Body dysmorphic disorder and self-esteem: a meta-analysis

Studies were selected if they fulfilled the following eligibility criteria. BDD symptom severity had to be measured with a questionnaire or interview that captures symptoms as described in the fifth or fourth edition of the Diagnostic and Statistical Manual of Mental Disorders, DSM-5 or DSM-IV [1, 32]. This comprised detailed measures of BDD symptom severity as well as shorter screening measures for BDD symptoms. Alternatively, categorial diagnostic measures of BDD based on DSM-IV or DSM-5 were also considered. Hence, the Yale-Brown Obsessive Compulsive Scale for Body Dysmorphic Disorder (BDD-YBOCS) [33], the self-report and clinician-administered versions of the Body Dysmorphic Disorder Examination (BDDE) [34], the Body Dysmorphic Symptoms Inventory (Fragebogen körperdysmorpher Symptome; FKS) [35], the Questionario sul Dismorfismo Corporeo (QDC) [36], the Dysmorphic Concern Questionnaire (DCQ) [37], the Body Dysmorphic Disorder Questionnaire (BDDQ) [38], and the Body Dysmorphic Disorder Diagnostic Module (BDD-DM) [39] were included in this meta-analysis. Measures of body image or body dissatisfaction were excluded. Also, measures which specifically address muscle dysmorphia were not included, as we intended to investigate BDD symptoms in general and because of the overlap between muscle dysmorphia and eating disorders. This meta-analysis relied on the definition and operationalization of self-esteem by Rosenberg [4]. Thus, self-esteem needed to be assessed via the Rosenberg Self-Esteem Scale (RSES), the most widely used self-report measure for global self-esteem [4]. For inclusion in the meta-analysis of partial correlations, studies were required to use a questionnaire or interview for the assessment of depressive symptom severity. The Beck Depression Inventory (BDI) [40‐42], the Hamilton Depression Rating Scale (HAMD) [43], the depression subscale of the Depression Anxiety Stress Scales (DASS) [44], the depression subscale of the Hospital Anxiety and Depression Scale (HADS) [45], the depression subscale of the Symptom Checklist-90 (SCL-90) [46], and the Patient Health Questionnaire-9 Depression module (PHQ-9) [47] were used in the studies.

Clinical, subclinical, and non-clinical samples were examined. Studies could target BDD patients, mentally healthy control participants, students, community persons, and cosmetic surgery patients. Participants were allowed to have secondary comorbid mental disorders. However, samples with another primary mental disorder (e.g., eating disorders, social anxiety disorder) were excluded. Studies that were recruited according to the presence or absence of a physical condition (e.g., rheumatic arthritis, obesity) were not included in this analysis. Also, samples that were selected according to related factors (e.g., body dissatisfaction) were not considered. No restrictions concerning age or gender of the sample were applied. Studies could be designed as correlational surveys or intervention studies. Since we investigated the cross-sectional relationship, data on all our variables of interest had to be collected at a single measurement point. In the case of more than one measurement point, baseline measures were analyzed. Case studies were omitted. For inclusion, manuscripts were required to be written in English or German.

Several sources were used to identify relevant studies. The databases PubMed, PsycInfo, PsycArticles, Medline, Web of Science, Psyndex, and Dissertation Abstracts International were searched for eligible studies. Furthermore, ongoing trials were found in the http://​ClinicalTrials.​gov registry, the Cochrane Central Register of Controlled Trials (CENTRAL), the WHO International Clinical Trials Registry Platform (ICTRP), and the ISRCTN registry. We also tried to obtain unpublished data by searching OpenGrey (http://​www.​opengrey.​eu). The keyword-based literature search was carried out by the second author in April 2017. Subsequently published or registered studies were identified in January 2019, August 2019, and in May 2020. The following search term was applied: (body dysmorphic AND self-esteem) or (dysmorphophobia AND self-esteem) or (dysmorphophobic AND self-esteem) or (body dysmorphic AND self-worth) or (dysmorphophobia AND self-worth) or (dysmorphophobic AND self-worth). The corresponding German search terms were: (körperdysmorphe AND Selbstwert) or (Dysmorphophobie AND Selbstwert) or (dysmorphophobe AND Selbstwert). Additionally, 24 well-known researchers in the field of BDD were contacted for unpublished studies in September 2019.

