OBJECTIVES:

Mobile apps are suggested for supporting home monitoring and reducing emergency department (ED) visits and hospitalizations for children with medical complexity (CMC). None have been implemented. We sought to assess the MyChildCMC app (1) feasibility for CMC home monitoring, (2) ability to detect early deteriorations before ED and hospital admissions, and (3) preliminary impact.

METHODS:

Parents of CMC (aged 1–21 years) admitted to a children’s hospital were randomly assigned to MyChildCMC or usual care. MyChildCMC subjects recorded their child’s vital signs and symptoms daily for 3 months postdischarge and received real-time feedback. Feasibility measures included parent’s enrollment, retention, and engagement. The preliminary impact was determined by using quality of life, parent satisfaction with care, and subsequent ED and hospital admissions and hospital days.

RESULTS:

A total of 62 parents and CMC were invited to participate: 50 enrolled (80.6% enrollment rate) and were randomly assigned to MyChildCMC (n = 24) or usual care (n = 26). Retention at 1 and 3 months was 80% and 74%, and engagement was 68.3% and 62.6%. Run-chart shifts in vital signs were common findings preceding admissions. The satisfaction score was 26.9 in the MyChildCMC group and 24.1 in the control group (P = .035). No quality of life or subsequent admission differences occurred between groups. The 3-month hospital days (pre-post enrollment) decreased from 9.25 to 4.54 days (rate ratio = 0.49; 95% confidence interval = 0.39–0.62; P < .001) in the MyChildCMC group and increased from 1.08 to 2.46 days (rate ratio = 2.29; 95% confidence interval = 1.47–3.56; P < .001) in the control group.

CONCLUSIONS:

MyChildCMC was feasible and appears effective, with the potential to detect early deteriorations in health for timely interventions that might avoid ED and hospitalizations. A larger and definitive study of MyChildCMC’s impact and sustainability is needed.

Children with medical complexity (CMC) have high acute health care use with recurrent emergency department (ED) visits and frequent and/or prolonged hospitalizations.18  Although composing <1% of children, CMC account for 30% of pediatric health care costs and 86% of hospital charges in the United States.911 

CMC have significant daily care needs at home and require continuous parent (caregiver) attention and care coordination.12  These children often have frequent acute health deteriorations and issues with supportive technologies that lead to recurrent ED and/or hospital admissions. For example, in a sample of CMC with gastrostomy tubes, 73% had device-related complications that led to hospitalizations, most of which could have been identified and managed at home.13  Novel approaches are needed to reduce hospitalizations in CMC.

Many programs to support CMC ongoing care outside the hospital (eg, medical home, home nursing care, specialty clinics, etc) exist.1431  In observational studies, researchers suggest these programs can reduce hospitalizations, lengths of stay, and costs.3237  However, they are costly, underfunded, nonaccessible to many CMC, and lack home-monitoring systems for early recognition of child’s health deteriorations to avoid admissions.

Mobile health (mHealth) apps have the potential to support home monitoring and reduce the risk of acute deteriorations of CMC’s health.38,39  Although mHealth apps can improve outcomes of patients with chronic diseases,4046  studies have largely been targeted at single, noncomplex conditions (eg, asthma, hypertension, etc).4752  No home-monitoring app has been tested for feasibility or impact on CMC outcomes.

In a previous study, our team described preferences and key functionalities that parents found important for a CMC home-monitoring app.53  The results were used to design MyChildCMC and test it for usability with parents. The current pilot study’s objectives were to (1) assess the feasibility of MyChildCMC as a home-monitoring app for CMC, (2) explore the app’s ability to identify early signs of health deteriorations before ED and/or hospital admissions, and (3) determine the preliminary impact of the app on child-health outcomes. We hypothesized that MyChildCMC use would be feasible and achieve >60% enrollment, retention, and engagement rates and54  would help detect early signs of health deterioration and, that, compared with controls, MyChildCMC users would have better outcomes for the child (fewer ED and/or hospital use and hospital days and higher quality of life [QoL]) and parent (higher satisfaction with child’s care).

The study was conducted at a 289-bed freestanding, tertiary-care children’s hospital in the Intermountain West in the United States. CMC represent 30% of admissions and 53% of hospital charges. Annually, the hospital has 3500 ED visits, 1880 admissions, 4000 hospital days, and $257 million in hospital charges for CMC.

MyChildCMC app was designed for parent use to support CMC home monitoring and generate early warnings for acute health deteriorations that often lead to ED and/or hospital admissions.53  MyChildCMC has multiple features (Supplemental Fig 4) including the following:

  • 1. User interface: This allows parents to record daily the following parameters (Supplemental Table 3), determined during focus groups with CMC parents53 : temperature, heart rate (HR), oxygen saturation (O2Sat), respiratory rate, pain, seizure status, fluid intake and feeding, mental status, and level of parent worry about the child’s health.

    • a. For temperature, HR, and respiratory rate, measured values are recorded.

    • b. For pain, the numeric rating scale is used to score and record pain level.55  The numeric rating scale is a 1-item parent proxy scale with scores categorized as mild (0–3), moderate (4–7), and severe (8–10) pain.

    • c. Feeding and fluid intake (1 item) is recorded as 25%, 50%, 75%, or 100% of usual food and fluid intake.

    • d. Mental status is determined by using the behavior (1 item) question of the pediatric early warning scores, scored as (1) playing and/or appropriate, (2) sleepy, (3) irritable and/or agitated, and (4) lethargic and/or unresponsive.

    • e. Seizures are recorded as (1) fewer than usual, (2) usual, (3) more than usual, and (4) way more than usual.

    • f. Caregiver worry, a nonspecific sense of looming health deterioration (even in the absence of abnormal vital signs and/or symptoms), is recorded as (1) not worried, (2) a little worried, (3) worried, and (4) very worried. Excess parental worry about a child’s health increases the likelihood of the child receiving care.56 

  • 2. Decision rules: 2 rules are used to identify significant changes in vital signs and symptoms: (1) specific thresholds and (2) run-chart rules to identify deviation from individualized baselines,57,58  which are established with a minimum of 14 days of data, whereas the child is medically stable as recorded by parents (parents indicate if the child’s health status is “typical” or “normal” for each day’s data entry). Subsequent data are compared with the baseline until a significant change is detected, and, then, a new baseline is established with new 14 normal days after patient’s signs and symptoms return to a stable state, and so on.

    • a. For thresholds, significant changes are defined as O2Sat <90% (Salt Lake City altitude), a pain score >7, temperature <96.8°F or >101.3°F, <75% hydration or ≤25% nutrition, and scores 3 or 4 for mental status and worry.

    • b. Run-chart rules are used for temperature, HR, respiratory rate, pain, and O2Sat. Significant changes are defined as having the following:

      • • An outlier (data point >2 SD from patient’s baseline).

      • • Shift (≥6 consecutive points below or above the patient’s baseline mean but within 2 SD).

