Analyzing Nonpharmacological Behavior Management in Children With Dental Anxiety: A Systematic Review and Meta‐Analysis

ABSTRACT

Background and Aims

Dental anxiety in children is a prevalent concern that affects compliance and treatment outcomes in pediatric dentistry. Nonpharmacological behavioral interventions (NPBI), including virtual reality (VR) and tell‐show‐do (TSD), are commonly used to manage dental anxiety. This systematic review and meta‐analysis aim to evaluate the effectiveness of these interventions in reducing anxiety and pain perception in pediatric dental patients.

Methods

Following PRISMA‐2020 guidelines, a comprehensive search of PubMed, Scopus, Cochrane, and Web of Science was conducted until April 2024. Randomized controlled trials (RCTs) comparing NPBIs to controls in children undergoing dental procedures were included, focusing on changes in dental anxiety and pain. The risk of bias was assessed using the Cochrane RoB 2 tool for RCTs and the ROBINS‐I tool for non‐randomized studies. Meta‐analysis was performed using a random‐effects model, with standardized mean differences (SMD) and 95% Confidence Intervals (CI) calculated for anxiety and pain outcomes. Statistical heterogeneity was assessed using I² and Cochran's Q test.

Results

Of 2,957 records, 76 RCTs with 6,723 participants were included. Distraction techniques did not significantly reduce dental anxiety compared to Tell‐Show‐Do (SMD −0.34, 95% CI −0.71 to 0.04) but were effective in reducing pain (SMD −0.43, 95% CI −0.76 to −0.10). Virtual Reality (VR) distraction showed no significant reduction in anxiety or pain compared to traditional techniques.

Conclusion

While certain distraction techniques may alleviate pain, their impact on anxiety is comparable to that of traditional methods such as TSD. Despite this innovative approach, VR distraction did not demonstrate superiority over traditional techniques in reducing anxiety or pain.

Abbreviations

CFSS‐DS

children's fear survey schedule ‐ dental subscale

CI

confidence interval

FBRS

Frankl's behavior rating scale

FLACC

face, Legs, activity, cry, consolability scale

INPLASY

International platform for registered systematic review and meta‐analysis

NPBI

nonpharmacological behavioral interventions

PICOS

population, intervention, comparator, outcomes, study design

PRISMA

preferred reporting items for systematic reviews and meta‐analyses

RCTs

Randomized controlled trials

ROBINS‐I

risk of bias in non‐randomized studies ‐ of interventions

RoB‐II

risk of bias assessment tool 2

SD

standard deviation

SMD

standardized mean difference

TSD

tell‐show‐do

VAS

visual analog scale

VPT

Venham picture test

VR

virtual reality

WBFS

Wong‐baker faces scale

1. Introduction

Dental anxiety, a prevalent concern among pediatric patients, significantly hinders the delivery of effective dental care [1]. It is characterized by a spectrum of fearful responses to dental stimuli that not only affect children's immediate emotional well‐being but also have long‐term implications for oral health [2]. Dental anxiety manifests across various age groups and impacts individuals differently as they grow older. For example, studies have shown that the prevalence of dental anxiety in preschool‐aged children varies between 4% and 20% [3], whereas in school‐aged children aged 6 to 12 years, it fluctuates between 8% and 23% [4]. Among adolescents, the range is slightly narrower, from 7% to 18% [5], whereas in adults, dental anxiety prevalence is notably higher, ranging from 14% to 30% [6].

Studies indicate that early experiences of dental anxiety can lead to avoidance of dental care, resulting in poor oral hygiene, an increased incidence of dental caries, and a higher likelihood of requiring extensive dental interventions later in life [7, 8, 9]. The traditional approach to managing dental anxiety in children predominantly relies on pharmacological intervention. However, concerns about possible side effects, the potential for sedation‐related complications, and the growing preference for nonpharmacological alternatives have shifted the focus towards behavioral management techniques [10].

Behavioral management strategies in pediatric dentistry encompass a range of interventions designed to alleviate anxiety, improve patient cooperation, and facilitate the delivery of dental care without resorting to sedation or pharmacological means [11]. These methods include but are not limited to cognitive‐behavioral therapy, desensitization [12], distraction techniques [13], virtual reality [14], and parent‐child interaction therapies [10]. Despite the appeal of these nonpharmacological approaches, evidence regarding their efficacy, applicability, and best practices remains fragmented and inconclusive.

Responding to the critical need for effective, safe, and child‐friendly management of dental anxiety, this systematic review and meta‐analysis was conducted to provide evidence on nonpharmacological behavioral management techniques for children with dental anxiety. This review aimed to evaluate the effectiveness of these interventions in reducing dental anxiety levels and improving behavioral outcomes during dental visits.

2. Methods

This study was performed adhering to the Preferred Reporting Items for Systematic Reviews and Meta‐analysis (PRISMA‐2020) guidelines [15]. The methodologies and analyses were performed in full adherence to the principles outlined in the Cochrane Handbook of Systematic Reviews and Meta‐analyses and the Guide for Comparative Effectiveness Reviews [16]. The protocol used for this systematic review was registered on the international platform for systematic reviews and meta‐analysis (INPLASY 202440064).

2.1. Search Strategies

A systematic search of electronic databases, including PubMed, Scopus, Cochrane, and Web of Science, was conducted through April 2024. Specific keywords were used to identify the potentially relevant studies. Further records were manually extracted. The specific keywords and results for each database are listed in Table 1.

Table 1.

Search strategies used in different databases.

Database	Search strategy	Records retrieved
PubMed	(“Dental anxiety” OR “Dental anxiet*” OR “Dental distress” OR “Dental fear” OR “Dental phobia”) AND (Child* OR Pediatric*) AND (nonpharmacological OR Behavior* OR “Tell‐Show‐Do Method” OR “Positive Reinforcement” OR “Distraction Techniques” OR “Desensitization” OR “Parental Involvement” OR “Behavior Modeling” OR “Communication”)	926
Web of Science core collection	(“Dental anxiety” OR “Dental anxiet*” OR “Dental distress” OR “Dental fear” OR “Dental phobia”) AND (Child* OR Pediatric*) AND (nonpharmacological OR Behavior* OR “Tell‐Show‐Do Method” OR “Positive Reinforcement” OR “Distraction Techniques” OR “Desensitization” OR “Parental Involvement” OR “Behavior Modeling” OR “Communication”)	535
Scopus	TITLE‐ABS‐KEY ((“dental anxiety” OR “dental anxiet*” OR “dental distress” OR “dental fear” OR “dental phobia”) AND (child* OR pediatric*) AND (nonpharmacological OR behavior* OR “tell‐show‐do method” OR “positive reinforcement” OR “distraction techniques” OR “desensitization” OR “parental involvement” OR “behavior modeling” OR “communication”))	1170
Cochrane	(“Dental anxiety” OR “Dental anxiet*” OR “Dental distress” OR “Dental fear” OR “Dental phobia”) AND (Child* OR Pediatric*) AND (nonpharmacological OR Behavior* OR “Tell‐Show‐Do Method” OR “Positive Reinforcement” OR “Distraction Techniques” OR “Desensitization” OR “Parental Involvement” OR “Behavior Modeling” OR “Communication”) in Title Abstract Keyword ‐ (Word variations have been searched)	326