In a first step, the abstracts of identified studies were screened. The abstract screening of studies which were published after April 2017 was performed by two research assistants. The abstracts were required to suggest that BDD symptoms and self-esteem were captured in the study. Subsequently, a full text assessment was conducted by the second author (or a research assistant for studies with dates of publication after April 2017) according to the eligibility criteria described above.

A coding scheme for extraction of relevant data was developed. The coding scheme contained the following information: First, the sample was described with regard to the number of participants (in total and in the subgroups), clinical status, age, sex, education, ethnicity, sample type (e.g., students, cosmetic surgery patients), comorbidities, and other study-specific inclusion criteria (e.g., a certain cut-off on a BDD questionnaire). Second, the assessment of BDD symptom severity was specified. The interview or questionnaire used to examine BDD symptoms, diagnostic criteria, the diagnostic method (self-report vs. clinician-administered), as well as means and standard deviations of the diagnostic measure in the sample were coded. Additionally, the range of BDD symptom severity (e.g., only clinical participants) and whether the study compared two extreme groups (e.g., BDD patients versus healthy controls) were rated. Third, mean and standard deviation of the RSES in the total sample were gathered. Fourth, information on the assessment of depressive symptoms was collected. This included the measure for depressive symptom severity, the applied diagnostic criteria, the diagnostic method, as well as mean and standard deviation of the measure for depressive symptoms. Fifth, the reported effect size data were compiled. Preferably, the correlations between BDD symptom severity and self-esteem, between BDD symptom severity and depressive symptom severity, and between self-esteem and depressive symptom severity were gathered. Additionally, we coded whether the correlation was reported in the study or obtained by the authors afterwards. The type of correlation and the number of participants, for whom the correlation was calculated, were also coded. Alternatively, Cohen’s d for the difference in self-esteem and depressive symptoms of participants with BDD compared to participants without BDD were entered. If Cohen’s d was not reported, the mean and standard deviation of self-esteem and depressive symptom severity, and the number of participants in each comparison group were collected.

Data were coded independently by the first and second author. Interrater agreement was 97% and consensus was achieved after discussion of divergent coding. If studies did not report all data that were needed for the meta-analysis, authors were asked for the missing information. Altogether, 30 authors were contacted (concerning 35 studies) and 17 authors provided the required information (for 20 studies).

The effect sizes in the individual studies might have been subject to bias. We considered the selection of the sample (e.g., clinical BDD patients versus non-clinical students) and the diagnostic method for assessing BDD symptoms (self-report versus clinician-administered) as possible sources of bias. Consequently, these aspects were included in our coding scheme and controlled for in moderator analysis. Furthermore, we dealt with potential selective reporting by contacting all authors of studies which assessed our variables of interest without reporting an effect size for the relationship between BDD symptoms and self-esteem.

Effect sizes for the relationship between BDD symptom severity and self-esteem were calculated in three ways depending on the level of measurement of BDD symptom severity. For the majority of studies (k = 21), Fisher’s z transformed Pearson correlations between BDD symptom severity and self-esteem were analyzed. If effect sizes could not be based on a continuous measure of BDD symptom severity, we either used the pointbiseral correlation (k = 1) between BDD (coded 1 for BDD and 0 for healthy controls) and self-esteem or Cohen’s d (k = 1) which was transformed to Fisher’s z [48, 49]. In this case Cohen’s d described the difference in mean self-esteem between participants with BDD compared to participants without BDD. This categorial effect size is not based on the individual values of participants but rather on the group means. Thus, it mirrors the relationship between BDD symptom severity and self-esteem on a less precise group level. Nevertheless, we preferred to integrate these categorial effect sizes in the meta-analysis to achieve an extensive overview of the field and to avoid complete loss of the information. Two studies [12, 50] followed an ordinal approach and reported correlations between the number of items endorsed on the BDDQ and self-esteem. As this represents a gain in information compared to mere nominal data, this procedure was applied for studies which used the BDDQ.

