@Article{info:doi/10.2196/65710, author="Drexl, Konstantin and Ralisa, Vanisha and Rosselet-Amoussou, Jo{\"e}lle and Wen, K. Cheng and Urben, S{\'e}bastien and Plessen, Jessica Kerstin and Glaus, Jennifer", title="Readdressing the Ongoing Challenge of Missing Data in Youth Ecological Momentary Assessment Studies: Meta-Analysis Update", journal="J Med Internet Res", year="2025", month="Apr", day="30", volume="27", pages="e65710", keywords="youth", keywords="adolescents", keywords="children", keywords="ecological momentary assessment", keywords="experience sampling methodology", keywords="ambulatory assessment", keywords="missing data", keywords="dropout", keywords="meta-analysis", keywords="mobile devices", abstract="Background: Ecological momentary assessment (EMA) is pivotal in longitudinal health research in youth, but potential bias associated with nonparticipation, omitted reports, or dropout threatens its clinical validity. Previous meta-analytic evidence is inconsistent regarding specific determinants of missing data. Objective: This meta-analysis aimed to update and expand upon previous research by examining key participation metrics---acceptance, compliance, and retention---in youth EMA studies. In addition, it sought to identify potential moderators among sample and design characteristics, with the goal of better understanding and mitigating the impact of missing data. Methods: We used a bibliographic database search to identify EMA studies involving children and adolescents published from 2001 to November 2023. Eligible studies used mobile-delivered EMA protocols in samples with an average age up to 18 years. We conducted separate meta-analyses for acceptance, compliance, and retention rates, and performed meta-regressions to address sample and design characteristics. Furthermore, we extracted and pooled sample-level effect sizes related to correlates of response compliance. Risk of publication bias was assessed using funnel plots, regression tests, and sensitivity analyses targeting inflated compliance rates. Results: We identified 285 samples, including 17,441 participants aged 5 to 17.96 years (mean age 14.22, SD 2.24 years; mean percentage of female participants 55.7\%). Pooled estimates were 67.27\% (k=88, 95\% CI 62.39-71.96) for acceptance, 71.97\% (k=216, 95\% CI 69.83-74.11) for compliance, and 96.57\% (k=169, 95\% CI 95.42-97.56) for retention. Despite overall poor moderation of participation metrics, acceptance rates decreased as the number of EMA items increased (log-transformed b=?0.115, SE 0.036; 95\% CI ?0.185 to ?0.045; P=.001; R2=19.98), compliance rates declined by 0.8\% per year of publication (SE 0.25, 95\% CI ?1.3 to ?0.3; P=.002; R2=4.17), and retention rates dropped with increasing study duration (log-transformed b=?0.061, SE 0.015; 95\% CI ?0.091 to 0.032; P<.001; R2=10.06). The benefits of monetary incentives on response compliance diminished as the proportion of female participants increased (b=?0.002, SE 0.001; 95\% CI ?0.003 to ?0.001; P=.003; R2=9.47). Within-sample analyses showed a small but significant effect indicating higher compliance in girls compared to boys (k=25; g=0.18; 95\% CI 0.06-0.31; P=.003), but no significant age-related effects were found (k=14; z score=0.05; 95\% CI ?0.01 to 0.16). Conclusions: Despite a 5-fold increase in included effect sizes compared to the initial review, the variability in rates of missing data that one can expect based on specific sample and design characteristics remains substantial. The inconsistency in identifying robust moderators highlights the need for greater attention to missing data and its impact on study results. To eradicate any health-related bias in EMA studies, researchers should collectively increase transparent reporting practices, intensify primary methodological research, and involve participants' perspectives on missing data. Trial Registration: PROSPERO CRD42022376948; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022376948 ", doi="10.2196/65710", url="https://www.jmir.org/2025/1/e65710", url="http://www.ncbi.nlm.nih.gov/pubmed/40305088" } @Article{info:doi/10.2196/69457, author="Owino, Geofrey and Shibwabo, Bernard", title="Advances in Infant Cry Paralinguistic Classification---Methods, Implementation, and Applications: Systematic Review", journal="JMIR Rehabil Assist Technol", year="2025", month="Apr", day="29", volume="12", pages="e69457", keywords="infant cry classification", keywords="machine learning", keywords="audio feature extraction", keywords="pitch analysis", keywords="hybrid models", keywords="denoising techniques", keywords="federated learning", keywords="real-time analysis", keywords="pediatric healthcare", keywords="signal processing", keywords="data confidentiality", abstract="Background: Effective communication is essential for human interaction; yet, infants can only express their needs through various types of suggestive cries. Traditional approaches of interpreting infant cries are often subjective, inconsistent, and slow, leaving gaps in timely, precise caregiving responses. A precise interpretation of infant cries can potentially provide valuable insights into the infant's health, needs, and well-being, enabling prompt medical or caregiving actions. Objective: This study seeks to systematically review the advancements in methods, coverage, deployment schemes, and applications of infant cry classification over the last 24 years. The review focuses on the different infant cry classification techniques, feature extraction methods, and practical applications. Furthermore, we aimed to identify recent trends and directions in the field of infant cry signal processing to address both academic and practical needs. Methods: A systematic literature review was conducted using 9 electronic databases: Cochrane Database of Systematic Reviews, JSTOR, Web of Science Core Collection, Scopus, PubMed, ACM, MEDLINE, IEEE Xplore, and Google Scholar. A total of 5904 search results were initially retrieved, with 126 studies meeting the eligibility criteria after screening by 2 independent reviewers. The methodological quality of these studies was assessed using the Cochrane risk of bias tool (version 2; RoB2), with 92\% (n=116) of the studies indicating a low risk of bias and 8\% (n=10) of the studies showing some concerns regarding bias. The overall quality assessment was performed using TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines. The data analysis was conducted using R (version 3.64; R Foundation). Results: Notable advancements in infant cry classification methods were realized, particularly from 2019 onward, using machine learning, deep learning, and hybrid approaches. Common audio features included Mel-frequency cepstral coefficients, spectrograms, pitch, duration, intensity, formants, 0-crossing rate, and chroma. Deployment methods included mobile apps and web-based platforms for real-time analysis, with 90\% (n=113) of the remaining models remaining undeployed to real-world applications. Denoising techniques and federated learning were limitedly used to enhance model robustness and ensure data confidentiality from 5\% (n=6) of the studies. Some of the practical applications spanned health care monitoring, diagnostics, and caregiver support. Conclusions: The evolution of infant cry classification methods has progressed from traditional classical statistical methods to machine learning models but with minimal considerations of data privacy, confidentiality, and ultimate deployment to practical use. Further research is thus proposed to develop standardized foundational audio multimodal approaches, incorporating a broader range of audio features and ensuring data confidentiality through methods such as federated learning. Furthermore, a preliminary layer is proposed for denoising the cry signal before the feature extraction stage. These improvements will enhance the accuracy, generalizability, and practical applicability of infant cry classification models in diverse health care settings. ", doi="10.2196/69457", url="https://rehab.jmir.org/2025/1/e69457" } @Article{info:doi/10.2196/60367, author="Wu, Rong and Zhang, Yu and Huang, Peijie and Xie, Yiying and Wang, Jianxun and Wang, Shuangyong and Lin, Qiuxia and Bai, Yichen and Feng, Songfu and Cai, Nian and Lu, Xiaohe", title="Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study", journal="J Med Internet Res", year="2025", month="Apr", day="23", volume="27", pages="e60367", keywords="retinopathy of prematurity", keywords="reactivation", keywords="prediction", keywords="machine learning", keywords="deep learning", keywords="anti-VEGF", abstract="Background: Retinopathy of prematurity (ROP) is the leading preventable cause of childhood blindness. A timely intravitreal injection of antivascular endothelial growth factor (anti-VEGF) is required to prevent retinal detachment with consequent vision impairment and loss. However, anti-VEGF has been reported to be associated with ROP reactivation. Therefore, an accurate prediction of reactivation after treatment is urgently needed. Objective: To develop and validate prediction models for reactivation after anti-VEGF intravitreal injection in infants with ROP using multimodal machine learning algorithms. Methods: Infants with ROP undergoing anti-VEGF treatment were recruited from 3 hospitals, and conventional machine learning, deep learning, and fusion models were constructed. The areas under the curve (AUCs), accuracy, sensitivity, and specificity were used to show the performances of the prediction models. Results: A total of 239 cases with anti-VEGF treatment were recruited, including 90 (37.66\%) with reactivation and 149 (62.34\%) nonreactivation cases. The AUCs for the conventional machine learning model were 0.806 and 0.805 in the internal validation and test groups, respectively. The average AUC, sensitivity, and specificity in the test for the deep learning model were 0.787, 0.800, and 0.570, respectively. The specificity, AUC, and sensitivity for the fusion model were 0.686, 0.822, and 0.800 in a test, separately. Conclusions: We constructed 3 prediction models for ROP reactivation. The fusion model achieved the best performance. Using this prediction model, we could optimize strategies for treating ROP in infants and develop better screening plans after treatment. ", doi="10.2196/60367", url="https://www.jmir.org/2025/1/e60367" } @Article{info:doi/10.2196/66491, author="Park, Soo Ji and Park, Sa-Yoon and Moon, Won Jae and Kim, Kwangsoo and Suh, In Dong", title="Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis", journal="J Med Internet Res", year="2025", month="Apr", day="18", volume="27", pages="e66491", keywords="machine learning", keywords="respiratory disease classification", keywords="wheeze detection", keywords="auscultation", keywords="mel-spectrogram", keywords="abnormal lung sound detection", keywords="artificial intelligence", keywords="pediatric", keywords="lung sound analysis", keywords="systematic review", keywords="asthma", keywords="pneumonia", keywords="children", keywords="morbidity", keywords="mortality", keywords="diagnostic", keywords="respiratory pathology", abstract="Background: Pediatric respiratory diseases, including asthma and pneumonia, are major causes of morbidity and mortality in children. Auscultation of lung sounds is a key diagnostic tool but is prone to subjective variability. The integration of artificial intelligence (AI) and machine learning (ML) with electronic stethoscopes offers a promising approach for automated and objective lung sound. Objective: This systematic review and meta-analysis assess the performance of ML models in pediatric lung sound analysis. The study evaluates the methodologies, model performance, and database characteristics while identifying limitations and future directions for clinical implementation. Methods: A systematic search was conducted in Medline via PubMed, Embase, Web of Science, OVID, and IEEE Xplore for studies published between January 1, 1990, and December 16, 2024. Inclusion criteria are as follows: studies developing ML models for pediatric lung sound classification with a defined database, physician-labeled reference standard, and reported performance metrics. Exclusion criteria are as follows: studies focusing on adults, cardiac auscultation, validation of existing models, or lacking performance metrics. Risk of bias was assessed using a modified Quality Assessment of Diagnostic Accuracy Studies (version 2) framework. Data were extracted on study design, dataset, ML methods, feature extraction, and classification tasks. Bivariate meta-analysis was performed for binary classification tasks, including wheezing and abnormal lung sound detection. Results: A total of 41 studies met the inclusion criteria. The most common classification task was binary detection of abnormal lung sounds, particularly wheezing. Pooled sensitivity and specificity for wheeze detection were 0.902 (95\% CI 0.726-0.970) and 0.955 (95\% CI 0.762-0.993), respectively. For abnormal lung sound detection, pooled sensitivity was 0.907 (95\% CI 0.816-0.956) and specificity 0.877 (95\% CI 0.813-0.921). The most frequently used feature extraction methods were Mel-spectrogram, Mel-frequency cepstral coefficients, and short-time Fourier transform. Convolutional neural networks were the predominant ML model, often combined with recurrent neural networks or residual network architectures. However, high heterogeneity in dataset size, annotation methods, and evaluation criteria were observed. Most studies relied on small, single-center datasets, limiting generalizability. Conclusions: ML models show high accuracy in pediatric lung sound analysis, but face limitations due to dataset heterogeneity, lack of standard guidelines, and limited external validation. Future research should focus on standardized protocols and the development of large-scale, multicenter datasets to improve model robustness and clinical implementation. ", doi="10.2196/66491", url="https://www.jmir.org/2025/1/e66491" } @Article{info:doi/10.2196/58337, author="Kato, Daigo and Okuno, Akiko and Ishikawa, Tetsuo and Itakura, Shoji and Oguchi, Shinji and Kasahara, Yoshiyuki and Kanenishi, Kenji and Kitadai, Yuzo and Kimura, Yoshitaka and Shimojo, Naoki and Nakahara, Kazushige and Hanai, Akiko and Hamada, Hiromichi and Mogami, Haruta and Morokuma, Seiichi and Sakurada, Kazuhiro and Konishi, Yukuo and Kawakami, Eiryo", title="Multilevel Factors and Indicators of Atypical Neurodevelopment During Early Infancy in Japan: Prospective, Longitudinal, Observational Study", journal="JMIR Pediatr Parent", year="2025", month="Apr", day="4", volume="8", pages="e58337", keywords="early developmental signs", keywords="neurodevelopmental screening", keywords="risk factors", keywords="prediction", keywords="early intervention", keywords="longitudinal study", abstract="Background: The early identification of developmental concerns requires understanding individual differences that may represent early signs of neurodevelopmental conditions. However, few studies have longitudinally examined how child and maternal factors interact to shape these early developmental characteristics. Objective: We aim to identify factors from the perinatal to infant periods associated with early developmental characteristics that may precede formal diagnoses and propose a method for evaluating individual differences in neurodevelopmental trajectories. Methods: A prospective longitudinal observational study of 147 mother-child pairs was conducted from gestation to 12 months post partum. Assessments included prenatal questionnaires and blood collection, cord blood at delivery, and postpartum questionnaires at 1, 6, and 12 months. The Modified Checklist for Autism in Toddlers (M-CHAT) was used to evaluate developmental characteristics that might indicate early signs of atypical neurodevelopment. Polychoric or polyserial correlation coefficients assessed relationships between M-CHAT scores and longitudinal variables. L2-regularized logistic regression and Shapley Additive Explanations predicted M-CHAT scores and determined feature contributions. Results: Twenty-one factors (4 prenatal, 3 at birth, and 14 postnatal) showed significant associations with M-CHAT scores (adjusted P values<.05). The predictive