      • • Trend (≥5 increasing or decreasing consecutive data points).

  • 3. Real-time feedback system: includes a simplified summary report page and individual graphs.

    • a. Summary report: provides a quick view of “today’s” vital signs and symptoms, categorized in 3 zones: green (no outlier, trend, or shift), yellow (trend or shift with data within 2 SD), or red (outlier, shift, or trend with data >2 SD). Vital sign and symptom data in the green zone means “good,” the yellow zone means “could be better,” and the red zone means “not good.”

    • b. Individual graphs: provide a longitudinal view of changes over time for each vital sign and symptom.

  • 4. Real-time reminder: automated e-mail and/or text reminders sent daily to support user engagement with daily use.

  • 5. Other features: The app works across popular smartphone platforms. Data are stored behind the university firewall within a Health Insurance Portability and Accountability Act compliant database (HeidiSQL).

We conducted a randomized controlled trial of parents (or primary caregivers) whose CMC were admitted to the hospital. The inclusion criteria were (1) CMC determined by the care team using an operational definition, including having multisystem chronic conditions with (a) ≥3 organ systems affected, (b) ≥3 health care providers involved in their care, (c) ≥2 previous year admissions, and/or d) technology dependence; (2) aged 1 to 21 years; (3) parent owns a smartphone or tablet with Internet access; and (4) English speakers. The exclusion criteria were (1) critically ill, (2) intensive care admission, (3) receiving palliative care, (4) not discharged from the hospital, (5) non-English speakers, and (6) families without a smartphone or tablet or Internet access. The institutional review board approved the study. The trial is registered with ClinicalTrials.gov (identifier: NCT04470193).

Each morning, research coordinators contacted inpatient-team attending physicians to identify CMC admissions and review the medical records to confirm eligibility. Participants were approached during hospitalization, enrolled, and randomly assigned to the MyChildCMC app or usual care, by using a computer-generated allocation sequence uploaded into REDCap for use by research coordinators. Participants randomly assigned to MyChildCMC received help to download the app and training on use. Research coordinators reviewed vital sign measurements with parents (control and intervention) and contacted their nurse for training if they had difficulty and provided parents in both groups with needed equipment (pulse oximeter and stethoscope). Participants were enrolled between June 10, 2019, and December 19, 2019, and asked to monitor their child’s vital signs and symptoms daily for 3 months after hospital discharge, by using MyChildCMC (intervention group) or paper (control group). There was no other interaction or interference with the participant’s usual care.

QoL was collected at enrollment (baseline) and at 1- and 3-months postenrollment, by using a survey adapted from Ellzey et al,59  (Supplemental Fig 8), assessing physical health, mental health, sleep, pain, activities, and general QoL. Follow-up surveys included QoL, collected at 1- and 3-months postenrollment, and parent satisfaction with overall child’s care, collected at 3 months, by using Research Electronic Data Capture. Parent satisfaction (Supplemental Fig 9) was collected by using a survey adapted from the Client Satisfaction Questionnaire60  and assessed (1) parent confidence in care provision, (2) parent consistency to manage child’s health, (3) medical professional support, (4) positive social support, (5) care structure (monitoring and responding to symptoms) at home, and (6) parental stress. During satisfaction survey, we asked 4 additional questions (only to the intervention group) to assess parental perception of the app’s impact on their child’s health. Both groups received a gift card of $40 for completing each baseline and follow-up QoL surveys.

We used 3 metrics to assess MyChildCMC feasibility: (1) enrollment rate (percent of eligible parents who enrolled after being invited to participate), (2) retention rate (percent of participants who completed the QoL survey at 1 and 3 months), and (3) engagement rate (percent of daily vital signs and symptoms recorded in MyChildCMC out of all opportunities over the study period). The percentage of participants who used MyChildCMC each day during the study is depicted in Supplemental Fig 5.

MyChildCMC’s ability to detect health deteriorations was determined among users by plotting vital sign and symptom data on run charts and exploring any significant change in the 3 months postenrollment and its relationship with subsequent ED or hospital admission.

Preliminary impact was determined by comparing outcomes between the MyChildCMC and control groups. The primary outcome was the child’s QoL. Secondary outcomes included parent satisfaction with the overall child’s care and subsequent child’s ED or hospital admissions and hospital days (3 months pre- and postenrollment), collected after the study was completed, by using the electronic health record, which captures >95% of pediatric ED and hospital admissions in our state.

We used a generalized estimating equation model for repeated assessments, with a common unstructured residual covariance matrix to account for correlation in repeated measurements in the same patient, to compare QoL total score at baseline, 1 month, and 3 months between the groups. Using intention-to-treat analysis, we compared numbers of ED or hospital admissions and hospital days (3-month pre- and 3-month postenrollment; enrollment hospitalization excluded) between the groups using generalized linear models with logarithmic link and Poisson regression, estimating rate ratios (RRs) and 95% confidence intervals (CIs). A generalized linear model was also used to compare parent satisfaction. Covariates included age, sex, and race and ethnicity.

To achieve our planned sample size of 50 subjects, we randomly assigned 68 participants to provide 50 evaluable subjects, allowing up to 16% attrition, to estimate the proportions of patients satisfying enrollment, retention, engagement, and dropout to within margins of error (half-widths of 90% CIs) of ±0.11, 0.11, 0.16, and 0.13, if the true proportions for enrollment, retention, engagement, and dropout, were 0.60, 0.60, 0.60, and 0.20, respectively. The 50 evaluable subjects were used to estimate the SD of QoL, with a margin of error of ∼20%. The sample size also allowed us to provide provisional estimates of the effect size for the QoL outcome to within a margin of error of ±7.1 points, assuming a true SD of 15 points.

A total of 68 CMC parents were invited to participate: 12 declined enrollments, and 6 did not meet eligibility criteria (5 were non-English speaking parents, and 1 was on palliative care). Of the 50 out of 62 eligible parents who agreed (80.6%) to participate, 24 were randomly assigned to MyChildCMC, and 26 were randomly assigned to usual care. CMC were 42% female, 76% non-Hispanic white, 22% Hispanic, and 2% other race and/or ethnicity, with an overall mean age of 8.5 (SD: 5.7) years, with no significant differences between groups (Table 1). A total of 6 of the 50 parents participated in a previous usability study of MyChildCMC; 3 were randomly assigned to MyChildCMC, and 3 were randomly assigned to usual care.

