# iTransform: Wearable Biosensors to Detect the Evolution of Opioid Tolerance in Opioid Naïve Individuals

> **NIH NIH K23** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2020 · $188,616

## Abstract

PROJECT SUMMARY
The integrated research and training plans outlined in this K23 submission will prepare me for a career as a
clinician-scientist conducting translational substance abuse research. My career goal is to perform hypothesis-
driven original research investigations directed toward reducing morbidity and mortality from opioid overdose. In
this proposal, I intend to deploy wearable biosensors (small devices that continuously record physiology) to study
the effects of therapeutic administration of opioid analgesics. I have already studied wearable biosensors in
individuals receiving opioids; my preliminary data demonstrates that opioid-tolerant individuals have different
biometric signals than non-tolerant individuals. This observation suggests that biosensors can be used to identify
the onset of tolerance, an important event that correlates with higher doses of opioid analgesics, and higher risk
of death from opioid overdose. Biosensor data management and analysis requires signal processing, data
analytic, and machine learning techniques; these approaches are beyond the areas of traditional medical
training. My short-term goal is to utilize this K23 award to fill my knowledge gaps in wearable biosensing and
advanced data analysis so that I can generate ever more innovative responses to the problem of opioid
prescribing, tolerance, misuse, addiction, and overdose. To optimize this important line of investigation, I have
developed a training plan that includes: 1) completing a PhD through the Millennium PhD program; 2) expanding
my skills in wearable biosensing and behavioral health-based research; 3) developing an understanding of signal
processing and machine learning; 4) developing data analytic and data science skills; and 5) expanding my
research presentation and dissemination skills. I will achieve these goals through directed coursework, focused
seminars, and practical experience. My mentorship team of expert investigators who will ensure my productivity
and success includes E. Boyer (primary mentor), D. Smelson, J. Fang, and P. Indic (secondary mentors), and
D. Ganesan (advisor) My research plan has three specific aims: 1) to deploy a wearable biosensor technology
to detect digital biomarkers associated with the initiation of opioid analgesic therapy in an opioid naïve population;
2) to use signal-processing analytics to identify transitions in digital biomarkers with progressive opioid use and
to identify individual characteristics associated with this transition; and, 3) to apply and explore supervised
learning algorithms that can predict transitions in digital biomarkers that herald the onset of opioid tolerance. To
identify dynamic patterns in response to opioids, I will study the digital biomarkers of opioid-naïve patients with
acute fractures who are prescribed opioid analgesics. Results will be used to develop “big data” approaches to
apply predictive algorithms to identify the onset of opioid tolerance. This work has the po...

## Key facts

- **NIH application ID:** 9889092
- **Project number:** 5K23DA045242-02
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** STEPHANIE P CARREIRO
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $188,616
- **Award type:** 5
- **Project period:** 2019-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9889092

## Citation

> US National Institutes of Health, RePORTER application 9889092, iTransform: Wearable Biosensors to Detect the Evolution of Opioid Tolerance in Opioid Naïve Individuals (5K23DA045242-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9889092. Licensed CC0.

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