accuracy for M-CHAT scores demonstrated reasonable predictive accuracy (area under the receiver operating characteristic curve=0.79). Key predictors included infant sleep status after 6 months (nighttime sleep duration, bedtime, and difficulties falling asleep), maternal Kessler Psychological Distress Scale scores, and Mother-to-Infant Bonding Scale scores after late gestation. Conclusion: Maternal psychological distress, mother-infant bonding, and infant sleep patterns were identified as significant predictors of early developmental characteristics that may indicate emerging developmental concerns. This study advances our understanding of early developmental assessment by providing a novel approach to identifying and evaluating early indicators of atypical neurodevelopment. ", doi="10.2196/58337", url="https://pediatrics.jmir.org/2025/1/e58337" } @Article{info:doi/10.2196/58334, author="Misra, Gauri and Wegerif, Simon and Fairlie, Louise and Kapoor, Melissa and Fok, James and Salt, Gemma and Halbert, Jay and Maconochie, Ian and Mullen, Niall", title="The Measurement of Vital Signs in Pediatric Patients by Lifelight Software in Comparison to the Standard of Care: Protocol for the VISION-Junior Observational Study", journal="JMIR Res Protoc", year="2025", month="Mar", day="14", volume="14", pages="e58334", keywords="vital signs", keywords="remote photoplethysmography", keywords="pediatric health assessment", keywords="pediatric health monitoring", keywords="pediatric", keywords="infant", keywords="infants", keywords="infancy", keywords="child", keywords="children", keywords="Lifelight", keywords="software", keywords="app", keywords="observational study", keywords="study protocol", keywords="clinical deterioration", keywords="COVID-19", keywords="SARS-CoV-2", keywords="pandemic", keywords="telemedicine", keywords="medical device", keywords="photoplethysmography", keywords="eHealth", keywords="mobile health", keywords="mHealth", abstract="Background: Measuring vital signs (VS) is important in potentially unwell children, as a change in VS may indicate a more serious infection than is clinically apparent or herald clinical deterioration. However, currently available methods are not suitable for regular measurement of VS in the home or community setting, and adherence can be poor. The COVID-19 pandemic highlighted a need for the contactless measurement of VS by nonclinical personnel, reinforced by the growing use of telemedicine. The Lifelight app is being developed as a medical device for the contactless measurement of VS using remote photoplethysmography via the camera on smart devices. The VISION-D (Measurement of Vital Signs by Lifelight Software in Comparison to the Standard of Care---Development) and -V (Validation) studies demonstrated the accuracy of the app compared with standard of care (SOC) measurement of blood pressure, pulse rate (PR), and respiratory rate (RR) in adults, supporting certification of Lifelight as a class I Conformit{\'e} Europ{\'e}enne medical device. Objective: To support the development of the Lifelight app for pediatric patients, the VISION-Junior study is collecting high-quality data that will be used to develop algorithms for the measurement of VS (PR, RR, and oxygen saturation) in pediatric patients. The accuracy of the app will be assessed against SOC measurements made simultaneously with app measurements. Methods: The study is recruiting pediatric patients (younger than 18?years of age) attending the Sunderland Royal Hospital pediatric emergency department of the South Tyneside and Sunderland National Health Service Foundation Trust. High-resolution videos of the face (and torso in children younger than 5 years of age) and audio recordings (to explore the value of crying, wheezing, coughing, and other sounds in predicting illness) are made using the Lifelight Data Collect app. VS are measured simultaneously using SOC methods (finger clip sensor for PR and oxygen saturation; manual counting of RR). Feedback from patients, parents, carers, and nurses who use Lifelight is collected via questionnaires. Anticipated recruitment is 500 participants, with subtargets for age, sex, and skin tone distribution (Fitzpatrick 6-point scale). Early data will be used to refine the algorithms. A separate dataset will be retained to test the performance of the app against predefined targets. Results: The study started on June 12, 2023, and reached its recruitment target (n=532) in April 2024 after extending the deadline. Algorithm refinement is in progress, after which the performance of Lifelight will be compared with the SOC measurement of VS. The analyses are expected to be completed by mid-August 2024. Conclusions: Data collected in this study will be used to develop and assess the accuracy of the app for the measurement of VS in pediatric patients of all ages. Trial Registration: ClinicalTrials.gov NCT05850013; https://clinicaltrials.gov/study/NCT05850013 International Registered Report Identifier (IRRID): DERR1-10.2196/58334 ", doi="10.2196/58334", url="https://www.researchprotocols.org/2025/1/e58334" } @Article{info:doi/10.2196/59377, author="Gao, Jing and Jie, Xu and Yao, Yujun and Xue, Jingdong and Chen, Lei and Chen, Ruiyao and Chen, Jiayuan and Cheng, Weiwei", title="Fetal Birth Weight Prediction in the Third Trimester: Retrospective Cohort Study and Development of an Ensemble Model", journal="JMIR Pediatr Parent", year="2025", month="Mar", day="10", volume="8", pages="e59377", keywords="fetal birthweight", keywords="ensemble learning model", keywords="machine learning", keywords="prediction model", keywords="ultrasonography", keywords="macrosomia", keywords="low birth weight", keywords="birth weight", keywords="fetal", keywords="AI", keywords="artificial intelligence", keywords="prenatal", keywords="prenatal care", keywords="Shanghai", keywords="neonatal", keywords="maternal", keywords="parental", abstract="Background: Accurate third-trimester birth weight prediction is vital for reducing adverse outcomes, and machine learning (ML) offers superior precision over traditional ultrasound methods. Objective: This study aims to develop an ML model on the basis of clinical big data for accurate prediction of birth weight in the third trimester of pregnancy, which can help reduce adverse maternal and fetal outcomes. Methods: From January 1, 2018 to December 31, 2019, a retrospective cohort study involving 16,655 singleton live births without congenital anomalies (>28 weeks of gestation) was conducted in a tertiary first-class hospital in Shanghai. The initial set of data was divided into a train set for algorithm development and a test set on which the algorithm was divided in a ratio of 4:1. We extracted maternal and neonatal delivery outcomes, as well as parental demographics, obstetric clinical data, and sonographic fetal biometry, from electronic medical records. A total of 5 basic ML algorithms, including Ridge, SVM, Random Forest, extreme gradient boosting (XGBoost), and Multi-Layer Perceptron, were used to develop the prediction model, which was then averaged into an ensemble learning model. The models were compared using accuracy, mean squared error, root mean squared error, and mean absolute error. International Peace Maternity and Child Health Hospital's Research Ethics Committee granted ethical approval for the usage of patient information (GKLW2021-20). Results: Train and test sets contained a total of 13,324 and 3331 cases, respectively. From a total of 59 variables, we selected 17 variables that were readily available for the ``few feature model,'' which achieved high predictive power with an accuracy of 81\% and significantly exceeded ultrasound formula methods. In addition, our model maintained superior performance for low birth weight and macrosomic fetal populations. Conclusions: Our research investigated an innovative artificial intelligence model for predicting fetal birth weight and maximizing health care resource use. In the era of big data, our model improves maternal and fetal outcomes and promotes precision medicine. ", doi="10.2196/59377", url="https://pediatrics.jmir.org/2025/1/e59377" } @Article{info:doi/10.2196/55720, author="Heuvelink, Annerieke and Saini, Privender and Ta?ar, {\"O}zg{\"u}r and Nauts, Sanne", title="Improving Pediatric Patients' Magnetic Resonance Imaging Experience With an In-Bore Solution: Design and Usability Study", journal="JMIR Serious Games", year="2025", month="Feb", day="13", volume="13", pages="e55720", keywords="MRI", keywords="magnetic resonance imaging", keywords="imaging", keywords="radiology", keywords="pediatrics", keywords="children", keywords="patient guidance", keywords="patient experience", keywords="design", keywords="usability", keywords="breath hold", abstract="Background: Annually, millions of children undergo a magnetic resonance imaging (MRI) examination. Hospitals increasingly aim to scan young children awake, as doing so benefits both patients and health care systems. To help hospitals reduce the need for anesthesia, we have developed solutions to prepare pediatric patients at home and in the hospital. Objective: The goal of our project was to design, develop, and test a solution that extends our preparation solutions by guiding and engaging children during their MRI examination. Methods: Pediatric In-bore was designed to deliver a familiar experience by reusing design elements from our preparation solutions. It offers child-friendly movies and auditory and visual guidance about examination progress and breath holding. To evaluate children's liking and understanding of the solution, we conducted a usability study. Ten healthy children participated in a mock MRI examination featuring pediatric In-bore. We observed task compliance (ability to lie still and hold one's breath) and conducted guided interviews to assess their experience and understanding of the guidance offered. Results: Participants (aged 5 to 10 years) were generally positive about pediatric In-bore. They liked the main character (Ollie the elephant) and her movie. Auditory and visual guidance were generally liked and understood. All but one participant successfully managed to lie still during the mock examination, and 6 (60\%) out of 10 participants successfully held their breath. Conclusions: Pediatric In-bore appears promising for engaging and guiding young children during awake MRI. It completes the Pediatric Coaching solution that now offers guidance throughout the MRI journey. Future research can expand on this work by evaluating the clinical impact of the Pediatric Coaching solution in a larger and more diverse sample of pediatric patients. ", doi="10.2196/55720", url="https://games.jmir.org/2025/1/e55720" } @Article{info:doi/10.2196/58421, author="Huang, Chien-Yu and Yu, Yen-Ting and Chen, Kuan-Lin and Lien, Jenn-Jier and Lin, Gong-Hong and Hsieh, Ching-Lin", title="Predicting Age and Visual-Motor Integration Using Origami Photographs: Deep Learning Study", journal="JMIR Form Res", year="2025", month="Jan", day="10", volume="9", pages="e58421", keywords="artificial intelligence", keywords="origami", keywords="child development screening", keywords="child development", keywords="visual motor integration", keywords="children", keywords="developmental status", keywords="activity performance", keywords="deep learning", abstract="Background: Origami is a popular activity among preschool children and can be used by therapists as an evaluation tool to assess children's development in clinical settings. It is easy to implement, appealing to children, and time-efficient, requiring only simple materials---pieces of paper. Furthermore, the products of origami may reflect children's ages and their visual-motor integration (VMI) development. However, therapists typically evaluate children's origami creations based primarily on their personal background knowledge and clinical experience, leading to subjective and descriptive feedback. Consequently, the effectiveness of using origami products to determine children's age and VMI development lacks empirical support. Objective: This study had two main aims. First, we sought to apply artificial intelligence (AI) techniques to origami products to predict children's ages and VMI development, including VMI level (standardized scores) and VMI developmental status (typical, borderline, or delayed). Second, we explored the performance of the AI models using all combinations of photographs taken from different angles. Methods: A total of 515 children aged 2-6 years were recruited and divided into training and testing groups at a 4:1 ratio. Children created origami dogs, which were photographed from 8 different angles. The Beery--Buktenica Developmental Test of Visual-Motor Integration, 6th Edition, was used to assess the children's VMI levels and developmental status. Three AI models---ResNet-50, XGBoost, and a multilayer perceptron---were combined sequentially to predict age z scores and VMI z scores using the training group. The trained models were then tested using the testing group, and the accuracy of the predicted VMI developmental status was also calculated. Results: The R2 of the age and the VMI trained models ranged from 0.50 to 0.73 and from 0.50 to 0.66, respectively. The AI models that obtained an R2>0.70 for the age model and an R2>0.60 for the VMI model were selected for model testing. Those models were further examined for the accuracy of the VMI developmental status, the correlations, and the mean absolute error (MAE) of both the age and the VMI models. The accuracy of the VMI developmental status was about 71\%-76\%. The correlations between the final predicted age z score and the real age z score ranged from 0.84 to 0.85, and the correlations of the final predicted VMI z scores to the real z scores ranged from 0.77 to 0.81. The MAE of the age models ranged from 0.42 to 0.46 and those of the VMI models ranged from 0.43 to 0.48. Conclusion: Our findings indicate that AI techniques have a significant potential for predicting children's development. The insights provided by AI may assist therapists in better interpreting children's performance in activities. ", doi="10.2196/58421", url="https://formative.jmir.org/2025/1/e58421" } @Article{info:doi/10.2196/58686, author="Qin, Chenlong and Peng, Li and Liu, Yun and Zhang, Xiaoliang and Miao, Shumei and Wei, Zhiyuan and Feng, Wei and Zhang, Hongjian and Wan, Cheng and Yu, Yun and Lu, Shan and Huang, Ruochen and Zhang, Xin", title="Development and Validation of a Nomogram-Based Model to Predict Primary Hypertension Within the Next Year in Children and Adolescents: Retrospective Cohort Study", journal="J Med Internet Res", year="2024", month="Dec", day="30", volume="26", pages="e58686", keywords="independent risk factors", keywords="prediction model", keywords="primary hypertension", keywords="clinical applicability", keywords="development", keywords="validation", keywords="pediatrics", keywords="electronic health records", abstract="Background: Primary hypertension (PH) poses significant risks to children and adolescents. Few prediction models for the risk of PH in children and adolescents currently exist, posing a challenge for doctors in making informed clinical decisions. Objective: This study aimed to investigate the incidence and risk factors of PH in Chinese children and adolescents. It also aimed to establish and validate a nomogram-based model for predicting the next year's PH risk. Methods: A training cohort (n=3938, between January 1, 2008, and December 31, 2020) and a validation cohort (n=1269, between January 1, 2021, and July 1, 2023) were established for model training and validation. An independent cohort of 576 individuals was established for external validation of the model. The result of the least absolute shrinkage and selection operator regression technique was used to select the optimal predictive features, and multivariate logistic regression to construct the nomogram. The performance of the nomogram underwent assessment and validation through the area under the receiver operating characteristic curve, concordance index, calibration curves, decision curve analysis, clinical impact curves, and sensitivity analysis. Results: The PH risk factors that we have ultimately identified include gender (odds ratio [OR] 3.34, 95\% CI 