2.2. Inclusion and Exclusion Criteria

Predetermined criteria were utilized for the selection and exclusion of the obtained records. The parameters for inclusion were defined using the PICOS model as follows: Population: Children undergoing dental procedures; Intervention: all types of nonpharmacological behavioral interventions (NPBIs) aimed at reducing dental anxiety; Comparator: No intervention, no distraction, standard care, or another type of NPBI; Outcomes: Dental anxiety was measured using the heart rate‐facial image scale, Venham's picture test, Abeer Children Dental Anxiety Scale, and visual analog scale (VAS), among others; Study design: Randomized controlled trials (RCTs).

Studies conducted in languages other than English, as well as theses, conference abstracts, and studies with unreliable data that could not be extracted or analyzed, were excluded.

2.3. Selection of Studies and Data Extraction

Two authors independently implemented the selection criteria. The eligibility screening process consisted of two steps. First, abstracts were examined to determine eligibility, and subsequently, full‐text articles of the qualifying abstracts were obtained and reviewed for eligibility. Then, an online data extraction sheet was created, accessible to the two authors who conducted the data extraction. The extracted data included (1) study ID, (2) study publication year, (3) country, (4) study design, (5) number of participants, (6) age range, (7) intervention and control, (8) methods of assessment, (9) evaluation time, (10) changes in fear and anxiety, (11) changes in pain, and (12) completion of treatment.

2.4. Assessment of Risk of Bias in Included Studies

In accordance with the guidelines of the Cochrane Handbook of Systematic Review and Meta‐analysis [16], the Cochrane Risk of Bias Assessment Tool 2 (RoB‐II) was used to assess the risk of bias in RCTs. This tool assesses seven essential areas of potential bias: (1) sequence generation, (2) allocation concealment, (3) blinding of participants and personnel, (4) blinding of outcome evaluation, (5) insufficient outcome data, (6) selective reporting, and (7) other possible sources of bias. Two authors independently assessed each of the aforementioned domains across the studies, assigning ratings of unclear, low, or high risks of bias. The Risk of Bias in Non‐Randomized Studies of Intervention (ROBINS‐I) tool was used to assess the bias in non‐randomized studies. Publication bias was evaluated using funnel plots and Egger's regression test, where a p‐value < 0.05 indicated significant asymmetry.

2.5. Measures of Intervention Effect and Data Synthesis

The parameters assessed were the extent of dental fear and anxiety using multiple assessment tools, including the Dental Subscale of Children's Fear Survey Schedule (CFSS‐DS), Facial Image Scale (FIS), Raghavendra Mahuriu Sujata Pictorial Scale (RMS‐PS), Frankl's Behavior Scale for Ratings (FBRS), and Venham Images Test (VPT). Pain modification was assessed using self‐reported pain measures (WBFS) and the Face, Legs, Activity, Cry, Consolability (FLACC) scale. The SMD and its related 95% CI were computed by aggregating all scales in the paired meta‐analysis models. Statistical analysis was conducted using the STATA software version 18. Statistical significance was set at p < 0.05. A random‐effects model was employed to account for the expected variability among studies, resulting from differences in interventions and study populations. Additionally, subgroup analyses were conducted to investigate potential sources of heterogeneity, including variations in anxiety measurement scales and intervention types.

2.6. Statistical Analysis

All statistical analyses were conducted using RevMan 5.4 (Cochrane Collaboration) and R software (metafor package). A random‐effects model was employed to conduct the meta‐analysis, taking into account the expected variability among studies due to differences in interventions and study populations.

• – Heterogeneity Assessment

Statistical heterogeneity was assessed using Cochran's Q test and quantified with the I² statistic. The I² values were interpreted as follows:

• 25%: Low heterogeneity

• 50%: Moderate heterogeneity

• 75%: High heterogeneity

A p‐value < 0.10 in Cochran's Q test was considered indicative of significant heterogeneity.

• – Effect Size Calculation

The Standardized Mean Difference (SMD) with 95% Confidence Intervals (CI) was used to estimate the pooled effect size for anxiety and pain reduction. The inverse variance method was applied to weigh individual study contributions based on sample sizes and standard deviations.

• – Subgroup and Sensitivity Analysis

To explore potential sources of heterogeneity, subgroup analyses were performed based on:

• Intervention type (Virtual Reality vs. Tell‐Show‐Do)

• Age groups

• Anxiety measurement scales

A leave‐one‐out sensitivity analysis was conducted by systematically omitting one study at a time to assess the robustness of the pooled effect size. To investigate potential moderators contributing to the observed heterogeneity, a meta‐regression analysis was performed using the ‘metafor’ package in R. The following moderators were examined: (1) mean age of participants, (2) type of intervention (e.g., VR vs. TSD vs. other distraction techniques), (3) type of dental procedure (invasive vs. noninvasive), and (4) type of anxiety assessment tool (self‐reported vs. observational vs. physiological). Meta‐regression was conducted only for outcomes with at least 10 studies to ensure sufficient statistical power, as recommended by the Cochrane Handbook [16]. Results were considered statistically significant at p < 0.05.

A random‐effects model was used to account for the expected variability among studies due to differences in interventions and study populations. Additionally, subgroup analyses were conducted to investigate potential sources of heterogeneity, including variations in anxiety measurement scales and intervention types.

3. Results

3.1. Search Results

The search strategy yielded 926 records from PubMed, 1170 records from Scopus, 535 records from the Web of Science Core Collection, and 326 records from the Cochrane Library, totaling 2957 records. These records were imported into the EndNote software, which was then used to automatically detect and eliminate 1109 duplicate records. After duplicates were removed, 1848 records remained and underwent preliminary screening based on their titles and abstracts. This process resulted in the exclusion of 1718 records. The full texts of the remaining 130 studies were then assessed against more detailed criteria, resulting in 76 studies that met the inclusion criteria and were incorporated into the review. The PRISMA flow diagram is shown in Figure 1.

Figure 1.

PRISMA flow diagram exhibiting the study selection process.

