Does psychological well-being influence oral-health-related quality of life reports in children receiving orthodontic treatment?


Although the associations between oral biologic variables such as malocclusion and oral-health-related quality of life (OHRQOL) have been explored, little research has been done to address the influence of psychological characteristics on perceived OHRQOL. The aim of this study was to assess OHRQOL outcomes in orthodontics while controlling for individual psychological characteristics. We postulated that children with better psychological well-being (PWB) would experience fewer negative OHRQOL impacts, regardless of their orthodontic treatment status.


One hundred eighteen children (74 treatment and 44 on the waiting list), aged 11 to 14 years, seeking treatment at the orthodontic clinics at the University of Toronto, participated in this study. The child perception questionnaire (CPQ11-14) and the PWB subscale of the child health questionnaire were administered at baseline and follow-up. Occlusal changes were assessed by using the dental aesthetic index. A waiting-list comparison group was used to account for age-related effects.


Although the treatment subjects had significantly better OHRQOL scores at follow-up, the results were significantly modified by each subject’s PWB status ( P <0.01). Furthermore, multivariate analysis showed that PWB contributed significantly to the variance in CPQ11-14 scores (26%). In contrast, the amount of variance explained by the treatment status alone was relatively small (9%).


The results of this study support the postulated mediator role of PWB when evaluating OHRQOL outcomes in children undergoing orthodontic treatment. Children with better PWB are, in general, more likely to report better OHRQOL regardless of their orthodontic treatment status. On the other hand, children with low PWB, who did not receive orthodontic treatment, experienced worse OHRQOL compared with those who received treatment. This suggests that children with low PWB can benefit from orthodontic treatment. Nonetheless, further work, with larger samples and longer follow-ups, is needed to confirm this finding and to improve our understanding of how other psychological factors relate to patients’ OHRQOL.

As orthodontic outcome research continues to move away from the traditional biomedical model toward a biopsychosocial perspective, more attention is being given to the concept of oral-health-related quality of life (OHRQOL). OHRQOL is defined as the absence of negative impacts of oral conditions on social life and a positive sense of dentofacial self-confidence. Studies with reliable OHRQOL measures have identified differences between treated and untreated orthodontic patients. For example, a Brazilian study of 1675 adolescents indicated that children who had completed orthodontic treatment reported fewer OHRQOL impacts than those who were never treated. These differences were mostly related to socio-emotional aspects of well-being such as smiling, laughing, and showing teeth without embarrassment.

Such differences between treated and untreated subjects are expected in light of studies emphasizing the importance of dentofacial esthetics in daily social interactions. For instance, an unattractive dentition has been associated with teasing, bullying, and negative OHRQOL impacts. Improving dental esthetics and, subsequently, psychological well-being (PWB) are frequently stated reasons for seeking orthodontic treatment during childhood and adolescence. However, the bulk of evidence denoting the relative stability of PWB undermines this assumption. Furthermore, no studies have described how OHRQOL and PWB change during orthodontic treatment.

Attempts to correlate OHRQOL reports with clinical orthodontic indicators, on the other hand, have often reached equivocal conclusions. In many of these studies, children reporting worse OHRQOL were not consistently those with worse malocclusions. It is possible that some children with a severe malocclusion are more emotionally resilient to the challenges caused by their condition. Hence, accurate interpretation of OHRQOL measures requires an understanding of not only their psychometric properties, but also the contextual factors that might influence their assessments of health and well-being. A recent long-term study evaluating psychosocial outcomes in orthodontics suggested that analyzing the effects of orthodontic treatment on psychological health without considering intervening factors might lead to invalid conclusions about the efficacy of treatment. This was corroborated by cross-sectional reports recognizing the effects of innate personality traits on children’s perceptions of dentofacial esthetics and patients’ evaluations of the impact of their health on daily functioning.

