# Evaluation techniques for mHealth outcome measures using patient generated health data

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2022 · $371,673

## Abstract

PROJECT SUMMARY
This proposal investigates statistical models for developing mobile health (mHealth) measures using patient
generated health data (PGHD) with high complexity and temporality. The emergence of mHealth technologies
and computational tools are rapidly expanding their use in research and clinical settings, and engaging patients
in self-management. mHealth technology further allows integration of multifarious data streams to improve
outcome measurement and prediction to aid clinical decision making. To maximize their actionability, however,
there is a need to investigate novel approaches for design, development and evaluation of mHealth-based
measures. We ground our investigation in chronic pelvic pain (CPP) as the disease model, a prevalent,
complex disorder with high societal burden and quality of life (QoL) impact. There is substantial heterogeneity
between patients and day-to-day variations in how CPP unfolds. Therefore, mHealth methods are particularly
valuable for capturing the complex disease scenarios. There are no CPP-specific self-reported measures to
assess disease status or treatment response. We propose to investigate models that can handle the inherent
challenges of PGHD to derive ecologically valid and actionable self-tracking measures for patient outcomes in
health settings. The Specific Aims are: Specific Aim 1. Investigate “critical windows of tracking” for
mHealth-based disease outcome measurement. We will enroll 90 participants undergoing 12 weeks of
physical therapy treatment for their CPP to use a mHealth app for tracking their symptoms, daily function, and
medications. We will triangulate these data with clinician assessments and passive data on sleep and activity
to build distributed lag models (DLMs) to identify predictors that can be used for outcome monitoring. Specific
Specific Aim 2. Investigate a functional data analytic framework grounded in CPP to develop self-
tracking pain and QoL measures. We will enroll 180 CPP patients to track their disease symptoms through a
mHealth app and wear activity monitors for 3 months. Through a series of supervised and unsupervised
models leveraging functional data analytic methods, we will identify variables to inform the design of the
composite pain and QoL measures. Aim 2a. Design and develop a multidimensional self-tracking pain
measure. We will build estimation models where the unit of observation is a set of curves (i.e., pain location,
severity, type) over time, leveraging functional data analytic methods. Aim 2b. Design and develop a flexible
self-tracking QoL measure. We will assess the relative predictive ability of individual items on CPP symptoms
to derive a CPP-specific QoL measure that can be used at the day- vs week-level. Exploratory Aim 2: We will
assess disease specificity of the models by comparing output from a non-CPP control group. Flexible, non-
parametric data approaches allow maximizing the features of the available mHealth technology, which can a...

## Key facts

- **NIH application ID:** 10412721
- **Project number:** 1R01HD108263-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Ipek Ensari
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $371,673
- **Award type:** 1
- **Project period:** 2022-09-22 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10412721

## Citation

> US National Institutes of Health, RePORTER application 10412721, Evaluation techniques for mHealth outcome measures using patient generated health data (1R01HD108263-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10412721. Licensed CC0.

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