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Ambulatory Management of Childhood Asthma Using a Novel Self-management Application

Flory L. Nkoy, Bernhard A. Fassl, Victoria L. Wilkins, Joseph Johnson, Eun Hea Unsicker, Karmella J. Koopmeiners, Andrea Jensen, Michelle Frazier, Jordan Gaddis, Lis Malmgren, Stacey Williams, Heather Oldroyd, Tom Greene, Xiaoming Sheng, Derek A. Uchida, Christopher G. Maloney and Bryan L. Stone
Pediatrics June 2019, 143 (6) e20181711; DOI: https://doi.org/10.1542/peds.2018-1711
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Abstract

Video Abstract

BACKGROUND AND OBJECTIVES: Pediatric ambulatory asthma control is suboptimal, reducing quality of life (QoL) and causing emergency department (ED) and hospital admissions. We assessed the impact of the electronic-AsthmaTracker (e-AT), a self-monitoring application for children with asthma.

METHODS: Prospective cohort study with matched controls. Participants were enrolled January 2014 to December 2015 in 11 pediatric clinics for weekly e-AT use for 1 year. Analyses included: (1) longitudinal changes for the child (QoL, asthma control, and interrupted and missed school days) and parents (interrupted and missed work days and satisfaction), (2) comparing ED and hospital admissions and oral corticosteroid (OCS) use pre- and postintervention, and (3) comparing ED and hospital admissions and OCS use between e-AT users and matched controls.

RESULTS: A total of 327 children and parents enrolled; e-AT adherence at 12 months was 65%. Compared with baseline, participants had significantly (P < .001) increased QoL, asthma control, and reduced interrupted and missed school and work days at all assessment times. Compared with 1 year preintervention, they had reduced ED and hospital admissions (rate ratio [RR]: 0.68; 95% confidence interval [CI]: 0.49–0.95) and OCS use (RR: 0.74; 95% CI: 0.61–0.91). Parent satisfaction remained high. Compared with matched controls, participants had reduced ED and hospital admissions (RR: 0.41; 95% CI: 0.22–0.75) and OCS use (RR: 0.65; 95% CI: 0.46–0.93).

CONCLUSIONS: e-AT use led to high and sustained participation in self-monitoring and improved asthma outcomes. Dissemination of this care model has potential to broadly improve pediatric ambulatory asthma care.

  • Abbreviations:
    ACT —
    asthma control test
    CI —
    confidence interval
    e-AT —
    electronic-AsthmaTracker
    ED —
    emergency department
    IH —
    Intermountain Healthcare
    OCS —
    oral corticosteroid
    PCP —
    primary care provider
    QoL —
    quality of life
    RR —
    rate ratio

    • What’s Known on This Subject:

    Despite recommendations and potential benefits, asthma self-management interventions for children are rare, and uptake by patients is poor. Little data exist about their effectiveness on outcomes of children with asthma.

    What This Study Adds:

    In this study, we report participants’ adherence and the impact on multiple outcomes of implementing a novel self-management tool for children with asthma at 11 clinics.

    Poorly controlled childhood asthma is associated with frequent exacerbations and a significant impact on child and family quality of life (QoL) and health care use.1 In 2016, about 8.3 million children <18 years had asthma, and 53.7% experienced an exacerbation.2 Asthma is one of the most common causes of school absenteeism and missed work for parents.2,3 In 2008, children aged 5 to 17 years with at least 1 asthma exacerbation in the previous year missed 10.5 million school days, and their parents missed 14.2 million work days,1 a productivity loss of $3.8 billion per year.4 The annual cost of pediatric asthma in the United States is estimated at $20.7 billion.5

    Despite broad distribution of guidelines, >50% of children with asthma still have poorly controlled disease,6,7 leading to poor QoL, increased exacerbation risk, and frequent acute health care use.8 Multiple factors are associated with poor control, including9–13 (1) insufficient education and support for self-management, (2) limited self-management skills, (3) failure to recognize and act on early changes in asthma control,14,15 (4) nonadherence with therapy,16 and (5) inadequate therapy prescribing by physicians.8,17–19

    To reduce the risk of exacerbations and acute health care use, guidelines recommend control assessments and preventive measures at clinical encounters, including initiating self-management support to help patients achieve and maintain optimal control.18,20–22 However, self-management support interventions are rarely implemented in general pediatric ambulatory clinics.18

    With input from parents, we developed a Web and mobile-Web application (https://asthmatracker.utah.edu/public/index.php), the electronic-AsthmaTracker (e-AT).23 The e-AT was designed to support the self-monitoring and management of children with asthma by using the asthma control test (ACT)24 (modified and validated for weekly assessment) coupled with decision support for proactive care.25,26 Our objective in this study was to assess the impact of implementing the e-AT in general pediatric ambulatory clinics.

    Methods

    Setting

    We contacted and enrolled 11 general pediatric ambulatory clinics (no clinic refused participation) on the basis of their proximity to our team, and/or readiness to implement the e-AT, with (1) a physician champion to facilitate clinic e-AT use, (2) a clinic care coordinator with 15% full-time equivalent dedicated to using the e-AT in patient care, and (3) capacity to accommodate enrollment and e-AT training (eg, private room). We involved 11 clinics to achieve our targeted sample size of 30 patients per clinic for a total of 330 patients.