Contemporary models of diseases and disorders and their consequences, which integrate both biologic and psychologic aspects of health, support this holistic thinking paradigm. For example, according to the model of Wilson and Cleary, health-related quality of life outcomes experienced by a patient are determined not only by the nature and severity of the disease or disorder, but also by the patient’s characteristics and his or her environment. A thorough examination of the orthodontic OHRQOL literature with the Wilson-Cleary model as the conceptual framework showed that, for the most part, studies have focused on the associations between biologic variables and OHRQOL, with little emphasis on the psychological characteristics of children receiving orthodontic treatment. This is surprising, since research has shown that determinants of health-related quality of life are mainly psychological. Hence, psychological factors such as PWB are certainly important mediators of OHRQOL.

Since the relationship between psychological factors and OHRQOL is largely unexamined in orthodontic patients, this study was undertaken to answer the question: do individual psychological characteristics affect children’s OHRQOL reports? The specific objectives of this longitudinal investigation were to explore the effect of PWB on reported OHRQOL in children receiving orthodontic treatment and to compare this effect with a sample of untreated waiting-list controls. We hypothesized that children with better PWB would experience fewer negative impacts, regardless of their orthodontic treatment status. According to this hypothesis, the children’s assessment of OHRQOL would be influenced by their PWB. To demonstrate this mediating role of PWB, we compared OHRQOL in children with high and low PWB scores. We expected that OHRQOL outcomes would not change for those in the high PWB group but might change for those in the low group.

Since medical research has shown that psychological variables are likely to affect the more subjective domains of quality of life reports, we expected that the influence of PWB would be more pronounced for the more subjective social and emotional dimensions of the OHRQOL measure used in this study than for the more objective dimensions addressing functional limitations and oral symptoms.

Material and methods

In this study, we used a 2-group before-and-after design to assess changes in OHRQOL after orthodontic treatment. Patients receiving treatment were the focal group of interest, whereas patients awaiting treatment comprised the comparison group.

To be eligible, a child had to be fluent in English and have good general health. Children with severe dentofacial deformities were excluded. Parents’ consents and children’s assents were obtained, and the Research Ethics Board of the University of Toronto, Ontario, Canada, approved all study procedures. Subjects were not offered incentives or compensation for participating in the study. The treatment subjects were consecutively recruited from the graduate orthodontic clinic at the University of Toronto during their first assessment visit. The control subjects were consecutively recruited from the Faculty of Dentistry clinics during their first orthodontic screening visit. All 11- to 14-year-old subjects who met the eligibility criteria were recruited by the first author (S.A.).

All children completed the child perception questionnaire (CPQ11-14) and the PWB subscale of the child health questionnaire at baseline (T1) and follow-up (T2). The questionnaires were completed by the children unassisted by parents or investigators. The dental aesthetic index (DAI) was used to determine the clinical severity of the malocclusion. Table 1 summarizes the main study variables. Age and sex were recorded because of their potential associations with outcome and explanatory variables. The treatments were completed at the graduate orthodontic clinic as routinely prescribed with fixed appliance therapy. On average, treatment lasted for 26 months. The T2 data were collected at the first retention check appointment for the treatment subjects and after an equivalent time interval for the control subjects.

Table I
Main study variables and their interpretations
CPQ11-14 Child perception questionnaire
Lower CPQ11-14 scores represent better OHRQOL
OS Oral symptoms
Lower CPQ11-14 scores represent better OHRQOL
FL Functional limitations
Lower CPQ11-14 scores represent better OHRQOL
EWB Emotional well-being
Lower CPQ11-14 scores represent better OHRQOL
SWB Social well-being
Lower CPQ11-14 scores represent better OHRQOL
PWB Psychological well-being
Lower PWB scores represent worse psychological well-being
DAI Dental aesthetic index
Lower DAI scores represent better occlusion

The CPQ11-14 is a child OHRQOL instrument. The age-specific questionnaire (11-14 years) consists of 37 items, grouped into 4 domains: oral symptoms (OS), functional limitations (FL), emotional well-being (EWB), and social well-being (SWB). Each item asks about the frequency of events, as applied to the teeth, lips, and jaws, in the previous 3 months. The response options were “never,” “once or twice,” “sometimes,” “often,” and “every day or almost every day.” Additive scale and subscale scores for the CPQ11-14 were calculated by summing the item response codes. Although the instrument was designed to yield an overall score, a separate score can be generated for each subscale. Higher scores signify worse OHRQOL. The validity, reliability, and responsiveness of this measure have been established in various settings. This measure examines the impacts of oral conditions on children’s EWB and SWB; nonetheless, it is important not to confuse these 2 domains with the more generic PWB. EWB and SWB focus specifically on the impacts of oral health conditions of children’s daily functioning, whereas PWB takes into account the effect of all aspects of health and daily life on well-being.