2.88 to 3.86; P<.001), age (OR 1.11, 95\% CI 1.08 to 1.14; P<.001), family history of hypertension (OR 42.74, 95\% CI 23.07 to 79.19; P<.001), fasting blood glucose (OR 6.07, 95\% CI 4.74 to 7.78; P<.001), low-density lipoprotein cholesterol (OR 2.03, 95\% CI 1.60 to 2.57; P<.001), and uric acid (OR 1.01, 95\% CI 1.01 to 1.01; P<.001), while factor breastfeeding (OR 0.04, 95\% CI 0.03 to 0.05; P<.001) has been identified as a protective factor. Subsequently, a nomogram has been constructed incorporating these factors. Areas under the receiver operating characteristic curves of the nomogram were 0.892 in the training cohort, 0.808 in the validation cohort, and 0.790 in the external validation cohort. Concordance indexes of the nomogram were 0.892 in the training cohort, 0.808 in the validation cohort, and 0.790 in the external validation cohort. The nomogram has been proven to have good clinical benefits and stability in calibration curves, decision curve analysis, clinical impact curves, and sensitivity analysis. Finally, we observed noteworthy differences in uric acid levels and family history of hypertension among various subgroups, demonstrating a high correlation with PH. Moreover, the web-based calculator of the nomogram was built online. Conclusions: We have developed and validated a stable and reliable nomogram that can accurately predict PH risk within the next year among children and adolescents in primary care and offer effective and cost-efficient support for clinical decisions for the risk prediction of PH. ", doi="10.2196/58686", url="https://www.jmir.org/2024/1/e58686" } @Article{info:doi/10.2196/55986, author="Zou, Zhuan and Chen, Bin and Xiao, Dongqiong and Tang, Fajuan and Li, Xihong", title="Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis", journal="J Med Internet Res", year="2024", month="Dec", day="11", volume="26", pages="e55986", keywords="epileptic seizures", keywords="machine learning", keywords="deep learning", keywords="electroencephalogram", keywords="EEG", keywords="children", keywords="pediatrics", keywords="epilepsy", keywords="detection", abstract="Background: Real-time monitoring of pediatric epileptic seizures poses a significant challenge in clinical practice. In recent years, machine learning (ML) has attracted substantial attention from researchers for diagnosing and treating neurological diseases, leading to its application for detecting pediatric epileptic seizures. However, systematic evidence substantiating its feasibility remains limited. Objective: This systematic review aimed to consolidate the existing evidence regarding the effectiveness of ML in monitoring pediatric epileptic seizures with an effort to provide an evidence-based foundation for the development and enhancement of intelligent tools in the future. Methods: We conducted a systematic search of the PubMed, Cochrane, Embase, and Web of Science databases for original studies focused on the detection of pediatric epileptic seizures using ML, with a cutoff date of August 27, 2023. The risk of bias in eligible studies was assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies--2). Meta-analyses were performed to evaluate the C-index and the diagnostic 4-grid table, using a bivariate mixed-effects model for the latter. We also examined publication bias for the C-index by using funnel plots and the Egger test. Results: This systematic review included 28 original studies, with 15 studies on ML and 13 on deep learning (DL). All these models were based on electroencephalography data of children. The pooled C-index, sensitivity, specificity, and accuracy of ML in the training set were 0.76 (95\% CI 0.69-0.82), 0.77 (95\% CI 0.73-0.80), 0.74 (95\% CI 0.70-0.77), and 0.75 (95\% CI 0.72-0.77), respectively. In the validation set, the pooled C-index, sensitivity, specificity, and accuracy of ML were 0.73 (95\% CI 0.67-0.79), 0.88 (95\% CI 0.83-0.91), 0.83 (95\% CI 0.71-0.90), and 0.78 (95\% CI 0.73-0.82), respectively. Meanwhile, the pooled C-index of DL in the validation set was 0.91 (95\% CI 0.88-0.94), with sensitivity, specificity, and accuracy being 0.89 (95\% CI 0.85-0.91), 0.91 (95\% CI 0.88-0.93), and 0.89 (95\% CI 0.86-0.92), respectively. Conclusions: Our systematic review demonstrates promising accuracy of artificial intelligence methods in epilepsy detection. DL appears to offer higher detection accuracy than ML. These findings support the development of DL-based early-warning tools in future research. Trial Registration: PROSPERO CRD42023467260; https://www.crd.york.ac.uk/prospero/display\_record.php?ID=CRD42023467260 ", doi="10.2196/55986", url="https://www.jmir.org/2024/1/e55986" } @Article{info:doi/10.2196/64353, author="Miladinovi{\'c}, Aleksandar and Quaia, Christian and Kresevic, Simone and Aj{\v c}evi{\'c}, Milo{\vs} and Diplotti, Laura and Michieletto, Paola and Accardo, Agostino and Pensiero, Stefano", title="High-Resolution Eye-Tracking System for Accurate Measurement of Short-Latency Ocular Following Responses: Development and Observational Study", journal="JMIR Pediatr Parent", year="2024", month="Dec", day="9", volume="7", pages="e64353", keywords="ocular following response", keywords="stereopsis", keywords="video-oculography", keywords="ocular", keywords="tracker", keywords="vision", keywords="pediatric", keywords="children", keywords="youth", keywords="infrared", keywords="algorithm", keywords="eye tracking", abstract="Background: Ocular following responses (OFRs)---small-amplitude, short-latency reflexive eye movements---have been used to study visual motion processing, with potential diagnostic applications. However, they are difficult to record with commercial, video-based eye trackers, especially in children. Objective: We aimed to design and develop a noninvasive eye tracker specialized for measuring OFRs, trading off lower temporal resolution and a smaller range for higher spatial resolution. Methods: We developed a high-resolution eye-tracking system based on a high-resolution camera operating in the near-infrared spectral range, coupled with infrared illuminators and a dedicated postprocessing pipeline, optimized to measure OFRs in children. To assess its performance, we: (1) evaluated our algorithm for compensating small head movements in both artificial and real-world settings, (2) compared OFRs measured simultaneously by our system and a reference scleral search coil eye-tracking system, and (3) tested the system's ability to measure OFRs in a clinical setting with children. Results: The simultaneous measurement by our system and a reference system showed that our system achieved an in vivo resolution of approximately 0.06{\textdegree}, which is sufficient for recording OFRs. Head motion compensation was successfully tested, showing a displacement error of less than 5 $\mu$m. Finally, robust OFRs were detected in 16 children during recording sessions lasting less than 5 minutes. Conclusions: Our high-resolution, noninvasive eye-tracking system successfully detected OFRs with minimal need for subject cooperation. The system effectively addresses the limits of other OFR measurement methods and offers a versatile solution suitable for clinical applications, particularly in children, where eye tracking is more challenging. The system could potentially be suitable for diagnostic applications, particularly in pediatric populations where early detection of visual disorders like stereodeficiencies is critical. ", doi="10.2196/64353", url="https://pediatrics.jmir.org/2024/1/e64353" } @Article{info:doi/10.2196/54641, author="Song, Kyungchul and Ko, Taehoon and Chae, Wook Hyun and Oh, Suk Jun and Kim, Ho-Seong and Shin, Joo Hyun and Kim, Jeong-Ho and Na, Ji-Hoon and Park, Jung Chae and Sohn, Beomseok", title="Development and Validation of a Prediction Model Using Sella Magnetic Resonance Imaging--Based Radiomics and Clinical Parameters for the Diagnosis of Growth Hormone Deficiency and Idiopathic Short Stature: Cross-Sectional, Multicenter Study", journal="J Med Internet Res", year="2024", month="Nov", day="27", volume="26", pages="e54641", keywords="dwarfism", keywords="pituitary", keywords="idiopathic short stature", keywords="child", keywords="adolescent", keywords="machine learning", keywords="magnetic resonance imaging", keywords="MRI", abstract="Background: Growth hormone deficiency (GHD) and idiopathic short stature (ISS) are the major etiologies of short stature in children. For the diagnosis of GHD and ISS, meticulous evaluations are required, including growth hormone provocation tests, which are invasive and burdensome for children. Additionally, sella magnetic resonance imaging (MRI) is necessary for assessing etiologies of GHD, which cannot evaluate hormonal secretion. Recently, radiomics has emerged as a revolutionary technique that uses mathematical algorithms to extract various features for the quantitative analysis of medical images. Objective: This study aimed to develop a machine learning--based model using sella MRI--based radiomics and clinical parameters to diagnose GHD and ISS. Methods: A total of 293 children with short stature who underwent sella MRI and growth hormone provocation tests were included in the training set, and 47 children who met the same inclusion criteria were enrolled in the test set from different hospitals for this study. A total of 186 radiomic features were extracted from the pituitary glands using a semiautomatic segmentation process for both the T2-weighted and contrast-enhanced T1-weighted image. The clinical parameters included auxological data, insulin-like growth factor-I, and bone age. The extreme gradient boosting algorithm was used to train the prediction models. Internal validation was conducted using 5-fold cross-validation on the training set, and external validation was conducted on the test set. Model performance was assessed by plotting the area under the receiver operating characteristic curve. The mean absolute Shapley values were computed to quantify the impact of each parameter. Results: The area under the receiver operating characteristic curves (95\% CIs) of the clinical, radiomics, and combined models were 0.684 (0.590-0.778), 0.691 (0.620-0.762), and 0.830 (0.741-0.919), respectively, in the external validation. Among the clinical parameters, the major contributing factors to prediction were BMI SD score (SDS), chronological age--bone age, weight SDS, growth velocity, and insulin-like growth factor-I SDS in the clinical model. In the combined model, radiomic features including maximum probability from a T2-weighted image and run length nonuniformity normalized from a T2-weighted image added incremental value to the prediction (combined model vs clinical model, P=.03; combined model vs radiomics model, P=.02). The code for our model is available in a public repository on GitHub. Conclusions: Our model combining both radiomics and clinical parameters can accurately predict GHD from ISS, which was also proven in the external validation. These findings highlight the potential of machine learning--based models using radiomics and clinical parameters for diagnosing GHD and ISS. ", doi="10.2196/54641", url="https://www.jmir.org/2024/1/e54641" } @Article{info:doi/10.2196/59564, author="Chua, Chien Mei and Hadimaja, Matthew and Wong, Jill and Mukherjee, Subhra Sankha and Foussat, Agathe and Chan, Daniel and Nandal, Umesh and Yap, Fabian", title="Exploring the Use of a Length AI Algorithm to Estimate Children's Length from Smartphone Images in a Real-World Setting: Algorithm Development and Usability Study", journal="JMIR Pediatr Parent", year="2024", month="Nov", day="22", volume="7", pages="e59564", keywords="computer vision", keywords="length estimation", keywords="artificial intelligence", keywords="smartphone images", keywords="children", keywords="AI", keywords="algorithm", keywords="imaging", keywords="height", keywords="length", keywords="measure", keywords="pediatric", keywords="infant", keywords="neonatal", keywords="newborn", keywords="smartphone", keywords="mHealth", keywords="mobile health", keywords="mobile phone", abstract="Background: Length measurement in young children younger than 18 months is important for monitoring growth and development. Accurate length measurement requires proper equipment, standardized methods, and trained personnel. In addition, length measurement requires young children's cooperation, making it particularly challenging during infancy and toddlerhood. Objective: This study aimed to develop a length artificial intelligence (LAI) algorithm to aid users in determining recumbent length conveniently from smartphone images and explore its performance and suitability for personal and clinical use. Methods: This proof-of-concept study in healthy children (aged 0-18 months) was performed at KK Women's and Children's Hospital, Singapore, from November 2021 to March 2022. Smartphone images were taken by parents and investigators. Standardized length-board measurements were taken by trained investigators. Performance was evaluated by comparing the tool's image-based length estimations with length-board measurements (bias [mean error, mean difference between measured and predicted length]; absolute error [magnitude of error]). Prediction performance was evaluated on an individual-image basis and participant-averaged basis. User experience was collected through questionnaires. Results: A total of 215 participants (median age 4.4, IQR 1.9-9.7 months) were included. The tool produced a length prediction for 99.4\% (2211/2224) of photos analyzed. The mean absolute error was 2.47 cm for individual image predictions and 1.77 cm for participant-averaged predictions. Investigators and parents reported no difficulties in capturing the required photos for most participants (182/215, 84.7\% participants and 144/200, 72\% participants, respectively). Conclusions: The LAI algorithm is an accessible and novel way of estimating children's length from smartphone images without the need for specialized equipment or trained personnel. The LAI algorithm's current performance and ease of use suggest its potential for use by parents or caregivers with an accuracy approaching what is typically achieved in general clinics or community health settings. The results show that the algorithm is acceptable for use in a personal setting, serving as a proof of concept for use in clinical settings. Trial Registration: ClinicalTrials.gov NCT05079776; https://clinicaltrials.gov/ct2/show/NCT05079776 ", doi="10.2196/59564", url="https://pediatrics.jmir.org/2024/1/e59564" } @Article{info:doi/10.2196/57814, author="Wang, Yipei and Zhang, Pei and Xing, Yan and Shi, Huifeng and Cui, Yunpu and Wei, Yuan and Zhang, Ke and Wu, Xinxia and Ji, Hong and Xu, Xuedong and Dong, Yanhui and Jin, Changxiao", title="Telemedicine Integrated Care Versus In-Person Care Mode for Patients With Short Stature: Comprehensive Comparison of a Retrospective Cohort Study", journal="J Med Internet Res", year="2024", month="Nov", day="19", volume="26", pages="e57814", keywords="telemedicine", keywords="telemedicine integrated care mode", keywords="short stature", keywords="clinical outcomes", keywords="health-seeking behaviors", keywords="cost analysis", keywords="in-person care", keywords="mobile health", keywords="mHealth", keywords="telehealth", keywords="eHealth", keywords="video virtual visit", keywords="access to care", keywords="children", keywords="pediatrics", keywords="China", keywords="accessibility", keywords="temporal", keywords="spatial constraints", keywords="chronic disease", abstract="Background: Telemedicine has demonstrated efficacy as a supplement to traditional in-person care when treating certain diseases. Nevertheless, more investigation is needed to comprehensively assess its potential as an alternative to in-person care and its influence on access to care. The successful treatment of short stature relies on timely and regular intervention, particularly in rural and economically disadvantaged regions where the disease is more prevalent. Objective: This study evaluated the clinical outcomes, health-seeking behaviors, and cost of telemedicine integrated into care for children with short stature in China. Methods: Our study involved 1241 individuals diagnosed with short stature at the pediatric outpatient clinic of Peking