    Overall, 9 of the 11 clinics are owned by Intermountain Healthcare (IH), whereas 2 are non-IH clinics. IH, a regional not-for-profit integrated health care delivery system, has 22 hospitals and 185 clinics and urgent care facilities in Utah and southeastern Idaho,27 providing care to about 1 680 000 patients and serving about 60% of Utah’s residents and 85% of Utah’s children.28 The University of Utah Institutional Review Board approved the study.

    Parent and Stakeholder Engagement

    The AsthmaTracker was developed and tested in a paper version,25,29 translated into the electronic version (e-AT),23 and implemented in participating clinics with the assistance of 6 parents and 7 community stakeholders who provided input on study design and implementation. Community stakeholders included 2 primary care providers (PCPs), 2 insurance company representatives, 2 representatives for the Asthma Program of the Utah Department of Health, and the IH Pediatric and Primary Care Clinical Program Directors.

    Study Population and Study Design

    Inclusion criteria consisted of the following: (1) children 2 to 17 years with persistent asthma (determined by the PCP based on National Institutes of Health criteria of ongoing need for a controller medication) who received asthma care in the previous year at participating clinics (and their parents), (2) English speakers, and (3) having Internet access. Exclusion criteria included children who previously used the AsthmaTracker (paper version) because this may influence results.25,29 We conducted a prospective cohort study, comparing e-AT participants to their own baselines and to a concurrent matched-control population.

    Participant Identification, Enrollment, and Follow-up

    The research team provided training to clinical staff at each clinic, addressing gaps in asthma care and opportunities to improve care with the e-AT. Eligible patients were identified electronically at each clinic and invited to enroll in the study by clinic care coordinators from January 2014 to December 2015. Families who consented received education about the e-AT using a standardized teaching flipchart, were given e-AT access, and were asked to use it weekly for 1 year.

    e-AT Description

    The e-AT features include the following: (1) automated reminders to sustain use, (2) real-time results graphing, (3) alerts for patients and parents (via e-mail or text) and PCP’s office (via e-mail or clinic dashboard) for early signs of asthma control deterioration, and (4) real-time recommendations using the colors green, yellow, and red. Scores (range 5–25) had 3 categories: 20 to 25 (well controlled or green), 15 to 19 (not well controlled or yellow), and ≤15 (poorly controlled or red).

    The e-AT Web-based clinic dashboard facilitates patient management in the clinic and provides real-time data on patient’s asthma control status with longitudinal graphs, adherence to e-AT use, and adherence to medications. After an alert, the clinic proactively contacts patients and parents to identify and address care issues and adjust treatment using step therapy.30 Additional features to support participant adherence include the following: (1) a progress bar, adding 25 points per completion of an assessment and, at 100 points (every 4 completions), generating a congratulatory message with fireworks and a notice of an incentive ($10 gift certificate); and (2) a leader board, allowing users to see weekly adherence of the top 5 deidentified users.

    Outcomes Measures, Data Collection, and Sources

    The child’s QoL (primary outcome) was measured at baseline, 3, 6, and 12 months by using the Integrated Therapeutics Group Child Asthma Short Form.31,32 Asthma control was collected weekly using the e-AT, which was also used to collect data on the number of child interrupted or missed school days and parent interrupted or missed work days. Parent satisfaction with overall asthma care was measured at baseline and 12 months by using the client satisfaction survey.33 Collection of the above outcomes was restricted to e-AT users.

    We used the IH enterprise data warehouse34 to identify a concurrent matched-control group and collected patient demographics (age, sex, race, and insurance) and usage outcomes data for the whole study population, including the number of oral corticosteroid (OCS) prescriptions and number of emergency department (ED) and hospital admissions during 1 year before and 1 year after e-AT initiation. The enterprise data warehouse is an integrated database, linking administrative and clinical data from IH hospitals and clinics, and captures about 95% of pediatric ED and hospital admissions in Utah. Controls with persistent asthma were matched two to one to cases by age, sex, clinic visit dates within ±2 months of enrollment of cases, and clinic visit types (asthma versus nonasthma). Controls were drawn from 42 IH ambulatory clinics not contacted to participate in the study in the same geographical regions as participating clinics. Control patients were identified by using International Classification of Diseases, Ninth Revision and International Classification of Diseases, Tenth Revision codes, and persistent asthma was assumed on the basis of having at least 2 controller prescriptions in the previous year.

    Statistical Analysis

    Descriptive analyses were used to summarize the study population baseline characteristics, by using means and SDs for continuous variables, and frequencies for categorical and ordinal variables. Three sets of analyses were used to assess the e-AT impact, including (1) longitudinal changes in outcomes (QoL, asthma control, interrupted or missed school and work days, and satisfaction) within subjects (e-AT users), comparing baseline (before e-AT initiation) to follow-up values (while using the e-AT); (2) comparisons of ED or hospital admission and OCS use rates within subjects, during 1 year before versus 1 year after e-AT initiation; and (3) comparisons of ED or hospital admission and OCS use rates between subjects in clinics that received the e-AT versus matched controls from clinics that did not receive it.

    Longitudinal Analysis

    Outcome measurements obtained at enrollment (before e-AT exposure) were used as baseline values, reflecting patient outcomes under usual care. For QoL, we fitted a linear fixed effect repeated measures model to estimate the adjusted mean QoL score change between baseline and each follow-up (3, 6, 12 months), with clinic, time (month), and clinic-by-time interaction terms used as fixed effects, using a common unstructured covariance matrix to account for correlations in QoL measurements in the same patient.