TABLE 1

Demographic Characteristics

Patient (child) characteristicsControl (n = 26)Intervention (n = 24)P
Sex, n (%)    
  Male 14 (53.9) 15 (62.5) .536 
  Female 12 (46.2) 9 (37.5) — 
 Age, mean (95% CI) 9.65 (7.40–11.91) 7.29 (4.90–9.69) .144 
 Patient race and/or ethnicity, n (%)    
  White 18 (69.2) 20 (83.3) .477 
  Hispanic 8 (30.8) 3 (12.5) — 
  Other 0 (0) 1 (4.2) — 
Most common clinical conditions among CMC, n (%)    
 Technology dependent (n = 24) 11 (42.3) 13 (54.2) .402 
 Epilepsy and/or seizures (n = 19) 8 (30.8) 11 (45.6) .273 
 Respiratory failure (n = 17) 7 (26.9) 10 (41.6) .272 
 Fever (n = 17) 7 (26.9) 10 (41.6) .272 
 Infection of known cause (n = 15) 9 (34.6) 6 (25.0) .459 
 Cerebral palsy (n = 14) 6 (23.1) 8 (33.5) .420 
 GI or feeding issues (n = 10) 4 (15.4) 6 (25.0) .396 
Parent or caregiver characteristics, n (%)    
 Phone use type    
  iPhone 0 (0) 12 (50) — 
  Android 0 (0) 12 (50) — 
  Unknown 26 (100) 0 (0) — 
 Parent relation to patient, n (%)    
  Mother 23 (88.5) 20 (83.3) .723 
  Father 2 (7.7) 2 (8.3) — 
  Unknown 1 (3.9) 2 (8.3) — 
 Parent race and/or ethnicity, n (%)    
  White 20 (76.9) 19 (79.2) .716 
  Hispanic 5 (19.2) 2 (8.3) — 
  Other 1 (3.9) 3 (12.5) — 
 Parent marital status, n (%)    
  Married 19 (73.1) 15 (62.5) .291 
  Divorced 2 (7.7) 4 (16.7) — 
  Widowed 2 (7.7) 0 (0) — 
  Never married 2 (7.7) 3 (12.5) — 
  Unmarried couple 1 (3.9) 0 (0) — 
  Unknown 0 (0) 2 (8.3) — 
 Parent home status, n (%)    
  Not a single parent 20 (76.9) 15 (62.5) .109 
  Single parent 6 (23.1) 7 (29.2) — 
  Unknown 0 (0) 2 (8.3) — 
 Parent education, n (%)    
  College incomplete 13 (50) 9 (37.5) .056 
  College completed 13 (50) 12 (50) — 
  Unknown 0 (0) 3 (12.5) — 
 Parent family income, n (%)    
  <25 000 6 (24) 4 (16.7) .421 
  25 000–39 000 5 (20) 4 (16.7) — 
  40 000–49 000 2 (8) 1 (4.2) — 
  50 000–74 000 3 (12) 5 (20.8) — 
  75 000–99 000 1 (4) 4 (16.7) — 
  100 000–124 000 0 (0) 2 (8.3) — 
  125 000–149 000 3 (12) 1 (4.2) — 
  >150 000 5 (20) 1 (4.2) — 
  Unknown 1 (0) 2 (8.3) — 
 Parent employment status, n (%)    
  Unemployed 8 (30.8) 6 (25) .066 
  Employed 18 (69.2) 15 (62.5) — 
  Unknown 0 (0) 3 (12.5) — 
Patient (child) characteristicsControl (n = 26)Intervention (n = 24)P
Sex, n (%)    
  Male 14 (53.9) 15 (62.5) .536 
  Female 12 (46.2) 9 (37.5) — 
 Age, mean (95% CI) 9.65 (7.40–11.91) 7.29 (4.90–9.69) .144 
 Patient race and/or ethnicity, n (%)    
  White 18 (69.2) 20 (83.3) .477 
  Hispanic 8 (30.8) 3 (12.5) — 
  Other 0 (0) 1 (4.2) — 
Most common clinical conditions among CMC, n (%)    
 Technology dependent (n = 24) 11 (42.3) 13 (54.2) .402 
 Epilepsy and/or seizures (n = 19) 8 (30.8) 11 (45.6) .273 
 Respiratory failure (n = 17) 7 (26.9) 10 (41.6) .272 
 Fever (n = 17) 7 (26.9) 10 (41.6) .272 
 Infection of known cause (n = 15) 9 (34.6) 6 (25.0) .459 
 Cerebral palsy (n = 14) 6 (23.1) 8 (33.5) .420 
 GI or feeding issues (n = 10) 4 (15.4) 6 (25.0) .396 
Parent or caregiver characteristics, n (%)    
 Phone use type    
  iPhone 0 (0) 12 (50) — 
  Android 0 (0) 12 (50) — 
  Unknown 26 (100) 0 (0) — 
 Parent relation to patient, n (%)    
  Mother 23 (88.5) 20 (83.3) .723 
  Father 2 (7.7) 2 (8.3) — 
  Unknown 1 (3.9) 2 (8.3) — 
 Parent race and/or ethnicity, n (%)    
  White 20 (76.9) 19 (79.2) .716 
  Hispanic 5 (19.2) 2 (8.3) — 
  Other 1 (3.9) 3 (12.5) — 
 Parent marital status, n (%)    
  Married 19 (73.1) 15 (62.5) .291 
  Divorced 2 (7.7) 4 (16.7) — 
  Widowed 2 (7.7) 0 (0) — 
  Never married 2 (7.7) 3 (12.5) — 
  Unmarried couple 1 (3.9) 0 (0) — 
  Unknown 0 (0) 2 (8.3) — 
 Parent home status, n (%)    
  Not a single parent 20 (76.9) 15 (62.5) .109 
  Single parent 6 (23.1) 7 (29.2) — 
  Unknown 0 (0) 2 (8.3) — 
 Parent education, n (%)    
  College incomplete 13 (50) 9 (37.5) .056 
  College completed 13 (50) 12 (50) — 
  Unknown 0 (0) 3 (12.5) — 
 Parent family income, n (%)    
  <25 000 6 (24) 4 (16.7) .421 
  25 000–39 000 5 (20) 4 (16.7) — 
  40 000–49 000 2 (8) 1 (4.2) — 
  50 000–74 000 3 (12) 5 (20.8) — 
  75 000–99 000 1 (4) 4 (16.7) — 
  100 000–124 000 0 (0) 2 (8.3) — 
  125 000–149 000 3 (12) 1 (4.2) — 
  >150 000 5 (20) 1 (4.2) — 
  Unknown 1 (0) 2 (8.3) — 
 Parent employment status, n (%)    
  Unemployed 8 (30.8) 6 (25) .066 
  Employed 18 (69.2) 15 (62.5) — 
  Unknown 0 (0) 3 (12.5) — 

The table reveals that there were no statistical differences in demographic characteristics between the study groups. GI, gastrointestinal; —, not applicable.

Among the 50 participating parents, the retention rate was 80% (40 of 50) at 1 month and 76% (38 of 50) at 3 months. Among the 24 families randomly assigned to MyChildCMC, 5 families used it only once at the time of enrollment. Of families who used the app after discharge, the engagement (daily vital sign and symptom recordings) was 68.3% at 1 month and 62.6% at 3 months, and the average length of time from the start to discontinuing use of the app was 78.4 days (median: 89 days), with a range of 13 to 91 days. There were no technical issues with the app or safety concerns during the study.

