https://pediatrics.jmir.org/issue/feed 2025-01-03T09:30:05-05:00 JMIR Publications editor@jmir.org Open Journal Systems Unless stated otherwise, all articles are open-access distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work ("first published in the Journal of Medical Internet Research...") is properly cited with original URL and bibliographic citation information. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. Improving pediatric and adolescent health outcomes and empowering and educating parents. https://pediatrics.jmir.org/2026/1/e78937 2026-02-03T16:30:09-05:00 Rawan AlSaad Raghad Burjaq Majid AlAbdulla Alaa Abd-alrazaq Javaid Sheikh Rajat Thomas

Background: Postpartum maternal mental health (MMH) symptoms, including depression, anxiety, and childbirth-related posttraumatic stress disorder (CBPTSD), are known to influence infant sleep trajectories. While previous research has examined their individual and combined associations, the predictive utility of these MMH symptoms for early identification of infant sleep problems through machine learning remains understudied. Objective: This study aimed to examine whether postpartum MMH measures can predict infant sleep outcomes during the first year of life. The analysis focused on two clinically relevant sleep indicators: (1) nocturnal sleep duration and (2) night awakening frequency. Methods: A total of 409 mother-infant dyads were included in the study. Predictor variables comprised postpartum MMH symptoms assessed between 3 and 12 months postpartum, along with sociodemographic characteristics of mothers and infants. MMH symptoms were measured using three validated instruments: the Edinburgh Postnatal Depression Scale (EPDS), the Hospital Anxiety and Depression Scale (HADS), and the City Birth Trauma Scale (City BiTS). Infant sleep outcomes were assessed using the Brief Infant Sleep Questionnaire (BISQ). Six supervised machine learning algorithms were evaluated: logistic regression, random forest, support vector classifier (SVC), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and multilayer perceptron (MLP). Post-hoc feature importance analysis was conducted to identify the most influential predictors associated with each infant sleep outcome. Results: All models demonstrated high predictive performance. The best model achieved a precision-recall area under the curve (PR-AUC) of 0.92, F1 score of 0.84, and accuracy of 0.88 for predicting short nocturnal sleep duration. For frequent night awakenings, the top PR-AUC was 0.91, with an F1 score of 0.78 and accuracy of 0.85. Key predictors included maternal age and total scores from the EPDS, HADS-A, and City BiTS, with individual symptom items offering additional discriminative value. Conclusions: Machine learning models can accurately predict which infants are at risk for suboptimal sleep based on maternal mental health measures, enabling personalized, responsive, and developmentally informed postpartum care that promotes long-term maternal and infant well-being.

2026-02-03T16:30:09-05:00 https://pediatrics.jmir.org/2026/1/e76712 2026-02-02T15:00:16-05:00 Berhanu Fikadie Endehabtu Kassahun Alemu Shegaw Anagaw Mengiste Meseret Zelalem Monika Knudsen Gullslett Binyam Tilahun

Background: Despite progress in childhood vaccination, many children in Low- and Middle-Income Countries (LMICs), including Ethiopia, remain unvaccinated, presenting a significant public health challenge. The Immunization Agenda 2030 (IA2030) seeks to halve the number of unvaccinated children by identifying at-risk populations, but effective strategies are limited. This study leverages machine learning (ML) to identify Ethiopian children aged 12 to 35 months who are at higher risk of being zero-dose. By analyzing demographic, socio-economic, and healthcare access data, the study developed predictive models using different algorithms. The findings aim to inform targeted interventions, ultimately improving vaccination coverage and health outcomes. Objective: This study aimed to develop a machine learning model to predict zero-dose children and to identify the most influential predictors of zero dose in Ethiopia. Methods: We examined how well the predictive algorithms can characterize a child at risk of being zero-dose based on predictor variables sourced from the recent national Immunization survey data. We applied supervised machine learning algorithms with the survey data sets, which included 13,666 children aged 12 to 35 months. Model performance was assessed using accuracy, area under the curve, precision, recall and F1 score. We applied Shapley Additive analysis to identify the most important predictors. Results: The Light Gradient Boosting Machine (LightGBM), Random Forest, Extreme Gradient Boosting (XGBoost), and AdaBoost classifiers effectively identified most zero-dose (ZD) children as being at high risk. Among these, LightGBM demonstrated the best performance, achieving an accuracy of 93%, an Area Under the Curve(AUC) of 97%, a precision of 94%, and a recall of 91%. The most significant features impacting the model included poor perception of vaccination benefits, lack of antenatal care (ANC) utilization, distance from Immunization services, and absence of maternal Tetanus Toxoid vaccinations. Conclusions: The developed machine learning models effectively predict children at risk of being zero-dose, with the LGBM model showing the best performance. This model can guide targeted interventions to reduce zero-dose prevalence and address vaccination inequities. Key predictors include access to Immunization sites, maternal health service utilization, and perceptions of Immunization benefits. By focusing on these vulnerable groups, public health efforts can tackle disparities in vaccination coverage. Enhancing maternal care, raising caregiver awareness, and improving Immunization access through outreach can significantly reduce the number of zero-dose children.

