# Adolescents’ involvement in decision making during specialty care visits for pediatric chronic illness: Development and evaluation of a new measure and implications for self-management

> **NIH NIH R01** · CHILDREN'S HOSP OF PHILADELPHIA · 2022 · $465,651

## Abstract

PROJECT SUMMARY/ABSTRACT
Shared decision making (SDM) between providers, parents, and youth is posited to be one of the processes of
self-management for a chronic condition. Adequate conceptual models for involving youth in decision making
must attend to the youth-parent-provider triad, recognize that there are multiple ways for youth to be involved
in the process of decision making, and underscore that parent, provider, and youth decision making behaviors
and roles should change with development. The field lacks empirical research to understand the nature of
parent-youth-provider interactions about decisions and outcomes of different patterns of behavior over time,
including adherence and health outcomes in youth with a chronic illness. This lack of research is due, at least
in part, to the absence of feasible, reliable, valid, and conceptually sound measures that assess the complex
interplay of decision making behaviors during medical encounters. The primary objective of this proposal is to
develop a measure of youths’ involvement in decision making during outpatient visits for pediatric chronic
illness (specifically, type 1 diabetes, sickle cell disease, juvenile idiopathic arthritis, and inflammatory bowel
disease). Aim 1 is to utilize semi-structured qualitative interviews with youth, parents, and providers (Study 1,
Phase 1) and cognitive interviews with youth and parents (Study 1, Phase 2) to develop items for a new
measure- the Decision Making Involvement Scale-Medical Encounters (DMIS-ME)- and ensure alignment
between participant interpretation and intent of the items. Aim 2 is to evaluate the psychometric properties and
validity of the DMIS-ME, utilizing both classical and modern test theory. Validity will be assessed by examining
whether DMIS-ME subscales are associated with youth age, decision self-efficacy, perceived global health,
self-management skills, and adherence. Secondary Aim 2 is to develop a typology of visit profile classes based
on DMIS-ME subscales, using latent class analysis, and examine whether the classes vary based on socio-
demographics and variables tested in Aim 2. For Aim 2 (Study 2), we will enroll and assess youth and their
parents, immediately after attending an outpatient specialty care visit related to their chronic illness. They will
complete the DMIS-ME and measures of decision self-efficacy, perceived global health, self-management
skills, and adherence. Aim 3 is to identify and describe ethnic/racial disparities in youths’ perceived
involvement in decision making based on data from Aim 1 and 2. The development of the DMIS-ME addresses
a critical gap in the field of pediatric self-management and decision making. The DMIS-ME can be used in
future research to describe youths’ decision making involvement in a multidimensional way that accounts for
the youth-parent-provider triad, identify outcomes of involvement, and inform the development and evaluation
of interventions to enhance youth involvement in deci...

## Key facts

- **NIH application ID:** 10471891
- **Project number:** 5R01NR020005-02
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** Victoria Allison Miller
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $465,651
- **Award type:** 5
- **Project period:** 2021-08-19 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10471891

## Citation

> US National Institutes of Health, RePORTER application 10471891, Adolescents’ involvement in decision making during specialty care visits for pediatric chronic illness: Development and evaluation of a new measure and implications for self-management (5R01NR020005-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10471891. Licensed CC0.

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