Dataset Information

A Serious Game-Derived Index for Detecting Children With Heterogeneous Developmental Disabilities: Randomized Controlled Trial.

ABSTRACT: BACKGROUND:Developmental disabilities are a set of heterogeneous delays or difficulties in one or more areas of neuropsychological development. Considering that childhood is an essential stage of brain development and developmental delays lead to personal or social burdens, the early detection of childhood developmental disabilities is important. However, early screening for developmental disabilities has been a challenge because of the fear of positive results, expensive tests, differences in diagnosis depending on examiners' abilities, and difficulty in diagnosis arising from the need for long-term follow-up observation. OBJECTIVE:This study aimed to assess the feasibility of using a serious game-derived index to identify heterogeneous developmental disabilities. This study also examines the correlation between the game-derived index and existing neuropsychological test results. METHODS:The randomized controlled trial involved 48 children with either normal development or developmental disabilities. In this clinical trial, we used 19 features (6 from the Korean-Wechsler Preschool and Primary Scale of Intelligence, 8 from the Psychoeducational Profile Revised, 2 from the Bruininks-Oseretsky Test of Motor Proficiency, Second Edition, and 3 from the Pediatric Evaluation of Disability Inventory) from neuropsychological tests and 9 (7 game scores, path accuracy, and completion rate) from the serious game, DoBrain. The following analysis was conducted based on participants' baseline information and neuropsychological test and game-derived index data for one week: (1) we compared the baseline information between the normal development and developmental disabilities groups; (2) then we measured the correlation between the game-derived index and the neuropsychological test scores for each group; and (3) we built a classifier based on the game-derived index with a Gaussian process method and then compared the area under the curve (AUC) with a model based on neuropsychological test results. RESULTS:A total of 16 children (normal development=9; developmental disabilities=7) were analyzed after selection. Their developmental abilities were assessed before they started to play the serious games, and statistically significant differences were found in both groups. Specifically, the normal development group was more developed than the developmental disabilities group in terms of social function, gross motor function, full-scale IQ, and visual motor imitation, in that order. Similarly, the normal development group obtained a higher score on the game-derived index than the developmental disabilities group. In the correlation analysis between the game-derived index and the neuropsychological tests, the normal development group showed greater correlation with more variables than the developmental disabilities group. The game-derived index-based model had an AUC=0.9, a similar detection value as the neuropsychological test-based model's AUC=0.86. CONCLUSIONS:A game-derived index based on serious games can detect children with heterogenous developmental disabilities. This suggests that serious games can be used as a potential screening tool for developmental disabilities. TRIAL REGISTRATION:Clinical Research Information Service KCT0003247; https://cris.nih.go.kr/cris/en/search/search_result_st01 .jsp?seq=12365.

SUBMITTER: Bang C

PROVIDER: S-EPMC6838693 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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A Serious Game-Derived Index for Detecting Children With Heterogeneous Developmental Disabilities: Randomized Controlled Trial.

Bang Changbae C Nam Yelin Y Ko Eun Jae EJ Lee Wooseong W Kim Byungjae B Choi Yejin Y Park Yu Rang YR

JMIR serious games 20191024 4

<h4>Background</h4>Developmental disabilities are a set of heterogeneous delays or difficulties in one or more areas of neuropsychological development. Considering that childhood is an essential stage of brain development and developmental delays lead to personal or social burdens, the early detection of childhood developmental disabilities is important. However, early screening for developmental disabilities has been a challenge because of the fear of positive results, expensive tests, differen ...[more]

