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The impact of age and BMI on impairment due to disordered eating in a large female community sample
Eating Behaviors 13 (2012) 342–346
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Eating Behaviors
The impact of age and BMI on impairment due to disordered eating in a large female community sample Øyvind Rø a,⁎, Lasse Bang a, Deborah L. Reas a, Jan H. Rosenvinge b a b
Regional Eating Disorders Service (RASP), Division of Mental Health and Addiction, Oslo University Hospital, Ullevål, Oslo, Norway Department of Psychology, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway
a r t i c l e
i n f o
Article history: Received 14 November 2011 Received in revised form 13 April 2012 Accepted 31 May 2012 Available online 7 June 2012 Keywords: Clinical Impairment Assessment Functional impairment Disease-specific quality of life Age BMI Eating disorders
a b s t r a c t The impact of age and BMI on functional impairment in eating disorders was assessed by the Clinical Impairment Assessment (CIA) scale in a representative community sample. The CIA was administered to 1080 women aged 16–50 years (M = 36.2, SD = 9.5) with a range of BMI from 13.5 to 55.0 (M = 24.6, SD = 4.9) randomly selected from the Norwegian National Population Register. The average global CIA score was 5.3 (SD = 8.5). Impairment tended to decrease with age (rs = −.20, p b .01), yet increased with greater BMI (rs = .31, p b .01). Approximately 30% of the participants with obesity scored in the clinical range compared to 7% of the underweight and normal-weight participants. Data supported the utility and feasibility of the CIA as a measure of functional impairment secondary to weight, shape, and eating concerns. It is recommended that age and BMI be considered during the interpretation of CIA data. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction Most measures of eating disorder psychopathology assess the presence and severity of eating disorder symptoms, which include restrictive food-intake, binge eating, purging behavior, and concerns about weight and shape. However, these measures fail to consider the impact eating disorder-related symptoms have on daily functioning. It is often the repercussions and the negative aspects of eating disorders that motivate people to seek treatment (Pettersen & Rosenvinge, 2002). Thus, functional impairment secondary to eating disorder symptoms is important to consider during assessment. In recent years, several eating disorder-specific quality of life measures have been developed (e.g. Engel, Adair, Hayas, & Abraham, 2009; Jenkins, Hoste, Meyer, & Blissett, 2011). However, several of these fail to distinguish between eating disorder symptoms versus the consequent functional impairment. Furthermore, impairment due to weight and shape concerns is often overlooked, despite constituting a core feature of eating disorder psychopathology (Fairburn, Cooper, & Shafran, 2003). Lastly, the psychometric properties of quality of life measures developed for eating disorders have not been adequately investigated. A recently developed eating disorder-specific measure of functional impairment is the Clinical Impairment Assessment scale (CIA), ⁎ Corresponding author at: Regional Eating Disorders Service (RASP), Division of Mental Health and Addiction, Oslo University Hospital, P.O. Box 4250, Nydalen, 0407 Oslo, Norway. E-mail address: [email protected] (Ø. Rø). 1471-0153/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.eatbeh.2012.05.010
which was designed to address these shortcomings. The CIA is a brief self-report measure (Bohn et al., 2008) developed as a companion instrument to the widely utilized Eating Disorder ExaminationQuestionnaire (EDE-Q, Fairburn & Beglin, 2008; Fairburn & Beglin, 1994). Initial psychometric studies of the CIA are promising, and clinical norms with a screening cut-off score to determine caseness have been established (Bohn et al., 2008). A Norwegian version and a Fijian version of the CIA have been tested, both of which have demonstrated acceptable psychometric properties using university students and adolescent girls, respectively (Becker et al., 2010; Reas, Rø, Kapstad, & Lask, 2010). Normative data from both non-clinical and clinical populations are necessary to interpret CIA data. To date, three papers have investigated the CIA among non-clinical samples (Becker et al., 2010; Reas et al., 2010; Welch, Birgegard, Parling, & Ghaderi, 2010). Only one of these studies extracted their sample from the general population, as opposed to a school or college population. As scores on eating disorder instruments may vary across cultures and populations, the accumulation of cross-cultural normative data is important in the clinical interpretation of scores (Clausen, Rokkedal, & Rosenvinge, 2009; Clausen, Rosenvinge, Friborg, & Rokkedal, 2011). Moreover, to our knowledge no studies have investigated the impact of age and BMI on impairment level. Young age as well as elevated BMI may trigger body dissatisfaction (McLean, Paxton, & Wertheim, 2010; Mond, Hay, Rodgers, & Owen, 2006), which several studies have established as a risk factor for the development of eating disorder psychopathology (e.g. Cattarin & Thompson, 1994; Stice, Presnell, & Spangler, 2002; Stice & Shaw, 2002).
Ø. Rø et al. / Eating Behaviors 13 (2012) 342–346
Using data from a nationally representative sample of women, the aims of the present study were to 1) report normative data and internal consistency of the CIA, 2) investigate the impact of age and BMI on CIA scores, and 3) investigate the relationship between EDE-Q and CIA scores to determine convergent validity. 2. Methods 2.1. Study design and participants
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for each subscale. Tukey's HSD tests were used to investigate posthoc comparisons. The recommended CIA cut-off score of 16 was used to determine clinically significant impairment (Bohn et al., 2008), and Pearson's Chi-squares were used to test for group differences in such status. We also calculated the proportion of participants scoring above the recommended severity cut-off point for the EDE-Q (i.e. > 4.0), for comparison with the CIA. To correct for multiple comparisons, statistical significance levels were set to p b .01. The Statistical Package for Social Sciences (SPSS version 14) was used for all analyses.
The present study is a cross-sectional survey of Norwegian women aged 16–50 years. A random community sample of 3000 women was extracted from the Norwegian National Population Register. A questionnaire packet was mailed to participants along with instructions to complete and return the questionnaires by mail or electronically online. A reminder was sent to all participants after about four weeks. No monetary rewards were given for participation in the study. However, all participants were invited to participate in a lottery, in which 30 winners were given a gift card worth approximately $80 USD. The study was reported to the Regional Committee for Medical and Health Research Ethics in Northern Norway and qualified for an exemption due to the anonymous nature of the data.
Using scoring procedures outlined by Bohn et al. (2008), the global CIA score was obtained by adding the items together with pro‐ratings of missing items so long as at least 12 of the 16 items had been rated. Using guidelines recommended by Fairburn and Cooper (1993), the EDE-Q subscale scores were calculated by averaging the available item responses when less than half of the relevant items were missing, while the EDE-Q global score was calculated when scores on more than half of the four subscales were available. Approximately 1% of the items required for scoring the EDE-Q and CIA scores were missing.