2026-02-02T15:00:16-05:00 https://pediatrics.jmir.org/2026/1/e82133 2026-01-26T11:15:08-05:00 Marta Castillo-Rodenas Núria Solanas Bacardit Clotilde Farràs Company Queralt Miró Catalina Laia Solà Reguant Aïna Fuster-Casanovas Francesc López Seguí Josep Vidal-Alaball

Background: In Catalonia, Spain, pediatric primary care is undergoing restructuring, including greater integration of information and communication technologies. The adoption of digital health solutions has also risen significantly since the onset of the COVID-19 pandemic. In areas with limited availability of healthcare professionals, digital tools are a key strategy for facilitating access to services and ensuring continuity of care. Objective: To evaluate the feasibility, diagnostic accuracy, and satisfaction of users and providers of an acute pediatric video consultation model, referred to as video interconsultation, that includes a remote physical examination and takes place between healthcare professionals, one of whom is physically present with the patient. Methods: This study aimed to evaluate the feasibility, diagnostic accuracy, and user and provider satisfaction of an acute pediatric video interconsultation model in a rural primary care setting in Catalonia. This 20-month prospective observational study included 200 cases involving children aged 0-14 years who received video interconsultations for acute conditions as part of routine practice. Each video interconsultation was conducted between a nurse physically present with the patient and a pediatrician connected virtually, and incorporated a virtual physical examination using a digital camera, video otoscope, and digital stethoscope. All patients were subsequently assessed in person. Evaluated outcomes were feasibility, diagnostic accuracy, consultation duration, and satisfaction among users and healthcare providers. Results: The video interconsultation model was feasible in 64.5% of cases, achieving 78.2% diagnostic agreement with subsequent in-person examinations. Mean accuracy was 0.99 (95% CI 0.98–1.00), with a specificity of 0.99 (95% CI 0.98–1.00) and a sensitivity of 0.90 (95% CI 0.84–0.95). Diagnostic agreement was highest for otoscopic, oropharyngeal, and dermatologic examinations, and lowest for abdominal assessments. Video consultations lasted approximately twice as long as in-person visits. Satisfaction was high, with 94.5% of users and 74% of providers rating their experience positively. Conclusions: Video interconsultations involving physical examinations and interprofessional collaboration appear to be a feasible, accurate, and well-received model for managing multiple acute pediatric conditions. This model may improve access to care in rural primary care and help reduce disparities in healthcare delivery. However, further research is needed to determine the specific settings in which this approach may be most beneficial, as well as its potential limitations in diverse clinical settings.

2026-01-26T11:15:08-05:00 https://pediatrics.jmir.org/2026/1/e67085 2026-01-19T11:00:04-05:00 Martine Jeukens-Visser Monique Flierman Eline Möller Renate Giezeman Raoul Engelbert Daniël Bossen

<strong>Background:</strong> After hospital discharge, parents of preterm infants need accessible and reliable information to gain confidence and skills in their child-caring abilities and parental autonomy. Parental need for information after hospital discharge includes topics related to prematurity, such as crying, feeding, sleeping, infant care, general health, and neuromotor development. However, parents report difficulty in finding and understanding this information. Mobile apps have the potential to improve information provision. <strong>Objective:</strong> The aim of this systematic app review was to (1) identify mobile apps for parents of preterm infants targeting the period after hospital discharge and (2) evaluate the content, quality of the app, and understandability and actionability of the information material. <strong>Methods:</strong> We systematically searched for apps in the Apple App Store, Google Play Store, and Google, along with a literature search using PubMed. Multiple keywords were used (eg, “preterm baby,” “app,” and “home”). Apps were included when they provided information for parents on topics and content related to prematurity after hospital discharge. To examine app content related to the postdischarge period, apps were reviewed, and topics were identified. The Mobile App Rating Scale (MARS) was used to measure the app’s quality, and the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-AV) was used to measure the understandability and actionability of the information material. <strong>Results:</strong> After the initial search, the titles and descriptions of 196 apps were screened for eligibility. Eventually, 9 English apps were included in the review. Information related to the postdischarge period constituted only a small part of the app’s content. Most commonly addressed topics related to the period at home were vaccinations, follow-up, feeding, and using home oxygen. Using the MARS, only one of the 9 apps received a good score for overall quality (“MyPreemie app”; Graham’s Foundation), and 7 apps received an acceptable score. Only 4 apps scored high on understandability of the PEMAT-AV, and 6 apps scored high on actionability. No Dutch apps were identified. <strong>Conclusions:</strong> The current availability of mobile information apps for parents of preterm infants targeting the period after hospital discharge is limited. A total of 9 English apps were identified, which contained a small portion related to the postdischarge period. This content is not comprehensive for the postdischarge period: topics indicated as relevant by parents, such as crying in preterm infants, diaper change in preterm, or parental well-being after preterm birth, are often missing. The overall quality of the apps is only acceptable. Although the reliability of the information was close to good, the understandability of the apps was moderate. Recommendations for future app development include more relevant and understandable information related to the postdischarge period, which meets the demand of parents of preterm infants.