PMID: 31651408

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Project description:BackgroundEarly detection of developmental disabilities in children is essential because early intervention can improve the prognosis of children. Meanwhile, a growing body of evidence has indicated a relationship between developmental disability and motor skill, and thus, motor skill is considered in the early diagnosis of developmental disability. However, there are challenges to assessing motor skill in the diagnosis of developmental disorder, such as a lack of specialists and time constraints, and thus it is commonly conducted through informal questions or surveys to parents.ObjectiveThis study sought to evaluate the possibility of using drag-and-drop data as a digital biomarker and to develop a classification model based on drag-and-drop data with which to classify children with developmental disabilities.MethodsWe collected drag-and-drop data from children with typical development and developmental disabilities from May 1, 2018, to May 1, 2020, via a mobile application (DoBrain). We used touch coordinates and extracted kinetic variables from these coordinates. A deep learning algorithm was developed to predict potential development disabilities in children. For interpretability of the model results, we identified which coordinates contributed to the classification results by applying gradient-weighted class activation mapping.ResultsOf the 370 children in the study, 223 had typical development, and 147 had developmental disabilities. In all games, the number of changes in the acceleration sign based on the direction of progress both in the x- and y-axes showed significant differences between the 2 groups (P<.001; effect size >0.5). The deep learning convolutional neural network model showed that drag-and-drop data can help diagnose developmental disabilities, with an area under the receiving operating characteristics curve of 0.817. A gradient class activation map, which can interpret the results of a deep learning model, was visualized with the game results for specific children.ConclusionsThrough the results of the deep learning model, we confirmed that drag-and-drop data can be a new digital biomarker for the diagnosis of developmental disabilities.

Project description:BackgroundChildren with learning disabilities (CLD) have worse health outcomes than children with no learning disabilities (CNLD). This systematic review compared caries experience and met dental care need for CLD to CNLD using Decayed, Missing, Filled Permanent Teeth (DMFT) and decayed, missing/extracted, filled primary teeth (dmft/deft), care index (CI), and restorative index (RI) values.MethodsWithout date or language restrictions four databases were searched for; cross-sectional studies comparing caries experience and CI/ RI in CLD matched to groups of CNLD. Screening and data extraction were carried out independently and in duplicate. Risk of bias was assessed using the Newcastle-Ottawa Scale. Meta-analyses were carried out (random effects model).ResultsThere were 25 articles with 3976 children (1 to 18?years old), from 18 countries, fitting the inclusion criteria. Children with; Down syndrome were investigated in 11 studies, autism in 8 and mixed learning disabilities in 6. The overall mean DMFT for CLD was 2.31 (standard deviation±1.97; range 0.22 to 7.2) and for CNLD was 2.51 (±2.14; 0.37 to 4.76). Using standardised mean difference (SMD), meta-analysis showed no evidence of a difference between CLD and CNLD (n =?16 studies) for caries experience (SMD?=?-0.43; 95%CI?=?-0.91 to 0.05). This was similar for sub-groups of children with autism (SMD?=?-0.28; 95%CI?=?1.31 to 0.75) and mixed disabilities (SMD?=?0.26; 95%CI?=?-0.94 to 1.47). However, for children with Down syndrome, caries experience was lower for CLD than CNLD (SMD?=?-0.73; 95%CI?=?-1.28 to -?0.18). For primary teeth, mean dmft/deft was 2.24 for CLD and 2.48 for CNLD (n =?8 studies). Meta-analyses showed no evidence of a difference between CLD and CNLD for caries experience across all disability groups (SMD?=?0.41; 95% CI?=?-0.14 to 0.96), or in sub-groups: Down syndrome (SMD?=?0.55; 95%CI-?=?-?0.40 to 1.52), autism (SMD?=?0.43; 95%CI?=?-0.53 to 2.39) and mixed disabilities (SMD?=?-0.10; 95%CI?=?-0.34 to 0.14). The studies' risk of bias were medium to high.ConclusionThere was no evidence of a difference in caries levels in primary or permanent dentitions for CLD and CNLD. This was similar for learning disability sub-groups, except for Down syndrome where dental caries levels in permanent teeth was lower. Data on met need for dental caries was inconclusive.Trial registrationThe protocol was published in PROSPERO: CRD42017068964 (June 8th, 2017).

