# A Novel mHealth Intervention to Improve Outcomes of Children with Medical Complexity

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $631,544

## Abstract

PROJECT SUMMARY/ABSTRACT
Children with medical complexity (CMC) are the most vulnerable of children with chronic diseases, who have
complex, multisystem chronic diseases affecting ≥3 organ systems, severe functional limitations and technology
dependencies. CMC have high health care needs, and account for 40% of hospitalized children and 35% of all
pediatric health care costs. Due to high medical fragility, CMC have frequent acute deteriorations superimposed
on their chronic conditions, leading to recurrent emergency department (ED)/hospital admissions and affecting
quality of life (QOL). To reduce ED/hospital admissions for CMC, remote monitoring is suggested, with use of
mHealth apps to regularly assess their health status remotely and identify early signs of acute deterioration,
allowing for early interventions to prevent ED/hospital admissions. Yet no app to support remote monitoring of
CMC exists. Variable, multisystem conditions among CMC make it difficult to develop an app. Also, many CMC
are at high-risk for health care inequities, with minorities having higher unmet needs, but the impacts of health
care inequities and social determinants of health (SDOH) on ED/hospital admissions in CMC are rarely studied.
Fortunately, ED/hospital admissions for CMC are often preceded by a limited set of shared (crosscutting) acute
symptoms. These crosscutting symptoms rarely occur suddenly. Studies suggest that they usually start as subtle
signs, often unnoticed by parents until they escalate to prompt an ED/hospital visit. Thus, crosscutting symptoms
offer an opportunity for a novel and practical approach for developing a remote monitoring app for CMC,
despite their multiple, variable underlying conditions. In a focus group, parents identified the crosscutting
symptoms that most often preceded their children’s hospital admissions, and conveyed their needs, preferences
and key functionalities that led to MyChildCMC, the first app designed to monitor and identify early signs of
crosscutting symptoms in CMC. In a pilot trial of 50 subjects, we confirmed feasibility of MyChildCMC use by
parents, ability to detect early signs 2-14 days prior to ED/hospital admissions, and use leading significantly to
fewer hospital days than controls. The current study will assess the efficacy and sustainability of MyChildCMC
in a fully-powered 6-month, 2-arm (MyChildCMC vs usual care) trial of CMC (age 1-18 years) and their parents.
Parents assigned to MyChildCMC will use the app daily for 6 months, both arms will receive financial incentive
for participation, then we will stop the incentive and follow subjects for 6 more months to assess sustainability at
12 months. We will also assess if MyChildCMC use will help reduce or eliminate inequities in ED/hospital
admissions. Specific Aims are: 1) Determine MyChildCMC’s efficacy on 1.a. Child (ED/hospital use, hospital
days and QOL) and 1.b. Parent (satisfaction, self-efficacy and stress) outcomes; 2) Compare ED/hospital use
amo...

## Key facts

- **NIH application ID:** 10785881
- **Project number:** 1R01NR020784-01A1
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Flory L Nkoy
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $631,544
- **Award type:** 1
- **Project period:** 2024-06-12 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10785881

## Citation

> US National Institutes of Health, RePORTER application 10785881, A Novel mHealth Intervention to Improve Outcomes of Children with Medical Complexity (1R01NR020784-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10785881. Licensed CC0.

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