2026-01-19T11:00:04-05:00 https://pediatrics.jmir.org/2026/1/e81844 2026-01-09T15:30:04-05:00 Jessica N Rivera Rivera Marjanna Smith Shrey Mathur Katarina E AuBuchon Angela D Thomas Hannah Arem

Background: The 42 days following childbirth are a high-risk period for birthing individuals and newborns. We created two rule-based chatbots – one for birthing individuals and one for newborn caregivers – to deliver information on postpartum and newborn warning signs, follow-up care, and other relevant resources during this high-risk period. Objective: This study examines strategies for implementing the chatbot following discharge from a large hospital center, initial chatbot reach, and subsequent reach after chatbot refinement based on end-user feedback. Methods: Reach was defined as the number of users opening the chatbot out of those who received it. Birthing individuals’ demographic (age, ethnicity, race, language, and insurance type) and clinical characteristics (delivery method and prenatal care location), and newborns’ time in the hospital were obtained from the medical record. Descriptive statistics, chi-square tests, t-tests, and multiple logistic regression models were used to analyze the association between demographic and clinical characteristics and chatbot reach. Results: Both chatbots were developed and revised based on clinician, community, and patient feedback. 4,933/7,489 (65.9%) of newborn caregivers discharged between 10/2/2022 and 1/15/2025 opened the newborn chatbot, and 4,140/6,505 (63.6%) of birthing individuals discharged between 11/21/2022 and 1/15/2025 opened the postpartum chatbot. Older age (OR=1.02, 95% CI [1.01, 1.03]), Black race (OR=0.73, 95% CI [0.61, 0.88]; ref. White), other language (OR=1.90, 95% CI [1.21, 2.98]; ref. English), receipt of prenatal care external to the hospital system (FQHCs: OR=0.52, 95% CI [0.45, 0.60]; Kaiser: OR=0.34, 95% CI [0.29, 0.39], ref. within the hospital system), and public insurance (OR=0.72, 95% CI [0.64, 0.82]; ref. private insurance) were significant predictors of postpartum chatbot reach. Older age (OR=1.02, 95% CI [1.01, 1.03]), Black race (OR=0.61, 95% CI [0.50, 0.74]; ref. White), receipt of prenatal care external to the hospital system (FQHCs: OR=0.50, 95% CI [0.44, 0.57]; Kaiser: OR=0.30, 95% CI [0.26, 0.35]; ref. within the hospital system), public insurance (OR=0.63, 95% CI [0.55, 0.71]) and self-pay (OR=0.56, 95% CI [0.38, 0.83]; ref. private insurance), and newborn time in the hospital of 2-4 days (OR=1.21, 95% CI [1.09, 1.35]; ref. less than 2 days) were significant predictors of newborn chatbot reach. Including a Spanish-language version in the newborn chatbot improved reach among Spanish-preferring caregivers (from 58.0% to 66.2%), but additional chatbot content revision and the addition of chatbot information to discharge paperwork did not change chatbot reach. Conclusions: While there were differences in chatbot reach by patient demographics, the chatbot showed delivery of time-sensitive information and support to >60% of individuals. This intervention demonstrated that chatbots can be used to supplement patient care and help bridge the gaps in connecting patients to care and support after hospital discharge. Future work should address additional ways to improve chatbot reach and explore the impact on targeted health outcomes.