    For asthma control, we used random patient effects models with weekly ACT scores as a continuous variable, assessing mean changes from baseline to first, second, third, and fourth quarter. Models included patient’s adherence (cumulative proportion of weeks of e-AT use) as an additional covariate. For parent satisfaction, we used linear fixed effects like in QoL. For interrupted or missed school and work days, we used generalized estimating equations with robust SEs and a compound symmetry working covariance model under negative binomial model. The model estimated rate ratios (RRs) for the outcomes between baselines and follow-up periods. All models were controlled for sine and cosine transformations of calendar months to account for seasonality.

    Comparisons of ED or Hospital Admission and OCS Use Rates (Within Subjects)

    We used clustered negative binomial regression to compare 1 year before (used as baseline) to 1 year after e-AT initiation periods, accounting for nesting of the pre- and postperiod within each subject.

    Comparisons of ED or Hospital Admission and OCS Use Rates Between Subjects and Matched Controls

    We used intention-to-treat analysis, comparing ED or hospital admission and OCS use rates over a 1-year follow-up period between cases and controls, using a generalized linear model with a negative binomial outcome, logarithmic link and random site effects, and matching variables and ED or hospital admission and OCS use rates in the preceding 1 year as covariates.

    Results

    General Summary

    We enrolled 327 children (and 327 parents or primary caregivers) from the 11 clinics. Two clinics had only 1 patient enrolled and were excluded, leaving 325 children from 9 clinics for analyses, of which 318 (97.2%) completed baseline assessments and were included in the longitudinal analysis. Overall, enrollment included 40% female patients and 76% white patients, with a mean age of 7.9 years (4.0 SD). Adherence with weekly e-AT use at 12 months was 65%.

    Longitudinal Analyses

    Overall, the average child QoL score (Fig 1) significantly (P < .001) increased from 79.1 at baseline to 90.9, 90.0, and 90.6 at 3, 6, and 12 months, respectively. This ∼12-point increase occurred early at 3 months and remained high thereafter. Increases (P < .001) in QoL occurred in 8 of 9 clinics, with Fig 2 showing SEs of changes (from baseline) at 3, 6, and 12 months overall and at individual clinics, with 0 representing no change. We also found improvement (P < .001) in the overall average asthma control score (Fig 3) from 18.8 at baseline to 22.3, 22.8, 22.8, and 22.9 at quarters 1, 2, 3, and 4, respectively. The increase occurred early (quarter 1) after e-AT initiation, stayed high thereafter, and was seen in 8 clinics. In Fig 4, we show SE of changes (from baseline) at quarters 1, 2, 3, and 4 overall and at individual clinics. Supplemental Figs 9 and 10 are QoL and asthma control changes overall and across clinics.

    FIGURE 1
    FIGURE 1

    Adjusted mean in QoL scores at baseline and 3, 6, and 12 months. Overall adjusted mean QoL scores with SEs at baseline (0 months) and 3, 6, and 12 months. There was a significant increase (from baseline) of about 12 points in QoL scores, starting early at 3 months and remaining high through the end of the study follow-up period. Y-axis units show only QoL score ranges between 50 and 100 (rather than 0–100) for easy reading.

    FIGURE 2
    FIGURE 2

    Adjusted mean change in QoL scores at 3, 6, and 12 months. Adjusted mean change (from baseline) in QoL scores (x-axis), overall (top) and by individual clinics, at months 3, 6, and 12, with SE bars of the adjusted means. The vertical line at 0 is a reference, and each bar crossing this line indicates no change (from baseline) at a specified follow-up time. For instance, clinic 6 did not change at any follow-up time period, as the 3 SE bars are crossing the vertical line. SE bars on the right, not crossing the vertical line, indicate significant improvement from baseline. For instance, there was an improvement of ∼10 points at 3-, 6-, and 12-month follow-up times overall.

    FIGURE 3
    FIGURE 3

    Adjusted mean in asthma control at baseline and quarters 1, 2, 3, and 4. Overall adjusted mean asthma control scores with SEs at baseline (0) and at quarters 1, 2, 3, and 4. The overall mean asthma control score shows a significant increase (from baseline) of close to 4 points early after initiation of the e-AT at quarter 1, with scores remaining high through the end of the study follow-up period.

    FIGURE 4
    FIGURE 4

    Adjusted mean change in asthma control at quarters 1, 2, 3, and 4. Adjusted mean change (from baseline) in asthma control scores (x-axis), overall (top) and by individual clinics, at quarters 1, 2, 3, and 4, with SE bars of the adjusted means. The vertical line at 0 is a reference, and each bar crossing this line indicates no change (from baseline) at a specified follow-up time. For instance, clinic 6 did not change at any follow-up time period, as the 3 SE bars are crossing the vertical line. SE bars on the right, not crossing the vertical line, indicate significant improvement from baseline. For instance, there was an improvement of ∼4 points at quarters 1, 2, 3, and 4 overall (all clinics).

    The overall average number of interrupted or missed school days significantly decreased (P < .001) from 1.91 at baseline to 0.79, 0.52, and 0.79 at 3, 6, and 12 months, respectively. Significant reductions occurred at all follow-up time periods, overall and at most clinics (Fig 5). The overall average number of interrupted or missed work days significantly decreased (P < .001) from 0.72 at baseline to 0.27, 0.25, and 0.20 at 3, 6, and 12 months, respectively. Significant reductions occurred at all follow-up time periods, overall and at most clinics (Fig 6). The parent satisfaction score was high at baseline (4.7) and slightly reduced (P < .001) but remained high (4.5) at 12 months.