2.2. Measures
3. Results
The Clinical Impairment Assessment (CIA, version 3.0) is a 16-item self-report measure of functional impairment secondary to eating disorder psychopathology during the past 28 days (Bohn et al., 2008). Items probe impairment in domains of life typically affected by an eating disorder, including mood and self-perception, cognitive functioning, interpersonal functioning, and work performance. Respondents are instructed to rate how their eating habits, exercising, or feelings about eating, shape or weight have affected their life over the past four weeks. Item responses are rated on a 4-point Likert scale from ‘not at all’ to ‘a lot’. A global score and three subscale scores (Personal impairment, Cognitive impairment, and Social impairment) can be computed, giving both general and domain-specific indices of functional impairment. The CIA was administered immediately following completion of the EDE-Q, as recommended by the developers (Bohn et al., 2008). The Norwegian version of the CIA (Reas et al., 2010) has shown satisfactory internal consistency (Cronbach's alpha = .94) and test–retest reliability (rs = .94). The Eating Disorder Examination-Questionnaire (EDE-Q, version 6.0) is a widely employed self-report measure of eating disorder psychopathology (Fairburn & Beglin, 1994; Fairburn & Beglin, 2008). The EDE-Q consists of 28 items addressing core eating disorder features during the past 28 days, including restrictive eating, binge eating, purging behavior and the undue importance of weight and shape in determining self-worth. It is comprised of four subscales: Dietary restraint, Eating concern, Weight concern, and Shape concern. Except for items probing the frequency of specific behaviors, all responses are rated on a 7-point Likert scale. The Norwegian version of the EDE-Q has shown acceptable psychometric properties (Rø, Reas, & Lask, 2010).
3.1. Sample characteristics
2.3. Statistical analyses The internal consistency of the CIA was calculated using Cronbach's alpha. Spearman's coefficients (rs) were used to test correlations between variables. Participants were classified into four age groups: 16–20 years, 21–30 years, 31–40 years, and 41–50 years. Body mass index (BMI) was calculated using the formula: kg/m 2. Four BMI groups were defined according to established WHO guidelines (World Health Organization, 2011) for “underweight” (BMI b 18.5), “normal-weight” (BMI ≥ 18.5–24.9), “overweight” (BMI ≥ 25–29.9) and “obese” (BMI ≥ 30). To investigate group-specific effects of BMI and age, two-way general linear model ANOVAs were performed
2.4. Missing data
A total of 1094 (37.4%) questionnaires were returned after accounting for incorrect addresses. The response rate among the age groups was significantly different ((χ2 = 77.6, p b .01): 16–20 years = 26.7%, 21–30 years = 27.5%, 31–40 years = 32.2% and 41–50 years = 45.8%). After excluding participants whose global EDE-Q or CIA scores could not be computed due to missing data (approximately 1%), the final sample comprised 1080 women aged 16–50 years (M = 36.22, SD= 9.45, Mdn= 38.00). Self-reported BMI ranged from 13.5 to 55.0 (M = 24.61, SD= 4.85, Mdn = 23.39). 3.2. Descriptive data and internal consistency Mean global CIA for the total sample was 5.32 (8.5). Table 1 presents descriptive data and percentile ranks for the global and subscale Table 1 Mean, standard deviation, median, range and percentile data for CIA global and subscale scores.
Mean (SD) Median (range) Percentile rank 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
CIA global
Personal
Social
Cognitive
5.32 (8.50) 1.10 (0–48)
3.39 (4.63) 1.00 (0–18)
0.80 (1.98) 0.00 (0–12)
0.85 (2.06) 0.00 (0–15)
0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.01 2.00 3.00 4.00 5.00 6.30 8.00 12.00 17.00 25.00 48.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 2.00 2.00 3.00 4.00 5.00 6.00 8.00 11.00 15.00 18.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 2.00 3.00 5.00 12.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 2.00 3.00 5.00 15.00
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scores of the CIA. Eleven percent of the entire sample scored above the CIA cut-off value of 16.0. Cronbach's alpha was .96, .96, .91, and .91 for the Global, Personal, Social and Cognitive scales, respectively, indicating satisfactory internal consistency. 3.3. Age and BMI-specific effects As illustrated in Table 2, higher global CIA scores were correlated with younger age (rs = −.20, p b .01) and with higher BMI-values (rs = .31, p b .01). In line with the correlations, there was a statistically significant main effect of age for the global CIA and the Personal impairment subscale, with lower levels of impairment among older women. The Chisquare test revealed a significantly higher percentage of younger participants scoring above the global cut-off score (χ² = 24.6, p b .01). The youngest group (16–20 years) scored approximately four points higher than the eldest group (41–50 years) on the global CIA score, which is close to a two-fold increase. The same trend was observed for the Social and Cognitive impairment subscales as well, although this did not reach statistical significance. A main effect of BMI was also found for the global score and subscales, such that scores increased significantly with higher BMI. The obese group demonstrated a global CIA score that was approximately eight points higher than the normal-weight group, which constitutes a three-fold increase. Thirty percent of the obese group scored above the severity cut-off, compared to 7% of the normal weight group. The Chi-square test revealed that these differences were statistically significant (χ² = 63.5, p b .01). The only exception was the Social impairment subscale, for which a U-shaped trend was observed. Specifically, level of social impairment decreased from the underweight to the normal weight group, before increasing steadily across the overweight and obese groups. The interaction effect between BMI and age on CIA scores was non-significant. Table 3 presents age‐ and BMI-specific norms for global and subscale scores of the CIA, as well as results from the between-group analyses.
significantly with the global EDE-Q, with coefficients ranging from rs = .59 to .82 (p b .01). 4. Discussion The present study investigated functional impairment secondary to eating disorder pathology among a general population sample of Norwegian women. The internal consistency of the CIA was satisfactory, as has been previously reported by other studies (Bohn et al., 2008; Reas et al., 2010). Significant positive correlations between eating pathology and impairment were found, providing evidence to support convergent validity. Younger age was associated with greater levels of personal and global impairment. Higher BMI was associated with greater impairment with the exception of social impairment, which decreased from the underweight to the normal weight group, before increasing steadily across the overweight and obese groups. Generally, the effects of age were more selective than the effects of BMI and the magnitude of these effects was smaller compared to the associations with BMI. 4.1. Normative data Participants in our study obtained an average global CIA score of 5.3, which is lower than what has been reported in previous publications. Thus, the two other studies in which norms from a non-clinical sample have been established have reported mean global CIA scores of 6.4 (Reas et al., 2010) and 8.3 (Welch et al., 2010). Normative scores from a non-clinical sample was also reported in the study by Becker et al. (2010), however, this sample was divided into asymptomatic and symptomatic groups with a corresponding presentation of results and are therefore less suitable for comparison to our data. Given the agerelated trends suggested by our data, the older age of our sample arguably accounts for the lower level of impairment we observed. The mean age in our sample was 11–12 years older than in the studies by Reas et al., (2010) and Welch et al., (2010), respectively. As discussed below, this finding underscores the importance of considering age when interpreting scores especially for community studies.