University Third Hospital between 2012 and 2023. Patients were divided into in-person care (IPC; 1183 patients receiving only in-person care) and telemedicine integrated care (TIC; 58 patients receiving both in-person and virtual care) groups. For both groups, the initial 71.43\% (average of 58 percentages, with each percentage representing the ratio of patients in the treatment group) of visits were categorized into the pretelemedicine phase. We used propensity score matching to select individuals with similar baseline conditions. We used 7 variables such as age, gender, and medical insurance for the 1:5 closest neighbor match. Eventually, 115 patients in the IPC group and 54 patients in the TIC group were selected. The primary clinical outcome was the change in the standard height percentage. Health-seeking behavior was described by visit intervals in the pre- and post-telemedicine phases. The cost analysis compared costs both between different groups and between different visit modalities of the TIC group in the post-telemedicine phase. Results: In terms of clinical effectiveness, we demonstrated that the increase in height among the TIC group ($\Delta$zTIC=0.74) was more substantial than that for the IPC group ($\Delta$zIPC=0.51, P=.01; paired t test), while no unfavorable changes in other endpoints such as BMI or insulin-like growth factor 1 (IGF-1) levels were observed. As for health-seeking behaviors, the results showed that, during the post-telemedicine phase, the IPC group had a visit interval of 71.08 (IQR 50.75-90.73) days, significantly longer than the prior period (51.25 [IQR 34.75-82.00] days, P<.001; U test), whereas the TIC group's visit interval remained unchanged. As for the cost per visit, there was no difference in the average cost per visit between the 2 groups nor between the pre- and post-telemedicine phases. During the post-telemedicine phase, within the TIC group, in-person visits had a higher average total cost, elevated medical and labor expenses, and greater medical cost compared with virtual visits. Conclusions: We contend that the rise in medical visits facilitated by integrating telemedicine into care effectively restored the previously constrained number of medical visits to their usual levels, without increasing costs. Our research underscores that administering prompt treatment may enable physicians to seize a crucial treatment opportunity for children with short stature, thus attaining superior results. ", doi="10.2196/57814", url="https://www.jmir.org/2024/1/e57814" } @Article{info:doi/10.2196/57641, author="Zhu, Jinpu and Yang, Fushuang and Wang, Yang and Wang, Zhongtian and Xiao, Yao and Wang, Lie and Sun, Liping", title="Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis", journal="J Med Internet Res", year="2024", month="Nov", day="18", volume="26", pages="e57641", keywords="machine learning", keywords="artificial intelligence", keywords="Kawasaki disease", keywords="febrile illness", keywords="coronary artery lesions", keywords="systematic review", keywords="meta-analysis", abstract="Background: Kawasaki disease (KD) is an acute pediatric vasculitis that can lead to coronary artery aneurysms and severe cardiovascular complications, often presenting with obvious fever in the early stages. In current clinical practice, distinguishing KD from other febrile illnesses remains a significant challenge. In recent years, some researchers have explored the potential of machine learning (ML) methods for the differential diagnosis of KD versus other febrile illnesses, as well as for predicting coronary artery lesions (CALs) in people with KD. However, there is still a lack of systematic evidence to validate their effectiveness. Therefore, we have conducted the first systematic review and meta-analysis to evaluate the accuracy of ML in differentiating KD from other febrile illnesses and in predicting CALs in people with KD, so as to provide evidence-based support for the application of ML in the diagnosis and treatment of KD. Objective: This study aimed to summarize the accuracy of ML in differentiating KD from other febrile illnesses and predicting CALs in people with KD. Methods: PubMed, Cochrane Library, Embase, and Web of Science were systematically searched until September 26, 2023. The risk of bias in the included original studies was appraised using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Stata (version 15.0; StataCorp) was used for the statistical analysis. Results: A total of 29 studies were incorporated. Of them, 20 used ML to differentiate KD from other febrile illnesses. These studies involved a total of 103,882 participants, including 12,541 people with KD. In the validation set, the pooled concordance index, sensitivity, and specificity were 0.898 (95\% CI 0.874-0.922), 0.91 (95\% CI 0.83-0.95), and 0.86 (95\% CI 0.80-0.90), respectively. Meanwhile, 9 studies used ML for early prediction of the risk of CALs in children with KD. These studies involved a total of 6503 people with KD, of whom 986 had CALs. The pooled concordance index in the validation set was 0.787 (95\% CI 0.738-0.835). Conclusions: The diagnostic and predictive factors used in the studies we included were primarily derived from common clinical data. The ML models constructed based on these clinical data demonstrated promising effectiveness in differentiating KD from other febrile illnesses and in predicting coronary artery lesions. Therefore, in future research, we can explore the use of ML methods to identify more efficient predictors and develop tools that can be applied on a broader scale for the differentiation of KD and the prediction of CALs. ", doi="10.2196/57641", url="https://www.jmir.org/2024/1/e57641" } @Article{info:doi/10.2196/58439, author="Oyovwi, Sr Mega Obukohwo and Ohwin, Peggy Ejiro and Rotu, Arientare Rume and Olowe, Gideon Temitope", title="Internet-Based Abnormal Chromosomal Diagnosis During Pregnancy Using a Noninvasive Innovative Approach to Detecting Chromosomal Abnormalities in the Fetus: Scoping Review", journal="JMIR Bioinform Biotech", year="2024", month="Oct", day="16", volume="5", pages="e58439", keywords="internet-based", keywords="abnormal chromosomal diagnosis", keywords="pregnancy", keywords="noninvasive", keywords="innovative approach", keywords="detecting", keywords="preventing", keywords="chromosomal abnormalities", keywords="fetus", abstract="Background: Chromosomal abnormalities are genetic disorders caused by chromosome errors, leading to developmental delays, birth defects, and miscarriages. Currently, invasive procedures such as amniocentesis or chorionic villus sampling are mostly used, which carry a risk of miscarriage. This has led to the need for a noninvasive and innovative approach to detect and prevent chromosomal abnormalities during pregnancy. Objective: This review aims to describe and appraise the potential of internet-based abnormal chromosomal preventive measures as a noninvasive approach to detecting and preventing chromosomal abnormalities during pregnancy. Methods: A thorough review of existing literature and research on chromosomal abnormalities and noninvasive approaches to prenatal diagnosis and therapy was conducted. Electronic databases such as PubMed, Google Scholar, ScienceDirect, CENTRAL, CINAHL, Embase, OVID MEDLINE, OVID PsycINFO, Scopus, ACM, and IEEE Xplore were searched for relevant studies and articles published in the last 5 years. The keywords used included chromosomal abnormalities, prenatal diagnosis, noninvasive, and internet-based, and diagnosis. Results: The review of literature revealed that internet-based abnormal chromosomal diagnosis is a potential noninvasive approach to detecting and preventing chromosomal abnormalities during pregnancy. This innovative approach involves the use of advanced technology, including high-resolution ultrasound, cell-free DNA testing, and bioinformatics, to analyze fetal DNA from maternal blood samples. It allows early detection of chromosomal abnormalities, enabling timely interventions and treatment to prevent adverse outcomes. Furthermore, with the advancement of technology, internet-based abnormal chromosomal diagnosis has emerged as a safe alternative with benefits including its cost-effectiveness, increased accessibility and convenience, potential for earlier detection and intervention, and ethical considerations. Conclusions: Internet-based abnormal chromosomal diagnosis has the potential to revolutionize prenatal care by offering a safe and noninvasive alternative to invasive procedures. It has the potential to improve the detection of chromosomal abnormalities, leading to better pregnancy outcomes and reduced risk of miscarriage. Further research and development in this field is needed to make this approach more accessible and affordable for pregnant women. ", doi="10.2196/58439", url="https://bioinform.jmir.org/2024/1/e58439", url="http://www.ncbi.nlm.nih.gov/pubmed/39412876" } @Article{info:doi/10.2196/57325, author="DeMatteo, Carol and Randall, Sarah and Jakubowski, Josephine and Stazyk, Kathy and Obeid, Joyce and Noseworthy, Michael and Mazurek, Michael and Timmons, W. Brian and Connolly, John and Giglia, Lucia and Hall, Geoffrey and Lin, Chia-Yu and Perrotta, Samantha", title="Fact or Fiction---Accelerometry Versus Self-Report in Adherence to Pediatric Concussion Protocols: Prospective Longitudinal Cohort Study", journal="JMIR Pediatr Parent", year="2024", month="Oct", day="9", volume="7", pages="e57325", keywords="pediatric concussion", keywords="guidelines", keywords="adherence", keywords="return to school", keywords="return to sport", keywords="actigraphy", abstract="Background: Concussion, or mild traumatic brain injury, is a growing public health concern, affecting approximately 1.2\% of the population annually. Among children aged 1?17 years, concussion had the highest weighted prevalence compared to other injury types, highlighting the importance of addressing this issue among the youth population. Objective: This study aimed to assess adherence to Return to Activity (RTA) protocols among youth with concussion and to determine if better adherence affected time to recovery and the rate of reinjury. Methods: Children and youth (N=139) aged 5?18 years with concussion were recruited. Self-reported symptoms and protocol stage of recovery were monitored every 48 hours until symptom resolution was achieved. Daily accelerometry was assessed with the ActiGraph. Data were collected to evaluate adherence to the RTA protocol based on physical activity cutoff points corresponding to RTA stages. Participants were evaluated using a battery of physical, cognitive, and behavioral measures at recruitment, upon symptom resolution, and 3 months post symptom resolution. Results: For RTA stage 1, a total of 13\% of participants were adherent based on accelerometry, whereas 11\% and 34\% of participants were adherent for stage 2 and 3, respectively. The median time to symptom resolution was 13 days for participants who were subjectively reported adherent to the RTA protocol and 20 days for those who were subjectively reported as nonadherent (P=.03). No significant agreement was found between self-report of adherence and objective actigraphy adherence to the RTA protocol as well as to other clinical outcomes, such as depression, quality of life, and balance. The rate of reinjury among the entire cohort was 2\% (n=3). Conclusions: Overall, adherence to staged protocols post concussion was minimal when assessed with accelerometers, but adherence was higher by self-report. More physical activity restrictions, as specified in the RTA protocol, resulted in lower adherence. Although objective adherence was low, reinjury rate was lower than expected, suggesting a protective effect of being monitored and increased youth awareness of protocols. The results of this study support the move to less restrictive protocols and earlier resumption of daily activities that have since been implemented in more recent protocols. ", doi="10.2196/57325", url="https://pediatrics.jmir.org/2024/1/e57325" } @Article{info:doi/10.2196/54577, author="Grazioli, Silvia and Crippa, Alessandro and Buo, Noemi and Busti Ceccarelli, Silvia and Molteni, Massimo and Nobile, Maria and Salandi, Antonio and Trabattoni, Sara and Caselli, Gabriele and Colombo, Paola", title="Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study", journal="JMIR Form Res", year="2024", month="Jul", day="29", volume="8", pages="e54577", keywords="digital-aided clinical assessment", keywords="machine learning", keywords="random forest", keywords="logistic regression", keywords="computational psychometrics", keywords="telemedicine", keywords="neurodevelopmental conditions", keywords="parent-report questionnaires", keywords="attention-deficit/hyperactivity disorder", keywords="autism spectrum disorder", keywords="ASD", keywords="autism", keywords="autistic", keywords="attention deficit", keywords="hyperactivity", keywords="classification", abstract="Background: Diagnosis of child and adolescent psychopathologies involves a multifaceted approach, integrating clinical observations, behavioral assessments, medical history, cognitive testing, and familial context information. Digital technologies, especially internet-based platforms for administering caregiver-rated questionnaires, are increasingly used in this field, particularly during the screening phase. The ascent of digital platforms for data collection has propelled advanced psychopathology classification methods such as supervised machine learning (ML) into the forefront of both research and clinical environments. This shift, recently called psycho-informatics, has been facilitated by gradually incorporating computational devices into clinical workflows. However, an actual integration between telemedicine and the ML approach has yet to be fulfilled. Objective: Under these premises, exploring the potential of ML applications for analyzing digitally collected data may have significant implications for supporting the clinical practice of diagnosing early psychopathology. The purpose of this study was, therefore, to exploit ML models for the classification of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) using internet-based parent-reported socio-anamnestic data, aiming at obtaining accurate predictive models for new help-seeking families. Methods: In this retrospective, single-center observational study, socio-anamnestic data were collected from 1688 children and adolescents referred for suspected neurodevelopmental conditions. The data included sociodemographic, clinical, environmental, and developmental factors, collected remotely through the first Italian internet-based screening tool for neurodevelopmental disorders, the Medea Information and Clinical Assessment On-Line (MedicalBIT). Random forest (RF), decision tree, and logistic regression models were developed and evaluated using classification accuracy, sensitivity, specificity, and importance of independent variables. Results: The RF model demonstrated robust accuracy, achieving 84\% (95\% CI 82-85; P<.001) for ADHD and 86\% (95\% CI 84-87; P<.001) for ASD classifications. Sensitivities were also high, with 93\% for ADHD and 95\% for ASD. In contrast, the DT and LR models exhibited lower accuracy (DT 74\%, 95\% CI 71-77; P<.001 for ADHD; DT 79\%, 95\% CI 77-82; P<.001 for ASD; LR 61\%, 95\% CI 57-64; P<.001 for ADHD; LR 63\%, 95\% CI 60-67; P<.001 for ASD) and sensitivities (DT: 82\% for ADHD and 88\% for ASD; LR: 62\% for ADHD and 68\% for ASD). The independent variables considered for classification differed in importance between the 2 models, reflecting the distinct characteristics of the 3 ML approaches. Conclusions: This study highlights the potential of ML models, particularly RF, in enhancing the diagnostic process of child and adolescent psychopathology. Altogether, the current findings underscore the significance of leveraging digital platforms and computational techniques in the diagnostic process. While interpretability remains crucial, the developed approach might provide valuable screening tools for clinicians, highlighting the significance of embedding computational techniques in the diagnostic process. ", doi="10.2196/54577", url="https://formative.jmir.org/2024/1/e54577", url="http://www.ncbi.nlm.nih.gov/pubmed/39073858" } @Article{info:doi/10.2196/58886, author="Weile, Synne