If possible, an effect size for the total sample (instead of separate effect sizes for the subgroups) was gathered. Still, samples with varying ranges of BDD symptom severity were examined. In some cases, this may have caused underestimation of the true effect, whereas in others the magnitude of the relationship might have been overestimated [51]. Restriction of range in samples with reduced variance of BDD symptom severity (e.g., only clinical BDD participants) may have led to underestimation of the true effect. Enhancement of range and corresponding overestimation of effect sizes may have been produced by comparison of extreme groups (BDD patients versus healthy controls). A meta-analysis without artifact correction was conducted to describe the actual observed effects. Additionally, we attempted to correct for the artifacts. Thereby, we intended to achieve an estimate of the effect scaled on the general population without variance restrictions. For this purpose, studies with potentially restricted or enhanced range of BDD symptom severity were identified on the basis of theoretical assumptions concerning the sample. The individual correlations of these studies were adjusted before conducting a meta-analysis using standard corrections for variance restrictions [52]. For the adjustment, an estimate of the standard deviation of the BDD symptom severity measure in the general population was used and applied to all studies included. If possible, this was drawn from studies with large community samples.

For the calculation of partial correlations between BDD symptom severity and self-esteem controlling for depressive symptom severity, Pearson correlations between BDD symptom severity and depressive symptom severity, as well as between self-esteem and depressive symptom severity were conducted and preprocessed in the same manner as described above. The partial correlations controlling for depressive symptom severity were also Fisher’s z transformed for a subsequent meta-analysis. A meta-analysis of (z-transformed) partial correlations was also conducted with and without artifact correction.

A random effects meta-analysis was chosen to account for heterogeneity in effect sizes across studies. The computation was performed in R [53] using the metafor package [54]. For the assessment of effect size variability I² and τ were used. A moderator analysis was conducted to examine the influence of participants’ mean age, percentage of females, sample type, diagnostic method, and BDD diagnosis on effect sizes. An alpha level of α = .05 was applied. To visualize a potential publication bias, we created funnel plots.

The process of study selection with the number of records screened and excluded at each stage is presented in the PRISMA flow diagram in Fig. 1 [31]. Altogether, 25 studies (and 27 effect sizes) with a total number of 6278 participants were included in the meta-analysis. The mean age was 26.35 with a mean percentage of females of 69.62%. Regarding the sample type, four samples were drawn from individuals with clinical BDD (n = 239), three from mentally healthy control participants and individuals with clinical BDD (n = 128), and five from cosmetic surgery settings (n = 614). Further, nine student samples (n = 3463), two community samples (n = 423), and three community samples with large proportions of students (n = 1310) were analyzed. For nine studies BDD was diagnosed by a clinician either prior to or during study participation. Twelve effect sizes were based on clinician-rated measures of BDD symptoms whereas 14 relied on self-report measures (for one study no precise information was available whether the BDD-YBOCS was administered by a clinician or applied as a self-report questionnaire). Seventeen studies assessed depressive symptoms and could be included in the meta-analysis of partial correlations. Table 1 provides an overview of the study characteristics and effect sizes which were extracted from the studies.

The meta-analysis of uncorrected zero-order correlations between BDD symptom severity and self-esteem yielded an overall effect size of r = −.42, CI = [−.48, −.35]. The Fisher’s z-transformed effect estimates and confidence intervals for the individual studies as well as the Fisher’s z-transformed overall effect size are illustrated in Fig. 2. With regard to heterogeneity, I² amounted to 85.87% and τ was .17, indicating substantial variability of effect sizes.

When correcting for variance restriction and enhancement of BDD symptom severity, a mean weighted correlation of r = −.45, CI = [−.51, −.39] was observed. The artifact-corrected Fisher’s z-transformed zero-order correlations and the corresponding overall effect size estimate are visualized in Fig. 3. The I² of 82.38% and τ = .14 implied considerable heterogeneity. The standard deviation estimates for the BDD symptom severity measures which were used for artifact correction can be found in the appendix (Additional file 2).

In the meta-analysis of uncorrected partial correlations between BDD symptom severity and self-esteem controlling for depressive symptom severity a mean weighted effect size of pr = −.20, CI = [−.25, −.15] was achieved. The forest plot of Fisher’s z-transformed partial correlations and confidence intervals for the individual studies and the total estimate are displayed in Fig. 4. Investigation of heterogeneity resulted in I² = 37.28% and τ = .06.

Basing the meta-analysis on the artifact-corrected partial correlations revealed a mean weighted effect size of pr = −.23, CI = [−.28, −.17]. Fisher’s z transformed coefficients and confidence intervals are presented in Fig. 5. This analysis produced an I² of 40.33% and τ = .06.

