was the most common cause of likely ARFID symptoms in a population-based study of 8 to 13 years in Switzerland (Kurz et al., 2016); likewise, in one U.S. sample, 9% of self-identified adult picky eaters met study criteria for a potential ARFID diagnosis (Zickgraf et al., 2016). There is consistent evidence that picky eating is associated with lower dietary variety and limited intake of fruits and vegetables in U.S. college students and other adults (Ellis et al., 2018; Zickgraf & Ellis, 2018; Zickgraf & Schepps, 2016). There is also evidence for nutritional impairment among picky eating Chinese children aged 3 to 12 years (Xue, Lee, et al., 2015; Xue, Zhao, et al., 2015). The picky eating presentation of ARFID may begin with nutritional impairment, and as the disorder develops, it may then progress to weight loss and psychosocial impairment (e.g., Zickgraf & Ellis, 2018). More research is needed to better understand the conditions under which picky eating can lead to specific ARFID symptoms in adults.

The current study showed that compared with U.S. college students, Chinese college students had significantly higher scores on Picky eating, Appetite, and Fear as measured by the C-NIAS. A recent literature review suggested that the prevalence of childhood picky eating was higher in China than in the U.S. samples (Taylor et al., 2015); for example, 54% to 59% among Chinese children aged 3 to 12 years (Xue, Lee, et al., 2015; Xue, Zhao, et al., 2015) versus 13% to 22% among U.S. children aged 3 to 11 years (Mascola et al., 2010). Picky eating is a relatively stable individual characteristic from early childhood to young adulthood (Marchi & Cohen, 1990; Mascola et al., 2010; Nicklaus et al., 2005). The finding of relatively higher mean levels of picky eating in Chinese young adults converges with findings in children to suggest overall higher rates of picky eating in China versus the United States.

It is well documented that appetite and eating behavior is closely related to obesity (Berthoud & Morrison, 2008; Suzuki et al., 2012). In representative samples in 2010, the prevalence of obesity was 35.5% and 35.8% among U.S. adult men and women (Flegal et al., 2012), and 11.9% and 12.1% among Chinese adult men and women (Li et al., 2012). Higher levels of picky eating and relatively higher risk of appetite disturbance and food-related fears in Chinese adults may contribute to the lower rates of overweight and obesity in China relative to U.S.

The finding of higher latent mean scores on all the C-NIAS subscales implies higher risk for ARFID among Chinese (vs. American) young adults. Nonfat phobic anorexia (e.g., low-weight restrictive eating with minimal shape/weight concerns) has been found to be more common in Hong Kong than in Western countries (Lee et al., 2001; Thomas et al., 2015). Although measures of implicit biases towards thinness has been found to distinguish ARFID from nonfat phobic anorexia in one U.S. sample (Izquierdo et al., 2019), objective/performance-based measures are not commonly used in making psychiatric diagnoses. Prior studies on the prevalence of nonfat phobic anorexia in Hong Kong were conducted prior to, or with patients diagnosed prior to, the introduction of ARFID to DSM-5. Even after ARFID was introduced, clinicians may not be fully aware of how to diagnose it; indeed, assessment tools for ARFID were not available until relatively recently (e.g., Bryant-Waugh et al., 2019; Zickgraf & Ellis, 2018), and as previously noted, are still unavailable in China. Therefore, a subset of patients exhibiting undereating and low weight with no fear of fatness might receive a diagnosis of nonfat phobic anorexia nervosa when depending on the reasons for restrictive eating (e.g., picky eating, poor appetite, and fear), an ARFID diagnosis would have been more appropriate. The C-NIAS may be useful in facilitating research on (a) distinguishing between ARFID and nonfat phobic anorexia nervosa in China and (b) comparisons with performance-based measures in Asian samples.

Functional Meaning of Food and Eating in China

As noted above, eating disorders in general are relatively understudied in non-Western countries, with no research on ARFID and very little research on its subclinical manifestations in China so far. The availability of psychometrically sound measures like the C-NIAS will support such research in the Chinese social and cultural context. Although much more research is needed to definitively establish potential cultural differences in patterns of picky eating, low appetite, and food-related fear, our results accentuate the importance of considering the cultural context of food and eating. As alluded to in the Introduction, food and meals play a particularly central role in cultural and social functioning in China. To facilitate cross-cultural discourse, a more detailed account that expands on the cultural symbolic role of food and social meaning of meals in China would be helpful to practitioners, clinicians, and researchers not familiar with the food culture in China (e.g., Ma, 2015).