Kathrine and Mathiasen, Ren{\'e} and Winther, Falck Jeanette and Hasle, Henrik and Henriksen, Tram Louise", title="Hjernetegn.dk---The Danish Central Nervous System Tumor Awareness Initiative Digital Decision Support Tool: Design and Implementation Report", journal="JMIR Med Inform", year="2024", month="Jul", day="25", volume="12", pages="e58886", keywords="digital health initiative", keywords="digital health initiatives", keywords="clinical decision support", keywords="decision support", keywords="decision support system", keywords="decision support systems", keywords="decision support tool", keywords="decision support tools", keywords="diagnostic delay", keywords="awareness initiative", keywords="pediatric neurology", keywords="pediatric CNS tumors", keywords="CNS tumor", keywords="CNS tumour", keywords="CNS tumours", keywords="co-creation", keywords="health systems and services", keywords="communication", keywords="central nervous system", abstract="Background: Childhood tumors in the central nervous system (CNS) have longer diagnostic delays than other pediatric tumors. Vague presenting symptoms pose a challenge in the diagnostic process; it has been indicated that patients and parents may be hesitant to seek help, and health care professionals (HCPs) may lack awareness and knowledge about clinical presentation. To raise awareness among HCPs, the Danish CNS tumor awareness initiative hjernetegn.dk was launched. Objective: This study aims to present the learnings from designing and implementing a decision support tool for HCPs to reduce diagnostic delay in childhood CNS tumors. The aims also include decisions regarding strategies for dissemination and use of social media, and an evaluation of the digital impact 6 months after launch. Methods: The phases of developing and implementing the tool include participatory co-creation workshops, designing the website and digital platforms, and implementing a press and media strategy. The digital impact of hjernetegn.dk was evaluated through website analytics and social media engagement. Implementation (Results): hjernetegn.dk was launched in August 2023. The results after 6 months exceeded key performance indicators. The analysis showed a high number of website visitors and engagement, with a plateau reached 3 months after the initial launch. The LinkedIn campaign and Google Search strategy also generated a high number of impressions and clicks. Conclusions: The findings suggest that the initiative has been successfully integrated, raising awareness and providing a valuable tool for HCPs in diagnosing childhood CNS tumors. The study highlights the importance of interdisciplinary collaboration, co-creation, and ongoing community management, as well as broad dissemination strategies when introducing a digital support tool. ", doi="10.2196/58886", url="https://medinform.jmir.org/2024/1/e58886" } @Article{info:doi/10.2196/51087, author="Navaneethan, Praveena and Mohammed, Pasha Imran and Shenoy, P. Rekha and Junaid, Junaid and Amanna, Supriya and Alsughier, Zeyad and Kolarkodi, Hameed Shaul", title="Evaluation of Staining Propensity of Silver Diamine Fluoride With and Without Potassium Iodide in Children (Project Healthy Smiles): Protocol for a Randomized Controlled Trial", journal="JMIR Res Protoc", year="2024", month="Jul", day="23", volume="13", pages="e51087", keywords="silver diamine fluoride", keywords="SDF", keywords="potassium iodide", keywords="KI", keywords="tooth discoloration", keywords="dental caries", keywords="dental esthetics", keywords="dental", keywords="teeth", keywords="healthy smile", keywords="staining", keywords="treatment", keywords="oral health", keywords="child", abstract="Background: Silver diamine fluoride (SDF) is becoming more widely recognized as a simple, cost-effective approach to minimize sensitivity and arrest caries. However, SDF results in caries that are stained black. Potassium iodide (KI) treatment with SDF may minimize or lessen the staining. However, the effectiveness of KI on staining has not been investigated. Studies demonstrating that potassium iodide reduces the black staining are still insufficient. This paper presents the study protocol for Healthy Smiles, a randomized controlled trial implemented to compare the staining propensity of SDF and SDF+KI. Objective: This study, Healthy Smiles, aims to evaluate the staining propensity of SDF and SDF+KI using a Nix Mini color sensor among children aged 4 to 6 years. Another objective of the study is to evaluate the caries-arresting effect of SDF and SDF+KI in the treatment of carious primary teeth. Methods: This study is a randomized controlled trial. A total of 60 children with caries that meet the criteria of the International Caries Detection and Assessment System (code 1 or above) will be randomly assigned to treatment groups, where group 1 will be treated with SDF and group 2 will be treated with SDF+KI. Discoloration of treated lesions will be assessed digitally using a Nix Mini color sensor. Participants will be followed up at 1, 3, and 6 months after treatment to digitally record the ?L and ?E values using the Nix Mini color sensor. Data will be analyzed using SPSS (version 28; IBM Corp). Independent sample t tests and the Mann-Whitney U test will be used to compare the 2 groups. Results: Enrollment started in October 2023. It is estimated that the enrollment period will be 12 months. Data collection is planned to be completed in 2024. Conclusions: The presented paper describes Happy Smiles, a project that provides an opportunity to address the aesthetic inconvenience of patients without compromising the effectiveness of the SDF treatment. The trial findings will contribute to the limited evidence base related to discoloration after SDF intervention to improve aesthetic appearances in child oral health. If the results from the trial are promising, it will lead to the development of a model for child oral health and pave the way for further research in child oral health. International Registered Report Identifier (IRRID): PRR1-10.2196/51087 ", doi="10.2196/51087", url="https://www.researchprotocols.org/2024/1/e51087" } @Article{info:doi/10.2196/54610, author="Pretorius, Kelly and Kang, Sookja and Choi, Eunju", title="Photos Shared on Facebook in the Context of Safe Sleep Recommendations: Content Analysis of Images", journal="JMIR Pediatr Parent", year="2024", month="Apr", day="23", volume="7", pages="e54610", keywords="SUID", keywords="SIDS", keywords="parenting", keywords="safe sleep", keywords="photo analysis", keywords="pediatric", keywords="pediatrics", keywords="paediatric", keywords="paediatrics", keywords="infant", keywords="infants", keywords="infancy", keywords="baby", keywords="babies", keywords="neonate", keywords="neonates", keywords="neonatal", keywords="newborn", keywords="newborns", keywords="sleep", keywords="safety", keywords="death", keywords="mortality", keywords="social media", keywords="picture", keywords="pictures", keywords="photo", keywords="photos", keywords="photographs", keywords="image", keywords="images", keywords="Facebook", keywords="mother", keywords="mothers", keywords="parent", keywords="co-sleeping", keywords="sudden infant death", keywords="sudden unexpected infant death", keywords="adherence", keywords="parent education", keywords="parents' education", keywords="awareness", abstract="Background: Sudden unexpected infant death (SUID) remains a leading cause of infant mortality; therefore, understanding parental practices of infant sleep at home is essential. Since social media analyses yield invaluable patient perspectives, understanding sleep practices in the context of safe sleep recommendations via a Facebook mothers' group is instrumental for policy makers, health care providers, and researchers. Objective: This study aimed to identify photos shared by mothers discussing SUID and safe sleep online and assess their consistency with infant sleep guidelines per the American Academy of Pediatrics (AAP). We hypothesized the photos would not be consistent with guidelines based on prior research and increasing rates of accidental suffocation and strangulation in bed. Methods: Data were extracted from a Facebook mothers' group in May 2019. After trialing various search terms, searching for the term ``SIDS'' on the selected Facebook group resulted in the most relevant discussions on SUID and safe sleep. The resulting data, including 20 posts and 912 comments among 512 mothers, were extracted and underwent qualitative descriptive content analysis. In completing the extraction and subsequent analysis, 24 shared personal photos were identified among the discussions. Of the photos, 14 pertained to the infant sleep environment. Photos of the infant sleep environment were then assessed for consistency with safe sleep guidelines per the AAP standards by 2 separate reviewers. Results: Of the shared photos relating to the infant sleep environment, 86\% (12/14) were not consistent with AAP safe sleep guidelines. Specific inconsistencies included prone sleeping, foreign objects in the sleeping environment, and use of infant sleeping devices. Use of infant monitoring devices was also identified. Conclusions: This study is unique because the photos originated from the home setting, were in the context of SUID and safe sleep, and were obtained without researcher interference. Despite study limitations, the commonality of prone sleeping, foreign objects, and the use of both infant sleep and monitoring devices (ie, overall inconsistency regarding AAP safe sleep guidelines) sets the stage for future investigation regarding parental barriers to practicing safe infant sleep and has implications for policy makers, clinicians, and researchers. ", doi="10.2196/54610", url="https://pediatrics.jmir.org/2024/1/e54610" } @Article{info:doi/10.2196/52468, author="Neves, Silveira Gabriela and Reis, Nogueira Zilma Silveira and Romanelli, Roberta and Batchelor, James", title="Assessment of Skin Maturity by LED Light at Birth and Its Association With Lung Maturity: Clinical Trial Secondary Outcomes", journal="JMIR Biomed Eng", year="2023", month="Dec", day="25", volume="8", pages="e52468", keywords="newborn infant", keywords="prematurity", keywords="neonatal respiratory distress syndrome", keywords="skin physiological phenomena", keywords="photometer", keywords="gestational age", abstract="Background: Clinicians face barriers when assessing lung maturity at birth due to global inequalities. Still, strategies for testing based solely on gestational age to predict the likelihood of respiratory distress syndrome (RDS) do not offer a comprehensive approach to addressing the challenge of uncertain outcomes. We hypothesize that a noninvasive assessment of skin maturity may indicate lung maturity. Objective: This study aimed to assess the association between a newborn's skin maturity and RDS occurrence. Methods: We conducted a case-control nested in a prospective cohort study, a secondary endpoint of a multicenter clinical trial. The study was carried out in 5 Brazilian urban reference centers for highly complex perinatal care. Of 781 newborns from the cohort study, 640 were selected for the case-control analysis. Newborns with RDS formed the case group and newborns without RDS were the controls. All newborns with other diseases exhibiting respiratory manifestations were excluded. Skin maturity was assessed from the newborn's skin over the sole by an optical device that acquired a reflection signal through an LED sensor. The device, previously validated, measured and recorded skin reflectance. Clinical data related to respiratory outcomes were gathered from medical records during the 72-hour follow-up of the newborn, or until discharge or death, whichever occurred first. The main outcome measure was the association between skin reflectance and RDS using univariate and multivariate binary logistic regression. Additionally, we assessed the connection between skin reflectance and factors such as neonatal intensive care unit (NICU) admission and the need for ventilatory support. Results: Out of 604 newborns, 470 (73.4\%) were from the RDS group and 170 (26.6\%) were from the control group. According to comparisons between the groups, newborns with RDS had a younger gestational age (31.6 vs 39.1 weeks, P<.001) and birth weight (1491 vs 3121 grams, P<.001) than controls. Skin reflectance was associated with RDS (odds ratio [OR] 0.982, 95\% CI 0.979-0.985, R2=0.632, P<.001). This relationship remained significant when adjusted by the cofactors antenatal corticosteroid and birth weight (OR 0.994, 95\% CI 0.990-0.998, R2=0.843, P<.001). Secondary outcomes also showed differences in skin reflectance. The mean difference was 0.219 (95\% CI 0.200-0.238) between newborns that required ventilatory support versus those that did not and 0.223 (95\% CI 0.205-0.241) between newborns that required NICU admission versus those that did not. Skin reflectance was associated with ventilatory support (OR 0.996, 95\% CI 0.992-0.999, R2=0.814, P=.01) and with NICU admission (OR 0.994, 95\% CI 0.990-0.998, R2=0.867, P=.004). Conclusions: Our findings present a potential marker of lung immaturity at birth using the indirect method of skin assessment. Using the RDS clinical condition and a medical device, this study demonstrated the synchrony between lung and skin maturity. Trial Registration: Registro Brasileiro de Ensaios Cl{\'i}nicos (ReBEC) RBR-3f5bm5; https://tinyurl.com/9fb7zrdb International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2018-027442 ", doi="10.2196/52468", url="https://biomedeng.jmir.org/2023/1/e52468", url="http://www.ncbi.nlm.nih.gov/pubmed/38875690" } @Article{info:doi/10.2196/40856, author="Gutierrez, Robert and McCrady, Allison and Masterson, Chelsea and Tolman, Sarah and Boukhechba, Mehdi and Barnes, Laura and Blemker, Silvia and Scharf, Rebecca", title="Upper Extremity Examination for Neuromuscular Diseases (U-EXTEND): Protocol for a Multimodal Feasibility Study", journal="JMIR Res Protoc", year="2022", month="Oct", day="27", volume="11", number="10", pages="e40856", keywords="mHealth", keywords="ubiquitous computing", keywords="neuromuscular disorders", keywords="inertial measurement unit", keywords="motor function", keywords="specific torque", keywords="cross-sectional area", keywords="echogenicity", abstract="Background: Neuromuscular diseases, such as spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD), may result in the loss of motor movements, respiratory failure, and early mortality in young children and in adulthood. With novel treatments now available, new evaluation methods are needed to assess progress that is not currently captured in existing motor scale tests. Objective: With our feasibility study, our interdisciplinary team of investigators aims to develop a novel, multimodal paradigm of measuring motor function in children with neuromuscular diseases that will revolutionize the way that clinical trial end points are measured, thereby accelerating the pipeline of new treatments for childhood neuromuscular diseases. Through the Upper Extremity Examination for Neuromuscular Diseases (U-EXTEND) study, we hypothesize that the novel objective measures of upper extremity muscle structure and function proposed herein will be able to capture small changes and differences in function that cannot be measured with current clinical metrics. Methods: U-EXTEND introduces a novel paradigm in which concrete, quantitative measures are used to assess motor function in patients with SMA and DMD. Aim 1 will focus on the use of ultrasound techniques to study muscle size, quality, and function, specifically isolating the biceps and pronator muscles of the upper extremities for follow-ups over time. To achieve this, clinical investigators will extract a set of measurements related to muscle structure, quality, and function by using ultrasound imaging and handheld dynamometry. Aim 2 will focus on leveraging wearable wireless sensor technology to capture motion data as participants perform activities of daily living. Measurement data will be examined and compared to those from a healthy cohort, and a motor function score will be calculated. Results: Data collection for both aims