We created run charts using participant data with >60% engagement in recording signs and symptoms and whose CMC had a hospitalization during follow-up. In 13 of 15 (87%) patients who had subsequent admissions, run charts revealed shifts (warning signs) and reduced variability in signs and symptoms as common changes occurring 1 to 2 weeks before admissions (Figs 13, Supplemental Figs 6 and 7). Two patients had shifts without admissions.

FIGURE 1

Average HR of patient A: positive shift and reduced variability. We depict the health-rate run-chart plot for patient A, revealing a positive shift (multiple points above the mean) and reduced variability, in the week leading to the admission. The shaded rectangle highlights the admission period, including the admission date and discharge date. The red points are related to run-chart rules in which data reveal significant changes. Control limits were (red dotted lines) determined with early data points when the parent indicated that the child’s health was “typical,” “usual,” or “normal state.”

FIGURE 1

Average HR of patient A: positive shift and reduced variability. We depict the health-rate run-chart plot for patient A, revealing a positive shift (multiple points above the mean) and reduced variability, in the week leading to the admission. The shaded rectangle highlights the admission period, including the admission date and discharge date. The red points are related to run-chart rules in which data reveal significant changes. Control limits were (red dotted lines) determined with early data points when the parent indicated that the child’s health was “typical,” “usual,” or “normal state.”

Close modal
FIGURE 2

Average temperature changes of patient B: negative shift and reduced variability. The figure reveals a negative shift (multiple points below the mean) and reduced variability in the temperature of patient B. The shaded rectangle highlights the admission period, including the admission date and discharge date. The red points are related to run-chart rules, in which data reveal significant changes.

FIGURE 2

Average temperature changes of patient B: negative shift and reduced variability. The figure reveals a negative shift (multiple points below the mean) and reduced variability in the temperature of patient B. The shaded rectangle highlights the admission period, including the admission date and discharge date. The red points are related to run-chart rules, in which data reveal significant changes.

Close modal
FIGURE 3

Average HR of patient B: positive shift and reduced variability. The figure reveals patient B with a positive shift (multiple points above the mean) in HR and reduced variability, before the admission. The shaded rectangle highlights the admission period, including the admission date and discharge date. The red points are related to run-chart rules, in which data reveal significant changes.

FIGURE 3

Average HR of patient B: positive shift and reduced variability. The figure reveals patient B with a positive shift (multiple points above the mean) in HR and reduced variability, before the admission. The shaded rectangle highlights the admission period, including the admission date and discharge date. The red points are related to run-chart rules, in which data reveal significant changes.

Close modal

The mean QoL score was 59.8 (standardized to 100) points in the MyChildCMC group and trended downward from 62.7 at baseline to 59.8 at 1 month and 55.2 at 3 months. Among controls, the mean QoL score was 52.5 and trended downward from 54.4 at baseline to 51.8 at 1 month and 50.8 at 3 months. QoL scores were not statistically significantly different (P > .05) at any assessment times between the groups. Two deaths (not related to the app use) and 1 hospice placement occurred in MyChildCMC group, and none occurred in controls.

The mean parent satisfaction with overall child’s care was high at 26.93 (SD = 2.22) points in the MyChildCMC group, compared with 24.14 (SD = 4.21) points in the control group (RR = 1.11; 95% CI = 1.01–1.22; P = .035), and subscale scores are provided in Supplemental Table 4, revealing the MyChildCMC group scoring significantly higher for medical and social support. Of the 15 participants randomly assigned to MyChildCMC and who responded to the 4 questions about the impact of the app on child’s health, 1 responded “did not know,” and 1 “strongly disagreed,” but 12 (80%) “agreed or strongly agreed” that the app made a positive contribution to their child’s health.

The mean number of ED and hospital admissions was 1.13 (median: 1 [interquartile range (IQR): 2]) before enrollment and 1.08 (median: 1 [IQR: 2]) after enrollment in the MyChildCMC group (RR = 0.96; 95% CI = 0.56–1.65; P = .891) and 0.85 (median: 1 [IQR: 1]) before and 0.88 (median: 0.5 (IQR: 1]) after enrollment in controls (RR = 1.05; 95% CI = 0.58–1.88; P = .882); there was no difference between the groups. The mean number of hospital days for the MyChildCMC group was 9.25 (median: 0 [IQR: 7.25]) days before and decreased to 4.54 (median: 0 [IQR: 5.75]) days (RR = 0.49; 95% CI = 0.39–0.62; P < .001) after enrollment but increased from 1.08 (median: 0 [IQR: 2]) before to 2.46 (median: 0 [IQR: 4.25]) days (RR = 2.29; 95% CI = 1.47–3.56; P < .001) after enrollment in the control group (Table 2).

TABLE 2

ED and Hospital Admissions and Hospital Days (3 Months Pre- and 3 Months Postenrollment)

No. PatientsNo. ED and/or Hospital AdmissionsMeanSDRR95% CIP
ED and/or hospital admissions        
 Intervention        
  Pre 24 27 1.13 1.26 — — — 
  Post 24 26 1.08 1.25 0.96 0.56–1.65 .891 
 Controls        
  Pre 26 22 0.85 1.05 — — — 
  Post 26 23 0.88 1.11 1.05 0.58–1.88 .882 
Hospital days        
Intervention        
  Pre 24 222a 9.25 18.30 — — — 
  Post 24 109a 4.54 6.95 0.49 0.39–0.62 <.001 
 Controls        
  Pre 26 28a 1.08 1.88 — — — 
  Post 26 64a 2.46 3.84 2.29 1.47–3.56 <.001 
No. PatientsNo. ED and/or Hospital AdmissionsMeanSDRR95% CIP
ED and/or hospital admissions        
 Intervention        
  Pre 24 27 1.13 1.26 — — — 
  Post 24 26 1.08 1.25 0.96 0.56–1.65 .891 
 Controls        
  Pre 26 22 0.85 1.05 — — — 
  Post 26 23 0.88 1.11 1.05 0.58–1.88 .882 
Hospital days        
Intervention        
  Pre 24 222a 9.25 18.30 — — — 
  Post 24 109a 4.54 6.95 0.49 0.39–0.62 <.001 
 Controls        
  Pre 26 28a 1.08 1.88 — — — 
  Post 26 64a 2.46 3.84 2.29 1.47–3.56 <.001 

The table reveals that there was no change in the cumulative number of ED and hospital admissions that occurred pre-enrollment compared with the cumulative number of ED and hospital admissions occurring postenrollment, for both the MyChildCMC and control groups. However, there was a significant reduction in the cumulative number of hospital days for hospital admissions occurring postenrollment for the MyChildCMC group, whereas there was a significant increase in the cumulative number of hospital days in admissions occurring postenrollment for the control group. —, not applicable.

a

No. hospital days

We found MyChildCMC use by parents feasible for CMC home monitoring, with high enrollment, retention, and engagement. Although QoL was higher in the MyChildCMC group, there was no statistical differences with controls. Yet, we found significantly greater parent satisfaction in child’s care and reduced hospital days among MyChildCMC users. MyChildCMC was able to detect signs (shifts) of acute health deteriorations 1 to 2 weeks before ED or hospital admissions in most (13 of 15) cases, meaning the app may be useful in alerting parents and their HCPs to promote timely home care to reduce ED and hospital use. Only 2 patients had shifts without admissions, possibly because of interventions at home.