The children’s PWB was measured by using the PWB subdomain of the child health questionnaire, which is a widely used and validated self-report instrument. The 16-item PWB scale measures the frequency of both negative and positive feelings. The items capture anxiety, depression, and happiness. Frequency is measured by using a 5-level continuum that ranges from “all of the time” to “none of the time.” The scores were calculated according to the user’s manual. Higher scores indicate better PWB; a score of a 100, for example, indicates that the child feels peaceful, happy, and calm all of the time. In contrast, lower scores indicate that the child has feelings of anxiety and depression. Specific instructions confirming the generic nature of the measure were added at the beginning of the questionnaire.

The severity of each treatment and control subjects’ orthodontic condition was assessed from study models taken at T1 and T2 and by using the DAI. Although other treatment-need indexes such as the index of orthodontic treatment need and the index of complexity, outcome, and need are available, the DAI was chosen because it incorporates the social acceptability of a child’s dental appearance. The rating is based on the measurement of 10 occlusal traits; each trait is multiplied by a weight derived from the judgment of laypersons. The products are summed, and a constant is added to give a DAI score. DAI scores range from 13 (the most acceptable) to 100 (the least acceptable). The DAI ratings were recorded by 3 trained and calibrated examiners. Intraexaminer and interexaminer reliabilities were evaluated by having the raters independently assess a random 10% sample of the models and then reassessing the models after a 1-week interval. Intraexaminer reliability for the DAI raters was high with intraclass correlation coefficients of 0.96, 0.91, and 0.97, respectively. The interexaminer reliability was also high (intraclass correlation coefficient of 0.81).

Statistical analysis

The data were analyzed by using SPSS software (version 16, SPSS, Chicago, Ill). Data analyses included descriptive statistics, and bivariate and multivariate analyses. Paired t tests were used to assess within-group changes over time for the treatment and control groups. The P value for all tests was set at <0.05.

Analysis of covariance (ANCOVA) models were then used to explore between-group differences. The first goal was to evaluate the relationship between the provision of orthodontic treatment and the changes in OHRQOL, represented by overall and individual CPQ11-14 scores, while controlling for the CPQ11-14 scores at T1, age, and malocclusion severity. This analysis plan, represented in model 1 ( Table II ), aims to address whether there is a difference in reported OHRQOL between treatment and control subjects.

Table II
Main study variables at T1 and T2 for the treatment and control groups
Group Treatment Control
Time T1 T2 T1 T2
Original baseline (n = 98) Retained baseline (n = 74) Follow-up (n = 74) Original baseline (n = 101) Retained baseline (n = 44) Follow-up
(n = 44)
Variable Mean, (SD),
Mean, (SD),
Mean, (SD),
Mean, (SD),
Mean, (SD),
Mean, (SD),
CPQ11-14 21.05 (15.09)
21.63 (14.19)
16.16 (10.99)
24.07 (16.15)
24.07 (16.15)
23.14 (17.97)
OS 5.58 (3.40)
5.75 (3.37)
5.26 (3.15)
5.93 (3.24)
6.07 (3.59)
6.34 (3.69)
FL 5.09 (4.15)
5.27 (4.15)
5.41 (4.26)
5.92 (4.95)
5.36 (4.69)
4.82 (4.57)
EWB 5.19 (5.09)
5.29 (5.14)
0.00- 24.00
2.51 (2.96)
6.83 (5.59)
6.75 (5.45)
6.82 (7.56)
SWB 5.18 (5.39)
5.32 (5.46)
2.99 (3.59)
6.01 (6.12)
5.89 (6.13)
5.16 (6.34)
PWB 80.66 (10.09)
79.78 (9.29)
81.68 (10.52)
78.33 (12.98)
78.05 (11.7)
78.84 (13.39)
DAI 34.21 (8.18)
33.72 (7.78)
22.49 (2.86)
36.53 (8.89)
36.25 (7.25)
33.56 (7.14)

Paired t statistics significant at P <0.01.