began in January 2021. As of July 2022, we have enrolled 44 participants (9 with SMA, 20 with DMD, and 15 healthy participants). We expect the initial results to be published in summer 2022. Conclusions: We hypothesize that by applying the described tools and techniques for measuring muscle structure and upper extremity function, we will have created a system for the precise quantification of changes in motor function among patients with neuromuscular diseases. Our study will allow us to track the minimal clinically important difference over time to assess progress in novel treatments. By comparing the muscle scores and functional scores over multiple visits, we will be able to detect small changes in both the ability of the participants to perform the functional tasks and their intrinsic muscle properties. International Registered Report Identifier (IRRID): DERR1-10.2196/40856 ", doi="10.2196/40856", url="https://www.researchprotocols.org/2022/10/e40856", url="http://www.ncbi.nlm.nih.gov/pubmed/36301603" } @Article{info:doi/10.2196/34969, author="Gustafsson, M. Berit and Korhonen, Laura", title="A Multiprofessional and Intersectoral Working Model to Detect and Support Preschool Children With Neurodevelopmental Difficulties (PLUSS Model): Protocol for an Evaluation Study", journal="JMIR Res Protoc", year="2022", month="Jun", day="15", volume="11", number="6", pages="e34969", keywords="early detection", keywords="early intervention", keywords="preschool children", keywords="multiprofessional", keywords="neurodevelopmental difficulties", keywords="parental support", keywords="preschool support", keywords="mental health", keywords="neurological", keywords="behavioural", keywords="emotional", keywords="paediatrics", keywords="pediatrics", keywords="parenting", keywords="children", keywords="neurodevelopmental", keywords="developmental", abstract="Background: Neurodevelopmental difficulties with various emotional and behavioral symptoms increase the risk of mental health problems later in life. Although we know that early detection and interventions are effective, there is a lack of intersectoral, integrative, and evidence-based working models to provide these services for preschool children and their parents. PLUSS (Psykisk h{\"a}lsa L{\"a}rande Utveckling Samverkan kring Sm{\aa} barn; English translation: mental health, learning, development, collaboration around preschool children) is a collaborative ``one way in'' model involving parents, health care providers, preschools, social services, and researchers. PLUSS provides coordinated services to screen, evaluate, and support toddlers with neurodevelopmental problems. It also offers parental interventions and education for preschool teachers. Objective: The model will be studied in a research project that aims to investigate (1) using a quasi-experimental study on longitudinal trajectories of neurodevelopmental difficulties and ability to function among participating preschoolers, (2) user satisfaction, and (3) implementation of the model and its effectiveness. The long-term goal is to provide evidence-based, coordinated services to reduce problems related to neurodevelopmental difficulties among preschool children and promote well-being and functioning in everyday life. Methods: The population of interest is children aged 1.5-5 years, whom the child health care nurse refers for further assessment due to suspected neurodevelopmental problems. Data are collected using questionnaires and semistructured interviews. Measures include sociodemographic data, longitudinal data on neurodevelopmental problems, parental well-being and satisfaction, the effectiveness of parental and preschool teacher training and implementation of the model, and fostered multisectoral collaborations. Data will be analyzed with qualitative and quantitative methods. Results: The PLUSS model has been approved by the National Ethics Review Board (2019--04839). This study was supported by FUTURUM grants 910161 and 910441. Data collection started in April 2019, with the data collection period planned to end in May 2024. Conclusions: PLUSS is an integrative working model with multiprofessional competence and intersectoral collaboration capacity to help preschool children with neurodevelopmental problems and their parents. It will be studied using quasi-experimental cross-sectional and longitudinal study designs. Data will be collected from parents, health care providers, and preschool teachers, and will be analyzed using quantitative and qualitative methods. The study will run in one Swedish county, and generalizability needs to be studied separately. Loss of follow-up could impact the longitudinal analysis. Trial Registration: ClinicalTrials.gov NCT04815889; https://clinicaltrials.gov/ct2/show/NCT04815889 International Registered Report Identifier (IRRID): DERR1-10.2196/34969 ", doi="10.2196/34969", url="https://www.researchprotocols.org/2022/6/e34969", url="http://www.ncbi.nlm.nih.gov/pubmed/35704376" } @Article{info:doi/10.2196/37927, author="Bello-Manga, Halima and Haliru, Lawal and Ahmed, Abdulkareem Kudrat and Tabari, Musa Abdulkadir and Farouk, Usman Bilkisu and Bahago, Yimi Gloria and Kazaure, Shuaibu Aisha and Muhammad, Sani Abdulrasheed and Gwarzo, Abubakar Samira and Baumann, A. Ana and DeBaun, R. Michael and King, A. Allison", title="Primary Prevention of Stroke in Children With Sickle Cell Anemia in Nigeria: Protocol for a Mixed Methods Implementation Study in a Community Hospital", journal="JMIR Res Protoc", year="2022", month="Jun", day="13", volume="11", number="6", pages="e37927", keywords="sickle cell anemia", keywords="stroke prevention", keywords="transcranial Doppler ultrasonography", abstract="Background: In Nigeria, approximately 150,000 children with sickle cell anemia (SCA) are born annually, accounting for more than half of all SCA births worldwide. Without intervention, about 11\% of children with SCA will develop a stroke before their 20th birthday. Evidence-based practices for primary stroke prevention include screening for abnormal transcranial Doppler (TCD) measurements coupled with regular blood transfusion therapy for at least one year, followed by hydroxyurea (HU) therapy indefinitely. In high-resource countries, this strategy contributes to a 92\% decrease in stroke incidence rates. In 2016, as part of a capacity building objective of the Stroke Prevention Trial in Nigeria (1R01NS094041: SPRING), TCD screening was adopted as standard care at Barau Dikko Teaching Hospital in Kaduna. However, with just 70 radiologists and only 3 certified in TCD screening in the state, just 5.49\% (1101/20,040) of eligible children with SCA were screened. Thus, there is a need to explore alternate implementation strategies to ensure children with SCA receive standard care TCD screening to decrease stroke incidence. Objective: This protocol describes a study to create a stroke prevention program in a community hospital in Kaduna through task shifting TCD screening to nurses and training medical officers to initiate and monitor HU utilization for stroke prevention. Methods: This study will be conducted at 2 sites (teaching hospital and community hospital) over a period of 3 years (November 2020 to November 2023), in 3 phases using both quasi-experimental and effectiveness-implementation study designs. In the needs assessment phase, focus groups and structured interviews will be conducted with health care providers and hospital administrators to identify barriers and facilitators to evidence-based stroke prevention practices. Results from the needs assessment will inform intervention strategies and a process plan to fit the needs of the community hospital. In the capacity building phase, nurses and medical officers at the community hospital will be trained on TCD screening and HU initiation and monitoring. In the implementation phase, children with SCA aged 2-16 years will be recruited into a nonrandomized single-arm prospective trial to determine the feasibility of initiating a task-shifted stroke prevention program by recording recruitment, retention, and adherence rates. The Reach and Effectiveness components of the RE-AIM (Reach, Effectiveness, Adoption, Implementation and Maintenance) framework will be used to evaluate implementation outcomes between the community and teaching hospitals. Results: The needs assessment phase of the study was completed in February 2021. Manuscript on findings is currently in preparation. Capacity building is ongoing with TCD training and sickle cell disease and stroke education sessions for nurses and doctors in the community hospital. Recruitment for the implementation trial is expected to commence in July 2022. Conclusions: This study proposes a structured, theory-driven approach to create a stroke prevention program in a community hospital in Kaduna, Nigeria, to decrease stroke incidence among children with SCA. Results will provide preliminary data for a definitive randomized clinical trial in implementation science. International Registered Report Identifier (IRRID): PRR1-10.2196/37927 ", doi="10.2196/37927", url="https://www.researchprotocols.org/2022/6/e37927", url="http://www.ncbi.nlm.nih.gov/pubmed/35700018" } @Article{info:doi/10.2196/37002, author="Moreno, P. Jennette and Dadabhoy, Hafza and Musaad, Salma and Baranowski, Tom and Thompson, Debbe and Alfano, A. Candice and Crowley, J. Stephanie", title="Evaluation of a Circadian Rhythm and Sleep-Focused Mobile Health Intervention for the Prevention of Accelerated Summer Weight Gain Among Elementary School--Age Children: Protocol for a Randomized Controlled Feasibility Study", journal="JMIR Res Protoc", year="2022", month="May", day="16", volume="11", number="5", pages="e37002", keywords="summer", keywords="circadian rhythms", keywords="sleep", keywords="child obesity", keywords="elementary school", abstract="Background: The i?rhythm project is a mobile health adaptation of interpersonal and social rhythm therapy designed to promote healthy sleep and behavioral rhythms among 5-8-year olds during summer for the prevention of accelerated summer weight gain. Objective: This pilot study will examine the feasibility, acceptability, and preliminary efficacy of the i?rhythm intervention. This will ensure that the research protocol and procedures work as desired and are acceptable to families in preparation for the fully powered randomized controlled trial. The proposed study will examine the willingness of participants to participate in the intervention and determine whether modifications to the intervention, procedures, and measures are needed before conducting a fully powered study. We will assess our ability to (1) recruit, consent, and retain participants; (2) deliver the intervention; (3) implement the study and assessment procedures; (4) assess the reliability of the proposed measures; and (5) assess the acceptability of the intervention and assessment protocol. Methods: This study will employ a single-blinded 2-group randomized control design (treatment and no-treatment control) with randomization occurring after baseline (Time 0) and 3 additional evaluation periods (postintervention [Time 1], and 9 months [Time 2] and 12 months after intervention [Time 3]). A sample of 40 parent-child dyads will be recruited. Results: This study was approved by the institutional review board of Baylor College of Medicine (H-47369). Recruitment began in March 2021. As of March 2022, data collection and recruitment are ongoing. Conclusions: This study will address the role of sleep and circadian rhythms in the prevention of accelerated summer weight gain and assess the intervention's effects on the long-term prevention of child obesity. Trial Registration: ClinicalTrials.gov NCT04445740; https://clinicaltrials.gov/ct2/show/NCT04445740. International Registered Report Identifier (IRRID): DERR1-10.2196/37002 ", doi="10.2196/37002", url="https://www.researchprotocols.org/2022/5/e37002", url="http://www.ncbi.nlm.nih.gov/pubmed/35576573" } @Article{info:doi/10.2196/31830, author="Varma, Maya and Washington, Peter and Chrisman, Brianna and Kline, Aaron and Leblanc, Emilie and Paskov, Kelley and Stockham, Nate and Jung, Jae-Yoon and Sun, Woo Min and Wall, P. Dennis", title="Identification of Social Engagement Indicators Associated With Autism Spectrum Disorder Using a Game-Based Mobile App: Comparative Study of Gaze Fixation and Visual Scanning Methods", journal="J Med Internet Res", year="2022", month="Feb", day="15", volume="24", number="2", pages="e31830", keywords="mobile health", keywords="autism spectrum disorder", keywords="social phenotyping", keywords="computer vision", keywords="gaze", keywords="mobile diagnostics", keywords="pattern recognition", keywords="autism", keywords="diagnostic", keywords="pattern", keywords="engagement", keywords="gaming", keywords="app", keywords="insight", keywords="vision", keywords="video", abstract="Background: Autism spectrum disorder (ASD) is a widespread neurodevelopmental condition with a range of potential causes and symptoms. Standard diagnostic mechanisms for ASD, which involve lengthy parent questionnaires and clinical observation, often result in long waiting times for results. Recent advances in computer vision and mobile technology hold potential for speeding up the diagnostic process by enabling computational analysis of behavioral and social impairments from home videos. Such techniques can improve objectivity and contribute quantitatively to the diagnostic process. Objective: In this work, we evaluate whether home videos collected from a game-based mobile app can be used to provide diagnostic insights into ASD. To the best of our knowledge, this is the first study attempting to identify potential social indicators of ASD from mobile phone videos without the use of eye-tracking hardware, manual annotations, and structured scenarios or clinical environments. Methods: Here, we used a mobile health app to collect over 11 hours of video footage depicting 95 children engaged in gameplay in a natural home environment. We used automated data set annotations to analyze two social indicators that have previously been shown to differ between children with ASD and their neurotypical (NT) peers: (1) gaze fixation patterns, which represent regions of an individual's visual focus and (2) visual scanning methods, which refer to the ways in which individuals scan their surrounding environment. We compared the gaze fixation and visual scanning methods used by children during a 90-second gameplay video to identify statistically significant differences between the 2 cohorts; we then trained a long short-term memory (LSTM) neural network to determine if gaze indicators could be predictive of ASD. Results: Our results show that gaze fixation patterns differ between the 2 cohorts; specifically, we could identify 1 statistically significant region of fixation (P<.001). In addition, we also demonstrate that there are unique visual scanning patterns that exist for individuals with ASD when compared to NT children (P<.001). A deep learning model trained on coarse gaze fixation annotations demonstrates mild predictive power in identifying ASD. Conclusions: Ultimately, our study demonstrates that heterogeneous video data sets collected from mobile devices hold potential for quantifying visual patterns and providing insights into ASD. We show the importance of automated labeling techniques in generating large-scale data sets while simultaneously preserving the privacy of participants, and we demonstrate that specific social engagement indicators associated with ASD can be identified and characterized using such data. ", doi="10.2196/31830", url="https://www.jmir.org/2022/2/e31830", url="http://www.ncbi.nlm.nih.gov/pubmed/35166683" } @Article{info:doi/10.2196/29857, author="Lee, Clement and Zhou, S. Melissa and Wang, R. Evelyn and Huber, Matthew and Lockwood, K. Katie and Parga, Joanna", title="Health Care Professional and Caregiver Attitudes Toward and Usage of Medical Podcasting: Questionnaire Study", journal="JMIR Pediatr Parent", year="2022", month="Feb", day="1", volume="5", number="1", pages="e29857", keywords="podcasts", keywords="social media", keywords="caregiver", keywords="parent", keywords="parenting", keywords="education", keywords="pediatrics", keywords="podcasting", keywords="patient