Digital psychological interventions in youth with neurological disorders: a systematic review.

Associations of weekday-to-weekend sleep differences with academic performance and health-related outcomes in school-age children and youths

Digital health interventions are a promising alternative to face-to-face psychosocial interventions that may improve psychological outcomes in youth with diabetes. Several reviews have explored the efficacy of digital health interventions for adults and youth with some chronic health conditions; however, their efficacy among youth with type 1 diabetes is not known. This systematic review aims to assess digital health interventions targeting both psychological and physical health outcomes in youth with type 1 diabetes as well as to evaluate study quality and efficacy, and provide directions for future research in this area. Relevant studies were identified through searches conducted in MEDLINE, Embase, APA PsycInfo, Scopus, Cochrane Central, and CINAHL Plus up until February 2021. Studies were included if they were randomized; reported at least one psychological outcome that was assessed at ≥2 time points; included a digital health intervention; and were conducted in youth aged 5 to 25 years with type 1 diabetes. The revised Cochrane risk-of-bias (RoB 2) tool was used to assess risk of bias. Of the 5159 articles found, 15 met the inclusion criteria and were included in the review. Self-efficacy improved in 2 of the 3 studies which assessed self-efficacy; however, no consistent improvements were found for other psychological, behavioral, or physical outcomes. All studies showed some risk of bias concerns. More research is needed to make firm conclusions on the efficacy of digital health interventions for youth with diabetes. More specifically, interventions based on psychological theories are needed and studies of higher quality methodologies.

Objectives Risk of bias (RoB) is an important to assess scientific evidence. Lack of detailed preregistration obfuscates reporting transparency and cast doubts about the required bijective relation between study protocol and the final scientific report of outcomes. This contribution examines whether the lack of preregistration affects the judgments required for risk of bias assessment according to the RoB 2.0 tool and whether the tool can adequatly capture flaws in preregistration. Method We examined the literature on RoB 2.0 and thoroughly evaluated the definition and criteria for the three different categories ‘high risk of bias’, ‘low risk of bias’, and some concerns of risk of bias’. Moreover, we investigated the literature on meta-science and performed a conceptual analysis of the epistemic merits and methodological benefits arising from various forms of preregistration. Accordingly, selective endpoint reporting, or endpoint modification raise serious and severe doubts about the scientific validity of biomedical randomized controlled trials (RCT) and preregistration is obligatory according to Article 35 of the Declaration of Helsinki of 2013. Results The upcoming revised Cochrane handbook introduces RoB 2.0 as a new research tool for assessing the risk of bias of an RCT. RoB 2.0 requires a comparison between the pre-specified analysis intentions and the reported analyses in order to assess potential selection bias of multiple outcomes or endpoints. In case a preregistered analysis plan is met, ‘low risk bias’ is assigned. ‘High risk of bias’ is assigned only if it is likely that reported outcomes have been selected based on the results, i.e. a deviation from the preregistered protocol is detected. If no information is available, RoB 2.0 suggests ‘some concern’. Furthermore, in cases where preregistration is lacking, RoB 2.0 suggests the methods section of an article as a source of the analysis intentions. Therefore, the lack of preregistration does not by default lead to the evaluation of a ‘high risk of bias’. Conclusions Although lack of preregistration can lead to ‘some concerns of risk of bias’, there is by default no assignment of ‘high risk of bias’even if a preregistration protocol is completely lacking. In light of the epistemic arguments in favour of preregistration, RoB 2.0 presents an untoward loophole in the risk of bias assessment with regard to selective outcome reporting or post hoc endpoint modification. Because any RoB 2.0 assessment is very effort-intensive and time-consuming, it is of utmost importance that all sort of biases are adequate considered and, thus, that future systematic reviews and meta-analysis benefit from risk of bias assessment tools that account for lack of preregistration as a source of ‘high risk of bias’ by default.

Background The Cochrane collaboration risk of bias assessment (RoB) tool is used in several fields to evaluate the methodological quality of studies. Its strengths and challenges are discussed. Objective To assess the sensitivity of the RoB tool in studies of pharmacist interventions. Setting DEPICT database was used to pool randomized controlled trials (RCTs) of complex interventions. Method A Guide for RoB Judgment in Pharmacy Services was created to help in the interpretation and judgment of bias criteria. The evaluation of bias (low, unclear, high risk) was performed by RCT. Sensitivity analyses were performed to assess the influence of different interpretations of eight elements of judgment in the RoB tool. Paired analysis and estimations of the effect size (95% confidence interval) of the criteria modifications compared to the original analyses were calculated. Main outcome measure Changes in the interpretations of judgment in the RoB tool. Results Overall, 8.3, 45.4, and 46.3% of the studies were determined to have low, unclear, and high risk of bias, respectively. High risk of bias was caused by attrition and detection domains. The number of studies classified with high risk of bias significantly increased for five of the eight interpretations, while unclear risk of bias increased for three interpretations (with a negligible effect size in all of them). Lack of blinding, loss of participants, and the use of subjective and self-reported outcomes were the main elements resulting in high risk of bias. Conclusion The RoB tool is useful for evaluating RCTs of pharmacist interventions if adapted criteria for judgment are used. Ignoring these adjustments produces a floor-effect with studies classified with high risk of bias.