3.2. Study Characteristics and Quality

This systematic review and meta‐analysis included 76 RCTs with a total of 6,723 participants, spanning a 39‐year period from 1985 to 2024. Regarding the geographical distribution, 28 RCTs were conducted in India, 7 in Iran, 5 in Saudi Arabia, 5 in Brazil, 5 in China, 3 in the USA, 3 in Spain, 2 in Egypt, 2 in Thailand, 2 in the UK, and finally, one RCT in each of the following countries: Bangladesh, France, Greece, Iraq, Ireland, Israel, Jordan, Korea, Lebanon, Mexico, Pakistan, Sweden, Syria, and Turkey. After applying the RoB‐II to the 76 included studies, the results were obtained and presented graphically. Additional details of the included studies are presented in Table 2. Figure 2 presents the authors' assessments of the risk of bias for each item in the individual studies. Approximately 25% of the 76 included studies were classified as low risk, 45% as moderate risk, and 30% as high risk of bias across all domains (Figure 3), with participant blinding and outcome assessment being the most common sources of bias. Figure 3 displays these assessments as percentages, summarizing the risk of bias across all included studies.

Table 2.

General characteristics of the included studies.

Study ID	Publication year	Study design	Country	Number of participants	Age range (years)	Intervention group	Control groups	Method of assessment	Evaluation time	Outcome
Mani 2020 [17]	2020	RCT	Bangladesh	Thirty	6–12	Audio distraction ‐ Audiovisual distraction	No intervention	Venham's clinical grading scale ‐ Venham's picture rate	First and second visits	Dental anxiety
CustÓdio 2020 [18]	2020	RCT	Brazil	Forty‐four	6–9	Distraction caused by augmented reality eyeglasses	Conventional NP‐BMT	VAS ‐ FLACC HR ‐ FPS‐R scale	Pre and Postoperative	Child's behavior, dental anxiety, dental discomfort, body movement, and perception of pain.
Ramos‐Jorge 2011 [19]	2011	RCT	Brazil	Seventy	4–11	Favourable perception of dentistry and dental care	Dentally neutral image	VPT	Pre‐op/post‐op ‘5 months’	Dental anxiety
Rank 2019 [20]	2019	RCT	Brazil	Three hundred and nine	4–6	a. Say–show–do b. Award‐based positive reinforcement following dental care	Say–show–do	VPT	Preoperative and Postoperative for ten months	Children's motivation in two dental visits and the difference occurring between genders
Serra‐Negra 2019 [21]	2019	RCT	Brazil	Thirty‐four	4–6	Restoration session 1: music No music in restoration #2.	First restoration session: Absence of music. Second restorative session: music	Pulse rate ‐ EPQ‐j ‘Brazilian version of the Eysenck Personality Questionnaire‐Junior’	Three 25‐min clinic consultations were separated by 7 days in the trial.	Effect of music on children's pulse rate
Du 2022 [22]	2022	RCT	China	Eighty‐six	4–9	VR relaxation	Traditional NP‐BMT	Modified CFSS‐DS ‐Wong‐Baker faces ‐ Houpt scale ‐ Simulator sickness questionnaire	NR	Dental anxiety ‐ Pain perception
Ran 2021 [23]	2021	RCT	Chintya	One hundred and ten	4–8	VR	Tell–show–do	CFSS‐DS ‐ WBFS ‐ FBRS	Before/during dental operation	Dental anxiety ‐ Dental pain ‐ Compliance score in perioperative children
Wang 2022 [24]	2022	RCT	China	Eighty	9–12	First treatment: anti‐anxiety auricular plaster treatment + tell–show–do Second treatment: regulate auricular plaster therapy + tell–show–do	Treatment 1: auricular plaster therapy (control) + tell–show–do Second treatment: anti‐anxiety auricular plaster therapy + tell–show–do	Heart rate, salivary cortisol, Venham's clinical anxiety obedience level rating scale, FCS, and MCDAS.	Preoperative and Postoperative for seven months	Dental anxiety
Xia 2016 [25]	2016	RCT	China	One hundred	3–12	A small notebook, a cartoon sticker, or a pencil eraser for rewards.	No intervention	CFSS‐DS	Before and after treatment	Dental anxiety
Zhu 2020 [26]	2020	RCT	China	Nine hundred and eighty‐eight	7–8	Experiential learning	Tell–show–do	(1) Modified CFSS‐DS (2) BP (3) HR	Pre‐op/post‐op	Dental anxiety
AlDhelai 2021 [27]	2021	RCT	Egypt	One hundred and fifty	3–6	Parental active presence	Parental passive presence	FIS ‐ FBRS ‐ IQ level	NR	Child's behavior
Kamel 2017 [28]	2017	RCT	Egypt	Sixty	4–6	favorable perceptions of dental care	Neutral cartoon images	Frankl rating scale ‐ Venham picture test	NR	Dental behavior ‐ Dental anxiety
Gómez‐Polo 2021 [29]	2021	RCT	Espain	Eighty	5–10	VR distraction	No distraction	Frankl test of facial image scaling	NR	‐ Dental anxiety ‐ patient's behavior
Guinot 2021 [30]	2021	RCT	Spain	Sixty‐eight	6–8	Video game ‘PlayStation’	Cartoon film	Corah's dental anxiety scale modified ‐ The Venham picture test ‐ Wong‐Baker faces scale ‐ Frankl scale ‐ Heart rate	10 months	Dental anxiety
Ramírez‐Carrasco 2017 [31]	2017	RCT	Spain	Forty	5–9	Using headphones for “classic directive hypnosis”	Headphones for noise cancellation during dental procedures	FLACC ‐ HR	During the dental visit	Dental anxiety ‐ dental pain
Huet 2011 [32]	2011	RCT	France	Thirty	5–12	Hypnosis	No Hypnosis	Modified Yale scale ‐ VAS ‐ Modified objective pain score	3 months	Dental anxiety ‐ Pain experience
Boka 2017 [33]	2017	RCT	Greece	Sixty‐one	3–8	Parental presence/absence + conventional NP‐BMT	No PPA + conventional NP‐BMT	Frankl scale	Pre‐op/post‐op	Childs' behavior
Aditya 2021 [34]	2021	RCT	India	Sixty	6–9	Fidget spinner‐ Kaleidoscope ‐ VR Distraction	NO distraction	Venham's picture test‐ Pulse oximeter ‐ Pulse rate ‐ SpO2	6 months	Dental anxiety
Asokan 2021 [35]	2021	RCT	India	Sixty	4–5	First group: diversionary magic trick. Second group: distracting mobile dental games	3rd group: TSD	Chotta Bheem–Chutki scale	Postoperative	Dental anxiety
Avisa 2018 [36]	2018	RCT	India	Two hundred and ten	8–12	Acupressure ‐ Sham	No intervention	MCDAS scale ‐ Frankl ‐ Pulse rate	Pre and postoperative	Dental anxiety
Azher 2020 [37]	2020	RCT	India	Forty‐eight	6–8	Play bubble breath therapy	Tell–show–do	Pulse rate ‐ Venham's anxiety and behavior rating scale	Pre and postoperative	Dental anxiety
Gala 2024 [38]	2024	RCT	India	Twenty‐two	5–8	VR distraction	Periorbital eye massager	HR‐SPO2‐modified behavior pain scale,	Pre and postoperative	Dental anxiety
