The ability to delay gratification (ATDG) is naturally linked to key regulatory psychological traits involved in self-discipline/regulation. The aim of this study was to ascertain the normalized impact of ATDG as an early predictor of oral health, on the primary dentition.
404 subjects [202 children (4–6 years old) and 202 mothers] were enrolled in a case-control study. Systematic data collection included: i) extraoral diagnostic parameters; ii) intraoral health status; iii) behavioral aspects; iv) baseline socio-demographic data. The ICC, the paired Student’s t -test and kappa statistic were used to evaluate intra-observer reliability. Distributions were explored with the chi-squared test [Odds ratio;95%CI;p < 0.05]. Conditional logistic regression was used to evaluate associations between all clinical diagnostic data and ATDG.
Overweight/obese children and those diagnosed with ADHD are more prone to lack ATDG (p < 0.001). Higher deft values were observed in children who lacked ATDG, who were also strongly associated with higher sugar consumption and more impulsive personalities (p < 0.001;OR:.107/0.031;95%CI:036–0.316/0.008–0.115). By contrast, children with responsible personality traits were associated with this skill (p < 0.028;OR:3.33;95%CI:1.1–9.7) and obtained the lowest deft (p < 0.306;OR:0.539;95%CI:0.165–0.176) and gingival index values (p < 0.001;OR:10.44;95%CI:2.6–40.9), which are clear indicators of better current and future oral health.
These data provide insights into a novel predictor for identifying individuals at a higher risk of dental caries in early childhood.
The present study offers a new hypothesis for identifying individuals with poor oral health status. Early tools to detect the most vulnerable population sectors are critically important to reduce the global burden of caries and other oral diseases.
Dental caries in early childhood arises from a complex interaction of genetic background and microbial, chemical, socio-physical environmental and behavioral factors, especially those associated with nutrition and oral care . Dental caries, along with other chronic oral diseases, is an irreversible pathology with a cumulative effect . Its onset is frequently in the early years. Onset and progression are dramatically conditioned by social factors and oral health behaviors . Various theoretical “life course” models have shown clear evidence of this , and structural equation modeling (SEM) has been used to estimate interactions in order to describe more accurately the direct or indirect influence of life course data on oral health and oral health-related quality of life . A well-established corpus of research documents has highlighted the need to understand psychosocial factors also associated with this oral condition. It has been shown that attention deficit hyperactivity disorder (ADHD) in children affects the child’s capacity to maintain appropriate daily oral care, which correlates with an increased incidence of caries . Obesity disease also provides a model of the relationship between dental caries and irregular dietary patterns, some of which have been associated with obesity . Both of these pathological conditions with a psychological basis have also been particularly associated with the inability to defer gratification . Delayed, or deferred, gratification is a social skill that refers to an individual’s ability to resist the temptation to accept an immediate reward in favor of a larger one later. The importance of this socially modifiable psychological trait is that a person’s ability to defer gratification (ATDG) is essentially linked to similar psychological traits, such as greater patience, impulse control, self-discipline and willpower, all of which are involved in self-regulation .
The primary aim of the present study was to ascertain the normalized impact of ATDG as an early predictor of oral health status and caries pathology on the primary dentition in a pediatric population. A secondary goal was to determine whether the inability to defer gratification could be associated with other patterns of risky oral health behavior.
Materials & methods
Every mother with a child taking part in this study received an informative letter, an invitation to participate and gave their informed consent beforehand. The study was performed in accordance with the principles of the Declaration of Helsinki with ethical approval provided by the Institutional Ethics Committee (PC 6/IC 0688-N-14 approved on 04/03/2015 ). In addition, the present study conforms to STROBE Guidelines (Supplementary material).
Study design and sampling
A case-control study was used to determine the primary objective of the present study. Sample size was estimated a priori using an unilateral contrast hypothesis, with 80% statistical power and 95% confidence interval, that the proportion of subjects with a high incidence of caries able and unable to delay gratification was 0.55 and 0.42, respectively, based on estimates from previous pilot studies. A 10% margin was considered to prevent loss of study power due to unexpected loss of subjects or diagnostic data collection.
A total of 202 children with primary dentition were included in the present research. 202 mothers were also examined and interviewed for the study. Identical inclusion/exclusion criteria were used for all participants, shown in Table 1 . Each child was included only once. Only data obtained from the first visit was used if there was more than one during the data collection period .