The second goal was to evaluate the role of PWB on mediating OHRQOL outcomes by using a second ANCOVA model (model 2 in Table II ). This model addresses whether there is a difference in OHRQOL scores between treatment and control subjects and whether it remains significant after controlling for PWB.


Of the 118 study subjects in this study, 50% were girls and 76% were white, with a mean age of 12.9 years (SD, 0.98) at T1. According to published DAI categories, 44.2% of the overall sample had handicapping malocclusions, 25.7% had severe malocclusions, 23.9% had definite malocclusions, and 6.2% had minor malocclusions.

Although follow-up data were successfully obtained from 118 subjects (74 treatment and 44 control), 199 children were recruited at the start of the study. To ensure that the relatively large percentage of dropouts (40.71%) did not compromise the comparability of T1 characteristics between the treatment and control groups, the data of the original and the retained subjects for the treatment and control groups were contrasted in Table II . The statistics indicated that all variables studied were comparable for both groups at T1. Hence, the subjects lost to follow-up did not influence the distribution of these variables.

Table II also summarizes the T2 data for the treatment and control subjects, with a guide to interpreting these scores in Table I . The CPQ11-14, EWB, SWB, and DAI scores for the treatment subjects were the only variables that changed significantly over the study period. In contrast, these scores did not change significantly for the control group. As expected, PWB scores remained relatively constant over time for both the treatment and control subjects. Furthermore, these PWB scores were slightly higher but not significantly different from those reported for normal schoolchildren.

As mentioned earlier, ANCOVA models were used to test the differences in reported OHRQOL between the treatment and control subjects. Treatment status was entered into the ANCOVA model as a fixed factor and tested for overall effect on CPQ11-14 overall and subscales scores at follow-up. For each scale, the first model controlled for age, T1 scores, and initial severity of malocclusion (DAI), and the second model also controlled for PWB. Table III provides a summary of the ANCOVA models and the total amount of variance explained by each model.

Table III
ANCOVA models showing contribution of covariates to overall and subscale (T2) CPQ11-14 scores
(T2) CPQ11-14 (T2) EWB (T2) SWB (T2) FL (T2) OS
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
F statistics F F F F F F F F F F
Age 0.57 5.79 .023 1.59 0.00 1.60 3.17 6.80 1.41 5.79
Baseline scores 6.51 7.81 1.89 2.09 6.52 7.10 5.27 5.23 3.45 3.77
DAI 0.58 0.39 0.21 0.11 4.85 4.88 0.00 0.03 0.27 0.58
PWB 27.09 22.15 18.07 7.14 13.97
Treatment status 4.17 1.31 14.97 10.17 3.62 1.29 1.47 3.29 1.77 0.31
Corrected model 3.79 8.9 5.02 9.08 4.78 7.88 2.73 3.71 2.01 4.46
Adjusted R 2 0.09 0.26 0.13 0.27 0.12 0.24 0.06 0.11 0.04 0.14
Model 1 controls for age, DAI, baseline scores, and treatment status; model 2 controls for all variables in model 1 in addition to PWB status.

P <0.05

P <0.01.

The results indicated a significant difference in overall CPQ11-14, SWB, and EWB scores between the treatment and control subjects ( P <0.05). However, after considering PWB as a covariate, the effect of providing orthodontic treatment was no longer significant, as measured by the overall CPQ11-14 and SWB scores ( P = 0.23). EWB was the only scale with the difference between treatment and control subjects remaining significant after controlling for clinical and psychological confounders. To illustrate the results, the adjusted mean CPQ11-14, SWB, and, EWB scores for the treatment and control groups are presented in Table IV .

Apr 13, 2017 | Posted by in Orthodontics | Comments Off on Does psychological well-being influence oral-health-related quality of life reports in children receiving orthodontic treatment?
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