education", abstract="Background: Podcasts are used increasingly in medicine. There is growing research into the role of podcasts in medical education, but the use of podcasting as a tool for pediatric parent/caregiver health education is largely unexplored. As parents/caregivers seek medical information online, an understanding of parental preferences is needed. Objective: We sought to explore health care professional and parent/caregiver awareness and views on podcasting as a health education tool. Methods: This survey study was conducted and distributed via in-person collection from parents/caregivers (?18 years old) in the waiting room of an academic pediatric primary care clinic, targeted social media promotion, and professional listservs for health care professionals in pediatrics. Statistical analysis included chi-square tests of independence between categorical variables. Results: In total, 125 health care professionals and 126 caregivers completed the survey. Of those surveyed, 81\% (101/125) of health care professionals and 55\% (69/126) of parents/caregivers listened to podcasts (P<.001). Health care professionals and parents/caregivers listed the same top 3 quality indicators for medical podcasts. Podcast listeners were more likely to have higher incomes and use professional websites for information. The survey elicited a variety of reasons for podcast nonengagement. Conclusions: Health care professionals appear to be more engaged in medical education podcasts than parents/caregivers. However, similar factors were valued when evaluating the quality of a pediatric podcast: accuracy, transparency, and credibility. Professional websites may be one avenue to increase podcast uptake. More needs to be done to explore the use of podcasts and digital media for medical information. ", doi="10.2196/29857", url="https://pediatrics.jmir.org/2022/1/e29857", url="http://www.ncbi.nlm.nih.gov/pubmed/35103616" } @Article{info:doi/10.2196/29656, author="Rossi, Silvia and Santini, Junior Silvano and Di Genova, Daniela and Maggi, Gianpaolo and Verrotti, Alberto and Farello, Giovanni and Romualdi, Roberta and Alisi, Anna and Tozzi, Eugenio Alberto and Balsano, Clara", title="Using the Social Robot NAO for Emotional Support to Children at a Pediatric Emergency Department: Randomized Clinical Trial", journal="J Med Internet Res", year="2022", month="Jan", day="13", volume="24", number="1", pages="e29656", keywords="children", keywords="emotional health", keywords="emergency department", keywords="social robots", keywords="anxiety", keywords="stress", abstract="Background: Social robots (SRs) have been used for improving anxiety in children in stressful clinical situations, such as during painful procedures. However, no studies have yet been performed to assess their effect in children while waiting for emergency room consultations. Objective: This study aims to assess the impact of SRs on managing stress in children waiting for an emergency room procedure through the assessment of salivary cortisol levels. Methods: This was an open randomized clinical trial in children attending a pediatric emergency department. Children accessing the emergency room were randomized to 1 of 3 groups: (1) playing with a NAO SR, (2) playing with a study nurse, or (3) waiting with parents. The salivary cortisol levels of all children were measured through a swab. Salivary cortisol levels before and after the intervention were compared in the 3 groups. We calculated the effect size of our interventions through the Cohen d-based effect size correlation (r). Results: A total of 109 children aged 3-10 years were enrolled in the study, and 94 (86.2\%) had complete data for the analyses. Salivary cortisol levels significantly decreased more in the group exposed to robot interaction than in the other two groups (r=0.75). Cortisol levels decreased more in girls (r=0.92) than in boys (r=0.57). Conclusions: SRs are efficacious in decreasing stress in children accessing the emergency room and may be considered a tool for improving emotional perceptions of children and their families in such a critical setting. Trial Registration: ClinicalTrials.gov NCT04627909; https://clinicaltrials.gov/ct2/show/study/NCT04627909 ", doi="10.2196/29656", url="https://www.jmir.org/2022/1/e29656", url="http://www.ncbi.nlm.nih.gov/pubmed/34854814" } @Article{info:doi/10.2196/32294, author="Young, J. William and Bover Manderski, T. Michelle and Ganz, Ollie and Delnevo, D. Cristine and Hrywna, Mary", title="Examining the Impact of Question Construction on Reporting of Sexual Identity: Survey Experiment Among Young Adults", journal="JMIR Public Health Surveill", year="2021", month="Dec", day="13", volume="7", number="12", pages="e32294", keywords="survey measurement", keywords="sexual identity", keywords="survey wording experiment", abstract="Background: Compared with heterosexuals, sexual minorities in the United States experience a higher incidence of negative physical and mental health outcomes. However, a variety of measurement challenges limit researchers' ability to conduct meaningful survey research to understand these disparities. Despite the prevalence of additional identities, many national health surveys only offer respondents 3 substantive options for reporting their sexual identities (straight/heterosexual, gay or lesbian, and bisexual), which could lead to measurement error via misreporting and item nonresponse. Objective: This study compared the traditional 3-option approach to measuring sexual identity with an expanded approach that offered respondents 5 additional options. Methods: An online survey experiment conducted among New Jersey residents between March and June 2021 randomly assigned 1254 young adults (ages 18-21) to answer either the 3-response measure of sexual identity or the expanded item. Response distributions for each measure were compared as were the odds of item nonresponse. Results: The expanded version of the question appeared to result in more accurate reporting among some subgroups and induced less item nonresponse; 12\% (77/642) of respondents in the expanded version selected a response that was not available in the shorter version. Females answering the expanded item were less likely to identify as gay or lesbian (2.1\% [10/467] vs. 6.6\% [30/457]). Females and Non-Hispanic Whites were slightly more likely to skip the shorter version than the longer version (1.1\% [5/457 for females and 3/264 for Non-Hispanic Whites] vs. 0\% [0/467 for females and 0/277 for Non-Hispanic Whites]). About 5\% (32/642) of respondents answering the longer item were unsure of their sexual identity (a similar option was not available in the shorter version). Compared with respondents answering the longer version of the question, those answering the shorter version had substantially greater odds of skipping the question altogether (odds ratio 9.57, 95\% CI 1.21-75.74; P=.03). Conclusions: Results favor the use of a longer, more detailed approach to measuring sexual identity in epidemiological research. Such a measure will likely allow researchers to produce more accurate estimates of health behaviors and outcomes among sexual minorities. ", doi="10.2196/32294", url="https://publichealth.jmir.org/2021/12/e32294", url="http://www.ncbi.nlm.nih.gov/pubmed/34898444" } @Article{info:doi/10.2196/29755, author="Ginsburg, Sarah Amy and Kinshella, Woo Mai-Lei and Naanyu, Violet and Rigg, Jessica and Chomba, Dorothy and Coleman, Jesse and Hwang, Bella and Ochieng, Roseline and Ansermino, Mark J. and Macharia, M. William", title="Multiparameter Continuous Physiological Monitoring Technologies in Neonates Among Health Care Providers and Caregivers at a Private Tertiary Hospital in Nairobi, Kenya: Feasibility, Usability, and Acceptability Study", journal="J Med Internet Res", year="2021", month="Oct", day="28", volume="23", number="10", pages="e29755", keywords="infants", keywords="Africa", keywords="medical technology design", keywords="user perspectives", keywords="in-depth interviews", keywords="direct observations", abstract="Background: Continuous physiological monitoring technologies are important for strengthening hospital care for neonates, particularly in resource-constrained settings, and understanding user perspectives is critical for informing medical technology design, development, and optimization. Objective: This study aims to assess the feasibility, usability, and acceptability of 2 noninvasive, multiparameter, continuous physiological monitoring technologies for use in neonates in an African health care setting. Methods: We assessed 2 investigational technologies from EarlySense and Sibel, compared with the reference Masimo Rad-97 technology through in-depth interviews and direct observations. A purposive sample of health care administrators, health care providers, and caregivers at Aga Khan University Hospital, a tertiary, private hospital in Nairobi, Kenya, were included. Data were analyzed using a thematic approach in NVivo 12 software. Results: Between July and August 2020, we interviewed 12 health care providers, 5 health care administrators, and 10 caregivers and observed the monitoring of 12 neonates. Staffing and maintenance of training in neonatal units are important feasibility considerations, and simple training requirements support the feasibility of the investigational technologies. Key usability characteristics included ease of use, wireless features, and reduced number of attachments connecting the neonate to the monitoring technology, which health care providers considered to increase the efficiency of care. The main factors supporting acceptability included caregiver-highlighted perceptions of neonate comfort and health care respondent technology familiarity. Concerns about the side effects of wireless connections, electromagnetic fields, and mistrust of unfamiliar technologies have emerged as possible acceptability barriers to investigational technologies. Conclusions: Overall, respondents considered the investigational technologies feasible, usable, and acceptable for the care of neonates at this health care facility. Our findings highlight the potential of different multiparameter continuous physiological monitoring technologies for use in different neonatal care settings. Simple and user-friendly technologies may help to bridge gaps in current care where there are many neonates; however, challenges in maintaining training and ensuring feasibility within resource-constrained health care settings warrant further research. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2019-035184 ", doi="10.2196/29755", url="https://www.jmir.org/2021/10/e29755", url="http://www.ncbi.nlm.nih.gov/pubmed/34709194" } @Article{info:doi/10.2196/28345, author="Schmucker, Michael and Haag, Martin", title="Automated Size Recognition in Pediatric Emergencies Using Machine Learning and Augmented Reality: Within-Group Comparative Study", journal="JMIR Form Res", year="2021", month="Sep", day="20", volume="5", number="9", pages="e28345", keywords="resuscitation", keywords="emergency medicine", keywords="mobile applications", keywords="mobile phone", keywords="user-computer interface", keywords="augmented reality", keywords="machine learning", abstract="Background: Pediatric emergencies involving children are rare events, and the experience of emergency physicians and the results of such emergencies are accordingly poor. Anatomical peculiarities and individual adjustments make treatment during pediatric emergency susceptible to error. Critical mistakes especially occur in the calculation of weight-based drug doses. Accordingly, the need for a ubiquitous assistance service that can, for example, automate dose calculation is high. However, few approaches exist due to the complexity of the problem. Objective: Technically, an assistance service is possible, among other approaches, with an app that uses a depth camera that is integrated in smartphones or head-mounted displays to provide a 3D understanding of the environment. The goal of this study was to automate this technology as much as possible to develop and statistically evaluate an assistance service that does not have significantly worse measurement performance than an emergency ruler (the state of the art). Methods: An assistance service was developed that uses machine learning to recognize patients and then automatically determines their size. Based on the size, the weight is automatically derived, and the dosages are calculated and presented to the physician. To evaluate the app, a small within-group design study was conducted with 17 children, who were each measured with the app installed on a smartphone with a built-in depth camera and a state-of-the-art emergency ruler. Results: According to the statistical results (one-sample t test; P=.42; $\alpha$=.05), there is no significant difference between the measurement performance of the app and an emergency ruler under the test conditions (indoor, daylight). The newly developed measurement method is thus not technically inferior to the established one in terms of accuracy. Conclusions: An assistance service with an integrated augmented reality emergency ruler is technically possible, although some groundwork is still needed. The results of this study clear the way for further research, for example, usability testing. ", doi="10.2196/28345", url="https://formative.jmir.org/2021/9/e28345", url="http://www.ncbi.nlm.nih.gov/pubmed/34542416" } @Article{info:doi/10.2196/25991, author="Kowalski, L. Rebecca and Lee, Laura and Spaeder, C. Michael and Moorman, Randall J. and Keim-Malpass, Jessica", title="Accuracy and Monitoring of Pediatric Early Warning Score (PEWS) Scores Prior to Emergent Pediatric Intensive Care Unit (ICU) Transfer: Retrospective Analysis", journal="JMIR Pediatr Parent", year="2021", month="Feb", day="22", volume="4", number="1", pages="e25991", keywords="pediatric intensive care unit", keywords="cardiorespiratory monitoring", keywords="hospital transfer", keywords="clinical deterioration", keywords="monitoring", keywords="ICU", keywords="intensive care unit", keywords="pediatric", keywords="retrospective", keywords="detection", keywords="deterioration", keywords="child", keywords="accuracy", keywords="cohort", abstract="Background: Current approaches to early detection of clinical deterioration in children have relied on intermittent track-and-trigger warning scores such as the Pediatric Early Warning Score (PEWS) that rely on periodic assessment and vital sign entry. There are limited data on the utility of these scores prior to events of decompensation leading to pediatric intensive care unit (PICU) transfer. Objective: The purpose of our study was to determine the accuracy of recorded PEWS scores, assess clinical reasons for transfer, and describe the monitoring practices prior to PICU transfer involving acute decompensation. Methods: We conducted a retrospective cohort study of patients ?21 years of age transferred emergently from the acute care pediatric floor to the PICU due to clinical deterioration over an 8-year period. Clinical charts were abstracted to (1) determine the clinical reason for transfer, (2) quantify the frequency of physiological monitoring prior to transfer, and (3) assess the timing and accuracy of the PEWS scores 24 hours prior to transfer. Results: During the 8-year period, 72 children and adolescents had an emergent PICU transfer due to clinical deterioration, most often due to acute respiratory distress. Only 35\% (25/72) of the sample was on continuous telemetry or pulse oximetry monitoring prior to the transfer event, and 47\% (34/72) had at least one incorrectly documented PEWS score in the 24 hours prior to the event, with a score underreporting the actual severity of illness. Conclusions: This analysis provides support for the routine assessment of clinical deterioration and advocates for more research focused on the use and utility of continuous cardiorespiratory monitoring for patients at risk for emergent transfer. ", doi="10.2196/25991", url="https://pediatrics.jmir.org/2021/1/e25991", url="http://www.ncbi.nlm.nih.gov/pubmed/33547772" } @Article{info:doi/10.2196/24061, author="Weijers, Miriam and Bastiaenen, Caroline and Feron, Frans and Schr{\"o}der, Kay", title="Designing