|Able to DG (n:125)||Unable to DG (n:77)||p value||OR||95%CI [inf-sup]|
|Baseline sociodemographic context|
|Parents/ family functional||113(0.4)||63(81.8)||0.114||1.915||0.847–4.328|
|Widowed father / mother||4(3.2)||0||0.113||0.968||0.938–0.999|
|Socioeconomic Status (Hi)|
|Number of inhabitants|
|Type of school|
|Behavioural data, oral care and nutrition|
|Dental anxiety response|
|<5 points (Lower dental anxiety)||99(79.2)||48(62.4)|
|>5 points (High dental anxiety)||26(20.8)||29(37.6)|
|Low anxiety response||89(71.2)||37(48.1)|
|High anxiety response||36(28.8)||40(51.9)|
|Type of personality (Baum test)|
|Diagnosed with ADHD hyperactivity||0||11(14.2)||**0.0001||1.167||1.065–1.278|
|Lifetime dental attendance patterns|
|Use of fluoride toothpaste||85(68)||40(51.9)||*0.023||1.966||1.096–3.525|
|High sugar intake in diet||44(35.2)||71(92.2)||**0.0001||0.046||0.018–0.114|
|Extraoral and intraoral diagnostic data|
|Body mass index (BMI)|
|Low BMI||9 (7.2)||7 (9.09)||0.631||0.776||0.277–2.17|
|Normal BMI||109 (87.2)||50 (64.9)||**0.001||3.47||1.71–7.03|
|High/severe BMI||7 (5.6)||20 (25.9)||**0.001||0.209||0.08–0.50|
|deft index (<0.5)||54 (43.2)||8 (10.3)||**0.0001||0.152||0.068–0.344|
|Mean deft index||3.42||4.10|
|Silness-Löe gingival index (grade 0)||51(40.8)||10(12.9)||**0.0001||4.618||2.172–9.816|
|Silness-Löe gingival index (grade 1)||50(40)||20(16)||*0.042||1.900||1.020–3.541|
|Silness-Löe gingival index (grade 2)||17(13.6)||30(38.9)||**0.0001||0.247||0.124–0.490|
|Silness-Löe gingival index(grade 3)||8(6.4)||17(22.1)||**0.001||0.241||0.098–0.591|
Diagnostic data collection
All data for this study were systematically collected by the same specialist in pediatric dentistry, using pre-piloted protocols. Data collection focused on four different areas: i) extraoral diagnostic parameters, ii) an intraoral health status assessment, iii) behavioral aspects, and iv) baseline socio-demographic information collected from each child and the parent participating in the study.
Extraoral diagnostic parameters ( Table 1 ): Body mass index (BMI) was recorded due its potential influence on oral health and particularly its potential association with ATDG . BMI was calculated on the basis of the height and weight of the child and was defined as weight in kilograms divided by the square of the height in meters, and was measured using a wall-mounted height meter and mechanical scales ( apps.who.int/bmi/index.jsp?introPage=intro_3.html ). Using the BMI classification, overweight was defined as 25–9.99 kg/m 2 and obesity as ≥30 kg/m 2 .
Based on previous reports in the literature about the influence of handedness on the prevalence of caries , handedness was determined using the Edinburgh Handedness Inventory. Left-handed children were rated using scores from −75 to −100, and right-handed subjects scores from 75 to 100. When a subject scored from −75 to 75, they were considered mixed-handed and were thus excluded from this study .
Intraoral health status assessment ( Table 1 ): The children’s teeth were not brushed or professionally treated before data collection. Teeth were explored using cotton rolls, plane mouth mirrors and a probe under illumination from a fixed light source. Caries was diagnosed on the surface, using the caries into dentin level (cavitated lesion). Diagnosis was performed by visual inspection, without using radiography. Experience of caries was assessed using the decayed, extracted tooth (due to caries only), filled teeth index for primary dentition ( deft ) and scored according to standard World Health Organization criteria ( www.who.int/iris/handle/10665/41905 ) . The SiC index (Significant Caries Index) was also calculated, as previously described . In brief, individuals were classified according to deft values and the third of the population with the highest caries scores were selected. The mean deft for this subgroup was then calculated to give the SiC index value .
The Silness–Löe plaque index was used to assess oral hygiene by evaluating plaque thickness on the buccal, lingual, mesial and distal surfaces of the teeth. At each assessment, the examiner observed and scored the amount of plaque on teeth using a four-degree scale .
Behavioral aspects: diet, self-care and use of dental care ( Table 1 ) . The mothers were also interviewed, using the modified protocol of Wagner and Heinrich-Weltzien, to identify specific risk factors associated with the development of malocclusion, as well as dietary patterns and behavior, and nonnutritive sucking and breathing patterns . The Venham anxiety scale , the Corah test and the Baum test were administered to all subjects in order to determine potential interactions between behavioral aspects such as dental anxiety, general anxiety response and type of personality respectively and ATDG. These variables were also recorded to control for confounders in the statistical analysis.
Baseline socio-demographic information ( Table 1 ): the following factors of relevance were collected: Socioeconomic position, socio-economic status (SES) according to Hollingshead’s classification of social class, based on parental education and income . Socio-demographic data were collected via structured questionnaires and included the child’s date of birth, gender, mother’s race (Caucasian vs other) child’s sex, child’s age, race, marital status, type of school, the number of people in the family household and the number of inhabitants in the town/neighborhood where they lived .
Experimental procedure. Delayed gratification assessment
The delayed gratification experiment was individually performed during the same hours of the afternoon in an adapted pediatric room with a controlled stimulus environment where the children felt comfortable; Neither parent was present . The experiment was performed following the protocol described by Kumst and Scarf . Briefly, a small open box (measuring 9 cm in width and 6 cm in height) containing candies was placed uncovered on the table in front of the children. The instructions given to the children were adapted from the method described by Schlam et al. . Each child was individually told: “There are two choices. Would you like one of these candies now or two of these candies in 15–20 min time?” If the child chose the candy immediately or was unable to wait during the 15–20 min anamnesis procedure, he/she was scored as having failed and received only one; conversely children who waited were considered to have the ability to defer gratification .
Intra-observer reliability was determined using the intraclass correlation coefficient (absolute agreement value) and the Student’s t -test for paired samples, considering values of p > 0.05 as indicative of good concordance between initial and repeated measurements. The kappa statistic was calculated to assess clinical agreement in categorical diagnosis. The Kolmogorov-Smirnov test was used to test normality of distribution. Conditional logistic regression was used to evaluate the association between all diagnostic variables and clinical data and the ATDG. Control for confounding factors was performed by preliminary stepwise regression analysis of each clinical variable. Odds ratio values and 95% confidence intervals were also calculated, with p values of less than 0.05 in regression analysis considered to be statistically significant.