Hemalatha 2024 [39]	2024	RCT	India	Forty‐eight	7–12	Voice control (Storytelling) distraction	Conventional behavior management strategies	FIS‐ FLACC ‐ Frankls scale	NR	Child pain‐related behavior
Jamil 2023 [40]	2023	RCT	India	Eighty	NR	Audiovisual distraction	Filmed modeling‐no technique	FIS‐fear assessment picture scale (FAPS)‐PR‐RR‐SPO2	Pre and postoperative	Dental anxiety
Karekar 2019 [41]	2019	RCT	India	Sixty three	5–8	Live modeling ‐ Film modeling	Tell–show–do	FIS ‐ HR	Before, during, and after diagnosis/preventive treatment	Dental anxiety
Khan 2019 [42]	2019	RCT	India	One hundred	7–9	Distraction from AV via VR Glasses 3D Box	(No intervention)	FIS ‐ MVARS ‐ BP ‐ HR	Pre and postoperative	Dental anxiety
Khandelwal 2018 [43]	2018	RCT	India	Four hundred	4–10	AVD ‐ TSD + AVD	No intervention ‐ Tell–show–do	FIS ‐ VPT ‐ BP ‐ HR ‐ SpO2	Before, during, and after Tx	Dental anxiety
Lekhwani 2023 [44]	2023	RCT	India	One hundred fifty	4–8	Tell–show–do	The Smartphone Dentist Game group utilizes the Tell‐Play‐Do approach.	VPT‐PR‐FIS	Pre and postoperative	Dental anxiety
Marwah 2005 [45]	2005	RCT	India	Forty	4–8	Music distraction can be categorized into subgroups based on the patient's choice. An instrumental music trio. b. musical ensemble specializing in nursery rhymes	No intervention	Venham's picture rate	Four dental visits	Identifying the music genre most likely to alleviate dental anxiety
Mehrotra 2023 [46]	2023	RCT	India	Twenty	6–14	VR distraction	Audio distraction	PR‐SPO2‐VPT	During both appointments at three‐time intervals	Dental anxiety
Mehrotra 2023 [47]	2023	RCT	India	Forty	6–14	VR distraction	Audio distraction	SPO2‐PR‐VPT	Pre and postoperative	Dental anxiety
Navit 2015 [48]	2015	RCT	India	One hundred and fifty	6–12	Instrumental ensemble ‐ Musical collection of children's songs Movie songs ensemble ‐ The audio story collection	No intervention	VPT ‐ VCRS ‐ HR	4 dental visits for 6 months	Dental anxiety
Niharika 2018 [49]	2018	RCT	India	Forty	4–8	Group A: Session I: Tell‐show‐do. Session II: utilizing virtual reality technology Session III: without virtual reality	Group B: Session I will focus on the tell‐show‐do method. Session II: without virtual reality Session III: utilizing virtual reality technology	Wong‐Baker faces ‐ MCDAS ‐ HR	Three dental sessions	Dental anxiety ‐ dental pain
Nuvvula 2014 [50]	2014	RCT	India	Ninety	4–11	Audio (fundamental methodology + music) ‐ Audiovisual (basic technology + three‐dimensional audiovisual)	Basic behavior guidance technique without distraction	Wright's modified FBRS and Houpt scale pulse rate	Pre and postoperative for 7 months	Dental anxiety
Obadiah 2020 [51]	2020	RCT	India	Sixty	7–10	Breathing exercise + bubble toy	No intervention	The scale of Frankl behavior ratings, Wong‐Baker Facial Pain Scale ‐ FLACC ‐ FIS	1st and 2nd visits for 5 months	Dental anxiety ‐ pain perception
Padminee 2022 [39]	2022	RCT	India	Seventy	6–12	Breathing relaxation using BrightHearts during IANB delivery in the first two visits	Virtual reality was experienced via augmented reality goggles during the initial two visits of the IANB administration.	HR ‐ Chotta Bheem–Chutki CBC scale (cartoon‐based anxiety measuring scale)	3 dental visits	Dental anxiety
Pande 2020 [52]	2020	RCT	India	Sixty	7–12	Distractions from sound ‐ AVD using VR ‐ Mobile phone games	Tell–show–do	BP ‐ HR ‐ FIS	Pre‐ and postoperation	Dental anxiety
Sabherwal1 2021 [53]	2021	RCT	India	Sixty	7–11	Hypnosis ‐ Progressive muscle relaxation	Communication and rapport‐building	Wong‐Baker facial pain scale, HR, SpO2, BP, and Visual Facial Anxiety Scale	Preoperative and postoperative for 5 months	Dental anxiety ‐ dental pain
Shekhar1 2022 [54]	2021	RCT	India	One hundred and twenty‐three	8–12	Utilizing verbal positive reinforcement and an active distraction tool such as a stress ball to facilitate communication. ‐ Utilizing verbal positive reinforcement and audiovisual distraction as a form of passive distraction in communication.	Communication with verbal positive reinforcement	MCDAS ‐ HR ‐ Venham's scale ‐ Self‐reporting and observational scale	Pre and postoperative	Dental anxiety ‐ dental pain
Shetty 2019 [55]	2019	RCT	India	One hundred and twenty	8–12	VR distraction	Conventional behavior management technique	Wong‐Baker facial pain rating scale, or MCDAS(f)r ‐ Cortisol levels in saliva	Pre and postoperative	Dental anxiety ‐ dental pain
Thakkar 2020 [56]	2020	RCT	India	One hundred and two	5–8	Pet therapy group	Conventional behavior management technique	MCDASf ‐ HR	Pre and postoperative	Dental anxiety
Thosar 2022 [57]	2022	RCT	India	Forty	5–8	1st visit: communication 2nd visit: magic thumb	1st visit: communication 2nd visit: favorite cartoon on mobile as AV	VPT ‐ Modified dental analogue scale ‐ HR ‐ SpO2	After the procedure, VPT and the modified oral analog scale were used. Before, during, and after the procedure, HR and SpO2 were used. For 3 months	Dental anxiety
Verma 2022 [58]	2022	RCT	India	Eighty	4–6	Utilize the technique of tell‐show‐do in the presence of the mother ‐ Mobile MG (3) Maternal presence during MG	Tell–show–do	Frankl behavior rating scale ‐ RMS‐PS ‐ FLACC	Pre and postoperative For ‘6 months’	Dental anxiety
Vishwakarma 2017 [59]	2017	RCT	India	Ninety	5–7	I. Phase (primary visit): live modeling Phase II (second visit): subjects underwent rotary treatment seven days later.	Phase I (first visit): Talk, play, and do. In Phase II (the second visit), rotary treatment was given to the subjects after 7 days.	HR ‐ FIS ‐ Venham 6‐point index	Pre and postoperative	Dental anxiety
Afshar 2011 [60]	2011	RCT	Iran	Sixty seven	5	Parental presence ‐ Parental absence	No control	HR ‐Venham scale ‐ Frankl BRS	NR	Dental anxiety
Aminabadi 2011 [61]	2011	RCT	Iran	Eighty	6–7	Pictorial story (dentist)	Pictorial story (barbershop)	Wong‐Baker faces ‐ MCDAS scale ‐ Sound, eye, and motor scale	Pre and postoperative	Dental pain ‐ dental anxiety
Bahrololoomi 2024 [62]	2024	RCT	Iran	Thirty	6–8	VR distraction	NO VR distraction	PR‐MCDAS‐Wong‐Baker Faces Pain Scale (WBFP)	Pre and postoperative	Dental anxiety‐pain perception