    FIGURE 5
    FIGURE 5

    Adjusted RR interrupted or missed school days at 3, 6, and 12 months. Adjusted RRs with their SE bars for interrupted or missed school days (x-axis) overall (all clinics) and by individual clinics at 3, 6, and 12 months. SE bars on the left of the line (not crossing 1) indicate significant reductions from baseline. SE bars crossing this line indicate no change at a specified follow-up. Overall (all clinics), a significant reduction occurred at 3, 6, and 12 months. RRs are displayed on the log scale.

    FIGURE 6
    FIGURE 6

    Adjusted RR of interrupted or missed work days at 3, 6, and 12 months. Adjusted RRs with their SE bars for interrupted or missed work days (x-axis) overall (all clinics) and by individual clinics at 3, 6, and 12 months. SE bars on the left of the line (not crossing 1) indicate significant reduction from baseline. SE bars crossing this line indicate no change at a specified follow-up. Overall (all clinics), a significant reduction occurred at 3, 6, and 12 months. RRs are displayed on the log scale.

    Comparisons of ED or Hospital Admissions and OCS Use (Within Subjects)

    The average ED and hospital asthma admission rates (per year) were 0.15 overall and 0.22 pre- and 0.09 postintervention, with an adjusted RR between pre- and postintervention of 0.68 (95% confidence interval [CI]: 0.49–0.95). The average OCS use was 0.63 days overall and 0.74 days pre– and 0.51 days post–e-AT, with an adjusted RR between pre- and postintervention of 0.74 (95% CI: 0.61–0.91). Overall and individual clinic outcomes are provided in Figs 7 and 8.

    FIGURE 7
    FIGURE 7

    Adjusted RR (pre- versus postintervention) of ED and hospital admissions. Adjusted RRs between 1 year pre– versus 1 year post–e-AT intervention, with SE bands, for ED and hospital admissions at each clinic and overall (all clinics). SE bars on the left of the line (not crossing 1) indicate significant reduction from baseline. SE bars crossing this line indicate no change. Overall (all clinics), a significant reduction occurred between 1 year pre- and 1 year postintervention. RRs are displayed on the log scale.

    FIGURE 8
    FIGURE 8

    Adjusted RR (pre- versus postintervention) of OCS use. Adjusted RRs between 1 year pre– versus 1 year post–e-AT intervention, with SE bars, for OCS use at each clinic and overall (all clinics). SE bars on the left of the line (not crossing 1) indicate significant reduction from baseline. SE bars crossing this line indicate no change. Overall (all clinics), a significant reduction occurred between 1 year pre- and 1 year postintervention. RRs are displayed on the log scale.

    Comparisons of ED or Hospital Admissions and OCS Use Between Subjects and Matched Controls

    Using intention-to-treat analysis, we compared 325 cases to 603 matched controls, with patient characteristics described in Table 1.

    View this table:
    TABLE 1

    Baseline Characteristics of Cases and Matched Controls

    ED and Hospital Admissions

    The overall average ED and hospital admission rates (per 1000 days) were 0.42 for cases and 0.23 for controls. Among cases, average ED and hospital admission rates were 0.59 over 1 year pre– and 0.24 over 1 year post–e-AT implementation. Among controls, ED and hospital admission rates were 0.23 over 1 year pre– and 0.24 over 1 year post–e-AT initiation. In analysis, it was shown that ED and hospital admissions were significantly reduced (RR: 0.43; 95% CI: 0.27–0.67) among cases but not among controls (RR: 1.04; 95% CI: 0.69–1.58), with an intervention effect RR of 0.41 (95% CI: 0.22–0.75) or 59% reduction among cases.

    OCS Use

    The overall average OCS use rates (per 1000 days) were 1.71 for cases and 1.93 for controls. Among cases, OCS use was 2.02 over 1 year pre– and 1.41 over 1 year post–e-AT initiation. Among controls, OCS use was 1.87 pre– and 1.99 post–e-AT initiation. The fixed effect model showed that OCS use was significantly reduced (RR: 0.69; 95% CI: 0.52–0.93) among cases but not among controls (RR: 1.06; 95% CI: 0.87–1.31), with an intervention effect RR of 0.65 (95% CI: 0.46–0.93) or 35% reduction in cases.

    Discussion

    When assessing longitudinal changes in outcomes, we found significant improvements from baseline in a child’s QoL and asthma control and reductions in a child’s interrupted or missed school days, parent’s interrupted or missed work days, OCS use, and ED and hospital admissions, with persisting high parent satisfaction. Compared with controls, e-AT users experienced significant reductions in ED and hospital admissions and OCS use during 1 year postintervention, whereas controls did not change, supporting a significant intervention effect.

    Previous self-monitoring and management support interventions have focused mostly on adults35–41 and have shown improved QoL, disease control, and less activity limitation.38,42–45 Studies of self-monitoring support interventions for children with asthma remain rare.18 In our results, it is demonstrated that multiple outcomes for the child and parent can be improved with integration of an effective technology that promotes parent and PCP engagement in asthma self-monitoring and identification of early signs of deterioration. With the e-AT, improvements in QoL and asthma control were sustained over the 12-month study duration, whereas Rikkers-Mutsaerts et al,46 using an Internet-based asthma self-management program, reported only short-term QoL improvement (at 3 but not 12 months) among adolescents recruited from general ambulatory practices. Also, asthma control scores increased about 4 points, twice the minimal clinically important difference of a 2-point change, reported with use of a Web-based asthma control monitoring diary in children.47

    Asthma guidelines recommend regular assessment of chronic asthma control to prompt timely adjustment of therapy.20 However, physicians in general do not regularly assess asthma control during clinical encounters.48 Lack of assessment leads to failure to initiate new or adjust asthma therapy, resulting in suboptimal treatment.14,15 Failure to regularly assess asthma control is attributed to busy schedules, lack of resources, and lack of an effective process for monitoring patients between clinic visits.14,15 The e-AT provides an effective way to regularly and remotely assess a patient’s asthma control and identify worsening control early, enabling proactive care.