3.4. Participants scoring above CIA and EDE-Q cut-off (%) 4.2. Impact of age and BMI Frequency and percentage of participants within each group scoring above the cut-off point for the global CIA score are also depicted in Table 3. The average global EDE-Q score was 1.27 (SD = 1.19) and 4% of the total participants scored above the EDE-Q severity cut-off score of 4.0, whereas 11% of the total sample scored above the CIA severity cut-off score of 16.0. Of the participants scoring above the EDE-Q cut-off, 94.6% also scored above the CIA cut-off. Of the participants scoring in the CIA clinical range, 31.6% also scored above the EDEQ cut-off score. 3.5. Relationship between EDE-Q and CIA Table 2 shows that the global scores of CIA and EDE-Q were highly correlated (rs = .83, p b .01). Also, the subscales of the CIA all correlated
Table 2 Spearman's correlation matrix for BMI, age, EDE-Q global, and CIA scores, respectively. BMI BMI Age EDE-Q global CIA global CIA personal CIA social CIA cognitive
Age
EDE-Q global
1 −.14 −.20 −.20 −.13 −.19
1 .83 .82 .63 .59
CIA global
CIA personal
CIA social
1 .15 .43 .31 .33 .25 .16
1 .98 .71 .70
Note: all coefficients are statistically significant at p b .01.
1 .65 .64
1 .65
A main effect of age was found for global and personal impairment. The results indicated that 19% of the youngest group scored in the clinical range for global impairment, compared to 8% of the eldest group. Our data are consistent with research by Mond et al. (2006), which showed that EDE-Q scores were negatively related to age in a general population sample, an effect also observed by Rø, Rosenvinge, and Reas (2012). A main effect of BMI was found for the global impairment and all three subscales. Post-hoc tests from our ANOVA analyses show that the obese group scored significantly higher than all other groups, across all CIA scales. The prevailing trend was that impairment increased with elevated BMI, with the exception of social impairment, for which a U-shaped trend was detected. Particularly dramatic was the proportion of participants (i.e. 30%) scoring above the severity cut-off in the obese group, compared with 7% of both the underweight and normal-weight groups. At least two factors may help explain these findings. First, it is feasible that an increase in impairment reflects eating disorder psychopathology, such that overweight and obese people exhibit a greater level of symptoms indicative of eating disorder psychopathology. In support of this argument, studies have demonstrated that elevated body mass is associated with higher scores on the EDE-Q in non-clinical samples (Mond, Hay, Rodgers, Owen, & Beumont, 2004; Rø et al., 2012) and research has shown that BMI predicts binge eating onset (Stice et al., 2002) as well as EDE-Q scores (McLean et al., 2010). Research has found that over 30% of treatment-seeking
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Table 3 CIA group-specific norms for age and BMI, with results from associated ANOVA (with Tukey's post hoc tests) and Chi-square tests. Variable/score
Group A
Group B
Group C
Group D
Age
16–20 (n = 92) Mean (SD)
21–30 (n = 212) Mean (SD)
31–40 (n = 335) Mean (SD)
41–50 (n = 435) Mean (SD)
F [χ²]
ηp²
Tukey's HSD
1. Global CIA 2. Personal 3. Social 4. Cognitive Frequency [%] above cut-off
7.85 (9.10) 5.14 (5.31) 1.12 (2.08) 1.30 (2.10) 17 [19%]
7.16 (9.72) 4.45 (5.25) 1.28 (2.57) 1.07 (2.09) 41 [19%]
5.11 (8.04) 3.35 (4.50) 0.71 (1.76) 0.78 (1.94) 33 [10%]
4.03 (7.80) 2.53 (4.01) 0.57 (1.73) 0.69 (2.12) 33 [8%]
5.9⁎ 9.1⁎ 2.8 2.8 [24.6]⁎
.02 .03 .01 .01
A > D; B > D A > C; A > D; B > D
BMI
b 18.5 (n = 27)
≥18.5–24.9 (n = 652)
≥ 25–29.9 (n = 256)
≥30 (n = 138)
1. Global CIA 2. Personal 3. Social 4. Cognitive Frequency [%] above cut-off
3.56 (7.78) 2.30 (4.34) 0.52 (1.78) 0.56 (1.65) 2 [7%]
3.64 (6.39) 2.40 (3.69) 0.46 (1.39) 0.60 (1.64) 45 [7%]
6.34 (9.25) 4.09 (4.98) 0.99 (2.20) 0.90 (2.22) 35 [14%]
11.75 (12.06) 7.01 (5.89) 2.10 (3.12) 1.99 (3.08) 42 [30%]
34.5⁎ 42.5⁎ 22.2⁎ 14.8⁎
.09 .11 .06 .04
A b D; B b C; B b D; C b D A b D; B b C; B b D; C b D A b D; B b C; B b D; C b D A b D; B b D; C b D
[63.5]⁎
χ² = Pearson's Chi-square test; ηp² = partial eta squared interpreted as small (.01), medium (.06), and large (.14) effects. ⁎ p b .01.