Ghaderi 2020 [63]	2020	RCT	Iran	Twenty‐ four	7–9	First group: First visit ‘treated with no lavender (control)’ Lavender intervention on 2nd visit	2nd group: ‐ 1st visit ‘treated with lavender (intervention)’ ‐ 2nd visit 'no lavender(control)	Anxiety: (1) Salivary cortisol (2) PR Pain perception: (1) Face rating scale	Two dental visits	Dental anxiety ‐ pain perception
Ghadimi 2018 [64]	2018	RCT	Iran	Twenty‐eight	4–5	The first group received the cartoon distraction intervention. Second ‘tell–show–do (control)’ visit	Second group: ‐ Initial “tell–show–do (control)” visit ‐ “Cartoon distraction” (intervention) at the second visit	Venham picture test ‐ PR ‐ FBRS	Two dental visits	Dental anxiety ‐ patient's behavior
Gozin 2022 [65]	2022	RCT	Iran	Forty‐five	6–9	Tell‐show ‐do	Audiovisual storytelling	VPT‐FIS	NR	Dental anxiety
Shokravi 2023 [66]	2023	RCT	Iran	Thirty	7–10	30‐min sessions of cognitive behavioral exercise	No intervention	Modified Child Dental Anxiety Scale (MCDAS)‐	Pre and postoperative	Dental anxiety
Aziz 2023 [67]	2023	RCT	Iraq	Sixty six	6–8	Tell‐show‐do‐ mobile app “Little Lovely Dentist”	Control	HR‐ SPO2‐VPT	Pre and postoperative	Dental anxiety
Bahrololoomi 2022 [68]	2022	RCT	Ireland	Thirty‐five	7–10	1st group: with breathing exercise 2nd group: No breathing exercise	1st group: No breathing exercise 2nd group: with breathing exercise	FIS ‐ BP ‐ FLACC ‐ WBFPS	NR	Dental anxiety
Peretz 2005 [69]	2005	RCT	Israel	Seventy	3–6	Magic tricks	Tell–show–do	The duration of the session from the start to the dentist chair ‐ The practitioner's capacity to conduct a dental examination ‐ Frankl's behavioral category	Pre and postoperative	Child's behavior
Alshatrat 2021 [70]	2021	RCT	Jordan	Fifty‐four	5–12	VR distraction	No intervention	VAS ‐ Wong‐Bakerr faces ‐ FLACC scale	NR	Dental pain ‐ dental anxiety
Song 2020 [71]	2020	RCT	Korea	Forty‐eight	3–7	The first therapy was “watching cartoon animation,” and the second was “using the program.”	Patients saw cartoon animation during the first and second treatments.	Heart rate using the Wong‐Baker face pain scale	Preoperative and postoperative for six months	Dental anxiety ‐ dental pain
Farhat‐McHayleh 2009 [72]	2009	RCT	Lebanon	One hundred and fifty‐five	5–9	Live modeling' for Groups 1 and 2	3rd Group: Tell–show–do	HR	NR	Dental anxiety ‐ Which parent served as the most appropriate model for live modeling in the child's case?
Garrocho‐Rangel 2018 [73]	2018	RCT	Mexico	Forty	5–8	Interventional dental visit ‘Video eyeglasses/earphones system distraction’	Control dental visit ‘Tradition non‐aversive behavior management’	FLACC ‐ HR ‐ O2 saturation	Two dental sessions	Dental anxiety ‐ pain perception
Abbasi 2021 [74]	2021	RCT	Pakistan	One hundred and sixty	6–11	“Little Lovely Dentist” app for phones ‐ YouTube “Video songs about teeth” ‐ Tell, show, and do	No intervention	Heart rate ‐ Facial image scale	Pre and postoperative	Dental anxiety
Al‐Khotani 2016 [75]	2016	RCT	Saudi Arabia	Fifty‐six	7–9	AV distraction	No intervention	FIS ‐ MVARS ‐ vital signs ‐ BP ‐ PR	Pre and postoperative	Dental anxiety
Almajed 2023 [76]	2023	RCT	Saudi Arabia	Twenty	4–6	VR distraction	NO VR distraction	VPT‐SEM	Three dental visits at two‐week intervals	Dental anxiety
Alnamankany 2019 [77]	2019	RCT	Saudi Arabia	Forty‐six	6–10	Modelling video	OH instruction video	Abeer Children Dental Anxiety Scale	Before watching the video/after watching the video	Dental anxiety
Alsaadoon 2022 [78]	2022	RCT	Saudi Arabia	Ninety‐three	6–8	Received storybook	No intervention	CFSS‐DS ‐ VCAS ‐ FBRS	Pre and postoperative	Dental anxiety
Bagher 2023 [79]	2023	RCT	Saudi Arabia	Thirty‐six	6–14	VR distraction	Cartoon film	Venham Anxiety and Behavioral Rating Scale‐Salivary cortisol level‐HR‐M‐ACDAS	Pre and postoperative	Dental anxiety
Schibbye 2023 [80]	2023	RCT	Sweden	Thirty‐three	NR	Internet‐based cognitive behavioral therapy (ICBT)	Control group	Picture‐Guided Behavioral Avoidance Test (PG‐BAT)	After a 12‐week follow up	Dental anxiety
Al‐Halabi 2018 [81]	2018	RCT	Syria	One hundred and one	6–10	Visual and auditory distractions (1) Glasses (2) VR box or tablet	Conventional NP‐BMT	WBFS ‐ HR ‐ FLACC‐BRS	6 months	Dental anxiety
Mitrakul 2015 [82]	2015	RCT	Thailand	Forty two	5–8	First visit: “without AV eyeglasses” and “Wearing AV eyeglasses” at the second visit	First visit: ‘wearing AV eyewear' Second visit: ‘without wearing an AV eyeglass'	Improved Faces Pain Assessment and Classification System (FLACC) ‐ HR	Before the procedure, install the RD. Use the handpiece for the first time. Take a 5‐min break during the rest of the treatment.	Dental pain ‐ Dental anxiety
Asvanud 2015 [82]	2015	RCT	Thailand	Forty‐nine	5–8	First visit: without AV glasses. Second visit: using AV eyewear	First visit: wearing AV eyeglasses. Second visit: Not wearing AV eyeglasses	Faces Pain Scale‐Revised ‐ Heart rate ‐ FLACC	2 visits, 1–4 weeks apart	Dental pain
Buldur 2020 [83]	2020	RCT	Turkey	Seventy‐six	7–11	First group: ‐ “Attention placebo control” (control) on the first visit ‐ “VR” (intervention) visit two	Second group: ‐ Initial “VR” (intervention) visit ‐ “Attention placebo control” (control) at the second visit	CFSS‐DS ‐ FIS ‐ HR	Pre and postoperative	Dental discomfort, anxiety, and behavior in children
HOWARD 2007 [84]	2007	RCT	United Kingdom	Seventy‐three	5–10	PALS model at the end of each treatment	Motivational rewards	MCDAS ‐ DMFT	NR	Dental anxiety ‐ Dental caries
Hine 2019 [85]	2019	RCT	USA	Forty	3–6	4 min Video modeling	14 min clip of a popular children's cartoon	15 s partial‐interval recording and included physical and vocal disruptions. ‐ Likert‐type scale	Pre and postoperative	Disruptive behavior
Kumari 2021 [86]	2021	RCT	USA	One hundred	6–12	Immersive VR	Non‐immersive VR	MCDAS ‐ VAS ‐ WBFRS	Pre and postoperative	Dental anxiety ‐ Pain perception
Zachary 1985 [87]	1985	RCT	USA	Fifty‐three	3–11	Stress relevant film	Stress irrelevant film	Palmer Sweat Index (Palmer Sweat Index, or VPT) ‐ A scoring system for behavior profiles ‐ The global anxiety rating scale ‐ Global Behavior Rating System	Pre and postoperative	‐ The impacts of stress‐relevant versus irrelevant film intervention: an analysis of modeling film's effectiveness on a representative, nonclinical group of children