2026-01-09T15:30:04-05:00 https://pediatrics.jmir.org/2026/1/e76512 2026-01-06T13:00:28-05:00 Jessica R Hanks Ashley M Hughes Safura Sultana Ryan Klute Kyle Formella Connor Flynn Allison Wallenfang Divya Krishnakumar Masah Mourad Yoonje Cho Morse Matthew J Mischler

<strong>Background:</strong> Children and young adults with special health care needs comprise a significant portion of the pediatric population in the United States, where 1 in every 5 children has a complex health care need. These patients are more likely to receive unsafe care and have their needs unmet in part due to lack of accessible information and limited training support. Barriers in communication may contribute to detrimental outcomes for this vulnerable, high-risk population. <strong>Objective:</strong> This project aims to identify barriers to communication in children and young adults with special health care needs in the health care setting. These barriers will inform prototype development using human-centered design approaches to create a web-based application. Feedback from patients, caregivers, and health care providers (HCPs) was obtained on the usability and usefulness of the tool within the health care setting. <strong>Methods:</strong> A needs assessment was conducted in which participants shared their experiences in providing or receiving health care services via a semistructured interview that was recorded and transcribed. Transcripts were analyzed inductively for themes, coded, and used to categorize the data. On the basis of these themes, iterative development of a web-based application for social stories took place. Focus groups were held to provide relevant feedback on the prototype. <strong>Results:</strong> There were 15 participants (n=10, 67% HCPs and n=5, 33% patients and caregivers) interviewed for the needs assessment that informed prototype development. A web-based application for social stories depicting different aspects of health care interactions was created. Focus group feedback from 19 participants (n=12, 63% HCPs and n=7, 37% patients and caregivers) on usability through the System Usability Scale, along with narrative feedback, was obtained. Overall, the usability of the application was supported by caregivers and HCPs. <strong>Conclusions:</strong> Children and young adults with special health care needs require medical services that their peers generally do not, thereby compounding potential barriers in communication surrounding health care delivery. Using social stories geared toward health care interactions may help reduce anxiety and difficulty.

2026-01-06T13:00:28-05:00 https://pediatrics.jmir.org/2025/1/e80420 2025-12-22T12:00:05-05:00 Amit Baumel Or Brandes Chen R Saar

<strong>Background:</strong> Digital parent training programs (DPTs) have emerged as a scalable solution for treating childhood oppositional defiant disorder (ODD), offering remote access and reduced barriers to care. However, there is limited data on their potential to reach untreated populations and their effectiveness during times of crisis, such as war. <strong>Objective:</strong> This study aimed to evaluate the reach, usage patterns, human support needs, and clinical outcomes of a fully remote guided DPT for child ODD, comparing 2 cohorts treated before and during wartime in Israel. <strong>Methods:</strong> Parents of children with ODD were enrolled in a human-supported DPT, with 25 families recruited before and 30 during wartime. Data included self-reported questionnaires (measured before-, postintervention, and 3 months after the end of the intervention), platform usage metrics, and clinician assessments. <strong>Results:</strong> Most families (62%, 34/55) had not previously received any intervention for their child’s behavior problems. Significant self-reported improvements in child behavior (Cohen <i>d</i>≥0.79) and parenting practices (0.39≤Cohen <i>d</i>≤0.87) were found post intervention. On average, families engaged with the program for 138.6 minutes across 31.4 unique logins, supported by 38.8 minutes of human interaction, primarily via messaging. During wartime, parents completed onboarding significantly faster (15.70 days vs 31.36 days) and were more likely to complete the critical “overcoming disobedience” phase (27/30, 90% vs 17/25, 68%). However, while self-reported changes were similar, clinician-rated recovery from ODD was marginally lower during wartime (13/30, 43% vs 17/25, 68%). <strong>Conclusions:</strong> DPTs present an acceptable avenue for care that could reach parents who have not sought treatment through traditional channels. However, this study’s results suggest that their clinical effectiveness may be lower under extreme stress conditions such as wartime, underscoring the need for future studies in this area. <strong>Trial Registration:</strong>

2025-12-22T12:00:05-05:00 https://pediatrics.jmir.org/2025/1/e82887 2025-12-19T16:30:08-05:00 Haonan Ning Wenyu Zheng Jinghui Zhang Fuhai Li Nana Qiao