overweight youth reported engaging in binge eating during the past 28 days (Eddy et al., 2007). Second, it is likely that the CIA taps the various personal, cognitive, and social aspects of overweight and obesity related to general quality of life, independent of eating disorder psychopathology. This could include the existence of medical complications associated with obesity (Pi-Sunyer, 1993), body dissatisfaction accompanying elevated BMI (McLean et al., 2010; Presnell, Bearman, & Stice, 2004; Stice & Shaw, 2002; Stice & Whitenton, 2002), depression (de Wit et al., 2010; Faith, Matz, & Jorge, 2002; Stunkard, Faith, & Allison, 2003), anxiety (Scott, McGee, Wells, & Oakley Browne, 2008; Strine et al., 2003), or the social discrimination, stigmatization, and bias which are consistently documented to adversely affect the mental health of obese individuals (Puhl & Brownell, 2001). Health-related quality of life has been found to decrease with obesity (Fontaine & Barofsky, 2001). Younger people are also afflicted, as found by a recent study reporting lower quality of life for children and adolescents who are obese (Griffiths, Parsons, & Hill, 2010). However, as the CIA is designed to be a disease-specific measure, it should theoretically be able to selectively measure quality of life specific to eating disorder psychopathology, and not overall quality of life. The extent to which this measure actually accomplishes this deserves future research, as some aspects of the CIA are highly relevant for individuals with overweight and obesity (i.e. concerns about eating, weight, and shape), without necessarily being indicative of eating disorder features. These findings should be considered when establishing criteria for determining clinical significance, particularly among community samples overrepresented by individuals with obesity and overweight, as a higher cut-point may be necessary. The extent to which community findings generalize to clinical settings also warrants further research. 4.3. Relationship between eating pathology and impairment As expected, correlations between EDE-Q and CIA were high. Similar results have been reported by other studies (Becker et al., 2010; Bohn et al., 2008; Reas et al., 2010), providing evidence of the convergent validity of the CIA and EDE-Q in both clinical and non-clinical samples. Eleven percent of our sample scored above the global CIA cut-off of 16.0, which is the same percentage as reported by Reas et al. (2010). No other studies have reported data regarding the cut-off score of the CIA in a non-clinical sample. For the sake of comparison, our data showed that 4% of women scored above the global EDE-Q cut-off, and this finding is consistent with the proportion reported by prior community studies (Luce, Crowther, & Pole, 2008; Rø et al.,
2010). Of the 4% of participants scoring above the EDE-Q cut-off, 95% also had an elevated CIA score of 16.0 or higher. In contrast, of the 11% of participants scoring in the clinical range for the CIA, approximately 30% also reported an elevated EDE-Q score, with twothirds scoring below the EDE-Q clinical cut-off. Based on these results, we argue that eating disorder symptoms, even when small in intensity, can be associated with a large degree of functional impairment. Alternatively, the CIA might be oversensitive in community samples, tapping general impairment related to eating, weight, and shape concerns prevalent in the community-at-large. A third possibility is that the established cut-off score of 4.0 on the global EDE-Q is too strict. A Swedish study found that nearly half of admitted patients scored below 4.0, with an average global EDE-Q score upon admission of 4.06 (SD = 1.2; Welch et al., 2010). Further research is warranted to investigate and replicate the severity cut-point among patients, in order to make reliable and meaningful comparisons to non-clinical samples. 4.4. Strengths and limitations Limitations of the present investigation deserve acknowledgment. First, the response rate of 37% in our study was slightly lower than prior studies and participants were on average two years older than non-participants, possibly introducing a selection bias affecting the representativeness and generalizability of our results. It could be argued, however, that the response rate affects the normative data to a greater extent than the analyses of BMI and age trends. Despite this, our sample size was large, with over 1000 participants randomly drawn from a national population, which is considered to be a methodological strength as it circumvents biases associated with other methods of recruitment. A typical problem with the use of a pathology measure on non-clinical populations is that many respondents achieve a low score, and as such, our data was positively skewed. Secondly, the study is also limited by self-reported BMI data, which may have produced an underestimate of their weight and an overestimate of their height (Rowland, 1990). However, high correlations (i.e. r's > .90) have been found between measured and self-reported BMI, indicating the suitability of these data (White, Masheb, BurkeMartindale, Rothschild, & Grilo, 2007). 4.5. Conclusions To date, our study represents the only investigation to examine the impact of age and BMI on impairment secondary to eating pathology as measured by the CIA. Our findings among a general population sample are valuable in providing a context in which clinical data can
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be interpreted. We recommend that age and BMI be considered during the interpretation and application of this self-report measure of impairment. Future studies should continue to investigate normative CIA data within both clinical and non-clinical samples. In particular, replications among a clinical sample are warranted to examine the cut-off and factor structure of the CIA originally established by Bohn et al. (2008). It will be particularly important to determine if, and to what extent, the CIA measures features of overweight and obesity related to eating disorder psychopathology. This will enable us to better determine the scope and recommended utilization of the measure. Role of funding sources This study received no external funding.
Contributors Authors Rosenvinge and Rø designed the study, wrote the protocol, and assisted with data collection. Authors Bang, Rø, and Reas assisted in data management and analyses. Dr. Reas, Drs. Bang, Rø, and Reas wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript. Conflict of interest No conflicts of interest exist by any author.
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Fairburn, C. G., Cooper, Z., & Shafran, R. (2003). Cognitive behaviour therapy for eating disorders: A “transdiagnostic” theory and treatment. Behaviour Research and Therapy, 41, 509–528. Faith, M. S., Matz, P. E., & Jorge, M. A. (2002). Obesity–depression associations in the population. Journal of Psychosomatic Research, 53, 935–942. Fontaine, K. R., & Barofsky, I. (2001). Obesity and health-related quality of life. Obesity Reviews, 2, 173–182. Griffiths, L. J., Parsons, T. J., & Hill, A. J. (2010). Self-esteem and quality of life in obese children and adolescents: A systematic review. International Journal of Pediatric Obesity, 5, 282–304. Jenkins, P. E., Hoste, R. R., Meyer, C., & Blissett, J. M. (2011). Eating disorders and quality of life: A review of the literature. Clinical Psychology Review, 31, 113–121. Luce, K. H., Crowther, J. H., & Pole, M. (2008). Eating Disorder Examination Questionnaire (EDE-Q): Norms for undergraduate women. The International Journal of Eating Disorders, 41, 273–276. McLean, S. A., Paxton, S. J., & Wertheim, E. H. (2010). Factors associated with body dissatisfaction and disordered eating in women in midlife. The International Journal of Eating Disorders, 43, 527–536. Mond, J. M., Hay, P. J., Rodgers, B., & Owen, C. (2006). Eating Disorder Examination Questionnaire (EDE-Q): Norms for young adult women. Behaviour Research and Therapy, 44, 53–62. Mond, J. M., Hay, P. J., Rodgers, B., Owen, C., & Beumont, P. J. V. (2004). Validity of the Eating Disorder Examination