That food and eating have a particularly strong social connotation and function in Chinese culture is evident in the cultural meanings attached to food and meals. For example, food has played a critical symbolic role in Chinese culture, with food, festivals/celebrations, and family reunion intricately related to each other. This relation manifests itself in the foods showcased in Spring Festival (e.g., fish, representing “surplus”), Mid-Autumn Festival (e.g., mooncakes and pomelos, representing “unity”), Lantern Festival (e.g., sweet round sticky rice balls, representing “together and reunion”), birthday celebration (e.g., noodles, representing “longevity”), and new-born celebration (e.g., red-dyed boiled eggs, representing “life,” “happiness,” and “luck”). In spiritual life, when worshipping gods and goddesses, as well as ancestors, the offerings most often will include a variety of foods and fruits—for them to enjoy.

Under this backdrop, it is a common practice in China to treat others with meals to enhance existing relations or to facilitate new relations (e.g., making a business deal). In welcoming a guest, the host will usually put food into the guest’s bowl with chopsticks (jia cai), as will the elder to the young in family gatherings; in both cases, it will only be polite for the guest or the young to finish all the food. Within the family, parenting advice notwithstanding, food is often used as reward or punishment: performing well at school may lead to sweets, desserts, or dining out at a favorite restaurant; misbehaving may mean no favorite food. In unfavorable situations when food is in short supply—a not so distant reality for many families in China and still the case for some—priority is typically given to children and the elderly, and then to the men and women; therefore, women in poorer families are more susceptible to malnutrition than men.

We suggest that the cultural context shapes the symptom manifestations and lived experiences of patients with eating disorders, including eating restrictions. Indeed, the public nature and performative function of meals and eating in Chinese culture might exacerbate the anxiety and social limitations associated with eating restrictions in ARFID. Our finding of higher latent mean scores in a Chinese sample versus a U.S. sample on measures of these constructs (i.e., Picky eating, Appetite, and Fear) is consistent with the idea that ARFID eating restrictions may be relatively more problematic and impairing in the Chinese versus Western food environment and culture.

Limitations and Future Directions

The current study has several limitations, including the use of convenience samples of college students that are not fully representative of the Chinese (or American) young adult population, with young adults who do not attend college being excluded. Because of the limited age ranges, these samples are also not representative of Chinese (or American) adults in general, and one should be cautious in generalizing the measurement invariance and latent mean differences to the general population. Another limitation is the lack of convergent validity trait/symptom measures for the Fear subscale. In the original development paper, the authors showed that Fear was strongly related to self-reported vomit phobia symptoms and awareness of visceral sensations (e.g., Boschen et al., 2013; Labus et al., 2004). There are no Chinese versions of these measures, and we encourage their development to enhance the descriptive psychopathology literature on ARFID in general, and the fear presentation in particular. We consider the current study a first step toward making instruments available for ARFID research in Chinese adults, and we strongly recommend that the C-NIAS be validated in both clinical and more broadly representative adult samples.

Conclusion

The current study supports the use of the C-NIAS to study subclinical ARFID symptoms in Chinese college populations. The C-NIAS is a brief tool that yields continuous symptom scores for three ARFID-related eating patterns: picky eating, poor appetite (limited interest in eating), and fear of aversive consequences from eating. We found higher levels of picky eating and relatively higher risk of appetite disturbance and food-related fears in Chinese adults than their Western counterparts. More research on the nature of disordered eating in Chinese population is needed, including on ARFID. To date, there has been no research related to ARFID or its subclinical presentations in Chinese adults; C-NIAS can help springboard this area of research. Future research is also needed to identify clinical cut-offs on the C-NIAS, so as to allow the tool to be used both in clinic and in research to identify individuals at risk for ARFID.

Footnotes

Authors’ Note

The data sets used and/or analyzed in the current study are available from the corresponding author on reasonable request.

Author Contributions

Jinbo He led the study design, performed the statistical analysis, helped the results interpretation, and drafted the manuscript. Hana F. Zickgraf participated in the study design, led the results interpretation, and drafted the manuscript. Jordan M. Ellis helped draft the manuscript. Zhicheng Lin helped draft the manuscript. Xitao Fan helped draft the manuscript. All authors read and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially supported by the Presidential Fund of the Chinese University of Hong Kong, Shenzhen, to Jinbo He (Grant Number: PF.01.001428) and Xitao Fan (Grant Number: PF.01.000670) and a grant from Guangdong Basic and Applied Basic Research Foundation to Zhicheng Lin (Grant Number: 2019A1515110574).

ORCID iD

Jinbo He

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