    The few studies of self-monitoring and management interventions for patients with asthma in general ambulatory practices have suffered from high attrition, with poor patient adherence over time.49,50 A study in adults with asthma after 6 months of follow-up achieved an adherence of only 55% with weekly Asthma Control Questionnaire use for self-monitoring.51 Van der Meer et al,41 also in a study of adults with asthma, reported low adherence (53% at 12 months) with weekly use of Internet-based Asthma Control Questionnaire for self-monitoring. In our study, adherence with weekly use of e-AT decreased over time but remained high at 65% after 12 months; one of the highest 1-year adherence levels reported. Our high adherence may be intrinsic to the e-AT model or due to user motivation to comply with weekly e-AT use knowing that their PCP would review their data and by a small financial incentive ($10 per 4 weeks of adherence).

    Our primary limitation is the study design recommended by our parent partners and community stakeholders who opposed withholding the e-AT from a control group, as would have occurred in the cluster randomized trial initially planned, and the subsequent lack of QoL data from the control group. These may have introduced unrecognized biases, yet the magnitude and sustainability of improvements observed in multiple outcomes among e-AT users (an ambulatory asthma population for whom enrollment was not driven by exacerbations) and the differences in outcomes when the study population was compared with a concurrent control group (which experienced no improvement over the study period despite matching on multiple characteristics) support the validity of our findings and the generalizability of our results. We excluded 2 clinics with only 1 patient enrolled in each from analyses because clinic was a variable used in the fixed effects model, which requires within-clinic variation analysis. The impact of this is negligible. We used controller prescriptions to define persistent asthma in matched controls but could not differentiate the level of severity (eg, mild, moderate, or severe persistent asthma). Having these data could have led to improved matching and different results. Baseline ED and hospital admission rate was lower for matched controls, suggesting a less-ill population; however, the 2 groups were equivalent in baseline OCS use, and controls did not change in either ED and hospital admissions or OCS use over the course of the study, whereas e-AT users improved, supporting a lack of extrinsic factors contributing to improvement among e-AT users. In addition, study participants were drawn from general pediatric ambulatory clinics and had a mean baseline asthma control, measured by ACT, of 18.8 (a score below 19 or not well controlled), not atypical in ambulatory care settings.52–54 The significant improvements in asthma control and other outcomes among e-AT users only bolsters the need for self-monitoring and management interventions in typical ambulatory settings. Lastly, we provided monetary incentive to support participant adherence with weekly use of the e-AT and do not know the impact this may have had on adherence or outcomes. However, if monetary incentive is needed to support a high level of adherence, a model involving payers sharing back a portion of their savings from reduced resource use may be appropriate for the e-AT care model.

    Conclusions

    The e-AT was effective in engaging parents in asthma self-management and in achieving sustained improvement in asthma outcomes for children and parents. Dissemination of this proactive care model can lead to major improvements in asthma care and outcomes, with potential for significant reductions in asthma-related health care costs.

    Acknowledgments

    The authors would like to acknowledge the University of Utah and Intermountain Healthcare leadership for their efforts and diligence in supporting implementation and use of the e-AT in participating primary care clinics. Specifically, we thank Dr Ed Clark (Chair of the Department of Pediatrics, Associate Vice President of Clinical Affairs, and President of the University of Utah Medical Group), Ms Carolyn Reynolds (Pediatric Clinical Program Director), Dr Wayne Cannon (former Primary Care Clinical Program Director), Dr Joe Hales (Primary Children’s Hospital Information Systems Director), Dr Lucy Savitz (former Assistant Vice President for Delivery System Science), and Dr Brent James (former Vice President for Medical Research and Chief Quality Officer and Executive Director of the Institute for Health Care Delivery Research at Intermountain Healthcare) for their support for this project. The primary author has full access to all the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis.

    Footnotes

      • Accepted February 19, 2019.
    • Address correspondence to Flory L. Nkoy, MD, MS, MPH, Division of Pediatric Inpatient Medicine, Primary Children’s Medical Center, The University of Utah School of Medicine, 100 North Medical Dr, Salt Lake City, UT 84113. E-mail: flory.nkoy{at}hsc.utah.edu

    • FINANCIAL DISCLOSURE: Primary author Dr Nkoy, coauthors (Drs Fassl, Wilkins, and Stone and Ms Koopmeiners, Ms Unsicker, and Ms Oldroyd; salary and travel support), and parent stakeholder coauthors (Ms Jensen, Ms Frazier, Ms Gaddis, Ms Malmgren, and Ms Williams; salary) received payment through the Patient-Centered Outcomes Research Institute grant; the other authors have indicated they have no financial relationships relevant to this article to disclose.

    • FUNDING: This study was supported by contract IH-12-11-5330 from the Patient-Centered Outcomes Research Institute. The sponsor did not participate in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript. The contents of this study are solely the responsibility of the authors and do not necessarily represent the official view of the Patient-Centered Outcomes Research Institute.

    • POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

    References

    1. ↵
      1. Akinbami LJ,
      2. Moorman JE,
      3. Liu X
      . Asthma prevalence, health care use, and mortality: United States, 2005-2009. Natl Health Stat Rep. 2011;(32):1–14pmid:21355352
      OpenUrlPubMed
    2. ↵
      1. Zahran HS,
      2. Bailey CM,
      3. Damon SA,
      4. Garbe PL,
      5. Breysse PN
      . Vital signs: asthma in children - United States, 2001-2016. MMWR Morb Mortal Wkly Rep. 2018;67(5):149–155pmid:29420459
      OpenUrlCrossRefPubMed
    3. ↵
      1. Sullivan P,
      2. Ghushchyan V,
      3. Navaratnam P, et al
      . School absence and productivity outcomes associated with childhood asthma in the USA. J Asthma. 2018;55(2):161–168pmid:2845370
      OpenUrlCrossRefPubMed
    4. ↵
      1. Walter H,
      2. Sadeque-Iqbal F,
      3. Ulysse R,
      4. Castillo D,
      5. Fitzpatrick A,
      6. Singleton J
      . The effectiveness of school-based family asthma educational programs on the quality of life and number of asthma exacerbations of children aged five to 18 years diagnosed with asthma: a systematic review protocol. JBI Database Syst Rev Implement Rep. 2015;13(10):69–81pmid:26571284
      OpenUrlPubMed
    5. ↵
      1. American Lung Association
      . Asthma & children fact sheet. 2012
    6. ↵
      1. Lieu TA,
      2. Quesenberry CP,
      3. Sorel ME,
      4. Mendoza GR,
      5. Leong AB
      . Computer-based models to identify high-risk children with asthma. Am J Respir Crit Care Med. 1998;157(4, pt 1):1173–1180pmid:9563736
      OpenUrlCrossRefPubMed
    7. ↵
      1. Bloomberg GR,
      2. Trinkaus KM,
      3. Fisher EB Jr,
      4. Musick JR,
      5. Strunk RC
      . Hospital readmissions for childhood asthma: a 10-year metropolitan study. Am J Respir Crit Care Med. 2003;167(8):1068–1076pmid:12684246
      OpenUrlCrossRefPubMed
    8. ↵
      1. Chapman KR,
      2. Boulet LP,
      3. Rea RM,
      4. Franssen E
      . Suboptimal asthma control: prevalence, detection and consequences in general practice. Eur Respir J. 2008;31(2):320–325pmid:17959642
      OpenUrlAbstract/FREE Full Text
    9. ↵
      1. Centers for Disease Control and Prevention (CDC)
      . Asthma hospitalizations and readmissions among children and young adults–Wisconsin, 1991-1995. MMWR Morb Mortal Wkly Rep. 1997;46(31):726–729pmid:9262074
      OpenUrlPubMed
      1. Li D,
      2. German D,
      3. Lulla S,
      4. Thomas RG,
      5. Wilson SR
      . Prospective study of hospitalization for asthma. A preliminary risk factor model. Am J Respir Crit Care Med. 1995;151(3 pt 1):647–655pmid:7881651
      OpenUrlPubMed
      1. Crane J,
      2. Pearce N,
      3. Burgess C,
      4. Woodman K,
      5. Robson B,
      6. Beasley R
      . Markers of risk of asthma death or readmission in the 12 months following a hospital admission for asthma. Int J Epidemiol. 1992;21(4):737–744pmid:1521979
      OpenUrlCrossRefPubMed
      1. Mitchell EA,
      2. Bland JM,
      3. Thompson JM
      . Risk factors for readmission to hospital for asthma in childhood. Thorax. 1994;49(1):33–36pmid:8153938
      OpenUrlAbstract/FREE Full Text
    13. ↵
      1. Vargas PA,
      2. Perry TT,
      3. Robles E, et al
      . Relationship of body mass index with asthma indicators in head start children. Ann Allergy Asthma Immunol. 2007;99(1):22–28pmid:17650825
      OpenUrlCrossRefPubMed
    14. ↵
      1. Barnes PJ
      . Achieving asthma control. Curr Med Res Opin. 2005;21(suppl 4):S5–S9pmid:16138939
      OpenUrlPubMed
    15. ↵
      1. Bloomberg GR,
      2. Banister C,
      3. Sterkel R, et al
      . Socioeconomic, family, and pediatric practice factors that affect level of asthma control. Pediatrics. 2009;123(3):829–835pmid:19255010
      OpenUrlAbstract/FREE Full Text
    16. ↵
      1. Bateman ED,
      2. Frith LF,
      3. Braunstein GL
      . Achieving guideline-based asthma control: does the patient benefit? Eur Respir J. 2002;20(3):588–595pmid:12358333
      OpenUrlAbstract/FREE Full Text
    17. ↵
      1. Rabe KF,
      2. Adachi M,
      3. Lai CK, et al
      . Worldwide severity and control of asthma in children and adults: the global asthma insights and reality surveys. J Allergy Clin Immunol. 2004;114(1):40–47pmid:15241342
      OpenUrlCrossRefPubMed
    18. ↵
      1. National Asthma Education and Prevention Program
      . Expert Panel Report 3 (EPR-3): guidelines for the diagnosis and management of asthma-summary report 2007. J Allergy Clin Immunol. 2007;120(suppl 5):S94–S138pmid:17983880
      OpenUrlCrossRefPubMed
    19. ↵
      1. Stempel DA,
      2. McLaughin TP,
      3. Stanford RH,
      4. Fuhlbrigge AL
      . Patterns of asthma control: a 3-year analysis of patient claims. J Allergy Clin Immunol. 2005;115(5):935–939pmid:15867848