Figure 2.

Summary of bias assessment: Authors' evaluations of each bias factor for each paper included.

Figure 3.

Risk of bias graph: Authors' percentage assessments of each risk of bias item across all studies.

3.3. Efficacy of Distraction Techniques When Compared to Tell‐Show‐Do (TSD)

The meta‐analysis was performed by pooling data on changes in dental anxiety ratings from nine studies. The results indicated that distraction strategies did not result in a statistically significant decrease in anxiety scores compared to controls (Standardized Mean Difference −0.34, 95% Confidence Interval −0.71 to 0.04, I² = 82.56%). Figure 4 displays the corresponding plot, with the I² score suggesting substantial heterogeneity in the data. To address this heterogeneity, an additional subgroup meta‐analysis was conducted using a specific assessment approach, with results presented in Figure 5. However, the analysis found that distraction strategies led to a statistically significant reduction in pain, with a standardized mean difference of −0.43 (95% CI −0.76 to −0.10, I² = 0.00%). Figure 6 shows the full plot. To further explore this outcome, a subgroup analysis was conducted based on the pain assessment method, and the results are illustrated in Figure 7.

Figure 4.

This forest plot presents a meta‐analysis comparing the effectiveness of distraction techniques versus Tell‐Show‐do (TSD) in managing dental anxiety in children. The effect size used is Cohen's d, with a random‐effects REML model applied for analysis.

Figure 5.

Subgroup analysis according to the assessment method for the association between distraction techniques and dental anxiety.

Figure 6.

This forest plot presents a meta‐analysis comparing the effectiveness of distraction techniques versus Tell‐Show‐do (TSD) in managing dental anxiety. The effect size used is Cohen's d, and a random‐effects REML model is applied.

Figure 7.

Subgroup analysis according to the assessment method for the association between distraction techniques and pain.

3.4. Efficacy of Virtual Reality (VR) Distraction Compared to Other Traditional Distraction Techniques

The meta‐analysis results showed that VR distraction was not associated with a statistically significant reduction in dental anxiety compared with traditional distraction techniques (SMD −0.47, 95% CI, −0.96, 0.02, I² 71.14%). The forest plot is shown in Figure 8. The I² value indicated significant heterogeneity in the results. An additional subgroup meta‐analysis was conducted using the same assessment method to attempt to resolve heterogeneity. The results of the subgroup meta‐analysis are shown in Figure 9. The results also showed that VR distraction, when compared to other traditional distraction techniques, was not associated with a statistically significant reduction in pain (SMD −058, 95% CI −2.14 to 0.98, I² 96.59%). The forest plot is shown in Figure 10. The I² value indicated significant heterogeneity in the results. Another subgroup meta‐analysis was conducted based on the assessment method used to resolve heterogeneity. The results of this subgroup meta‐analysis are presented in Figure 11.

Figure 8.

This forest plot presents a meta‐analysis comparing Virtual Reality (VR) distraction versus other traditional distraction techniques in managing dental anxiety. The effect size used is Cohen's d, and a random‐effects REML model is applied.

Figure 9.

Subgroup analysis according to the assessment method for the association between VR distraction techniques and dental anxiety.

Figure 10.

This forest plot presents a meta‐analysis comparing Virtual Reality (VR) distraction versus other traditional distraction techniques in managing dental anxiety. The effect size used is Cohen's d, and a random‐effects REML model is applied.

Figure 11.

Subgroup analysis according to the assessment method for the association between VR distraction techniques and pain.

3.5. Meta‐Regression Analysis

Meta‐regression was conducted to explore sources of heterogeneity in anxiety and pain outcomes. For anxiety outcomes (nine studies), the type of intervention (VR vs. TSD) and mean participant age were significant moderators (p = 0.03 and p = 0.04, respectively), explaining approximately 45% of the heterogeneity. Specifically, VR‐based interventions showed larger effect sizes in older children (mean age > 8 years) compared to younger ones. The type of dental procedure and assessment tool did not significantly influence heterogeneity (p > 0.10). For pain outcomes, due to the limited number of studies (n < 10), meta‐regression was not feasible, highlighting a limitation in exploring pain‐related heterogeneity.