We found no differences in ED- and hospital-admission frequency between groups. This may be due to our pilot study being underpowered for this outcome. The apparent reduction in hospital days in MyChildCMC users could be explained by real-time feedback empowering parents to be more vigilant at critical times, seek care, or intervene at home at earlier stages of deteriorations when the child’s condition is less severe, thus leading to shorter hospital stays when these children have unavoidable hospitalizations. However, we did not expect an increase in hospital days in controls and do not know the reasons for the increase.

Despite the availability of mobile phones and the relative low-cost opportunity to support the care of patients with chronic conditions,3846,61  no study has ever been targeted at CMC with home monitoring apps.6267  Available apps for home monitoring of children and adults are focused on noncomplex, single chronic conditions (eg, asthma, diabetes, etc).4752  The variable, multisystem conditions of CMC make it difficult to develop home monitoring apps. Only Cheng et al68  reported a home monitoring app for CMC, but this work was focused only on a subset of children with enteral feeding tubes and limited to usability testing, with no evaluation of impact. MyChildCMC’s uniqueness is that it introduces a practical approach to support home monitoring by focusing on crosscutting symptoms that often precede ED and hospital admissions in CMC rather than using multiple apps to monitor each CMC’s chronic conditions individually. With our study, we are the first to provide evidence that an app can help monitor CMC’s health at home, which may lead to improved outcomes, supporting the need for more studies of home monitoring apps for CMC.

Currently, there is no benchmark for feasibility outcomes, and no study has been conducted on CMC to determine enrollment, retention, and engagement targets of a daily symptom monitoring app. Studies of mHealth apps deemed feasible have various feasibility goals. For instance, a study designed to monitor daily depressive symptoms achieved a 43% user-engagement rate after 1 month.69  Similarly, the retention rate was 53% at 1 month in a study monitoring daily psychiatric symptoms.70  Also, an app for monitoring mental health symptoms in young people was declared feasible, with a recruitment of 52% and engagement rate of 45% at 3 months.71  Although the enrollment rate was high (86%) in a diabetes self-management program, the retention rate at 3 months was only 50%, but the study was declared feasible.72  In a study of a pain monitoring app in sickle cell disease, researchers reported a daily engagement of 86% in the first week but only 58% in the fourth week.73  Although our app is different from typical home monitoring apps (which often are targeted at a single disease or symptom) and there is no study for comparison, we selected our feasibility target a priori, with a goal of achieving >60% enrollment, retention, and engagement rates, on the basis of our previous experience with an app for children with asthma74  and the best practice guidance defined for the diet and exercise app in uterine cancer survivors.54 

Run-chart rules are widely used in quality improvement, although their application in monitoring vital signs and symptoms is not common.57,58  Because CMC often have atypical baselines for vital signs,75,76  run charts allowed us to (1) define individual patient’s baselines over 14 or more “normal” days as judged by their parents (studies report that ≥14 data points are needed to have adequate power to detect changes)77,78  and (2) identify significant changes from patient’s individualized baselines. We noted reduced variability in HRs as a signal of health status deterioration, which was also observed preceding preterm neonatal sepsis.79  Our plan included adding an alerting system74,80,81  for significant changes for parents and a population health dashboard for alerting HCPs that will allow real-time access to patient data to promote timely care.

Our study has several limitations, and results need to be interpreted with caution. This was a pilot study not powered for secondary outcomes but will inform a larger and definitive study of MyChildCMC’s impact and sustainability. In our study, we did not take into account planned admissions (eg, for sleep studies, surgeries, etc) in the ED and hospital admissions data, which may have biased our results. We grouped ED visits and hospital admissions as an outcome to capture early signs of child’s health deterioration that lead to any ED visit or hospital admission. Also, qualitative assessment was not included in our study but can be an important aspect of feasibility studies.

Although participants were randomly assigned, baseline hospital days were substantially higher in the MyChildCMC group. This may be due to our small sample size and skewing from a few patients, with more hospital days. However, the use of generalized linear models with logarithmic link and Poisson regression accounted for skewness of the data. Also, baselines in the number of ED and hospital admissions were similar in the groups, minimizing the risk of allocation bias. We also noted the random occurrence of 2 deaths and 1 hospice assignment in the MyChildCMC group, suggesting a sicker population in this group. This could be interpreted in 2 ways: our small sample size led to imbalance with regard to illness severity, suggesting caution in interpreting our results, or (2), despite sicker CMC being in the MyChildCMC group, we saw a significant drop in hospital days in these children, whereas hospital days increased in controls, suggesting the app’s effect size may be larger. In addition, we used intention-to-treat analysis to compare ED and hospital admissions and hospital days and did not exclude these 3 patients in our analysis. Even if we had excluded them, our results would reveal the same patterns of changes.

Although established baselines were stable, run charts reveal that vital signs post (subsequent not index) discharges were unstable for several days in some children. This could be due to either early discharge or a “signal” of an upcoming admission. Unfortunately, we did not collect data after 3 months. Our study was conducted in a single hospital with a less diverse patient population; therefore, generalizability of our results to other locations with diverse patients may be limited. Lastly, although parents reported it took ∼5 minutes per day to record vital signs and symptoms, we believe that automating vital sign data collection with a sensor device will minimize data burden and may increase long-term engagement.82 

We found MyChildCMC a feasible app for supporting home monitoring of CMC and may be effective in preventing recurrent ED and hospital admissions and/or reducing hospital days. In future studies, researchers will test its impact on a larger sample and will include a real-time alerting system for home parents and professional care teams to allow timely care in the home and facilitate medical decision-making that can impact ED and hospital visits.

Deidentified individual participant data (including data dictionaries), in addition to study protocols, the statistical analysis plan, and the informed consent form, will be made available to other researchers on request. Proposals should be submitted to flory.nkoy@hsc.utah.edu.

Dr Nkoy conceptualized and designed the study, drafted the initial manuscript, and oversaw the analysis; Drs Stone participated in the concept and design of the study, data analysis, interpretation of data, and manuscript revision; Drs Hofmann, Fassl, and Murphy and Mrs Zhu and Mrs Mahtta participated in the study concept and design, interpretation of data, and revision of the manuscript; and all authors have approved the final manuscript as submitted.

FUNDING: Supported by a grant from the Health Resources and Services Administration. The sponsors 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 article. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the Health Resources and Services Administration.