Questionnaire (EDE-Q) in screening for eating disorders in community samples. Behaviour Research and Therapy, 42, 551–567. Pettersen, G., & Rosenvinge, J. H. (2002). Improvement and recovery from eating disorders: A patient perspective. Eating Disorders: The Journal of Treatment & Prevention, 10, 61–71. Pi-Sunyer, F. X. (1993). Medical hazards of obesity. Annals of Internal Medicine, 119, 655–660. Presnell, K., Bearman, S. K., & Stice, E. (2004). Risk factors for body dissatisfaction in adolescent boys and girls: A prospective study. The International Journal of Eating Disorders, 36, 389–401. Puhl, R., & Brownell, K. D. (2001). Bias, discrimination, and obesity. Obesity, 9, 788–805. Reas, D. L., Rø, Ø., Kapstad, H., & Lask, B. (2010). Psychometric properties of the Clinical Impairment Assessment: Norms for young adult women. The International Journal of Eating Disorders, 43, 72–76. Rø, Ø., Reas, D. L., & Lask, B. (2010). Norms for the Eating Disorder Examination Questionnaire among female university students in Norway. Nordic Journal of Psychiatry, 64, 428–432. Rø, Ø., Rosenvinge, J. H., & Reas, D. L. (2012). The impact of age and BMI on Eating Disorder Examination Questionnaire (EDE-Q): Scores in a community sample. Eating Behaviors, 13, 158–161. Rowland, M. L. (1990). Self-reported weight and height. The American Journal of Clinical Nutrition, 52, 1125–1133. Scott, K. M., McGee, M. A., Wells, J. E., & Oakley Browne, M. A. (2008). Obesity and mental disorders in the adult general population. Journal of Psychosomatic Research, 64, 97–105. Stice, E., Presnell, K., & Spangler, D. (2002). Risk factors for binge eating onset in adolescent girls: A 2-year prospective investigation. Health Psychology, 21, 131–138. Stice, E., & Shaw, H. E. (2002). Role of body dissatisfaction in the onset and maintenance of eating pathology: A synthesis of research findings. Journal of Psychosomatic Research, 53, 985–993. Stice, E., & Whitenton, K. (2002). Risk factors for body dissatisfaction in adolescent girls: A longitudinal investigation. Developmental Psychology, 38, 669–678. Strine, T. W., Mokdad, A. H., Dube, S. R., Balluz, L. S., Gonzalez, O., Berry, J. T., et al. (2003). The association of depression and anxiety with obesity and unhealthy behaviors among community-dwelling US adults. General Hospital Psychiatry, 30, 127–137. Stunkard, A. J., Faith, M. S., & Allison, K. C. (2003). Depression and obesity. Biological Psychiatry, 54, 330–337. Welch, E., Birgegard, A., Parling, T., & Ghaderi, A. (2010). Eating Disorder Examination Questionnaire and Clinical Impairment Assessment Questionnaire: General population and clinical norms for young adult women in Sweden. Behaviour Research and Therapy, 49, 85–91. White, M. A., Masheb, R. M., Burke-Martindale, C., Rothschild, B., & Grilo, C. M. (2007). Accuracy of self-reported weight among bariatric surgery candidates: The influence of race and weight cycling. Obesity, 15, 2761–2768. World Health Organization (2011). BMI classification. 12-5-0011. Ref Type: Online Source.
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Eating Behaviors
The impact of age and BMI on impairment due to disordered eating in a large female community sample Øyvind Rø a,⁎, Lasse Bang a, Deborah L. Reas a, Jan H. Rosenvinge b a b
Regional Eating Disorders Service (RASP), Division of Mental Health and Addiction, Oslo University Hospital, Ullevål, Oslo, Norway Department of Psychology, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway
a r t i c l e
i n f o
Article history: Received 14 November 2011 Received in revised form 13 April 2012 Accepted 31 May 2012 Available online 7 June 2012 Keywords: Clinical Impairment Assessment Functional impairment Disease-specific quality of life Age BMI Eating disorders
a b s t r a c t The impact of age and BMI on functional impairment in eating disorders was assessed by the Clinical Impairment Assessment (CIA) scale in a representative community sample. The CIA was administered to 1080 women aged 16–50 years (M = 36.2, SD = 9.5) with a range of BMI from 13.5 to 55.0 (M = 24.6, SD = 4.9) randomly selected from the Norwegian National Population Register. The average global CIA score was 5.3 (SD = 8.5). Impairment tended to decrease with age (rs = −.20, p b .01), yet increased with greater BMI (rs = .31, p b .01). Approximately 30% of the participants with obesity scored in the clinical range compared to 7% of the underweight and normal-weight participants. Data supported the utility and feasibility of the CIA as a measure of functional impairment secondary to weight, shape, and eating concerns. It is recommended that age and BMI be considered during the interpretation of CIA data. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction Most measures of eating disorder psychopathology assess the presence and severity of eating disorder symptoms, which include restrictive food-intake, binge eating, purging behavior, and concerns about weight and shape. However, these measures fail to consider the impact eating disorder-related symptoms have on daily functioning. It is often the repercussions and the negative aspects of eating disorders that motivate people to seek treatment (Pettersen & Rosenvinge, 2002). Thus, functional impairment secondary to eating disorder symptoms is important to consider during assessment. In recent years, several eating disorder-specific quality of life measures have been developed (e.g. Engel, Adair, Hayas, & Abraham, 2009; Jenkins, Hoste, Meyer, & Blissett, 2011). However, several of these fail to distinguish between eating disorder symptoms versus the consequent functional impairment. Furthermore, impairment due to weight and shape concerns is often overlooked, despite constituting a core feature of eating disorder psychopathology (Fairburn, Cooper, & Shafran, 2003). Lastly, the psychometric properties of quality of life measures developed for eating disorders have not been adequately investigated. A recently developed eating disorder-specific measure of functional impairment is the Clinical Impairment Assessment scale (CIA), ⁎ Corresponding author at: Regional Eating Disorders Service (RASP), Division of Mental Health and Addiction, Oslo University Hospital, P.O. Box 4250, Nydalen, 0407 Oslo, Norway. E-mail address: [email protected] (Ø. Rø). 1471-0153/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.eatbeh.2012.05.010
which was designed to address these shortcomings. The CIA is a brief self-report measure (Bohn et al., 2008) developed as a companion instrument to the widely utilized Eating Disorder ExaminationQuestionnaire (EDE-Q, Fairburn & Beglin, 2008; Fairburn & Beglin, 1994). Initial psychometric studies of the CIA are promising, and clinical norms with a screening cut-off score to determine caseness have been established (Bohn et al., 2008). A Norwegian version and a Fijian version of the CIA have been tested, both of which have demonstrated acceptable psychometric properties using university students and adolescent girls, respectively (Becker et al., 2010; Reas, Rø, Kapstad, & Lask, 2010). Normative data from both non-clinical and clinical populations are necessary to interpret CIA data. To date, three papers have investigated the CIA among non-clinical samples (Becker et al., 2010; Reas et al., 2010; Welch, Birgegard, Parling, & Ghaderi, 2010). Only one of these studies extracted their sample from the general population, as opposed to a school or college population. As scores on eating disorder instruments may vary across cultures and populations, the accumulation of cross-cultural normative data is important in the clinical interpretation of scores (Clausen, Rokkedal, & Rosenvinge, 2009; Clausen, Rosenvinge, Friborg, & Rokkedal, 2011). Moreover, to our knowledge no studies have investigated the impact of age and BMI on impairment level. Young age as well as elevated BMI may trigger body dissatisfaction (McLean, Paxton, & Wertheim, 2010; Mond, Hay, Rodgers, & Owen, 2006), which several studies have established as a risk factor for the development of eating disorder psychopathology (e.g. Cattarin & Thompson, 1994; Stice, Presnell, & Spangler, 2002; Stice & Shaw, 2002).