Background: Protracted bacterial bronchitis (PBB) is a leading cause of chronic wet cough in children. Misdiagnosis and inadequate treatment may lead to the progression of diseases. Objective: The objective of this paper was to analyze factors influencing the cough duration prior to the diagnosis and assess health-related quality of life in children with PBB. Methods: Children diagnosed with PBB in the Qilu Hospital of Shandong University from November 2021 to November 2022 were included in this study. Clinical data were collected, parents completed the parent-proxy Cough-Specific Quality of Life (PC-QOL) questionnaire and the simplified Cough Symptom Score (sCSS). Children aged 6 years and older completed the Leicester Cough Questionnaire in Mandarin-Chinese (LCQ-MC). Results: As of November 2022, we enrolled 88 patients (N=88). Place of residence (B=9.35, 95% CI 0.36 to 18.35; P=.04) and rest status during the coughing episode (B=7.87, 95% CI 0.36 to 15.38; P=.04) were significantly associated with cough duration prior to the diagnosis. PC-QOL scores (physical: mean 3.10 (SD 1.36), psychological: mean 3.32 (SD 1.57), social: mean 3.67(SD 1.53), total: mean 10.09 (SD 4.21)) showed physical-social differences (t174=-2.58, P=.01). PC-QOL post-treatment scores were significantly higher than pre-treatment scores (physical: t18=-6.05, P＜.001; psychological: t18=-4.42, P＜.001; social: t18=-4.79, P＜.001; total: t18=-5.25, P＜.001). However, the scores of each PC-QOL domain were significantly lower than those of the LCQ-MC (physical: t34=8.31, P＜.001; psychological: t34=6.58, P＜.001; social: t34=5.09, P＜.001; total: t34=8.11, P＜.001). Conclusions: "Place of residence" and "rest status during the coughing episode" were significantly associated with cough duration prior to the diagnosis. Furthermore, PBB significantly reduces quality of life in physical, psychological, and social aspects.

2025-12-19T16:30:08-05:00 https://pediatrics.jmir.org/2025/1/e81860 2025-12-19T14:30:07-05:00 Kitiyawadee Srisim Supannikar Yingyongsaksri Janya Chuadthong

Background: Chronic ankle instability (CAI) in children affects ankle and foot function, balance, and mobility, inducing recurrent injuries, physical limitations, and low quality of life. To mitigate the consequences of CAI, the Jump Step Kids (JSKs) program is a rhythmic, multi-directional jumping and play-based intervention program designed to improve rehabilitation outcomes for children with CAI. Objective: The study was to investigate the effect of the JSKs program on functional movement and self-report outcomes for the foot and ankle in children with CAI aged 7-12 years. Methods: A stratified, randomized controlled trial was conducted with 34 school-aged children (aged 9 ± 2 years) diagnosed with CAI who were randomly assigned to either the intervention group (n = 17), which completed the JSKs program (supervised by a physiotherapist), or the control group (n = 17), which followed a self-administered home stretching program for ankle instability. Both groups participated in 30-minute sessions three times per week for 4 weeks. Outcome measures included heel raise test (HRT), standing long jump test (SLJT), 6-meter cross jump test (6mCHT), The Bruininks-Oseretsky Test of Motor Proficiency-second edition (BOT-2), and the Foot and Ankle Function Measure (FAAM) questionnaire. Assessments were conducted at baseline and after 4 weeks. Results: After 4 weeks of involvement, the JSKs program group showed significant gains in the 6mCHT (P =.019) and the two-legged side hop test of BOT-2 (P<.001). However, FAAM-ADL and sport scores did not differ significantly across groups (P>.05). Following the intervention, statistically significant differences were observed between groups in the one-legged stationary hop test (P =.019) and the HRT (P =.005). Conclusions: The findings illustrated that the JSKs program greatly improves functional movement, including muscle strength, dynamic balance, and agility. However, the JSKs program cannot alter self-report outcomes regarding functional status in individuals with CAI. The JSKs program is appropriate to apply as a supplement to standard therapy to achieve clinical physical outcomes for children with CAI. Clinical Trial: Thai Clinical Trials Registry (TCTR20220727002)

2025-12-19T14:30:07-05:00 https://pediatrics.jmir.org/2025/1/e71764 2025-12-08T13:45:23-05:00 Adam Korrick Lewkowitz Liana Lum Katrina Ursino Melissa Guillen Gabriela Garcia Sarai K Sales Saloni Taneja Crystal F Ware William A Grobman Caron Zlotnick Kate M Guthrie Emily S Miller

Participants in qualitative interviews designed to optimize the adaptation of a maternal postpartum depression intervention into a novel smartphone app noted that the app could be more useful if were delivered simultaneously to both parents; this calls for additional research on the feasibility and effectiveness of digital dyadic or stand-alone partner interventions to prevent postpartum depression.

2025-12-08T13:45:23-05:00