a Personalized Health Dashboard: Interdisciplinary and Participatory Approach", journal="JMIR Form Res", year="2021", month="Feb", day="9", volume="5", number="2", pages="e24061", keywords="visualization design model", keywords="dashboard", keywords="evaluation", keywords="personalized health care", keywords="International Classification of Functioning, Disability and Health (ICF)", keywords="patient access to records", keywords="human--computer interaction", keywords="health information visualization", abstract="Background: Within the Dutch Child Health Care (CHC), an online tool (360{\textdegree} CHILD-profile) is designed to enhance prevention and transformation toward personalized health care. From a personalized preventive perspective, it is of fundamental importance to timely identify children with emerging health problems interrelated to multiple health determinants. While digitalization of children's health data is now realized, the accessibility of data remains a major challenge for CHC professionals, let alone for parents/youth. Therefore, the idea was initiated from CHC practice to develop a novel approach to make relevant information accessible at a glance. Objective: This paper describes the stepwise development of a dashboard, as an example of using a design model to achieve visualization of a comprehensive overview of theoretically structured health data. Methods: Developmental process is based on the nested design model with involvement of relevant stakeholders in a real-life context. This model considers immediate upstream validation within 4 cascading design levels: Domain Problem and Data Characterization, Operation and Data Type Abstraction, Visual Encoding and Interaction Design, and Algorithm Design. This model also includes impact-oriented downstream validation, which can be initiated after delivering the prototype. Results: A comprehensible 360{\textdegree} CHILD-profile is developed: an online accessible visualization of CHC data based on the theoretical concept of the International Classification of Functioning, Disability and Health. This dashboard provides caregivers and parents/youth with a holistic view on children's health and ``entry points'' for preventive, individualized health plans. Conclusions: Describing this developmental process offers guidance on how to utilize the nested design model within a health care context. ", doi="10.2196/24061", url="https://formative.jmir.org/2021/2/e24061", url="http://www.ncbi.nlm.nih.gov/pubmed/33560229" } @Article{info:doi/10.2196/20172, author="Tanaka, Masanori and Saito, Manabu and Takahashi, Michio and Adachi, Masaki and Nakamura, Kazuhiko", title="Interformat Reliability of Web-Based Parent-Rated Questionnaires for Assessing Neurodevelopmental Disorders Among Preschoolers: Cross-sectional Community Study", journal="JMIR Pediatr Parent", year="2021", month="Feb", day="4", volume="4", number="1", pages="e20172", keywords="neurodevelopmental disorders", keywords="web-based questionnaire", keywords="preschoolers", keywords="parents", keywords="interformat reliability", abstract="Background: Early detection and intervention for neurodevelopmental disorders are effective. Several types of paper questionnaires have been developed to assess these conditions in early childhood; however, the psychometric equivalence between the web-based and the paper versions of these questionnaires is unknown. Objective: This study examined the interformat reliability of the web-based parent-rated version of the Autism Spectrum Screening Questionnaire (ASSQ), Attention-Deficit/Hyperactivity Disorder Rating Scale (ADHD-RS), Developmental Coordination Disorder Questionnaire 2007 (DCDQ), and Strengths and Difficulties Questionnaire (SDQ) among Japanese preschoolers in a community developmental health check-up setting. Methods: A set of paper-based questionnaires were distributed for voluntary completion to parents of children aged 5 years. The package of the paper format questionnaires included the ASSQ, ADHD-RS, DCDQ, parent-reported SDQ (P-SDQ), and several additional demographic questions. Responses were received from 508 parents of children who agreed to participate in the study. After 3 months, 300 parents, who were among the initial responders, were randomly selected and asked to complete the web-based versions of these questionnaires. A total of 140 parents replied to the web-based format and were included as a final sample in this study. Results: We obtained the McDonald $\omega$ coefficients for both the web-based and paper formats of the ASSQ (web-based: $\omega$=.90; paper: $\omega$=.86), ADHD-RS total and subscales (web-based: $\omega$=.88-.94; paper: $\omega$=.87-.93), DCDQ total and subscales (web-based: $\omega$=.82-.94; paper: $\omega$=.74-.92), and P-SDQ total and subscales (web-based: $\omega$=.55-.81; paper: $\omega$=.52-.80). The intraclass correlation coefficients between the web-based and paper formats were all significant at the 99.9\% confidence level: ASSQ (r=0.66, P<.001); ADHD-RS total and subscales (r=0.66-0.74, P<.001); DCDQ total and subscales (r=0.66-0.71, P<.001); P-SDQ Total Difficulties and subscales (r=0.55-0.73, P<.001). There were no significant differences between the web-based and paper formats for total mean score of the ASSQ (P=.76), total (P=.12) and subscale (P=.11-.47) mean scores of DCDQ, and the P-SDQ Total Difficulties mean score (P=.20) and mean subscale scores (P=.28-.79). Although significant differences were found between the web-based and paper formats for mean ADHD-RS scores (total: t132=2.83, P=.005; Inattention subscale: t133=2.15, P=.03; Hyperactivity/Impulsivity subscale: t133=3.21, P=.002), the effect sizes were small (Cohen d=0.18-0.22). Conclusions: These results suggest that the web-based versions of the ASSQ, ADHD-RS, DCDQ, and P-SDQ were equivalent, with the same level of internal consistency and intrarater reliability as the paper versions, indicating the applicability of the web-based versions of these questionnaires for assessing neurodevelopmental disorders. ", doi="10.2196/20172", url="https://pediatrics.jmir.org/2021/1/e20172", url="http://www.ncbi.nlm.nih.gov/pubmed/33455899" } @Article{info:doi/10.2196/25018, author="Sobolev, Michael and Vitale, Rachel and Wen, Hongyi and Kizer, James and Leeman, Robert and Pollak, P. J. and Baumel, Amit and Vadhan, P. Nehal and Estrin, Deborah and Muench, Frederick", title="The Digital Marshmallow Test (DMT) Diagnostic and Monitoring Mobile Health App for Impulsive Behavior: Development and Validation Study", journal="JMIR Mhealth Uhealth", year="2021", month="Jan", day="22", volume="9", number="1", pages="e25018", keywords="impulse control", keywords="impulsivity", keywords="self-regulation", keywords="self-control", keywords="mobile health", keywords="mHealth", keywords="ecological momentary assessment", keywords="active task", keywords="ResearchKit", abstract="Background: The classic Marshmallow Test, where children were offered a choice between one small but immediate reward (eg, one marshmallow) or a larger reward (eg, two marshmallows) if they waited for a period of time, instigated a wealth of research on the relationships among impulsive responding, self-regulation, and clinical and life outcomes. Impulsivity is a hallmark feature of self-regulation failures that lead to poor health decisions and outcomes, making understanding and treating impulsivity one of the most important constructs to tackle in building a culture of health. Despite a large literature base, impulsivity measurement remains difficult due to the multidimensional nature of the construct and limited methods of assessment in daily life. Mobile devices and the rise of mobile health (mHealth) have changed our ability to assess and intervene with individuals remotely, providing an avenue for ambulatory diagnostic testing and interventions. Longitudinal studies with mobile devices can further help to understand impulsive behaviors and variation in state impulsivity in daily life. Objective: The aim of this study was to develop and validate an impulsivity mHealth diagnostics and monitoring app called Digital Marshmallow Test (DMT) using both the Apple and Android platforms for widespread dissemination to researchers, clinicians, and the general public. Methods: The DMT app was developed using Apple's ResearchKit (iOS) and Android's ResearchStack open source frameworks for developing health research study apps. The DMT app consists of three main modules: self-report, ecological momentary assessment, and active behavioral and cognitive tasks. We conducted a study with a 21-day assessment period (N=116 participants) to validate the novel measures of the DMT app. Results: We used a semantic differential scale to develop self-report trait and momentary state measures of impulsivity as part of the DMT app. We identified three state factors (inefficient, thrill seeking, and intentional) that correlated highly with established measures of impulsivity. We further leveraged momentary semantic differential questions to examine intraindividual variability, the effect of daily life, and the contextual effect of mood on state impulsivity and daily impulsive behaviors. Our results indicated validation of the self-report sematic differential and related results, and of the mobile behavioral tasks, including the Balloon Analogue Risk Task and Go-No-Go task, with relatively low validity of the mobile Delay Discounting task. We discuss the design implications of these results to mHealth research. Conclusions: This study demonstrates the potential for assessing different facets of trait and state impulsivity during everyday life and in clinical settings using the DMT mobile app. The DMT app can be further used to enhance our understanding of the individual facets that underlie impulsive behaviors, as well as providing a promising avenue for digital interventions. Trial Registration: ClinicalTrials.gov NCT03006653; https://www.clinicaltrials.gov/ct2/show/NCT03006653 ", doi="10.2196/25018", url="http://mhealth.jmir.org/2021/1/e25018/", url="http://www.ncbi.nlm.nih.gov/pubmed/33480854" } @Article{info:doi/10.2196/18808, author="Espinoza, Juan and Crown, Kelly and Kulkarni, Omkar", title="A Guide to Chatbots for COVID-19 Screening at Pediatric Health Care Facilities", journal="JMIR Public Health Surveill", year="2020", month="Apr", day="30", volume="6", number="2", pages="e18808", keywords="chatbots", keywords="COVID-19: pediatrics", keywords="digital health", keywords="screening", doi="10.2196/18808", url="http://publichealth.jmir.org/2020/2/e18808/", url="http://www.ncbi.nlm.nih.gov/pubmed/32325425" } @Article{info:doi/10.2196/16204, author="Chou, H. Joseph and Roumiantsev, Sergei and Singh, Rachana", title="PediTools Electronic Growth Chart Calculators: Applications in Clinical Care, Research, and Quality Improvement", journal="J Med Internet Res", year="2020", month="Jan", day="30", volume="22", number="1", pages="e16204", keywords="growth charts", keywords="pediatrics", keywords="infant, newborn", keywords="infant, premature", keywords="failure to thrive", keywords="internet", keywords="software", abstract="Background: Parameterization of pediatric growth charts allows precise quantitation of growth metrics that would be difficult or impossible with traditional paper charts. However, limited availability of growth chart calculators for use by clinicians and clinical researchers currently restricts broader application. Objective: The aim of this study was to assess the deployment of electronic calculators for growth charts using the lambda-mu-sigma (LMS) parameterization method, with examples of their utilization for patient care delivery, clinical research, and quality improvement projects. Methods: The publicly accessible PediTools website of clinical calculators was developed to allow LMS-based calculations on anthropometric measurements of individual patients. Similar calculations were applied in a retrospective study of a population of patients from 7 Massachusetts neonatal intensive care units (NICUs) to compare interhospital growth outcomes (change in weight Z-score from birth to discharge [?Z weight]) and their association with gestational age at birth. At 1 hospital, a bundle of quality improvement interventions targeting improved growth was implemented, and the outcomes were assessed prospectively via monitoring of ?Z weight pre- and postintervention. Results: The PediTools website was launched in January 2012, and as of June 2019, it received over 500,000 page views per month, with users from over 21 countries. A retrospective analysis of 7975 patients at 7 Massachusetts NICUs, born between 2006 and 2011, at 23 to 34 completed weeks gestation identified an overall ?Z weight from birth to discharge of --0.81 (P<.001). However, the degree of ?Z weight differed significantly by hospital, ranging from --0.56 to --1.05 (P<.001). Also identified was the association between inferior growth outcomes and lower gestational age at birth, as well as that the degree of association between ?Z weight and gestation at birth also differed by hospital. At 1 hospital, implementing a bundle of interventions targeting growth resulted in a significant and sustained reduction in loss of weight Z-score from birth to discharge. Conclusions: LMS-based anthropometric measurement calculation tools on a public website have been widely utilized. Application in a retrospective clinical study on a large dataset demonstrated inferior growth at lower gestational age and interhospital variation in growth outcomes. Change in weight Z-score has potential utility as an outcome measure for monitoring clinical quality improvement. We also announce the release of open-source computer code written in R to allow other clinicians and clinical researchers to easily perform similar analyses. ", doi="10.2196/16204", url="https://www.jmir.org/2020/1/e16204", url="http://www.ncbi.nlm.nih.gov/pubmed/32012066" } @Article{info:doi/10.2196/14429, author="Ravindran, Vijay and Osgood, Monica and Sazawal, Vibha and Solorzano, Rita and Turnacioglu, Sinan", title="Virtual Reality Support for Joint Attention Using the Floreo Joint Attention Module: Usability and Feasibility Pilot Study", journal="JMIR Pediatr Parent", year="2019", month="Sep", day="30", volume="2", number="2", pages="e14429", keywords="autism spectrum disorder", keywords="interpersonal skills", keywords="virtual reality, instructional", abstract="Background: Advances in virtual reality (VR) technology offer new opportunities to design supports for the core behaviors associated with autism spectrum disorder (ASD) that promote progress toward optimal outcomes. Floreo has developed a novel mobile VR platform that pairs a user receiving instruction on target skills with an adult monitor. Objective: The primary objective of this pilot study was to explore the feasibility of using Floreo's Joint Attention Module in school-aged children with autism in a special education setting. A secondary objective was to explore a novel joint attention measure designed for use with school-aged children and to observe whether there was a suggestion of change in joint attention skills from preintervention to postintervention. Methods: A total of 12 participants (age range: 9 to 16 years) received training with the Joint Attention Module for 14 sessions over 5 weeks. Results: No serious side effects were reported, and no participants dropped out of the study because of undesirable side effects. On the basis of monitor data, 95.4\% (126/132) of the time participants tolerated the headset, 95.4\% (126/132) of the time participants seemed to enjoy using Floreo's platform, and 95.5\% (128/134) of the time the VR experience was reported as valuable. In addition, scoring of the joint attention measure suggested a positive change in participant skills related to the total number of interactions, use of eye contact, and initiation of interactions. Conclusions: The study results suggest that Floreo's Joint Attention Module is safe and well tolerated by students with ASD, and preliminary data also suggest that its use is related to improvements in fundamental joint attention skills. ", doi="10.2196/14429", url="http://pediatrics.jmir.org/2019/2/e14429/", url="http://www.ncbi.nlm.nih.gov/pubmed/31573921" }