# Using Mobile Health Technology and Real-Time Assessments to Address Multilevel Influences on Lumbar Spine Surgery Outcomes

> **NIH NIH K23** · WASHINGTON UNIVERSITY · 2024 · $172,766

## Abstract

PROJECT SUMMARY/ABSTRACT
Lumbar spine surgery for degenerative disease is one of the most common and most expensive surgeries
performed in the United States. However, there is substantial variation in lumbar surgery rates, approaches,
and patient-reported outcomes at the surgeon, hospital, and regional levels. One major cause of these
inconsistencies is the lack of evidence-based tools to both predict outcome and personalize treatment
recommendations. For example, while there has been growing recognition that pain symptoms reflect a
complex interplay of biochemical, psychological, and social factors that influence surgical outcomes, these
factors are not typically incorporated into surgical treatment plans. One important factor preventing the
expansion of evidence-based treatments, particularly related to behavioral and cognitive interventions, is a lack
of precision tools to measure dynamic symptom profiles for pain and related psychosocial comorbidities. In
particular, traditional patient assessments use cross-sectional (i.e., one-time) questionnaires that are subject to
recall bias and fail to capture longitudinal symptom dynamics. Mobile health (mHealth) technology has enabled
a fundamentally new approach to collect intensive longitudinal patient-reported and biometric data to support
individualized decision-making. In particular, ecological momentary assessment (EMA) is an emerging tool that
leverages brief mobile surveys to obtain momentary, longitudinal assessments of core disease constructs.
Complementing EMA, mobile fitness trackers, such as Fitbit, passively collect biometric data (e.g., activity,
heart-rate, and sleep) that reflect the physiologic manifestations of lumbar spine-related disability and impaired
psychosocial health. These innovative tools may provide a newfound ability to capture important
biopsychosocial features that impact surgical outcome. Recognizing these evidence gaps and the value of
emerging mHealth technology, this study’s overall objective is to establish the utility of using real-time
patient-reported and biometric data to prognosticate and stratify lumbar spine patients, and to establish the
value proposition for implementing these methods. This objective will be accomplished through the following
Aims. In Aim 1 I will investigate the ability of mHealth assessments to identify novel disease features with
prognostic importance for degenerative lumbar spine surgery patients. In Aim 2, I will use real-time mHealth
assessments to identify novel phenotypes of lumbar disease. In Aim 3, I will define the value proposition and
implementation context for using mHealth assessments to address functional and psychosocial influences on
lumbar surgery outcomes. These research activities will be combined with a rigorous mentored training
program incorporating chronic pain research, machine learning, behavioral intervention development, and
implementation science. At the completion of this award, I will be prepared to submit...

## Key facts

- **NIH application ID:** 10887003
- **Project number:** 1K23AR082986-01A1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** JACOB GREENBERG
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $172,766
- **Award type:** 1
- **Project period:** 2024-06-01 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10887003

## Citation

> US National Institutes of Health, RePORTER application 10887003, Using Mobile Health Technology and Real-Time Assessments to Address Multilevel Influences on Lumbar Spine Surgery Outcomes (1K23AR082986-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10887003. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*