Dataset Information

An App for Identifying Children at Risk for Developmental Problems Using Multidimensional Computerized Adaptive Testing: Development and Usability Study.

ABSTRACT: BACKGROUND:The use of multidomain developmental screening tools is a viable strategy for pediatric professionals to identify children at risk for developmental problems. However, a specialized multidimensional computer adaptive testing (MCAT) tool has not been developed to date. OBJECTIVE:We developed an app using MCAT, combined with Multidimensional Screening in Child Development (MuSiC) for toddlers, to help patients and their family members or clinicians identify developmental problems at an earlier stage. METHODS:We retrieved 75 item parameters from the MuSiC literature item bank for 1- to 3-year-old children, and simulated 1000 person measures from a normal standard distribution to compare the efficiency and precision of MCAT and nonadaptive testing (NAT) in five domains (ie, cognitive skills, language skills, gross motor skills, fine motor skills, and socioadaptive skills). The number of items saved and the cutoff points for the tool were determined and compared. We then developed an app for a Web-based assessment. RESULTS:MCAT yielded significantly more precise measurements and was significantly more efficient than NAT, with 46.67% (=(75-40)/75) saving in item length when measurement differences less than 5% were allowed. Person-measure correlation coefficients were highly consistent among the five domains. Significantly fewer items were answered on MCAT than on NAT without compromising the precision of MCAT. CONCLUSIONS:Developing an app as a tool for parents that can be implemented with their own computers, tablets, or mobile phones for the online screening and prediction of developmental delays in toddlers is useful and not difficult.

SUBMITTER: Hsu CF

PROVIDER: S-EPMC7193438 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An App for Identifying Children at Risk for Developmental Problems Using Multidimensional Computerized Adaptive Testing: Development and Usability Study.

Hsu Chen-Fang CF Chien Tsair-Wei TW Chow Julie Chi JC Yeh Yu-Tsen YT Chou Willy W

JMIR pediatrics and parenting 20200416 1

<h4>Background</h4>The use of multidomain developmental screening tools is a viable strategy for pediatric professionals to identify children at risk for developmental problems. However, a specialized multidimensional computer adaptive testing (MCAT) tool has not been developed to date.<h4>Objective</h4>We developed an app using MCAT, combined with Multidimensional Screening in Child Development (MuSiC) for toddlers, to help patients and their family members or clinicians identify developmental ...[more]

PMID: 32297867

Similar Datasets

Project description:BackgroundWorkplace bullying has been measured in many studies to investigate its effects on mental health issues. However, none have used web-based computerized adaptive testing (CAT) with bully classifications and convolutional neural networks (CNN) for reporting the extent of individual bullying in the workplace.ObjectiveThis study aims to build a model using CNN to develop an app for automatic detection and classification of nurse bullying-levels, incorporated with online Rasch computerized adaptive testing, to help assess nurse bullying at an earlier stage.MethodsWe recruited 960 nurses working in a Taiwan Ch-Mei hospital group to fill out the 22-item Negative Acts Questionnaire-Revised (NAQ-R) in August 2012. The k-mean and the CNN were used as unsupervised and supervised learnings, respectively, for: (1) dividing nurses into three classes (n=918, 29, and 13 with suspicious mild, moderate, and severe extent of being bullied, respectively); and (2) building a bully prediction model to estimate 69 different parameters. Finally, data were separated into training and testing sets in a proportion of 70:30, where the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve [AUC]), along with the accuracy across studies for comparison. An app predicting the respondent bullying-level was developed, involving the model's 69 estimated parameters and the online Rasch CAT module as a website assessment.ResultsWe observed that: (1) the 22-item model yields higher accuracy rates for three categories, with an accuracy of 94% for the total 960 cases, and accuracies of 99% (AUC 0.99; 95% CI 0.99-1.00) and 83% (AUC 0.94; 95% CI 0.82-0.99) for the lower and upper groups (cutoff points at 49 and 66 points) based on the 947 cases and 42 cases, respectively; and (2) the 700-case training set, with 95% accuracy, predicts the 260-case testing set reaching an accuracy of 97. Thus, a NAQ-R app for nurses that predicts bullying-level was successfully developed and demonstrated in this study.ConclusionsThe 22-item CNN model, combined with the Rasch online CAT, is recommended for improving the accuracy of the nurse NAQ-R assessment. An app developed for helping nurses self-assess workplace bullying at an early stage is required for application in the future.