Project description:ImportanceThe prevalence of asthma in US children with various developmental disabilities and delays is unclear, including how estimates vary by ethnic group.ObjectiveTo report asthma prevalence estimates by various disability categories and developmental delays in a diverse sample of the US pediatric population.Design, setting, and participantsThis population-based cross-sectional study encompassed a total of 71 811 families with children or adolescents aged 0 to 17 years (hereinafter referred to as children) who participated in the 2016 and 2017 National Survey of Children's Health. Data were collected from June 10, 2016, to February 10, 2017, for the 2016 survey and from August 10, 2017, to February 10, 2018, for the 2017 survey. Data were analyzed from September 20, 2019, to April 5, 2020.ExposuresDevelopmental disability, including attention-deficit/hyperactivity disorder, autism spectrum disorder, cerebral palsy, seizure, intellectual and/or learning disability, and vision, hearing, and/or speech delay. Delay was defined as not meeting growth milestones with unknown cause.Main outcomes and measuresWeighted asthma prevalence estimates and 95% CIs were generated for children with and without disabilities.ResultsA total of 71 811 participants (mean [SE] age, 8.6 [0.1] years; 36 800 boys [51.1%; 95% CI, 50.2%-52.0%]; 50 219 non-Hispanic white [51.4%; 95% CI, 50.6%-52.3%]) were included in our final analytical sample, of whom 5687 (7.9%; 95% CI, 7.5%-8.4%) had asthma and 11 426 (15.3%; 95% CI, 14.7%-16.0%) had at least 1 disability. Overall asthma prevalence estimates were 10 percentage points higher in children with a disability (16.1%; 95% CI, 14.3%-17.8%) vs children without a disability (6.5%; 95% CI, 6.0%-6.9%). The odds of asthma were significantly higher in children with a disability (odds ratio [OR], 2.77; 95% CI, 2.39-3.21) or delay (OR, 2.22; 95% CI, 1.78-2.77) vs typically growing children. Adjusted models remained significant for all disability categories (overall adjusted OR, 2.21; 95% CI, 1.87-2.62). Subgroup analyses showed ethnic minorities had a higher prevalence of concurrent asthma and developmental disabilities vs non-Hispanic whites (19.8% [95% CI, 16.6%-23.0%] vs 12.6% [95% CI, 11.1%-14.0%]; P?<?.001).Conclusions and relevanceThese results suggest that US children with various developmental disabilities or delay may have higher odds for developing asthma vs their typically developing peers. These findings support asthma screening in pediatric health care settings among patients with developmental disabilities and delays, particularly among those from ethnic minority backgrounds. In addition, very young children with asthma should be screened for disabilities and delays, because temporality cannot be determined by the current data source and analytical approach.

Project description:BACKGROUND:As pediatric ambulatory surgeries are rising and existing methods to reduce perioperative anxiety and pain are lacking in this population, a serious game for health (SGH), CliniPup, was developed to address this unmet need. CliniPup was generated using the SERES framework for serious game development. OBJECTIVE:The goal of the research was to clinically evaluate CliniPup as an adjunct therapy to existing pharmacological interventions aimed at reducing perioperative anxiety and pain in children undergoing ambulatory surgery. METHODS:CliniPup was evaluated in a prospective randomized controlled pilot trial in 20 children aged 6 to 10 years who underwent elective surgery and their parents. Study participants were randomly assigned to the test (n=12) or control group (n=8). Children in the test group played CliniPup 2 days prior to surgery, and children in the control group received standard of care. On the day of surgery, pediatric anxiety was measured with the modified Yale Preoperative Anxiety Scale and parental anxiety was assessed with the State-Trait Anxiety Inventory. Pediatric postoperative pain was assessed by the Wong-Baker Faces Pain Rating Scale. Child and parent user experience and satisfaction were also evaluated in the test group using structured questionnaires. RESULTS:Despite the small sample, preoperative anxiety scores were significantly lower (P=.01) in children who played CliniPup prior to surgery compared to controls. Parental preoperative anxiety scores were also lower in the test group (P=.10) but did not reach significance. No significant differences were observed in postoperative pain scores between groups (P=.54). The evaluation of user experience and satisfaction revealed that both children and parents were satisfied with CliniPup and would recommend the game to peers. CONCLUSIONS:Results of the pilot trial introduce CliniPup as a potentially effective and attractive adjunct therapy to reduce preoperative anxiety in children undergoing ambulatory surgery with a trend toward positive impact on parental preoperative anxiety. These results support the use of the SERES framework to generate an evidence-based SGH that results in positive health outcomes for patients. Based on these preliminary findings, we propose a research agenda to further develop and investigate this tool. TRIAL REGISTRATION:ClinicalTrials.gov NCT03874442; https://clinicaltrials.gov/ct2/show/NCT03874442 (Archived by WebCite at http://www.webcitation.org/78KZab8qc).

Dataset Information

A Serious Game-Derived Index for Detecting Children With Heterogeneous Developmental Disabilities: Randomized Controlled Trial.

Publications

A Serious Game-Derived Index for Detecting Children With Heterogeneous Developmental Disabilities: Randomized Controlled Trial.

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