Ø. Rø et al. / Eating Behaviors 13 (2012) 342–346
Using data from a nationally representative sample of women, the aims of the present study were to 1) report normative data and internal consistency of the CIA, 2) investigate the impact of age and BMI on CIA scores, and 3) investigate the relationship between EDE-Q and CIA scores to determine convergent validity. 2. Methods 2.1. Study design and participants
343
for each subscale. Tukey's HSD tests were used to investigate posthoc comparisons. The recommended CIA cut-off score of 16 was used to determine clinically significant impairment (Bohn et al., 2008), and Pearson's Chi-squares were used to test for group differences in such status. We also calculated the proportion of participants scoring above the recommended severity cut-off point for the EDE-Q (i.e. > 4.0), for comparison with the CIA. To correct for multiple comparisons, statistical significance levels were set to p b .01. The Statistical Package for Social Sciences (SPSS version 14) was used for all analyses.
The present study is a cross-sectional survey of Norwegian women aged 16–50 years. A random community sample of 3000 women was extracted from the Norwegian National Population Register. A questionnaire packet was mailed to participants along with instructions to complete and return the questionnaires by mail or electronically online. A reminder was sent to all participants after about four weeks. No monetary rewards were given for participation in the study. However, all participants were invited to participate in a lottery, in which 30 winners were given a gift card worth approximately $80 USD. The study was reported to the Regional Committee for Medical and Health Research Ethics in Northern Norway and qualified for an exemption due to the anonymous nature of the data.
Using scoring procedures outlined by Bohn et al. (2008), the global CIA score was obtained by adding the items together with pro‐ratings of missing items so long as at least 12 of the 16 items had been rated. Using guidelines recommended by Fairburn and Cooper (1993), the EDE-Q subscale scores were calculated by averaging the available item responses when less than half of the relevant items were missing, while the EDE-Q global score was calculated when scores on more than half of the four subscales were available. Approximately 1% of the items required for scoring the EDE-Q and CIA scores were missing.
2.2. Measures
3. Results
The Clinical Impairment Assessment (CIA, version 3.0) is a 16-item self-report measure of functional impairment secondary to eating disorder psychopathology during the past 28 days (Bohn et al., 2008). Items probe impairment in domains of life typically affected by an eating disorder, including mood and self-perception, cognitive functioning, interpersonal functioning, and work performance. Respondents are instructed to rate how their eating habits, exercising, or feelings about eating, shape or weight have affected their life over the past four weeks. Item responses are rated on a 4-point Likert scale from ‘not at all’ to ‘a lot’. A global score and three subscale scores (Personal impairment, Cognitive impairment, and Social impairment) can be computed, giving both general and domain-specific indices of functional impairment. The CIA was administered immediately following completion of the EDE-Q, as recommended by the developers (Bohn et al., 2008). The Norwegian version of the CIA (Reas et al., 2010) has shown satisfactory internal consistency (Cronbach's alpha = .94) and test–retest reliability (rs = .94). The Eating Disorder Examination-Questionnaire (EDE-Q, version 6.0) is a widely employed self-report measure of eating disorder psychopathology (Fairburn & Beglin, 1994; Fairburn & Beglin, 2008). The EDE-Q consists of 28 items addressing core eating disorder features during the past 28 days, including restrictive eating, binge eating, purging behavior and the undue importance of weight and shape in determining self-worth. It is comprised of four subscales: Dietary restraint, Eating concern, Weight concern, and Shape concern. Except for items probing the frequency of specific behaviors, all responses are rated on a 7-point Likert scale. The Norwegian version of the EDE-Q has shown acceptable psychometric properties (Rø, Reas, & Lask, 2010).
3.1. Sample characteristics
2.3. Statistical analyses The internal consistency of the CIA was calculated using Cronbach's alpha. Spearman's coefficients (rs) were used to test correlations between variables. Participants were classified into four age groups: 16–20 years, 21–30 years, 31–40 years, and 41–50 years. Body mass index (BMI) was calculated using the formula: kg/m 2. Four BMI groups were defined according to established WHO guidelines (World Health Organization, 2011) for “underweight” (BMI b 18.5), “normal-weight” (BMI ≥ 18.5–24.9), “overweight” (BMI ≥ 25–29.9) and “obese” (BMI ≥ 30). To investigate group-specific effects of BMI and age, two-way general linear model ANOVAs were performed
2.4. Missing data
A total of 1094 (37.4%) questionnaires were returned after accounting for incorrect addresses. The response rate among the age groups was significantly different ((χ2 = 77.6, p b .01): 16–20 years = 26.7%, 21–30 years = 27.5%, 31–40 years = 32.2% and 41–50 years = 45.8%). After excluding participants whose global EDE-Q or CIA scores could not be computed due to missing data (approximately 1%), the final sample comprised 1080 women aged 16–50 years (M = 36.22, SD= 9.45, Mdn= 38.00). Self-reported BMI ranged from 13.5 to 55.0 (M = 24.61, SD= 4.85, Mdn = 23.39). 3.2. Descriptive data and internal consistency Mean global CIA for the total sample was 5.32 (8.5). Table 1 presents descriptive data and percentile ranks for the global and subscale Table 1 Mean, standard deviation, median, range and percentile data for CIA global and subscale scores.