Efficacy of digital health interventions in youth with chronic medical conditions: A meta-analysis

BackgroundA substantial proportion of children and adolescents worldwide do not meet current physical activity (PA) guidelines. So digital tools interventions have been implemented worldwide. However, evidence regarding the effectiveness of these remains inconsistent, underscoring the need for a comprehensive synthesis of the available literature.ObjectiveThis umbrella review aimed to summarize and critically evaluate the characteristics and effectiveness of digital interventions designed to increase PA in children and adolescents.MethodsAn overview of systematic reviews (SRs) and meta-analyses of randomized controlled trials (RCTs) was conducted in accordance with the PRIOR (Preferred Reporting Items for Overviews of Reviews) and the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Literature Search Extension) checklists. Reviews published between 2018 and 2025 were searched in SCOPUS, PubMed or MEDLINE, Web of Science, Cochrane Database of Systematic Reviews, and SPORTDiscus or EBSCO, using a combination of terms addressing the type of digital tool (eg, apps, wearables, etc) and device-based PA outcomes. Eligible SRs and meta-analyses focused on populations aged 6‐17 years and evaluated digital interventions aimed at increasing PA. Methodological quality was assessed using AMSTAR 2 (A Measurement Tool to Assess Systematic Reviews) for SRs and meta-analyses, and Risk of Bias 2 for RCTs. Intervention characteristics and effectiveness outcomes were summarized as frequencies, and chi-square tests were applied to explore differences in effectiveness across study features.ResultsForty-eight SRs or meta-analyses comprising 62 RCTs were included. The reviews’ quality was moderate to poor, and 7/62 (11.3%) of RCTs were judged to have a high risk of bias. The mainly addressed PA outcomes were moderate-to-vigorous PA and step counts, commonly measured using accelerometers and pedometers. Overall, 45.2% of interventions demonstrated effectiveness for at least 1 PA outcome. Higher effectiveness rates were observed in RCTs that targeted PA as the sole primary outcome (19/32, 59.4%), used wearables as both the digital intervention component (7/11, 63.6%) and delivery device (8/12, 66.7%), and used pedometers for PA outcome measurement (13/16, 81.3%). Significant differences in effectiveness were found for the type of PA assessment device (P=.003) and for interventions targeting low-income populations, which showed lower effectiveness (P=.01). Additional trends were identified for geographic region (P=.06), intervention setting (P=.09), baseline activity level (P=.06), intervention focus (P=.09), and device brand (P=.09).ConclusionsThis novel umbrella review provides a comprehensive synthesis of digital PA interventions in youth, foreseeing potential factors that may influence their effectiveness, and highlighting methodological limitations. It offers evidence-based insights for practitioners, educators, and policymakers, helping to identify digital tools most likely to successfully increase PA in youth. Future research should prioritize stronger methodological rigor and more precise intervention designs. This has clear value for the public health practice to reduce long-term disease risk.

People with kidney failure have a high risk of death and poor quality of life. Mortality risk prediction models may help them decide which form of treatment they prefer. To systematically review the quality of existing mortality prediction models for people with kidney failure and assess whether they can be applied in clinical practice. MEDLINE, Embase, and the Cochrane Library were searched for studies published between January 1, 2004, and September 30, 2024. Studies were included if they created or evaluated mortality prediction models for people who developed kidney failure, whether treated or not treated with kidney replacement with hemodialysis or peritoneal dialysis. Studies including exclusively kidney transplant recipients were excluded. Two reviewers independently extracted data and graded each study at low, high, or unclear risk of bias and applicability using recommended checklists and tools. Reviewers used the Prediction Model Risk of Bias Assessment Tool and followed prespecified questions about study design, prediction framework, modeling algorithm, performance evaluation, and model deployment. Analyses were completed between January and October 2024. A total of 7184 unique abstracts were screened for eligibility. Of these, 77 were selected for full-text review, and 50 studies that created all-cause mortality prediction models were included, with 2 963 157 total participants, who had a median (range) age of 64 (52-81) years. Studies had a median (range) proportion of women of 42% (2%-54%). Included studies were at high risk of bias due to inadequate selection of study population (27 studies [54%]), shortcomings in methods of measurement of predictors (15 [30%]) and outcome (12 [24%]), and flaws in the analysis strategy (50 [100%]). Concerns for applicability were also high, as study participants (31 [62%]), predictors (17 [34%]), and outcome (5 [10%]) did not fit the intended target clinical setting. One study (2%) reported decision curve analysis, and 15 (30%) included a tool to enhance model usability. According to this systematic review of 50 studies, published mortality prediction models were at high risk of bias and had applicability concerns for clinical practice. New mortality prediction models are needed to inform treatment decisions in people with kidney failure.

BackgroundJuvenile idiopathic arthritis (JIA) is a chronic rheumatic condition requiring long-term, multidisciplinary treatment, which consumes significant health care resources and family energy. This study aims to analyze the effectiveness of digital interventions on patient outcomes in individuals with JIA.ObjectiveThis meta-analysis aimed to evaluate the impact of digital interventions on alleviating symptoms and improving overall well-being in children and adolescents with JIA.MethodsA systematic search of 5 databases identified randomized controlled trials assessing the impact of digital interventions on physiological and psychological outcomes in adolescents and children (average age ≤19 y). Outcomes included pain, physical activity, health-related quality of life, self-efficacy, and disease-related issues. A total of 2 reviewers independently screened papers and extracted data on intervention functionalities and outcomes, assessing the risk of bias. A meta-analysis using a random-effects model synthesized the results.ResultsThe review included 11 studies involving 885 patients with JIA. Digital interventions included educational (eg, self-management training), therapeutic (eg, pain management), and behavioral (eg, promoting physical activity) approaches. These were delivered through websites, telephone consultations, video conferences, apps, and interactive games, with durations ranging from 8 to 24 weeks and no clear link observed between intervention length and outcomes. Compared with conventional control groups, digital interventions were significantly effective in alleviating pain (standardized mean difference [SMD] −0.19, 95% CI −0.35 to −0.04) and enhancing physical activity levels (SMD 0.37, 95% CI 0.06-0.69). Marginal improvements in health-related quality of life, self-efficacy, and disease-related issues were observed, but these did not reach statistical significance (SMD −0.04, 95% CI −0.19 to 0.11; SMD 0.05, 95% CI −0.11 to 0.20; and SMD 0.09, 95% CI −0.11 to 0.29, respectively). The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach rated the quality of evidence for pain, health-related quality of life, self-efficacy, and disease-related issues as moderate, while the evidence quality for physical activity was assessed as low.ConclusionsDigital interventions can alleviate pain and enhance physical activity in patients with JIA. However, given the limited sample size and high risk of bias in some studies, further high-quality research is needed to improve the treatment and management of JIA.

Strategies to increase physical activity (PA) and improve nutrition would contribute to substantial health benefits in the population, including reducing the risk of several types of cancers. The increasing accessibility of digital technologies mean that these tools could potentially facilitate the improvement of health behaviours among young people. We conducted a review of systematic reviews to assess the available evidence on digital interventions aimed at increasing physical activity and good nutrition in sub-populations of young people (school-aged children, college/university students, young adults only (over 18 years) and both adolescent and young adults (<25 years)). Searches for systematic reviews were conducted across relevant databases including KSR Evidence (www.ksrevidence.com), Cochrane Database of Systematic Reviews (CDSR) and Database of Abstracts of Reviews of Effects (DARE; CRD). Records were independently screened by title and abstract by two reviewers and those deemed eligible were obtained for full text screening. Risk of bias (RoB) was assessed with the Risk of Bias Assessment Tool for Systematic Reviews (ROBIS) tool. We employed a narrative analysis and developed evidence gap maps. Twenty-four reviews were included with at least one for each sub-population and employing a range of digital interventions. The quality of evidence was limited with only one of the 24 of reviews overall judged as low RoB. Definitions of "digital intervention" greatly varied across systematic reviews with some reported interventions fitting into more than one category (i.e., an internet intervention could also be a mobile phone or computer intervention), however definitions as reported in the relevant reviews were used. No reviews reported cancer incidence or related outcomes. Available evidence was limited both by sub-population and type of intervention, but evidence was most pronounced in school-aged children. In school-aged children eHealth interventions, defined as school-based programmes delivered by the internet, computers, tablets, mobile technology, or tele-health methods, improved outcomes. Accelerometer-measured (Standardised Mean Difference [SMD] 0.33, 95% Confidence Interval [CI]: 0.05 to 0.61) and self-reported (SMD: 0.14, 95% CI: 0.05 to 0.23) PA increased, as did fruit and vegetable intake (SMD: 0.11, 95% CI: 0.03 to 0.19) (review rated as low RoB, minimal to considerable heterogeneity across results). No difference was reported for consumption of fat post-intervention (SMD: -0.06, 95% CI: -0.15 to 0.03) or sugar sweetened beverages(SSB) and snack consumption combined post-intervention (SMD: -0.02, 95% CI:-0.10 to 0.06),or at the follow up (studies reported 2 weeks to 36 months follow-up) after the intervention (SMD:-0.06, 95% CI: -0.15 to 0.03) (review rated low ROB, minimal to substantial heterogeneity across results). Smartphone based interventions utilising Short Messaging Service (SMS), app or combined approaches also improved PA measured using objective and subjective methods (SMD: 0.44, 95% CI: 0.11 to 0.77) when compared to controls, with increases in total PA [weighted mean difference (WMD) 32.35 min per day, 95% CI: 10.36 to 54.33] and in daily steps (WMD: 1,185, 95% CI: 303 to 2,068) (review rated as high RoB, moderate to substantial heterogeneity across results). For all results, interpretation has limitations in terms of RoB and presence of unexplained heterogeneity. This review of reviews has identified limited evidence that suggests some potential for digital interventions to increase PA and, to lesser extent, improve nutrition in school-aged children. However, effects can be small and based on less robust evidence. The body of evidence is characterised by a considerable level of heterogeneity, unclear/overlapping populations and intervention definitions, and a low methodological quality of systematic reviews. The heterogeneity across studies is further complicated when the age (older vs. more recent), interactivity (feedback/survey vs. no/less feedback/surveys), and accessibility (type of device) of the digital intervention is considered. This underscores the difficulty in synthesising evidence in a field with rapidly evolving technology and the resulting challenges in recommending the use of digital technology in public health. There is an urgent need for further research using contemporary technology and appropriate methods.

Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

Although exercise is recommended for children with asthma, inconsistencies in study findings limit the development of standardized exercise prescriptions. This study aimed to investigate the effect of karaoke training applied with telerehabilitation-based exercise training on health-related outcomes in children with asthma. Twenty-eight children with asthma were randomly assigned in equal numbers to a study group (SG) or a control group (CG), with 25 completing 24 individualized online exercise sessions over 8weeks. In addition, SG performed singing exercises using a karaoke application. The primary outcome was pulmonary function, with secondary outcomes including perception of dyspnea, perception of fatigue, thoracic expansion, respiratory and peripheral muscle function, functional capacity, asthma control, and quality of life. Both groups showed significant improvements in pulmonary functions, respiratory muscle strength, thoracic expansion, functional capacity, asthma control (ACT), and the Pediatric Asthma Quality of Life Questionnaire scores (PAQLQ) (p < 0.05). However, SG demonstrated significantly greater improvements in peak expiratory flow, maximal expiratory pressure, dyspnea on exertion, thoracic expansion (axillary and subcostal), 1-min sit-to-stand test, ACT, and PAQLQ (activity limitation, symptoms, total scores) compared to CG (p < 0.05).Conclusion:Telerehabilitation-based exercises have positive effects on health-related outcomes. Adding online karaoke training enhances engagement and increases therapeutic gains in children with asthma What is Known: • Ongoing inconsistencies in exercise-related studies prevent consensus on optimal exercise prescription for asthma patients. • Singing training has been studied in different research designs and shown beneficial effects in various respiratory diseases, especially COPD. What is New: • Telerehabilitation-based exercise programs, incorporating breathing and calisthenic exercises, have shown positive effects on health-related outcomes in children with asthma. • Karaoke-based singing integrated into online exercise sessions enhances engagement and increases therapeutic gains in children with asthma.

To systematically evaluate the methodological quality and statistical rigor of recent prediction model studies (2020-2025) for ovarian, cervical, and endometrial cancers. We performed a systematic assessment of PubMed literature (January 2020-April 2025), including studies developing, validating, or updating diagnostic/prognostic models for these cancers. Methodological quality and risk of bias were assessed using the Prediction Model Risk Of Bias Assessment Tool across 4 domains (participant selection, predictors, outcome, and analysis). Sub-group analyses compared studies by publication period and Journal Citation Report quartile. Among 192 included studies, Prediction Model Risk Of Bias Assessment Tool assessment revealed a high overall risk of bias in 96.9% (n = 189). Key issues included a high risk of bias in the analysis domain (89.1%, n = 171) and participant selection (85.9%, n = 165), primarily due to flawed methods and use of unsuitable cohorts (eg, public databases). External validation was critically lacking (62.5% performed none, only 6.8% performed ≥3), and statistician involvement was minimal (2.6%). While baseline reporting improved significantly from 2020-2022 (39.6%) to 2023-2025 (59.7%, p = .02), deficiencies in sample size, outcome definition, analytical methods, and validation practices showed no significant improvement. Current gynecological cancer prediction models exhibit widespread methodological shortcomings and a high risk of bias, severely limiting clinical utility. Urgent adherence to Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis standards, prioritized multi-center external validation, integration of statisticians, and reduced reliance on single public data sets are essential for developing reliable and applicable models.

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating “real-time model testing” stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.

BackgroundAlthough most countries in the European Union are richer and healthier than ever, health inequalities remain an important public health challenge. Health-related problems and premature death have disproportionately been reported in disadvantaged neighbourhoods. Neighbourhood social capital is believed to influence the association between neighbourhood deprivation and health in children and adolescents, making it a potentially interesting concept for policymakers.MethodsThis study aims to review the role of social capital in health inequalities and the social gradient in health and well-being of children and adolescents. A systematic review of published quantitative literature was conducted, focussing on (1) the mediating role of neighbourhood social capital in the relationship between socio-economic status (SES) and health-related outcomes in children and adolescents and (2) the interaction between neighbourhood social capital and socio-economic characteristics in relation to health-related outcomes in children and adolescents. Three electronic databases were searched. Studies executed between 1 January 1990 and 1 September 2011 in Western countries (USA, New Zealand, Australia and Europe) that included a health-related outcome in children or adolescents and a variable that measured neighbourhood social capital were included.ResultsEight studies met the inclusion criteria for the review. The findings are mixed. Only two of five studies confirmed that neighbourhood social capital mediates the association between neighbourhood deprivation and health and well-being in adolescents. Furthermore, two studies found a significant interaction between neighbourhood socio-economic factors and neighbourhood social capital, which indicates that neighbourhood social capital is especially beneficial for children who reside in deprived neighbourhoods. However, two other studies did not find a significant interaction between SES and neighbourhood social capital. Due to the broad range of studied health-related outcomes, the different operationalisations of neighbourhood social capital and the conceptual overlap between measures of SES and social capital in some studies, the factors that explain these differences in findings remain unclear.ConclusionsAlthough the findings of this study should be interpreted with caution, the results suggest that neighbourhood social capital might play a role in the health gradient among children and adolescents. However, only two of the included studies were conducted in Europe. Furthermore, some studies focussed on specific populations and minority groups. To formulate relevant European policy recommendations, further European-focussed research on this issue is needed.

To comprehensively evaluate the reporting quality, risk of bias, and radiomics methodology quality of radiomics models for predicting microvascular invasion in hepatocellular carcinoma. A systematic search of available literature was performed in PubMed, Embase, Web of Science, Scopus, and the Cochrane Library up to January 21, 2022. Studies that developed and/or validated machine learning models based on radiomics data to predict microvascular invasion in hepatocellular carcinoma were included. These studies were reviewed by two investigators and the consensus data were used for analyzing. The reporting quality, risk of bias, and radiomics methodological quality were evaluated by Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD), Prediction model Risk of Bias Assessment Tool, and Radiomics Quality Score (RQS), respectively. A total of 30 studies met eligibility criteria with 24 model developing studies and 6 model developing and external validation studies. The median overall TRIPOD adherence was 75.4% (range 56.7-94.3%). All studies were at high risk of bias with at least 2 of 20 sources of bias. Furthermore, 28 studies showed unclear risks of bias in up to 5 signaling questions because of the lack of specified reports. The median RQS score was 37.5% (range 25-61.1%). Current radiomic models for MVI-status prediction have moderate to good reporting quality, moderate radiomics methodology quality, and high risk of bias in model development and validation. • Current microvascular invasion prediction radiomics studies have moderate to good reporting quality, moderate radiomics methodology quality, and high risk of bias in model development and validation. • Data representativeness, feature robustness, events-per-variable ratio, evaluation metrics, and appropriate validation are five main aspects futures studies should focus more on to improve the quality of radiomics. • Both Radiomics Quality Score and Prediction model Risk of Bias Assessment Tool are needed to comprehensively evaluate a radiomics study.