1
Feudtner
C
,
Villareale
NL
,
Morray
B
,
Sharp
V
,
Hays
RM
,
Neff
JM
.
Technology-dependency among patients discharged from a children’s hospital: a retrospective cohort study
.
BMC Pediatr
.
2005
;
5
(
1
):
8
2
Dolk
H
,
Parkes
J
,
Hill
N
.
Trends in the prevalence of cerebral palsy in Northern Ireland, 1981–1997
.
Dev Med Child Neurol
.
2006
;
48
(
6
):
406
412; discussion 405
3
Farooqi
A
,
Hägglöf
B
,
Sedin
G
,
Gothefors
L
,
Serenius
F
.
Chronic conditions, functional limitations, and special health care needs in 10- to 12-year-old children born at 23 to 25 weeks’ gestation in the 1990s: a Swedish national prospective follow-up study
.
Pediatrics
.
2006
;
118
(
5
). Available at: www.pediatrics.org/cgi/content/full/118/5/e1466
4
Hack
M
,
Taylor
HG
,
Drotar
D
, et al
.
Chronic conditions, functional limitations, and special health care needs of school-aged children born with extremely low-birth-weight in the 1990s
.
JAMA
.
2005
;
294
(
3
):
318
325
5
Odding
E
,
Roebroeck
ME
,
Stam
HJ
.
The epidemiology of cerebral palsy: incidence, impairments and risk factors
.
Disabil Rehabil
.
2006
;
28
(
4
):
183
191
6
Reid
SM
,
Lanigan
A
,
Reddihough
DS
.
Post-neonatally acquired cerebral palsy in Victoria, Australia, 1970-1999
.
J Paediatr Child Health
.
2006
;
42
(
10
):
606
611
7
Strauss
D
,
Shavelle
R
,
Reynolds
R
,
Rosenbloom
L
,
Day
S
.
Survival in cerebral palsy in the last 20 years: signs of improvement?
Dev Med Child Neurol
.
2007
;
49
(
2
):
86
92
8
Cohen
E
,
Kuo
DZ
,
Agrawal
R
, et al
.
Children with medical complexity: an emerging population for clinical and research initiatives
.
Pediatrics
.
2011
;
127
(
3
):
529
538
9
Berry
JG
,
Hall
M
,
Neff
J
, et al
.
Children with medical complexity and Medicaid: spending and cost savings
. [
published correction appears in Health Aff (Millwood). 2015;34(1):189
]. Health Aff (Millwood)
.
2014
;
33
(
12
):
2199
2206
10
Berry
JG
,
Hall
M
,
Hall
DE
, et al
.
Inpatient growth and resource use in 28 children’s hospitals: a longitudinal, multi-institutional study
.
JAMA Pediatr
.
2013
;
167
(
2
):
170
177
11
Berry
JG
,
Agrawal
R
,
Kuo
DZ
, et al
.
Characteristics of hospitalizations for patients who use a structured clinical care program for children with medical complexity
.
J Pediatr
.
2011
;
159
(
2
):
284
290
12
Simon
TD
,
Berry
J
,
Feudtner
C
, et al
.
Children with complex chronic conditions in inpatient hospital settings in the United States
.
Pediatrics
.
2010
;
126
(
4
):
647
655
13
Friedman
JN
,
Ahmed
S
,
Connolly
B
,
Chait
P
,
Mahant
S
.
Complications associated with image-guided gastrostomy and gastrojejunostomy tubes in children
.
Pediatrics
.
2004
;
114
(
2
):
458
461
14
Thomas
CL
,
O’Rourke
PK
,
Wainwright
CE
.
Clinical outcomes of Queensland children with cystic fibrosis: a comparison between tertiary centre and outreach services
.
Med J Aust
.
2008
;
188
(
3
):
135
139
15
Williams
J
,
Sharp
GB
,
Griebel
ML
, et al
.
Outcome findings from a multidisciplinary clinic for children with epilepsy
.
Child Health Care
.
1995
;
24
(
4
):
235
244
16
Rahimy
MC
,
Gangbo
A
,
Ahouignan
G
, et al
.
Effect of a comprehensive clinical care program on disease course in severely ill children with sickle cell anemia in a sub-Saharan African setting
.
Blood
.
2003
;
102
(
3
):
834
838
17
Cooley
WC
;
American Academy of Pediatrics Committee on Children With Disabilities
.
Providing a primary care medical home for children and youth with cerebral palsy
.
Pediatrics
.
2004
;
114
(
4
):
1106
1113
18
Cooley
WC
,
McAllister
JW
.
Building medical homes: improvement strategies in primary care for children with special health care needs
.
Pediatrics
.
2004
;
113
(
suppl 5
):
1499
1506
19
Cooley
WC
,
McAllister
JW
,
Sherrieb
K
,
Kuhlthau
K
.
Improved outcomes associated with medical home implementation in pediatric primary care
.
Pediatrics
.
2009
;
124
(
1
):
358
364
20
Palfrey
JS
,
Sofis
LA
,
Davidson
EJ
,
Liu
J
,
Freeman
L
,
Ganz
ML
;
Pediatric Alliance for Coordinated Care
.
The Pediatric Alliance for Coordinated Care: evaluation of a medical home model
.
Pediatrics
.
2004
;
113
(
suppl 5
):
1507
1516
21
Goss
CH
,
Rubenfeld
GD
,
Ramsey
BW
,
Aitken
ML
.
Clinical trial participants compared with nonparticipants in cystic fibrosis
.
Am J Respir Crit Care Med
.
2006
;
173
(
1
):
98
104
22
Kelly
A
,
Golnik
A
,
Cady
R
.
A medical home center: specializing in the care of children with special health care needs of high intensity
.
Matern Child Health J
.
2008
;
12
(
5
):
633
640
23
Bergius
H
,
Eng
A
,
Fagerberg
M
, et al
.
Hospital-managed advanced care of children in their homes
.
J Telemed Telecare
.
2001
;
7
(
suppl 1
):
32
34
24
Jessop
DJ
,
Stein
RE
.
Who benefits from a pediatric home care program?
Pediatrics
.
1991
;
88
(
3
):
497
505
25
Jessop
DJ
,
Stein
RE
.
Providing comprehensive health care to children with chronic illness
.
Pediatrics
.
1994
;
93
(
4
):
602
607
26
Stein
R
A home care program for children with chronic illness
.
Child Health Care
.
1983
;
12
(
2
):
90
92
27
Stein
RE
,
Jessop
DJ
.
Does pediatric home care make a difference for children with chronic illness? Findings from the Pediatric Ambulatory Care Treatment Study
.
Pediatrics
.
1984
;
73
(
6
):
845
853
28
Sacchetti
A
,
Sacchetti
C
,
Carraccio
C
,
Gerardi
M
.
The potential for errors in children with special health care needs
.
Acad Emerg Med
.
2000
;
7
(
11
):
1330
1333
29
Berman
S
,
Rannie
M
,
Moore
L
,
Elias
E
,
Dryer
LJ
,
Jones
MD
 Jr
.