Mean (SD) Median (range) Percentile rank 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
CIA global
Personal
Social
Cognitive
5.32 (8.50) 1.10 (0–48)
3.39 (4.63) 1.00 (0–18)
0.80 (1.98) 0.00 (0–12)
0.85 (2.06) 0.00 (0–15)
0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.01 2.00 3.00 4.00 5.00 6.30 8.00 12.00 17.00 25.00 48.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 2.00 2.00 3.00 4.00 5.00 6.00 8.00 11.00 15.00 18.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 2.00 3.00 5.00 12.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 2.00 3.00 5.00 15.00
344
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scores of the CIA. Eleven percent of the entire sample scored above the CIA cut-off value of 16.0. Cronbach's alpha was .96, .96, .91, and .91 for the Global, Personal, Social and Cognitive scales, respectively, indicating satisfactory internal consistency. 3.3. Age and BMI-specific effects As illustrated in Table 2, higher global CIA scores were correlated with younger age (rs = −.20, p b .01) and with higher BMI-values (rs = .31, p b .01). In line with the correlations, there was a statistically significant main effect of age for the global CIA and the Personal impairment subscale, with lower levels of impairment among older women. The Chisquare test revealed a significantly higher percentage of younger participants scoring above the global cut-off score (χ² = 24.6, p b .01). The youngest group (16–20 years) scored approximately four points higher than the eldest group (41–50 years) on the global CIA score, which is close to a two-fold increase. The same trend was observed for the Social and Cognitive impairment subscales as well, although this did not reach statistical significance. A main effect of BMI was also found for the global score and subscales, such that scores increased significantly with higher BMI. The obese group demonstrated a global CIA score that was approximately eight points higher than the normal-weight group, which constitutes a three-fold increase. Thirty percent of the obese group scored above the severity cut-off, compared to 7% of the normal weight group. The Chi-square test revealed that these differences were statistically significant (χ² = 63.5, p b .01). The only exception was the Social impairment subscale, for which a U-shaped trend was observed. Specifically, level of social impairment decreased from the underweight to the normal weight group, before increasing steadily across the overweight and obese groups. The interaction effect between BMI and age on CIA scores was non-significant. Table 3 presents age‐ and BMI-specific norms for global and subscale scores of the CIA, as well as results from the between-group analyses.
significantly with the global EDE-Q, with coefficients ranging from rs = .59 to .82 (p b .01). 4. Discussion The present study investigated functional impairment secondary to eating disorder pathology among a general population sample of Norwegian women. The internal consistency of the CIA was satisfactory, as has been previously reported by other studies (Bohn et al., 2008; Reas et al., 2010). Significant positive correlations between eating pathology and impairment were found, providing evidence to support convergent validity. Younger age was associated with greater levels of personal and global impairment. Higher BMI was associated with greater impairment with the exception of social impairment, which decreased from the underweight to the normal weight group, before increasing steadily across the overweight and obese groups. Generally, the effects of age were more selective than the effects of BMI and the magnitude of these effects was smaller compared to the associations with BMI. 4.1. Normative data Participants in our study obtained an average global CIA score of 5.3, which is lower than what has been reported in previous publications. Thus, the two other studies in which norms from a non-clinical sample have been established have reported mean global CIA scores of 6.4 (Reas et al., 2010) and 8.3 (Welch et al., 2010). Normative scores from a non-clinical sample was also reported in the study by Becker et al. (2010), however, this sample was divided into asymptomatic and symptomatic groups with a corresponding presentation of results and are therefore less suitable for comparison to our data. Given the agerelated trends suggested by our data, the older age of our sample arguably accounts for the lower level of impairment we observed. The mean age in our sample was 11–12 years older than in the studies by Reas et al., (2010) and Welch et al., (2010), respectively. As discussed below, this finding underscores the importance of considering age when interpreting scores especially for community studies.
3.4. Participants scoring above CIA and EDE-Q cut-off (%) 4.2. Impact of age and BMI Frequency and percentage of participants within each group scoring above the cut-off point for the global CIA score are also depicted in Table 3. The average global EDE-Q score was 1.27 (SD = 1.19) and 4% of the total participants scored above the EDE-Q severity cut-off score of 4.0, whereas 11% of the total sample scored above the CIA severity cut-off score of 16.0. Of the participants scoring above the EDE-Q cut-off, 94.6% also scored above the CIA cut-off. Of the participants scoring in the CIA clinical range, 31.6% also scored above the EDEQ cut-off score. 3.5. Relationship between EDE-Q and CIA Table 2 shows that the global scores of CIA and EDE-Q were highly correlated (rs = .83, p b .01). Also, the subscales of the CIA all correlated
Table 2 Spearman's correlation matrix for BMI, age, EDE-Q global, and CIA scores, respectively. BMI BMI Age EDE-Q global CIA global CIA personal CIA social CIA cognitive
Age
EDE-Q global
1 −.14 −.20 −.20 −.13 −.19
1 .83 .82 .63 .59
CIA global
CIA personal
CIA social
1 .15 .43 .31 .33 .25 .16
1 .98 .71 .70
Note: all coefficients are statistically significant at p b .01.
1 .65 .64
1 .65
A main effect of age was found for global and personal impairment. The results indicated that 19% of the youngest group scored in the clinical range for global impairment, compared to 8% of the eldest group. Our data are consistent with research by Mond et al. (2006), which showed that EDE-Q scores were negatively related to age in a general population sample, an effect also observed by Rø, Rosenvinge, and Reas (2012). A main effect of BMI was found for the global impairment and all three subscales. Post-hoc tests from our ANOVA analyses show that the obese group scored significantly higher than all other groups, across all CIA scales. The prevailing trend was that impairment increased with elevated BMI, with the exception of social impairment, for which a U-shaped trend was detected. Particularly dramatic was the proportion of participants (i.e. 30%) scoring above the severity cut-off in the obese group, compared with 7% of both the underweight and normal-weight groups. At least two factors may help explain these findings. First, it is feasible that an increase in impairment reflects eating disorder psychopathology, such that overweight and obese people exhibit a greater level of symptoms indicative of eating disorder psychopathology. In support of this argument, studies have demonstrated that elevated body mass is associated with higher scores on the EDE-Q in non-clinical samples (Mond, Hay, Rodgers, Owen, & Beumont, 2004; Rø et al., 2012) and research has shown that BMI predicts binge eating onset (Stice et al., 2002) as well as EDE-Q scores (McLean et al., 2010). Research has found that over 30% of treatment-seeking
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Table 3 CIA group-specific norms for age and BMI, with results from associated ANOVA (with Tukey's post hoc tests) and Chi-square tests. Variable/score
Group A
Group B
Group C
Group D
Age
16–20 (n = 92) Mean (SD)
21–30 (n = 212) Mean (SD)
31–40 (n = 335) Mean (SD)
41–50 (n = 435) Mean (SD)
F [χ²]
ηp²
Tukey's HSD
1. Global CIA 2. Personal 3. Social 4. Cognitive Frequency [%] above cut-off
7.85 (9.10) 5.14 (5.31) 1.12 (2.08) 1.30 (2.10) 17 [19%]
7.16 (9.72) 4.45 (5.25) 1.28 (2.57) 1.07 (2.09) 41 [19%]
5.11 (8.04) 3.35 (4.50) 0.71 (1.76) 0.78 (1.94) 33 [10%]
4.03 (7.80) 2.53 (4.01) 0.57 (1.73) 0.69 (2.12) 33 [8%]
5.9⁎ 9.1⁎ 2.8 2.8 [24.6]⁎
.02 .03 .01 .01
A > D; B > D A > C; A > D; B > D
BMI
b 18.5 (n = 27)
≥18.5–24.9 (n = 652)
≥ 25–29.9 (n = 256)
≥30 (n = 138)
1. Global CIA 2. Personal 3. Social 4. Cognitive Frequency [%] above cut-off
3.56 (7.78) 2.30 (4.34) 0.52 (1.78) 0.56 (1.65) 2 [7%]
3.64 (6.39) 2.40 (3.69) 0.46 (1.39) 0.60 (1.64) 45 [7%]
6.34 (9.25) 4.09 (4.98) 0.99 (2.20) 0.90 (2.22) 35 [14%]
11.75 (12.06) 7.01 (5.89) 2.10 (3.12) 1.99 (3.08) 42 [30%]
34.5⁎ 42.5⁎ 22.2⁎ 14.8⁎
.09 .11 .06 .04
A b D; B b C; B b D; C b D A b D; B b C; B b D; C b D A b D; B b C; B b D; C b D A b D; B b D; C b D
[63.5]⁎
χ² = Pearson's Chi-square test; ηp² = partial eta squared interpreted as small (.01), medium (.06), and large (.14) effects. ⁎ p b .01.