3.6. Summary of Findings

Distraction techniques significantly reduced pain (SMD −0.43, 95% CI −0.76 to −0.10, Figure 6) and showed comparable anxiety reduction to TSD (SMD −0.34, 95% CI −0.71 to 0.04, Figure 4), suggesting clinical value in specific contexts, particularly for pain management.

4. Discussion

Distraction techniques have increasingly garnered attention in medical and dental settings as safe and cost‐effective methods to enhance patient experience during invasive and painful procedures [88]. These noninvasive interventions, which range from simple audiovisual aids to more sophisticated virtual reality systems, offer significant potential to alleviate patient discomfort and anxiety without complications associated with pharmacological options [89]. By integrating these techniques, healthcare professionals can significantly improve the procedural environment, making it less daunting for patients, especially those who are particularly sensitive to pain or anxiety [90].

The results of this study highlight the potential of nonpharmacological behavior interventions (NPBIs) in effectively managing dental anxiety in young patients. Despite the heterogeneity observed in the study outcomes, certain trends have emerged that offer valuable insights for clinical practice and future research. Furthermore, nonpharmacological behavior management methods have been emphasized as essential in pediatric dental practice to prevent overutilization of sedation and general anesthesia, highlighting the importance of these interventions in managing dental anxiety in young patients [91].

Distraction techniques, a core component of NPBIs, did not demonstrate a statistically significant reduction in dental anxiety compared to the Tell‐Show‐Do (TSD) method, according to the present analysis. This suggests that while distraction can be an effective tool for managing dental anxiety, its efficacy may not universally surpass that of traditional behavioral methods such as TSD. However, the observed reduction in pain associated with distraction techniques underscores their value in pediatric dental settings. The ability of these techniques to alleviate pain, a tangible outcome, suggests that they play a crucial role in improving the dental‐care experience of children, potentially increasing their willingness to undergo dental procedures.

The findings align with those reported by Almarzouq et al. [3], indicating that distraction techniques are effective in reducing self‐rated anxiety, improving cooperation, and lowering the pulse rate in contrast to TSD, except for more invasive dental procedures. This supports the effectiveness of diversion techniques in specific situations, while emphasizing their constraints in more demanding or unpleasant circumstances, as noted by Almarzouq et al. [3]. Considerable variability and heterogeneity were observed in the effects of NPBIs, suggesting that individual factors and specific contexts significantly influenced the outcomes.

In terms of specific interventions, Almarzouq et al. [3] found that distraction techniques were generally effective in reducing self‐rated anxiety and improving cooperation, particularly when compared to the Tell‐Show‐Do (TSD) method. In contrast, the current analysis showed that distraction techniques did not result in a statistically significant reduction in dental anxiety compared with TSD although they were effective in reducing pain. This discrepancy could be attributed to differences in the studies' inclusion criteria, dental procedures examined, or the anxiety assessment methods used.

VR techniques, such as VR eyeglasses, have been evaluated for their effectiveness in reducing anxiety in children during dental procedures such as extraction [92]. Some studies have shown promising results in reducing anxiety levels in children undergoing dental procedures [70, 83, 93]. Tanja et al. highlighted the potential of VR distraction to interrupt the cycle of dental anxiety by blocking the development of vivid memories during dental procedures [94].

However, Hamed et al. and Zhang et al. suggested that VR distraction may not be as effective in reducing anxiety levels in children during dental procedures [95, 96]. Additionally, Prado et al. reported mixed results, indicating that distraction techniques, including audiovisual distraction, showed varying effects in reducing dental anxiety and fear levels in children [97]. Similarly, Liu et al. found low‐quality evidence regarding the effectiveness of audiovisual distraction in relieving dental anxiety in children [98]. Furthermore, Du et al. concluded that there is no definitive conclusion regarding whether VR reduces dental anxiety in children [22].

An examination of VR distractions presents a nuanced perspective. Although VR did not show a statistically significant reduction in dental anxiety compared with other traditional distraction techniques, this finding should be interpreted with caution. The significant heterogeneity and marginal effect sizes suggest that VR distraction may still hold promise under certain conditions or for specific patient subsets. This highlights the need for further research to delineate the contexts in which VR and other innovative distraction methods are most effective. Additionally, the inconsistent efficacy of VR distraction, with no significant reduction in anxiety (SMD −0.47, 95% CI −0.96 to 0.02, Figure 8) or pain (SMD −0.58, 95% CI −2.14 to 0.98, Figure 10) compared to traditional methods, aligns with Hamed et al. [95] and Zhang et al. [96] but contrasts with Alshatrat et al. [70] and Nunna et al. [93]. Factors modulating VR's effectiveness include levels of interactivity, with passive VR (e.g., videos) providing temporary distraction and interactive VR (e.g., gamified environments) enhancing engagement, as reported by Koticha et al. [92]. Novelty effects may initially reduce anxiety, but wane with repeated exposure [22]. Child engagement, influenced by age and content relevance, is critical; younger children (3–6 years) may find VR headsets uncomfortable, while older children respond better to tailored content [93]. The high heterogeneity (I² = 71.14% for anxiety, Figure 8) suggests VR's efficacy is context‐dependent, with potential for subgroups like older children, as indicated by subgroup analyses (Figure 9).

The substantial heterogeneity observed in the meta‐analysis (I² = 82.56% for anxiety outcomes, I² = 71.14% for VR anxiety, I² = 96.59% for VR pain) suggests that various factors may influence the effectiveness of nonpharmacological behavioral interventions. Possible moderators include differences in study design, intervention types (VR‐based vs. traditional distraction), clinical settings, and the age distribution of participants. Future studies should focus on subgroup analyses to determine which populations benefit most from specific interventions. Table 3 details potential sources, including age demographics (3–14 years), with younger children (3–6 years) responding differently to interventions like Virtual Reality (VR) due to cognitive and emotional differences. Intervention types (e.g., TSD, VR, audiovisual distraction) target various psychological mechanisms, resulting in inconsistent effect sizes. Study methodologies, such as procedure type (invasive vs. noninvasive) and assessment timing, contribute to variability. Diverse assessment tools (CFSS‐DS, VPT, heart rate) differ in sensitivity, exacerbating heterogeneity. Cultural and clinical contexts may also influence outcomes. Subgroup analyses (Figures 5, 7, 9, 11) and meta‐regression identified age and intervention type as significant moderators for anxiety, explaining ~45% of variability, with VR showing larger effects in older children (mean age > 8 years).

Table 3.