Utilization and costs for children who have special health care needs and are enrolled in a hospital-based comprehensive primary care clinic
.
Pediatrics
.
2005
;
115
(
6
). Available at: www.pediatrics.org/cgi/content/full/115/6/e637
30
Gillette
Y
,
Hansen
NB
,
Robinson
JL
,
Kirkpatrick
K
,
Grywalski
R
.
Hospital-based case management for medically fragile infants: results of a randomized trial
.
Patient Educ Couns
.
1991
;
17
(
1
):
59
70
31
Gordon
JB
,
Colby
HH
,
Bartelt
T
,
Jablonski
D
,
Krauthoefer
ML
,
Havens
P
.
A tertiary care-primary care partnership model for medically complex and fragile children and youth with special health care needs
.
Arch Pediatr Adolesc Med
.
2007
;
161
(
10
):
937
944
32
Balaban
RB
,
Weissman
JS
,
Samuel
PA
,
Woolhandler
S
.
Redefining and redesigning hospital discharge to enhance patient care: a randomized controlled study
.
J Gen Intern Med
.
2008
;
23
(
8
):
1228
1233
33
Casey
PH
,
Lyle
RE
,
Bird
TM
, et al
.
Effect of hospital-based comprehensive care clinic on health costs for Medicaid-insured medically complex children
.
Arch Pediatr Adolesc Med
.
2011
;
165
(
5
):
392
398
34
Cohen
E
,
Friedman
JN
,
Mahant
S
,
Adams
S
,
Jovcevska
V
,
Rosenbaum
P
.
The impact of a complex care clinic in a children’s hospital
.
Child Care Health Dev
.
2010
;
36
(
4
):
574
582
35
Cohen
E
,
Lacombe-Duncan
A
,
Spalding
K
, et al
.
Integrated complex care coordination for children with medical complexity: a mixed-methods evaluation of tertiary care-community collaboration
.
BMC Health Serv Res
.
2012
;
12
:
366
36
Criscione
T
,
Walsh
KK
,
Kastner
TA
.
An evaluation of care coordination in controlling inpatient hospital utilization of people with developmental disabilities
.
Ment Retard
.
1995
;
33
(
6
):
364
373
37
Liptak
GS
,
Burns
CM
,
Davidson
PW
,
McAnarney
ER
.
Effects of providing comprehensive ambulatory services to children with chronic conditions
.
Arch Pediatr Adolesc Med
.
1998
;
152
(
10
):
1003
1008
38
Modi
AC
,
Pai
AL
,
Hommel
KA
, et al
.
Pediatric self-management: a framework for research, practice, and policy
.
Pediatrics
.
2012
;
129
(
2
). Available at: www.pediatrics.org/cgi/content/full/129/2/e473
39
Becker
S
,
Miron-Shatz
T
,
Schumacher
N
,
Krocza
J
,
Diamantidis
C
,
Albrecht
U-V
.
mHealth 2.0: experiences, possibilities, and perspectives
.
JMIR Mhealth Uhealth
.
2014
;
2
(
2
):
e24
40
Ahn
S
,
Basu
R
,
Smith
ML
, et al
.
The impact of chronic disease self-management programs: healthcare savings through a community-based intervention
.
BMC Public Health
.
2013
;
13
:
1141
41
Koegel
LK
,
Park
MN
,
Koegel
RL
.
Using self-management to improve the reciprocal social conversation of children with autism spectrum disorder
.
J Autism Dev Disord
.
2014
;
44
(
5
):
1055
1063
42
Rechenberg
K
,
Whittemore
R
,
Grey
M
,
Jaser
S
;
TeenCOPE Research Group
.
Contribution of income to self-management and health outcomes in pediatric type 1 diabetes
.
Pediatr Diabetes
.
2016
;
17
(
2
):
120
126
43
Adams
WG
,
Fuhlbrigge
AL
,
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
44
Cruz-Correia
R
,
Fonseca
J
,
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
189
45
Finkelstein
J
,
Cabrera
MR
,
Hripcsak
G
.
Internet-based home asthma telemonitoring: can patients handle the technology?
Chest
.
2000
;
117
(
1
):
148
155
46
Janson
SL
,
McGrath
KW
,
Covington
JK
,
Cheng
SC
,
Boushey
HA
.
Individualized asthma self-management improves medication adherence and markers of asthma control
.
J Allergy Clin Immunol
.
2009
;
123
(
4
):
840
846
47
Kirwan
M
,
Vandelanotte
C
,
Fenning
A
,
Duncan
MJ
.
Diabetes self-management smartphone application for adults with type 1 diabetes: randomized controlled trial
.
J Med Internet Res
.
2013
;
15
(
11
):
e235
48
Liu
WT
,
Huang
CD
,
Wang
CH
,
Lee
KY
,
Lin
SM
,
Kuo
HP
.
A mobile telephone-based interactive self-care system improves asthma control
.
Eur Respir J
.
2011
;
37
(
2
):
310
317
49
Marcano Belisario
JS
,
Huckvale
K
,
Greenfield
G
,
Car
J
,
Gunn
LH
.
Smartphone and tablet self management apps for asthma
.
Cochrane Database Syst Rev
.
2013
;
2013
(
11
):
CD010013
50
Nollen
NL
,
Mayo
MS
,
Carlson
SE
,
Rapoff
MA
,
Goggin
KJ
,
Ellerbeck
EF
.
Mobile technology for obesity prevention: a randomized pilot study in racial- and ethnic-minority girls
.
Am J Prev Med
.
2014
;
46
(
4
):
404
408
51
Wood
FG
,
Alley
E
,
Baer
S
,
Johnson
R
.
Interactive multimedia tailored to improve diabetes self-management
.
Nurs Clin North Am
.
2015
;
50
(
3
):
565
576
52
Woolford
SJ
,
Clark
SJ
,
Strecher
VJ
,
Resnicow
K
.
Tailored mobile phone text messages as an adjunct to obesity treatment for adolescents
.
J Telemed Telecare
.
2010
;
16
(
8
):
458
461
53
Nkoy
FL
,
Hofmann
MG
,
Stone
BL
, et al
.
Information needs for designing a home monitoring system for children with medical complexity
.
Int J Med Inform
.
2019
;
122
:
7
12
54
Koutoukidis
DA
,
Beeken
RJ
,
Manchanda
R
,
Burnell
M
,
Knobf
MT
,
Lanceley
A
.
Diet and exercise in uterine cancer survivors (DEUS pilot) - piloting a healthy eating and physical activity program: study protocol for a randomized controlled trial
. [
published correction appears in Trials. 2017;18(1):28
].
Trials
.
2016
;
17
(
1
):
130
55
Solodiuk
JC
,
Scott-Sutherland
J
,
Meyers
M
, et al
.
Validation of the Individualized Numeric Rating Scale (INRS): a pain assessment tool for nonverbal children with intellectual disability
.
Pain
.
2010
;
150
(
2
):
231
236
56
Janicke
DM
,
Finney
JW
,
Riley
AW
.
Children’s health care use: a prospective investigation of factors related to care-seeking