      OpenUrlCrossRefPubMed
    20. ↵
      New NHLBI guidelines for the diagnosis and management of asthma. National Heart, Lung and Blood Institute. Lippincott Health Promot Lett. 1997;2(7):1, 8–9pmid:9300898
      OpenUrlPubMed
      1. Ressel GW; Centers for Disease Control and Prevention; National Asthma Education and Prevention Program
      . NAEPP updates guidelines for the diagnosis and management of asthma. Am Fam Physician. 2003;68(1):169–170pmid:12887123
      OpenUrlPubMed
    22. ↵
      1. Williams SG,
      2. Schmidt DK,
      3. Redd SC,
      4. Storms W; National Asthma Education and Prevention Program
      . Key clinical activities for quality asthma care. Recommendations of the National Asthma Education and Prevention Program. MMWR Recomm Rep. 2003;52(RR-6):1–8pmid:12696781
      OpenUrlPubMed
    23. ↵
      1. Nkoy F,
      2. Stone B,
      3. Fassl B, et al
      . Development of a novel tool for engaging children and parents in asthma self-management. AMIA Annu Symp Proc. 2012;2012:663–672
      OpenUrl
    24. ↵
      1. Liu AH,
      2. Zeiger R,
      3. Sorkness C, et al
      . Development and cross-sectional validation of the Childhood Asthma Control Test. J Allergy Clin Immunol. 2007;119(4):817–825pmid:17353040
      OpenUrlCrossRefPubMed
    25. ↵
      1. Nkoy F,
      2. Stone B,
      3. Fassl B, et al
      . A self-monitoring tool for improving ambulatory asthma care in children. In: Proceedings from the Academy Health 2012 Annual Conference; June 24, 2012; Orlando, FL
    26. ↵
      1. Nkoy FL,
      2. Stone BL,
      3. Fassl BA, et al
      . Longitudinal validation of a tool for asthma self-monitoring. Pediatrics. 2013;132(6). Available at: www.pediatrics.org/cgi/content/full/132/6/e1554pmid:24218469
      OpenUrlAbstract/FREE Full Text
    27. ↵
      1. James BC,
      2. Savitz LA
      . How Intermountain trimmed health care costs through robust quality improvement efforts. Health Aff (Millwood). 2011;30(6):1185–1191pmid:21596758
      OpenUrlAbstract/FREE Full Text
    28. ↵
      1. Byington CL,
      2. Reynolds CC,
      3. Korgenski K, et al
      . Costs and infant outcomes after implementation of a care process model for febrile infants. Pediatrics. 2012;130(1). Available at: www.pediatrics.org/cgi/content/full/130/1/e16pmid:22732178
      OpenUrlAbstract/FREE Full Text
    29. ↵
      1. Nkoy F,
      2. Stone B,
      3. Fassl B, et al
      . Validation of a pediatric asthma self-monitoring tool. In: Proceedings from the Pediatric Academic Societies Conference; April 29, 2012; Boston, MA
    30. ↵
      1. National Heart, Lung, and Blood Institute
      . NHLBI-EPR-3. Guidelines for the Diagnosis and Management of Asthma. Bethesda, MD: National Heart, Lung, and Blood Institute; 2007
    31. ↵
      1. Gorelick MH,
      2. Brousseau DC,
      3. Stevens MW
      . Validity and responsiveness of a brief, asthma-specific quality-of-life instrument in children with acute asthma. Ann Allergy Asthma Immunol. 2004;92(1):47–51pmid:14756464
      OpenUrlPubMed
    32. ↵
      1. Kwok MY,
      2. Walsh-Kelly CM,
      3. Gorelick MH,
      4. Grabowski L,
      5. Kelly KJ
      . National Asthma Education and Prevention Program severity classification as a measure of disease burden in children with acute asthma. Pediatrics. 2006;117(4, pt 2):S71–S77pmid:16777834
      OpenUrlCrossRefPubMed
    33. ↵
      1. Varmi J
      . Development of the pediatric hematology/oncology parent satisfaction survey. Child Health Care. 2000;29:243–255
      OpenUrl
    34. ↵
      1. Evans RS,
      2. Lloyd JF,
      3. Pierce LA
      . Clinical use of an enterprise data warehouse.AMIA Annu Symp Proc. 2012;2012:189–198
      OpenUrlPubMed
    35. ↵
      1. Adams WG,
      2. Fuhlbrigge AL,
      3. Miller CW, et al
      . TLC-Asthma: an integrated information system for patient-centered monitoring, case management, and point-of-care decision support.AMIA Annu Symp Proc. 2003;2003:1–5
      OpenUrl
      1. Cruz-Correia R,
      2. Fonseca J,
      3. Lima L, et al
      . Web-based or paper-based self-management tools for asthma–patients’ opinions and quality of data in a randomized crossover study. Stud Health Technol Inform. 2007;127:178–189pmid:17901611
      OpenUrlPubMed
      1. Finkelstein J,
      2. Cabrera MR,
      3. Hripcsak G
      . Internet-based home asthma telemonitoring: can patients handle the technology? Chest. 2000;117(1):148–155pmid:10631213
      OpenUrlCrossRefPubMed
    38. ↵
      1. Janson SL,
      2. McGrath KW,
      3. Covington JK,
      4. Cheng SC,
      5. Boushey HA
      . Individualized asthma self-management improves medication adherence and markers of asthma control. J Allergy Clin Immunol. 2009;123(4):840–846pmid:19348923