The results of the moderator analysis for the meta-analysis of uncorrected zero-order correlations are presented in Table 2. The mean age of the sample, the percentage of females, and the diagnostic method did not show a significant influence on the magnitude of effect sizes in any of the analyses. The sample type turned out to be a significant moderator in the meta-analysis of uncorrected zero-order correlations (F (3, 22) = 4.83, p < .01). The weighted effect size estimates were z = −.40, CI = [−.58, −.22] for clinical BDD samples, z = −.83, CI = [− 1.06, −.60] for combined samples of mentally healthy control participants and individuals with clinical BDD, z = −.39, CI = [−.46, −.32] for student and community samples (which were analyzed as one category in the moderator analysis), and z = −.40, CI = [−.54, −.25] for the cosmetic surgery samples. The effect sizes for combined samples of clinical BDD and mentally healthy control participants differed significantly from the clinical BDD samples when contrasted in a dummy-coded moderator analysis (cf., Table 2). However, the moderation effect of the sample type was no longer significant for the artifact-corrected zero-order correlations. The weighted effect size estimates for the artifact-corrected zero-order correlations amounted to z = −.59, CI = [−.80, −.38] for clinical BDD samples, z = −.67, CI = [−.97, −.37] for combined samples of mentally healthy control participants and individuals with clinical BDD, z = −.46, CI = [−.55, −.37] for student and community samples, and z = −.45, CI = [−.63, −.27] for the cosmetic surgery samples. Regarding the partial correlations, the moderation effect of the sample type was no longer significant. Even more so, effect sizes for the different sample types were very much aligned after artifact correction (z = −.24, CI = [−.45, −.04] for clinical BDD samples, z = −.27,. CI = [−.52, −.02] for combined samples of mentally healthy control participants and individuals with clinical BDD, z = −.23, CI = [−.30, −.15] for student and community samples, z = −.21, CI = [−.37, −.05] for the cosmetic surgery samples) compared to the uncorrected weighted partial correlations (z = −.16, CI = [−.36, .03] for clinical BDD samples, z = −.32, CI = [−.54, −.11] for combined samples of mentally healthy control participants and individuals with clinical BDD, z = −.20, CI = [−.27, −.14] for student and community samples, z = −.18, CI = [−.32, −.03] for the cosmetic surgery samples). BDD diagnosis emerged as a significant moderator in the meta-analysis of uncorrected zero-order correlations (cf., Table 2). More precisely, studies in which BDD was diagnosed by a clinician prior to or during study participation appeared to have higher negative correlations between BDD symptom severity and self-esteem compared to studies without clinician-rated BDD diagnoses. However, this was no longer significant in all other analyses (b = −.118, CI = [−.278, .043], p = .144 for corrected zero-order correlations). In an attempt to explore other factors which could explain the heterogeneity of effect sizes, we additionally conducted moderator analysis with the year of publication and examined differences between different measures of BDD symptom severity. None of these analyses had significant explanatory value.

The funnel plots were rather symmetrical and did not point to any publication bias. Single effect sizes were positioned outside of the funnel which was in line with the heterogeneity of effect sizes, in particular with regard to the effect of the sample type. The funnel plots are attached as supplementary information (Additional files 3, 4, 5, 6).

The present meta-analysis has several limitations. First, we included studies using detailed clinician-administered measures of BDD symptom severity as well as shorter self-report screening instruments. These are of course not equally valid in assessing BDD symptom severity. For example, self-report measures might fail to differentiate BDD symptoms from preoccupation about actual defects (e.g., acne, scars) or weight-based concerns in the context of an eating disorder. Four of the 14 studies which applied self-report BDD measures tried to control for eating disorders. One of these studies excluded participants with elevated symptoms of an eating disorder [56], one study assessed comorbidities and reported that none of the participants were suffering from a comorbid eating disorder [6], one study excluded participants with a past or present eating disorder according to self-report [22], and one study ruled out the presence of any mental disorder according to self-report [11]. In order to address this limitation, we investigated the influence of the diagnostic method in moderator analysis. The diagnostic method appeared to have no systematic influence on the magnitude of effect sizes. On the one hand, this could imply that self-report measures were equally capable of capturing the relationship between BDD symptoms and self-esteem. On the other hand, this could signify that a preoccupation with actual appearance defects or weight-based concerns show a similar association with global self-esteem. Since this is the first meta-analysis on BDD and self-esteem we preferred to include all studies assessing BDD symptoms and self-esteem and controlled for the diagnostic method in moderator analysis.