overweight youth reported engaging in binge eating during the past 28 days (Eddy et al., 2007). Second, it is likely that the CIA taps the various personal, cognitive, and social aspects of overweight and obesity related to general quality of life, independent of eating disorder psychopathology. This could include the existence of medical complications associated with obesity (Pi-Sunyer, 1993), body dissatisfaction accompanying elevated BMI (McLean et al., 2010; Presnell, Bearman, & Stice, 2004; Stice & Shaw, 2002; Stice & Whitenton, 2002), depression (de Wit et al., 2010; Faith, Matz, & Jorge, 2002; Stunkard, Faith, & Allison, 2003), anxiety (Scott, McGee, Wells, & Oakley Browne, 2008; Strine et al., 2003), or the social discrimination, stigmatization, and bias which are consistently documented to adversely affect the mental health of obese individuals (Puhl & Brownell, 2001). Health-related quality of life has been found to decrease with obesity (Fontaine & Barofsky, 2001). Younger people are also afflicted, as found by a recent study reporting lower quality of life for children and adolescents who are obese (Griffiths, Parsons, & Hill, 2010). However, as the CIA is designed to be a disease-specific measure, it should theoretically be able to selectively measure quality of life specific to eating disorder psychopathology, and not overall quality of life. The extent to which this measure actually accomplishes this deserves future research, as some aspects of the CIA are highly relevant for individuals with overweight and obesity (i.e. concerns about eating, weight, and shape), without necessarily being indicative of eating disorder features. These findings should be considered when establishing criteria for determining clinical significance, particularly among community samples overrepresented by individuals with obesity and overweight, as a higher cut-point may be necessary. The extent to which community findings generalize to clinical settings also warrants further research. 4.3. Relationship between eating pathology and impairment As expected, correlations between EDE-Q and CIA were high. Similar results have been reported by other studies (Becker et al., 2010; Bohn et al., 2008; Reas et al., 2010), providing evidence of the convergent validity of the CIA and EDE-Q in both clinical and non-clinical samples. Eleven percent of our sample scored above the global CIA cut-off of 16.0, which is the same percentage as reported by Reas et al. (2010). No other studies have reported data regarding the cut-off score of the CIA in a non-clinical sample. For the sake of comparison, our data showed that 4% of women scored above the global EDE-Q cut-off, and this finding is consistent with the proportion reported by prior community studies (Luce, Crowther, & Pole, 2008; Rø et al.,
2010). Of the 4% of participants scoring above the EDE-Q cut-off, 95% also had an elevated CIA score of 16.0 or higher. In contrast, of the 11% of participants scoring in the clinical range for the CIA, approximately 30% also reported an elevated EDE-Q score, with twothirds scoring below the EDE-Q clinical cut-off. Based on these results, we argue that eating disorder symptoms, even when small in intensity, can be associated with a large degree of functional impairment. Alternatively, the CIA might be oversensitive in community samples, tapping general impairment related to eating, weight, and shape concerns prevalent in the community-at-large. A third possibility is that the established cut-off score of 4.0 on the global EDE-Q is too strict. A Swedish study found that nearly half of admitted patients scored below 4.0, with an average global EDE-Q score upon admission of 4.06 (SD = 1.2; Welch et al., 2010). Further research is warranted to investigate and replicate the severity cut-point among patients, in order to make reliable and meaningful comparisons to non-clinical samples. 4.4. Strengths and limitations Limitations of the present investigation deserve acknowledgment. First, the response rate of 37% in our study was slightly lower than prior studies and participants were on average two years older than non-participants, possibly introducing a selection bias affecting the representativeness and generalizability of our results. It could be argued, however, that the response rate affects the normative data to a greater extent than the analyses of BMI and age trends. Despite this, our sample size was large, with over 1000 participants randomly drawn from a national population, which is considered to be a methodological strength as it circumvents biases associated with other methods of recruitment. A typical problem with the use of a pathology measure on non-clinical populations is that many respondents achieve a low score, and as such, our data was positively skewed. Secondly, the study is also limited by self-reported BMI data, which may have produced an underestimate of their weight and an overestimate of their height (Rowland, 1990). However, high correlations (i.e. r's > .90) have been found between measured and self-reported BMI, indicating the suitability of these data (White, Masheb, BurkeMartindale, Rothschild, & Grilo, 2007). 4.5. Conclusions To date, our study represents the only investigation to examine the impact of age and BMI on impairment secondary to eating pathology as measured by the CIA. Our findings among a general population sample are valuable in providing a context in which clinical data can
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be interpreted. We recommend that age and BMI be considered during the interpretation and application of this self-report measure of impairment. Future studies should continue to investigate normative CIA data within both clinical and non-clinical samples. In particular, replications among a clinical sample are warranted to examine the cut-off and factor structure of the CIA originally established by Bohn et al. (2008). It will be particularly important to determine if, and to what extent, the CIA measures features of overweight and obesity related to eating disorder psychopathology. This will enable us to better determine the scope and recommended utilization of the measure. Role of funding sources This study received no external funding.
Contributors Authors Rosenvinge and Rø designed the study, wrote the protocol, and assisted with data collection. Authors Bang, Rø, and Reas assisted in data management and analyses. Dr. Reas, Drs. Bang, Rø, and Reas wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript. Conflict of interest No conflicts of interest exist by any author.
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