.
Med Care
.
2001
;
39
(
9
):
990
1001
57
Anhøj
J
.
Diagnostic value of run chart analysis: using likelihood ratios to compare run chart rules on simulated data series
.
PLoS One
.
2015
;
10
(
3
):
e0121349
58
Anhøj
J
,
Olesen
AV
.
Run charts revisited: a simulation study of run chart rules for detection of non-random variation in health care processes
.
PLoS One
.
2014
;
9
(
11
):
e113825
59
Ellzey
A
,
Valentine
KJ
,
Hagedorn
C
,
Murphy
NA
.
Parent perceptions of quality of life and healthcare satisfaction for children with medical complexity
.
J Pediatr Rehabil Med
.
2015
;
8
(
2
):
97
104
60
de Brey
H
.
A cross-national validation of the client satisfaction questionnaire: the Dutch experience
.
Eval Program Plann
.
1983
;
6
(
3–4
):
395
400
61
Perrin
JM
.
Patient-centered medical home for high-risk children with chronic illness
. [
published correction appears in JAMA. 2015;313(7):729
].
JAMA
.
2014
;
312
(
24
):
2625
2626
62
Birney
AJ
,
Gunn
R
,
Russell
JK
,
Ary
DV
.
MoodHacker mobile web app with email for adults to self-manage mild-to-moderate depression: randomized controlled trial
.
JMIR Mhealth Uhealth
.
2016
;
4
(
1
):
e8
63
Hui
CY
,
Walton
R
,
McKinstry
B
,
Jackson
T
,
Parker
R
,
Pinnock
H
.
The use of mobile applications to support self-management for people with asthma: a systematic review of controlled studies to identify features associated with clinical effectiveness and adherence
.
J Am Med Inform Assoc
.
2017
;
24
(
3
):
619
632
64
Lewis
J
,
Ray
P
,
Liaw
S-T
.
Recent worldwide developments in eHealth and mHealth to more effectively manage cancer and other chronic diseases - a systematic review
.
Yearb Med Inform
.
2016
;(
1
):
93
108
65
Nundy
S
,
Dick
JJ
,
Chou
CH
,
Nocon
RS
,
Chin
MH
,
Peek
ME
.
Mobile phone diabetes project led to improved glycemic control and net savings for Chicago plan participants
.
Health Aff (Millwood)
.
2014
;
33
(
2
):
265
272
66
Ramirez
V
,
Johnson
E
,
Gonzalez
C
,
Ramirez
V
,
Rubino
B
,
Rossetti
G
.
Assessing the use of mobile health technology by patients: an observational study in primary care clinics
.
JMIR Mhealth Uhealth
.
2016
;
4
(
2
):
e41
67
Sheehy
S
,
Cohen
G
,
Owen
KR
.
Self-management of diabetes in children and young adults using technology and smartphone applications
.
Curr Diabetes Rev
.
2014
;
10
(
5
):
298
301
68
Cheng
CF
,
Werner
NE
,
Doutcheva
N
, et al
.
Codesign and usability testing of a mobile application to support family-delivered enteral tube care
.
Hosp Pediatr
.
2020
;
10
(
8
):
641
650
69
Deady
M
,
Johnston
D
,
Milne
D
, et al
.
Preliminary effectiveness of a smartphone app to reduce depressive symptoms in the workplace: feasibility and acceptability study
.
JMIR Mhealth Uhealth
.
2018
;
6
(
12
):
e11661
70
Kidd
SA
,
Feldcamp
L
,
Adler
A
, et al
.
Feasibility and outcomes of a multi-function mobile health approach for the schizophrenia spectrum: App4Independence (A4i)
.
PLoS One
.
2019
;
14
(
7
):
e0219491
71
Edbrooke-Childs
J
,
Edridge
C
,
Averill
P
, et al
.
A feasibility trial of power up: smartphone app to support patient activation and shared decision making for mental health in young people
.
JMIR Mhealth Uhealth
.
2019
;
7
(
6
):
e11677
72
Yin
Z
,
Lesser
J
,
Paiva
KA
, et al
.
Using mobile health tools to engage rural underserved individuals in a diabetes education program in South Texas: feasibility study
.
JMIR Mhealth Uhealth
.
2020
;
8
(
3
):
e16683
73
Jonassaint
CR
,
Shah
N
,
Jonassaint
J
,
De Castro
L
.
Usability and feasibility of an mHealth intervention for monitoring and managing pain symptoms in sickle cell disease: the Sickle Cell Disease Mobile Application to Record Symptoms via Technology (SMART)
.
Hemoglobin
.
2015
;
39
(
3
):
162
168
74
Nkoy
FL
,
Fassl
BA
,
Wilkins
VL
, et al
.
Ambulatory management of childhood asthma using a novel self-management application
.
Pediatrics
.
2019
;
143
(
6
):
e20181711
75
Agrawal
S
.
Heart Rates in Kids With Complex Medical Conditions
.
Chicago, IL
:
Complex Child
;
2017
76
Lerner
CF
,
Kelly
RB
,
Hamilton
LJ
,
Klitzner
TS
.
Medical transport of children with complex chronic conditions
.
Emerg Med Int
.
2012
;
2012
:
837020
77
Ogrinc
GS
,
Headrick
LA
,
Moore
SM
,
Barton
AJ
,
Dolansky
MA
,
Madigosky
WS
.
Fundamentals of Health Care Improvement: A Guide to Improving Your Patients’ Care
. 2nd ed.
Oakbrook Terrace, IL
:
Joint Commission Resources
;
2012
78
Baldewijns
G
,
Luca
S
,
Vanrumste
B
,
Croonenborghs
T
.
Developing a system that can automatically detect health changes using transfer times of older adults
.
BMC Med Res Methodol
.
2016
;
16
:
23
79
Griffin
MP
,
O’Shea
TM
,
Bissonette
EA
,
Harrell
FE
 Jr
,
Lake
DE
,
Moorman
JR
.
Abnormal heart rate characteristics preceding neonatal sepsis and sepsis-like illness
.
Pediatr Res
.
2003
;
53
(
6
):
920
926
80
Nkoy
FL
,
Stone
BL
,
Fassl
BA
, et al
.
Development of a novel tool for engaging children and parents in asthma self-management
.
AMIA Annu Symp Proc
.
2012
;
2012
:
663
672
81
Nkoy
FL
,
Stone
BL
,
Fassl
BA
, et al
. A self-monitoring tool for improving ambulatory asthma care in children. In: Proceedings from the Academy Health Annual Conference; June 24, 2012; Orlando, FL
82
Nkoy
F
,
Fassl
B
,
Stone
B
, et al
.
Improving pediatric asthma care and outcomes across multiple hospitals
.
Pediatrics
.
2015
;
136
(
6
). Available at: www.pediatrics.org/cgi/content/full/136/6/e1602

Competing Interests

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

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data