      OpenUrlCrossRefPubMed
      1. Joshi A,
      2. Amelung P,
      3. Arora M,
      4. Finkelstein J
      . Clinical impact of home automated telemanagement in asthma.AMIA Annu Symp Proc. 2005;2005:1000
      OpenUrl
      1. Ostojic V,
      2. Cvoriscec B,
      3. Ostojic SB,
      4. Reznikoff D,
      5. Stipic-Markovic A,
      6. Tudjman Z
      . Improving asthma control through telemedicine: a study of short-message service. Telemed J E Health. 2005;11(1):28–35pmid:15785218
      OpenUrlCrossRefPubMed
    41. ↵
      1. van der Meer V,
      2. van Stel HF,
      3. Bakker MJ, et al; SMASHING (Self-Management of Asthma Supported by Hospitals, ICT, Nurses and General practitioners) Study Group
      . Weekly self-monitoring and treatment adjustment benefit patients with partly controlled and uncontrolled asthma: an analysis of the SMASHING study. Respir Res. 2010;11:74pmid:20537124
      OpenUrlCrossRefPubMed
    42. ↵
      1. Ahmad E,
      2. Grimes DE
      . The effects of self-management education for school-age children on asthma morbidity: a systematic review. J Sch Nurs. 2011;27(4):282–292pmid:21478414
      OpenUrlCrossRefPubMed
      1. Bonner S,
      2. Zimmerman BJ,
      3. Evans D,
      4. Irigoyen M,
      5. Resnick D,
      6. Mellins RB
      . An individualized intervention to improve asthma management among urban Latino and African-American families. J Asthma. 2002;39(2):167–179
      OpenUrlCrossRefPubMed
      1. Perneger TV,
      2. Sudre P,
      3. Muntner P, et al
      . Effect of patient education on self-management skills and health status in patients with asthma: a randomized trial. Am J Med. 2002;113(1):7–14pmid:12106617
      OpenUrlCrossRefPubMed
    45. ↵
      1. Rhee H,
      2. Belyea MJ,
      3. Hunt JF,
      4. Brasch J
      . Effects of a peer-led asthma self-management program for adolescents. Arch Pediatr Adolesc Med. 2011;165(6):513–519pmid:21646583
      OpenUrlCrossRefPubMed
    46. ↵
      1. Rikkers-Mutsaerts ER,
      2. Winters AE,
      3. Bakker MJ, et al; SMASHING Study Group
      . Internet-based self-management compared with usual care in adolescents with asthma: a randomized controlled trial. Pediatr Pulmonol. 2012;47(12):1170–1179pmid:22644646
      OpenUrlCrossRefPubMed
    47. ↵
      1. Voorend-van Bergen S,
      2. Vaessen-Verberne AA,
      3. Landstra AM, et al
      . Monitoring childhood asthma: web-based diaries and the asthma control test. J Allergy Clin Immunol. 2014;133(6):1599–1605.e2
      OpenUrlCrossRef
    48. ↵
      1. Garbutt JM,
      2. Highstein G,
      3. Yan Y,
      4. Strunk RC
      . Partner randomized controlled trial: study protocol and coaching intervention. BMC Pediatr. 2012;12:42pmid:22469168
      OpenUrlPubMed
    49. ↵
      1. Kaya Z,
      2. Erkan F,
      3. Ozkan M, et al
      . Self-management plans for asthma control and predictors of patient compliance. J Asthma. 2009;46(3):270–275
      OpenUrlCrossRefPubMed
    50. ↵
      1. Wensley DC,
      2. Silverman M
      . The quality of home spirometry in school children with asthma. Thorax. 2001;56(3):183–185pmid:11182009
      OpenUrlAbstract/FREE Full Text
    51. ↵
      1. Apter AJ,
      2. Wan F,
      3. Reisine S, et al
      . The association of health literacy with adherence and outcomes in moderate-severe asthma. J Allergy Clin Immunol. 2013;132(2):321–327pmid:23591273
      OpenUrlCrossRefPubMed
    52. ↵
      1. Bora M,
      2. Alpaydin AO,
      3. Yorgancioglu A, et al
      . Does asthma control as assessed by the asthma control test reflect airway inflammation? Multidiscip Respir Med. 2011;6(5):291–298pmid:22958759
      OpenUrlPubMed
      1. Gurková E,
      2. Popelková P
      . Validity of Asthma Control Test in assessing asthma control in Czech outpatient setting. Cent Eur J Public Health. 2015;23(4):286–291pmid:26841140
      OpenUrlPubMed
    54. ↵
      1. Malinow I,
      2. Rivera-Santiago R,
      3. López-Malpica F, et al
      . Association of the Asthma Control Test with peak flow measures in a population in Puerto Rico. P R Health Sci J. 2014;33(3):122–128pmid:25244881
      OpenUrlPubMed
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    Pediatrics
    Vol. 143, Issue 6
    1 Jun 2019
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    Ambulatory Management of Childhood Asthma Using a Novel Self-management Application
    Flory L. Nkoy, Bernhard A. Fassl, Victoria L. Wilkins, Joseph Johnson, Eun Hea Unsicker, Karmella J. Koopmeiners, Andrea Jensen, Michelle Frazier, Jordan Gaddis, Lis Malmgren, Stacey Williams, Heather Oldroyd, Tom Greene, Xiaoming Sheng, Derek A. Uchida, Christopher G. Maloney, Bryan L. Stone
    Pediatrics Jun 2019, 143 (6) e20181711; DOI: 10.1542/peds.2018-1711
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