Potential Sources of Heterogeneity and Their Hypothesized Impact.

Source of heterogeneity	Description	Hypothesized impact on results
Age demographics	Children aged 3–14 years with varying cognitive/emotional capacities.	Younger children may respond less to complex interventions (e.g., VR), leading to smaller effect sizes.
Intervention type	Varied from TSD to VR, audiovisual, and cognitive‐behavioral techniques.	Different mechanisms (e.g., distraction vs. desensitization) yield inconsistent effect sizes.
Study methodology	Variations in procedure type, duration, and assessment timing.	Invasive procedures or short‐term assessments may inflate anxiety scores, increasing variability.
Assessment tools	Diverse tools (e.g., CFSS‐DS, VPT, WBFS, heart rate) with differing sensitivity.	Variations in tool sensitivity lead to discrepancies in reported outcomes.
Cultural/clinical context	Studies in diverse settings (e.g., India, USA) with varying practices.	Cultural attitudes or clinic resources may influence intervention efficacy.

This review has several implications for clinical practice and policies. First, the diversity in responses to NPBIs suggests that a tailored approach in which dental practitioners assess individual patient needs and preferences could enhance the management of dental anxiety. The integration of NPBIs, including both traditional and innovative techniques such as VR, into routine dental care requires consideration of factors such as age, specific anxieties, and the procedural context.

Moreover, the risk of bias assessment (Figures 2 and 3) revealed that ~60% of studies had a high or unclear risk of bias in participant blinding and ~50% in outcome assessment, which impacted the reliability of the results. Unblinded participants in VR studies [70, 83] may overestimate anxiety reduction due to performance bias, inflating subjective measures like CFSS‐DS or VPT. Unblinded assessors using observational scales (Frankl Behavior Rating Scale [FBRS]) [34, 40] may exaggerate effect sizes, contributing to heterogeneity (I² = 82.56%, Figure 4). Objective measures (e.g., heart rate) [23, 79] are less biased but less common. Low‐risk studies (e.g., Ran et al. [23], Ghaderi et al. [63], ~25% of studies) reported smaller effect sizes, suggesting more reliable findings, while high‐risk studies [34] ( ~30%) drove variability. Subjective measures, which are predominant in the meta‐analysis, are prone to bias, whereas objective measures lack sensitivity for detecting nuanced changes in anxiety.

The present study had several limitations and challenges that may have influenced the results and their interpretations. Variations in the application and evaluation of interventions such as virtual reality (VR) and traditional distraction techniques are particularly notable. These differences make it difficult to assess and compare their effectiveness in different settings and populations definitively. Additionally, many studies included in this review exhibited a moderate to high risk of bias, particularly concerning the blinding of participants and outcome assessors, which is a common issue in behavioral studies where double‐blinding is often not feasible. In future research, these findings will highlight the importance of methodological rigor and the need for standardization in measuring outcomes related to dental anxiety and pain. The risk of bias assessment revealed moderate to high bias in several studies, particularly in participant blinding and selective reporting. These limitations could influence the reliability of the findings. Future trials should prioritize rigorous blinding procedures and pre‐registered protocols to minimize bias and improve study validity. Developing a standardized set of metrics and protocols for evaluating the effectiveness of NPBIs would not only facilitate more reliable comparisons across studies but also aid in the development of evidence‐based guidelines for implementing NPBIs in pediatric dentistry. The high heterogeneity (I² > 70%), moderate to high risk of bias, and variable efficacy of NPBIs (e.g., VR) posed challenges in synthesizing results. The inability to perform comprehensive meta‐regression for pain outcomes and reliance on subjective measures in high‐risk studies likely inflated effect sizes. The variable efficacy across patient and procedural contexts highlights the need for personalized intervention strategies. Future studies should standardize reporting, minimize bias, and investigate tailored approaches to improve reliability.

In clinical practice, selecting the appropriate nonpharmacological intervention depends on multiple factors, including patient anxiety levels, treatment type, and feasibility. For instance, VR distraction may be more effective for highly anxious children undergoing invasive procedures, whereas simpler techniques, such as tell‐show‐do (TSD), can be more practical in routine dental visits where minimal equipment is required. Pediatric dentists should assess each patient's specific needs to optimize anxiety management strategies.

Moreover, exploring the long‐term effects of NPBIs on dental anxiety and oral health behaviors remains a critical area for future investigation. Understanding how these interventions affect children's willingness to seek and receive dental care over time can inform strategies for mitigating dental anxiety and its associated negative outcomes. Finally, considering the practical aspects of implementing NPBIs in routine dental care, studies evaluating the cost‐effectiveness of such interventions are crucial. This includes not only the direct costs associated with the interventions themselves but also potential savings from the reduced need for sedation, improved patient cooperation, and fewer repeat visits due to untreated dental issues.

5. Conclusion

This meta‐analysis of 76 RCTs shows that nonpharmacological behavioral interventions (NPBIs), especially distraction techniques, significantly reduce pain in children during dental procedures (SMD −0.43, 95% CI −0.76 to −0.10, Figure 6) but not anxiety compared to tell‐show‐do (SMD −0.34, 95% CI −0.71 to 0.04, Figure 4). Virtual reality (VR) distraction is not superior (SMD −0.47 for anxiety, Figure 8; SMD −0.58 for pain, Figure 10), but it shows potential for use with older children or less invasive procedures (Figures 5, 9). Distraction techniques provide a noninvasive and cost‐effective approach to pain management. However, high heterogeneity (I² > 70%, Figures 4, 8, 10) and moderate to high risk of bias (Figures 2, 3) require cautious interpretation. Variable NPBI efficacy, influenced by age, interactivity, and assessment methods, necessitates tailored interventions based on patient and procedural factors. Future research should standardize outcomes, reduce bias, and explore long‐term effects to optimize NPBIs in pediatric dentistry.

Author Contributions

Kanwalpreet Kaur: conceptualization, methodology, formal analysis, data curation, investigation. Ravinder S. Saini: conceptualization, methodology, formal analysis, validation, resources, investigation, project administration, supervision. Rayan Ibrahim H. Binduhayyim: conceptualization, investigation, validation, project administration, resources, software. Seyed Ali Mosaddad: writing – review and editing, visualization. Artak Heboyan: validation, project administration, supervision, writing – review and editing, writing – original draft.

Ethics Statement

The authors have nothing to report.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Transparency Statement

All authors have reviewed and approved the final version of the manuscript. The first author had full access to all study data and assume full responsibility for its integrity and the accuracy of the data analysis. The lead author confirms that this manuscript provides an honest, accurate, and transparent account of the reported study, ensuring that no critical aspects have been omitted and that any deviations from the original study plan (or registered protocol, if applicable) have been clearly disclosed.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.