Second, concerning the assessment of global self-esteem, this meta-analysis relied on the Rosenberg Self-esteem Scale [4] and considered the level of self-esteem only. Thus, we cannot determine whether other definitions and operationalizations of self-esteem demonstrate the same pattern of results. We were not able to examine contingencies and instability of self-esteem and their associations with BDD symptoms, since most of the primary studies did not assess these aspects of self-esteem.

Third, no causal inference can be drawn from our correlational findings. It remains unclear whether low self-esteem represents a vulnerability for BDD or develops as a consequence of the disorder (cf., [3]). Orth and Robins described different models for linking low self-esteem to depression [20] and these models might also apply to the relationship between BDD and self-esteem. Apart from unidirectional pathways, reciprocal relations or a common cause (e.g., bullying experiences) of both variables are possible. Moreover, a diathesis-stress model might be appropriate in which only under certain conditions low self-esteem leads to elevated BDD symptoms. Also, if low self-esteem predisposed BDD symptoms, mediating (e.g., social avoidance) and moderating variables (e.g., instability of self-esteem) might have an effect. Schulte et al. investigated the temporal dynamics of insight, affect, and self-esteem in BDD over six consecutive days and found that the cross-lagged effect of state self-esteem on insight was stronger than the effect of insight on state self-esteem [68]. Altogether, more studies are required to investigate causal directions.

Fourth, we included studies with varying ranges of BDD symptom severity. This may have led to overestimation of effect sizes for extreme group comparisons and underestimation of the effect in clinical samples. We tried to adjust effect sizes using variance corrections. However, in the absence of standard deviation norms for the individual BDD measures in the general population, we used standard deviation estimates from community samples if these were available or had to rely on student samples. Therefore, the results of the meta-analysis of corrected correlations have to be interpreted with caution, and the corrections need to be regarded as an imperfect attempt to deal with the heterogeneous samples.

Fifth, we were only able to exploratively investigate moderators for which sufficient information was provided in the studies. For instance, we could not control for effects of medication, comorbidities, or personality disorders. Hence, moderator analysis should be replicated in the future with a larger number of studies and variability of moderators.

Future studies may examine causal directions concerning the relationship between BDD symptom severity and self-esteem. Furthermore, future research may seek to identify subgroups in which BDD symptoms are associated with particularly low self-esteem, as these groups might benefit from self-esteem interventions. In this regard, it could be important to consider different developmental phases and the impact of depressive symptoms. It might also be helpful to examine whether low self-esteem can help to distinguish individuals with BDD from individuals without BDD among cosmetic surgery patients. Moreover, future studies should focus on different aspects of self-esteem. For instance, Buhlmann et al. investigated implicit self-esteem [5, 6], whereas B. Phillips et al. examined contingent self-esteem in BDD [10]. More research on contingencies and stability of self-esteem in BDD is required. With regard to prevention and therapy of BDD, an important step will be to evaluate the specific effects of interventions targeting self-esteem. In their network analysis of BDD and major depressive disorder Summers et al. revealed a high centrality of feelings of worthlessness and discussed implications for treatment such as addressing maladaptive core beliefs about self-worth [18]. Hence, future work may further try to determine the role of feelings of worthlessness in etiology, maintenance, and treatment of BDD. Furthermore, future trials may compare the effects of interventions intended to boost self-esteem and enhance self-compassion. In particular, focusing on self-compassion may entail certain benefits because it appears to be independent of personal achievements and success and thereby may result in more stable self-evaluations and reduced processes of comparing oneself to others (e.g., in the domain of appearance) [69]. Higher levels of self-compassion were associated with fewer BDD symptoms in a sample of adolescents [70]. Veale and Gilbert proposed to improve current treatments for BDD by developing a functional and evolutionary understanding of the BDD symptoms and by learning to relate to oneself and others with compassion and kindness [71]. These strategies from compassion-focused therapy [72, 73] might complement or enhance cognitive approaches.