Project description:BackgroundThere is a lack of evidence-based app guidance for parents of children with crying, sleeping, and feeding problems who are often highly burdened and not likely to seek professional help. A new psychoeducational app for parents providing scientifically sound information via text and videos, a diary function, selfcare strategies, a chat forum and a regional directory of specialized counseling centers may serve as a low-threshold intervention for this target group.ObjectiveWe investigated how parents perceived the app in terms of the following: (1) overall impression and usability, (2) feedback on specific app functions regarding usefulness and (3) possible future improvements.MethodsOur clinical sample of N = 137 parents of children aged from 0 to 24 months was recruited from a cry baby outpatient clinic in Southern Germany between 2019 and 2022. A convergent parallel mixed methods design was used to collect and analyse cross-sectional data on app evaluation. After app use within the framework of a clinical trial, parents filled in an app evaluation questionnaire.ResultsMost participants used the app at least once a week (86, 62.8 %) over an average period of 19.06 days (SD = 15.00). Participants rated overall impression and usability as good, and the informational texts, expert videos and regional register of counseling centers as appealing and useful. The diary function and chat forum were found to be helpful in theory, but improvements in implementation were requested, such as a timer function for the diary entry. Regarding future functionality, parents posed several suggestions such as the option to contact counseling centers directly via app, and the inclusion of the profile of their partners.ConclusionsPositive ratings of overall impression, usability, and specific app functions are important prerequisites for the app to be effective. App-based guidance for this target group should include easy-to-use information. The app is intended to serve as a secondary preventive low-threshold offer and to complement professional counseling.

Project description:ObjectiveMultidimensional computerized adaptive testing enables precise measurements of patient-reported outcomes at an individual level across different dimensions. This study examined the construct validity of a multidimensional computerized adaptive test (CAT) for fatigue in rheumatoid arthritis (RA).MethodsThe 'CAT Fatigue RA' was constructed based on a previously calibrated item bank. It contains 196 items and three dimensions: 'severity', 'impact' and 'variability' of fatigue. The CAT was administered to 166 patients with RA. They also completed a traditional, multidimensional fatigue questionnaire (BRAF-MDQ) and the SF-36 in order to examine the CAT's construct validity. A priori criterion for construct validity was that 75% of the correlations between the CAT dimensions and the subscales of the other questionnaires were as expected. Furthermore, comprehensive use of the item bank, measurement precision and score distribution were investigated.ResultsThe a priori criterion for construct validity was supported for two of the three CAT dimensions (severity and impact but not for variability). For severity and impact, 87% of the correlations with the subscales of the well-established questionnaires were as expected but for variability, 53% of the hypothesised relations were found. Eighty-nine percent of the items were selected between one and 137 times for CAT administrations. Measurement precision was excellent for the severity and impact dimensions, with more than 90% of the CAT administrations reaching a standard error below 0.32. The variability dimension showed good measurement precision with 90% of the CAT administrations reaching a standard error below 0.44. No floor- or ceiling-effects were found for the three dimensions.ConclusionThe CAT Fatigue RA showed good construct validity and excellent measurement precision on the dimensions severity and impact. The dimension variability had less ideal measurement characteristics, pointing to the need to recalibrate the CAT item bank with a two-dimensional model, solely consisting of severity and impact.

Dataset Information

An App for Identifying Children at Risk for Developmental Problems Using Multidimensional Computerized Adaptive Testing: Development and Usability Study.

Publications

An App for Identifying Children at Risk for Developmental Problems Using Multidimensional Computerized Adaptive Testing: Development and Usability Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets