A Scoping Review and Content Analysis of Common Depressive Symptoms of Young People.

School nurses are the most accessible health care providers for many young people including adolescents and young adults. Early identification of depression results in improved outcomes, but little information is available comprehensively describing depressive symptoms specific to this population. The aim of this study was to develop a taxonomy of depressive symptoms that were manifested and described by young people based on a scoping review and content analysis. Twenty-five journal articles that included narrative descriptions of depressive symptoms in young people were included. A total of 60 depressive symptoms were identified and categorized into five dimensions: behavioral (n = 8), cognitive (n = 14), emotional (n = 15), interpersonal (n = 13), and somatic (n = 10). This comprehensive depression symptom taxonomy can help school nurses to identify young people who may experience depression and will support future research to better screen for depression.

Predicting Falls Among Community-Dwelling Older Adults: A Demonstration of Applied Machine Learning.

Data science skills are increasingly needed by informatics nurses and nurse scientists, but techniques such as machine learning can be daunting for those with clinical, rather than computer science or technical, backgrounds. With the increasing quantity of publicly available population-level datasets, identification of factors that predict clinical outcomes is possible using machine learning algorithms. This study demonstrates how to apply a machine learning approach to nursing-relevant questions, specifically an approach to predict falls among community-dwelling older adults, based on data from the 2014 Behavioral Risk Factor Surveillance System. A random forest algorithm, a common approach to machine learning, was compared to a logistic regression model. Explanations of how to interpret the models and their associated performance characteristics are included to serve as a tutorial to readers. Machine learning methods constitute an increasingly important approach for nursing as population-level data are increasingly being made available to the public.

The role of computer-based clinical decision support systems to deliver protective mechanical ventilation.

PURPOSE OF REVIEW: Mechanical ventilation of adults and children with acute respiratory failure necessitates balancing lung and diaphragm protective ventilation. Computerized decision support (CDS) offers advantages in circumstances where complex decisions need to be made to weigh potentially competing risks, depending on the physiologic state of the patient. RECENT FINDINGS: Significant variability in how ventilator protocols are applied still exists and clinical data show that there continues to be wide variability in ventilator management. We have developed a CDS, which we are currently testing in a Phase II randomized controlled trial. The CDS is called Real-time Effort Driven ventilator management (REDvent). We will describe the rationale and methods for development of CDS for lung and diaphragm protective ventilation, using the REDvent CDS as an exemplar. SUMMARY: Goals for achieving compliance and physiologic objectives can be met when CDS instructions are simple and explicit, provide the clinician with the underlying rule set, permit acceptable reasons for declining and allow for iterative adjustments.

Loneliness as a mediator of the impact of social isolation on cognitive functioning of Chinese older adults.

BACKGROUND AND OBJECTIVE: older adults have increased risk of social isolation, loneliness and cognitive functioning impairment, but the relationships among these factors are not conclusive. We investigated the potential mediation mechanism of loneliness on the association between social isolation and cognitive functioning among Chinese older adults within their cultural context. DESIGN: secondary analysis of the baseline wave (2011-12) of the harmonised China Health and Retirement Longitudinal Study. SETTING AND SUBJECTS: community-dwelling older adults in China (N = 7,410 participants aged 60-101 years). METHODS: we applied a multiple indicator multiple cause approach to determine whether the construct of social isolation is well defined by four indicators (social activity engagement, weekly adult children contact, caregiving for grandchildren and living alone) and used structural equation modelling to examine the direct and indirect effects among variables of interest. RESULTS: the results demonstrated that social activity engagement, weekly adult children contact and caregiving for grandchildren were significantly related to social isolation (ß = -0.26 to -0.28) (Living alone was fixed to 1 for model identification.) The indirect effect of social isolation on cognitive functioning through loneliness was significant (ß = -0.15), indicating loneliness was an important mediator. However, the direct effect of social isolation on cognitive functioning also remained significant (ß = -0.83), suggesting a partial mediation effect. CONCLUSIONS: our study highlights the mediation role of loneliness in the relationship between social isolation and cognitive functioning among Chinese older adults. The findings support the beneficial effects of maintaining social relations and coping with feelings of loneliness on older adults' cognitive functioning.

Role of Nursing Informatics in the Automation of Pneumonia Quality Measure Data Elements.

Core measures are standard metrics to reflect the processes of care provided by hospitals. Hospitals in the United States are expected to extract data from electronic health records, automated computation of core measures, and electronic submission of the quality measures data. Traditional manual calculation processes are time intensive and susceptible to error. Automated calculation has the potential to provide timely, accurate information, which could guide quality-of-care decisions, but this vision has yet to be achieved. In this study, nursing informaticists and data analysts implemented a method to automatically extract data elements from electronic health records to calculate a core measure. We analyzed the sensitivity, specificity, and accuracy of core measure data elements extracted via SQL query and compared the results to manually extracted data elements. This method achieved excellent performance for the structured data elements but was less efficient for semistructured and unstructured elements. We analyzed challenges in automating the calculation of quality measures and proposed a rule-based (hybrid) approach for semistructured and unstructured data elements.

Variability in Usual Care Mechanical Ventilation for Pediatric Acute Respiratory Distress Syndrome: Time for a Decision Support Protocol?

OBJECTIVES: Although pediatric intensivists philosophically embrace lung protective ventilation for acute lung injury and acute respiratory distress syndrome, we hypothesized that ventilator management varies. We assessed ventilator management by evaluating changes to ventilator settings in response to blood gases, pulse oximetry, or end-tidal CO2. We also assessed the potential impact that a pediatric mechanical ventilation protocol adapted from National Heart Lung and Blood Institute acute respiratory distress syndrome network protocols could have on reducing variability by comparing actual changes in ventilator settings to those recommended by the protocol. DESIGN: Prospective observational study. SETTING: Eight tertiary care U.S. PICUs, October 2011 to April 2012. PATIENTS: One hundred twenty patients (age range 17 d to 18 yr) with acute lung injury/acute respiratory distress syndrome. MEASUREMENTS AND MAIN RESULTS: Two thousand hundred arterial and capillary blood gases, 3,964 oxygen saturation by pulse oximetry, and 2,757 end-tidal CO2 values were associated with 3,983 ventilator settings. Ventilation mode at study onset was pressure control 60%, volume control 19%, pressure-regulated volume control 18%, and high-frequency oscillatory ventilation 3%. Clinicians changed FIO2 by ±5 or ±10% increments every 8 hours. Positive end-expiratory pressure was limited at ~10 cm H2O as oxygenation worsened, lower than would have been recommended by the protocol. In the first 72 hours of mechanical ventilation, maximum tidal volume/kg using predicted versus actual body weight was 10.3 (8.5-12.9) (median [interquartile range]) versus 9.2 mL/kg (7.6-12.0) (p < 0.001). Intensivists made changes similar to protocol recommendations 29% of the time, opposite to the protocol's recommendation 12% of the time and no changes 56% of the time. CONCLUSIONS: Ventilator management varies substantially in children with acute respiratory distress syndrome. Opportunities exist to minimize variability and potentially injurious ventilator settings by using a pediatric mechanical ventilation protocol offering adequately explicit instructions for given clinical situations. An accepted protocol could also reduce confounding by mechanical ventilation management in a clinical trial.

Potential Acceptability of a Pediatric Ventilator Management Computer Protocol.

OBJECTIVES: To examine issues regarding the granularity (size/scale) and potential acceptability of recommendations in a ventilator management protocol for children with pediatric acute respiratory distress syndrome. DESIGN: Survey/questionnaire. SETTING: The eight PICUs in the Collaborative Pediatric Critical Care Research Network. PARTICIPANTS: One hundred twenty-two physicians (attendings and fellows). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We used an online questionnaire to examine attitudes and assessed recommendations with 50 clinical scenarios. Overall 80% of scenario recommendations were accepted. Acceptance did not vary by provider characteristics but did vary by ventilator mode (high-frequency oscillatory ventilation 83%, pressure-regulated volume control 82%, pressure control 75%; p = 0.002) and variable adjusted (ranging from 88% for peak inspiratory pressure and 86% for FIO2 changes to 69% for positive end-expiratory pressure changes). Acceptance did not vary based on child size/age. There was a preference for smaller positive end-expiratory pressure changes but no clear granularity preference for other variables. CONCLUSIONS: Although overall acceptance rate for scenarios was good, there was little consensus regarding the size/scale of ventilator setting changes for children with pediatric acute respiratory distress syndrome. An acceptable protocol could support robust evaluation of ventilator management strategies. Further studies are needed to determine if adherence to an explicit protocol leads to better outcomes.

Quality of reporting randomized controlled trials in cancer nursing research.

BACKGROUND: Results of randomized controlled trials (RCTs) provide high-level evidence for evidence-based practice (EBP). The quality of RCTs has a substantial influence on providing reliable knowledge for EBP. Little is known about the quality of RCT reporting in cancer nursing. OBJECTIVE: The aim of this study was to assess the quality of reporting in published cancer nursing RCTs from 1984 to 2010. METHODS: A total of 227 RCTs in cancer nursing published in English-language journals and indexed in PubMed or Cumulative Index to Nursing and Allied Health Literature were reviewed using the Jadad scale, key methodologic index (KMI), and the Consolidated Standards of Reporting Trials (CONSORT) checklist to assess the quality of reporting methodological aspects of research and the overall quality of reporting RCTs. RESULTS: Adherence to reporting metrics was relatively low, based on the Jadad score (M = 1.94 out of 5, SD = 1.01), KMI scores (M = 0.84 out of 3, SD = .87), and adherence to CONSORT checklist items (M =16.92 out of 37, SD = 4.03). Only 11 of 37 items in the CONSORT checklist were reported in 80% or more of the studies reviewed. The quality of reporting showed some improvement over time. DISCUSSION: Adherence to reporting metrics for cancer nursing RCTs was suboptimal, and further efforts are needed to improve both methodology reporting and overall reporting. Journals are encouraged to adopt the CONSORT checklist to influence the quality of RCT reports.

A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation.

BACKGROUND: Increased workload, including workload related to electronic health record (EHR) documentation, is reported as a main contributor to nurse burnout and adversely affects patient safety and nurse satisfaction. Traditional methods for workload analysis are either administrative measures (such as the nurse-patient ratio) that do not represent actual nursing care or are subjective and limited to snapshots of care (eg, time-motion studies). Observing care and testing workflow changes in real time can be obstructive to clinical care. An examination of EHR interactions using EHR audit logs could provide a scalable, unobtrusive way to quantify the nursing workload, at least to the extent that nursing work is represented in EHR documentation. EHR audit logs are extremely complex; however, simple analytical methods cannot discover complex temporal patterns, requiring use of state-of-the-art temporal data-mining approaches. To effectively use these approaches, it is necessary to structure the raw audit logs into a consistent and scalable logical data model that can be consumed by machine learning (ML) algorithms. OBJECTIVE: We aimed to conceptualize a logical data model for nurse-EHR interactions that would support the future development of temporal ML models based on EHR audit log data. METHODS: We conducted a preliminary review of EHR audit logs to understand the types of nursing-specific data captured. Using concepts derived from the literature and our previous experience studying temporal patterns in biomedical data, we formulated a logical data model that can describe nurse-EHR interactions, the nurse-intrinsic and situational characteristics that may influence those interactions, and outcomes of relevance to the nursing workload in a scalable and extensible manner. RESULTS: We describe the data structure and concepts from EHR audit log data associated with nursing workload as a logical data model named RNteract. We conceptually demonstrate how using this logical data model could support temporal unsupervised ML and state-of-the-art artificial intelligence (AI) methods for predictive modeling. CONCLUSIONS: The RNteract logical data model appears capable of supporting a variety of AI-based systems and should be generalizable to any type of EHR system or health care setting. Quantitatively identifying and analyzing temporal patterns of nurse-EHR interactions is foundational for developing interventions that support the nursing documentation workload and address nurse burnout.

Social media use and mental health among older adults with multimorbidity: the role of self-care efficacy.

OBJECTIVES: To describe the prevalence and trends in the use of social media over time and explore whether social media use is related to better self-care efficacy and thus related to better mental health among United States older adults with multimorbidity. MATERIALS AND METHODS: Respondents aged 65 years+ and having 2 or more chronic conditions from the 2017-2020 Health Information National Trends Survey were analyzed (N = 3341) using weighted descriptive and logistic regression analyses. RESULTS: Overall, 48% (n = 1674) of older adults with multimorbidity used social media and there was a linear trend in use over time, increasing from 41.1% in 2017 to 46.5% in 2018, and then further up to 51.7% in 2019, and 54.0% in 2020. Users were often younger, married/partnered, and non-Hispanic White with high education and income. Social media use was associated with better self-care efficacy that was further related to better mental health, indicating a significant mediation effect of self-care efficacy in the relationship between social media use and mental health. DISCUSSION: Although older adults with multimorbidity are a fast-growing population using social media for health, significant demographic disparities exist. While social media use is promising in improving self-care efficacy and thus mental health, relying on social media for the management of multimorbidity might be potentially harmful to those who are not only affected by multimorbidity but also socially disadvantaged (eg, non-White with lower education). CONCLUSION: Great effort is needed to address the demographic disparity and ensure health equity when using social media for patient care.

Clinical equipoise regarding glycemic control: a survey of pediatric intensivist perceptions.

OBJECTIVES: To assess the willingness of pediatric intensivists to conduct a pediatric trial of blood glucose control, and to determine if self-reported practices were influenced by adult-specific data over the past 4 yrs. This was a follow-up to our previous 2005 survey. DESIGN: Electronic survey comprising a 30-item questionnaire. SETTING: North American PICUs that were members of, or connected to, the Pediatric Acute Lung Injury and Sepsis Network (n = 96 targeted institutions). PARTICIPANTS: North American pediatric intensivists (n = 209). INTERVENTIONS: None. METHODS: We conducted a survey of North American PICUs using a Web-based questionnaire. Invitations were sent to 96 institutions in 37 states/provinces. RESULTS: Response rate was 68% (141/209). The median definitions of hyperglycemia (150 mg/dL) and hypoglycemia (≤60 mg/dL) were similar to our 2005 survey results. Self-reported practice patterns remain variable. Although 75% of clinician respondents denied a change in clinical practice based on the published literature, the preferred blood glucose target range increased from 80-110 mg/dL in 2005 to 90-140 mg/dL in 2009. Intensivists who preferred a blood glucose target of 80-110 mg/dL decreased from 43% to 6% (p < 0.001). Many respondents (45%) indicated that the acceptable severe hypoglycemia rate (% patients) for a protocol was ≤2.5%. The majority (93%) indicated they would be willing to enroll patients in a pediatric trial of blood glucose control. CONCLUSIONS: Pediatric intensivists report that they control blood glucose with insulin in critically ill children and do not necessarily adopt adult-specific data or a single uniform blood glucose target. The published evidence does not adequately address PICU clinicians concerns. Unanswered questions and persistent variation in practice suggest a need for a multicenter clinical trial of blood glucose control in critically ill children.

What is the relationship between falls, functional limitations, and depressive symptoms among Chinese older adults? The role of living alone.

OBJECTIVES: Although the association between falls and depressive symptoms is well documented, the mechanisms underlying this association remain largely unexplored. We investigated the mediation role of functional limitations in the association between falls and depressive symptoms among Chinese older adults and determined whether the living arrangement (living alone or not) is a significant moderator of the above-mentioned mediation pathway. STUDY DESIGN: Cross-sectional study. MAIN OUTCOME MEASURES: Depressive symptoms were measured using the 10-item Center for Epidemiologic Studies Depression Scale short form (CESD-10), on which higher scores indicate higher levels of depressive symptoms. RESULTS: We used the harmonized China Health and Retirement Longitudinal Study national baseline data (2011-2012 year) involving 7410 participants aged 60 years and over. After adjusting for covariates (e.g., age and sex), the effects of falls on depressive symptoms were seen to be mediated by functional limitations among Chinese older adults (ß = 0.82, p < .001). The moderated mediation analysis, which assesses whether an indirect effect is conditional on values of a moderating variable, found that the mediation effect was contingent upon the living arrangement (ß = -0.60, p = .029). Specifically, the levels of functional limitations and depressive symptoms were higher for people with falls who were living with others relative to those living alone. CONCLUSIONS: These results suggest that functional limitations are an important intervening variable that links falls to depressive symptoms among Chinese older adults. Interventions to promote older adults' physical function and prevent falls are recommended to decrease the risk of depressive symptoms. These interventions can particularly benefit those who live with others.

Medication for Opioid Use Disorder During Pregnancy - Maternal and Infant Network to Understand Outcomes Associated with Use of Medication for Opioid Use Disorder During Pregnancy (MAT-LINK), 2014-2021.

Problem: Medication for opioid use disorder (MOUD) is recommended for persons with opioid use disorder (OUD) during pregnancy. However, knowledge gaps exist about best practices for management of OUD during pregnancy and these data are needed to guide clinical care. Period Covered: 2014-2021. Description of the System: Established in 2019, the Maternal and Infant Network to Understand Outcomes Associated with Medication for Opioid Use Disorder During Pregnancy (MAT-LINK) is a surveillance network of seven clinical sites in the United States. Boston Medical Center, Kaiser Permanente Northwest, The Ohio State University, and the University of Utah were the initial clinical sites in 2019. In 2021, three clinical sites were added to the network (the University of New Mexico, the University of Rochester, and the University of South Florida). Persons receiving care at the seven clinical sites are diverse in terms of geography, urbanicity, race and ethnicity, insurance coverage, and type of MOUD received. The goal of MAT-LINK is to capture demographic and clinical information about persons with OUD during pregnancy to better understand the effect of MOUD on outcomes and, ultimately, provide information for clinical care and public health interventions for this population. MAT-LINK maintains strict confidentiality through robust information technology architecture. MAT-LINK surveillance methods, population characteristics, and evaluation findings are described in this inaugural surveillance report. This report is the first to describe the system, presenting detailed information on funding, structure, data elements, and methods as well as findings from a surveillance evaluation. The findings presented in this report are limited to selected demographic characteristics of pregnant persons overall and by MOUD treatment status. Clinical and outcome data are not included because data collection and cleaning have not been completed; initial analyses of clinical and outcome data will begin in 2023. Results: The MAT-LINK surveillance network gathered data on 5,541 reported pregnancies with a known pregnancy outcome during 2014-2021 among persons with OUD from seven clinical sites. The mean maternal age was 29.7 (SD = ±5.1) years. By race and ethnicity, 86.3% of pregnant persons were identified as White, 25.4% as Hispanic or Latino, and 5.8% as Black or African American. Among pregnant persons, 81.6% had public insurance, and 84.4% lived in urban areas. Compared with persons not receiving MOUD during pregnancy, those receiving MOUD during pregnancy were more likely to be older and White and to have public insurance. The evaluation of the surveillance system found that the initial four clinical sites were not representative of demographics of the South or Southwest regions of the United States and had low representation from certain racial and ethnic groups compared with the overall U.S. population; however, the addition of three clinical sites in 2021 made the surveillance network more representative. Automated extraction and processing improved the speed of data collection and analysis. The ability to add new clinical sites and variables demonstrated the flexibility of MAT-LINK. Interpretation: MAT-LINK is the first surveillance system to collect comprehensive, longitudinal data on pregnant person-infant dyads with perinatal outcomes associated with MOUD during pregnancy from multiple clinical sites. Analyses of clinical site data demonstrated different sociodemographic characteristics between the MOUD and non-MOUD treatment groups. Public Health Actions: MAT-LINK is a timely and flexible surveillance system with data on approximately 5,500 pregnancies. Ongoing data collection and analyses of these data will provide information to support clinical and public health guidance to improve health outcomes among pregnant persons with OUD and their children.

Fatal and near-fatal asthma in children: the critical care perspective.

OBJECTIVE: To characterize the clinical course, therapies, and outcomes of children with fatal and near-fatal asthma admitted to pediatric intensive care units (PICUs). STUDY DESIGN: This was a retrospective chart abstraction across the 8 tertiary care PICUs of the Collaborative Pediatric Critical Care Research Network (CPCCRN). Inclusion criteria were children (aged 1-18 years) admitted between 2005 and 2009 (inclusive) for asthma who received ventilation (near-fatal) or died (fatal). Data collected included medications, ventilator strategies, concomitant therapies, demographic information, and risk variables. RESULTS: Of the 261 eligible children, 33 (13%) had no previous history of asthma, 218 (84%) survived with no known complications, and 32 (12%) had complications. Eleven (4%) died, 10 of whom had experienced cardiac arrest before admission. Patients intubated outside the PICU had a shorter duration of ventilation (median, 25 hours vs 84 hours; P < .001). African-Americans were disproportionately represented among the intubated children and had a shorter duration of intubation. Barotrauma occurred in 15 children (6%) before admission. Pharmacologic therapy was highly variable, with similar outcomes. CONCLUSION: Of the children ventilated in the CPCCRN PICUs, 96% survived to hospital discharge. Most of the children who died experienced cardiac arrest before admission. Intubation outside the PICU was correlated with shorter duration of ventilation. Complications of barotrauma and neuromyopathy were uncommon. Practice patterns varied widely among the CPCCRN sites.

Consent Builder: an innovative tool for creating research informed consent documents.

Objective: To describe process innovations related to research informed consent documents, and development and formative evaluation of Consent Builder, a platform for generating consent documents for multicenter studies. Materials and Methods: Analysis of Institutional Review Board workflows and documents, followed by process redesign, document redesign, and software development. Locally developed software leverages REDCap and LaTeX. A small-scale usability study was conducted. Results: Process innovations were combining document types, and conceptualizing 2-part informed consent documents: part 1 standardizing the study description and part 2 with local site verbiage. Consent Builder was implemented in the Trial Innovation Network. User survey scores were acceptable; but areas for improvement were noted. LaTeX coding was the biggest challenge for users. Discussion: The process changes were generally well accepted. The software implementation uncovered un-accounted for assumptions, and variability in IRB review workflow across centers. Technical modifications may be needed before widespread implementation. Conclusion: We demonstrated proof-of-concept of an approach to generate research consent documents that are consistent across sites in study description, but which allow for customization of local site verbiage. The Consent Builder tool is an example of an operational innovation, helping meet a need that arose in part due to regulations around use of Single IRB for multicenter trials.

Demonstration Project: Transitioning a Research Network to New Single IRB Platforms.

Since the 2016 National Institutes of Health (NIH) mandate to use a single IRB (sIRB) in multicenter research, institutions have struggled to operationalize the process. In this demonstration project, the University of Utah Trial Innovation Center assisted the Collaborative Pediatric Critical Care Research Network to transition from using individually negotiated reliance agreements and paper-based documentation to a new sIRB master agreement and an informatics platform to capture reliance documentation. Lessons learned that can guide other academic institutions and IRBs as they operationalize sIRBs included the need for sites to understand what type of engagement or reliance is required and their need to understand the difference between reliance and activation. Requirements around local review remain poorly understood. Further research is needed to determine approaches that can achieve the NIH vision of reviews becoming more efficient and improving study start-up times, relieving administrative burden while advancing human research protections.

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy.

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

Patient-Generated Health Data in Pediatric Asthma: Exploratory Study of Providers' Information Needs.

BACKGROUND: Adolescents are using mobile health apps as a form of self-management to collect data on symptoms, medication adherence, and activity. Adding functionality to an electronic health record (EHR) to accommodate disease-specific patient-generated health data (PGHD) may support clinical care. However, little is known on how to incorporate PGHD in a way that informs care for patients. Pediatric asthma, a prevalent health issue in the United States with 6 million children diagnosed, serves as an exemplar condition to examine information needs related to PGHD. OBJECTIVE: In this study we aimed to identify and prioritize asthma care tasks and decisions based on pediatric asthma guidelines and identify types of PGHD that might support the activities associated with the decisions. The purpose of this work is to provide guidance to mobile health app developers and EHR integration. METHODS: We searched the literature for exemplar asthma mobile apps and examined the types of PGHD collected. We identified the information needs associated with each decision in accordance with consensus-based guidelines, assessed the suitability of PGHD to meet those needs, and validated our findings with expert asthma providers. RESULTS: We mapped guideline-derived information needs to potential PGHD types and found PGHD that may be useful in meeting information needs. Information needs included types of symptoms, symptom triggers, medication adherence, and inhaler technique. Examples of suitable types of PGHD were Asthma Control Test calculations, exposures, and inhaler use. Providers suggested uncontrolled asthma as a place to focus PGHD efforts, indicating that they preferred to review PGHD at the time of the visit. CONCLUSIONS: We identified a manageable list of information requirements derived from clinical guidelines that can be used to guide the design and integration of PGHD into EHRs to support pediatric asthma management and advance mobile health app development. Mobile health app developers should examine PGHD information needs to inform EHR integration efforts.