Evaluation of a digital tool for detecting stress and craving in SUD recovery: An observational trial of accuracy and engagement.

Craving Digital biomarker Digital health Stress Substance use disorder

Journal

Drug and alcohol dependence
ISSN: 1879-0046
Titre abrégé: Drug Alcohol Depend
Pays: Ireland
ID NLM: 7513587

Informations de publication

Date de publication:
15 Jun 2024
Historique:
received: 20 09 2023
revised: 13 05 2024
accepted: 30 05 2024
medline: 26 6 2024
pubmed: 26 6 2024
entrez: 25 6 2024
Statut: aheadofprint

Résumé

Digital health interventions offer opportunities to expand access to substance use disorder (SUD) treatment, collect objective real-time data, and deliver just-in-time interventions: however implementation has been limited. RAE (Realize, Analyze, Engage) Health is a digital tool which uses continuous physiologic data to detect high risk behavioral states (stress and craving) during SUD recovery. This was an observational study to evaluate the digital stress and craving detection during outpatient SUD treatment. Participants were asked to use the RAE Health app, wear a commercial-grade wrist sensor over a 30-day period. They were asked to self-report stress and craving, at which time were offered brief in-app de-escalation tools. Supervised machine learning algorithms were applied retrospectively to wearable sensor data obtained to develop group-based digital biomarkers for stress and craving. Engagement was assessed by number of days of utilization, and number of hours in a given day of connection. Sixty percent of participants (N=30) completed the 30-day protocol. The model detected stress and craving correctly 76 % and 69 % of the time, respectively, but with false positive rates of 33 % and 28 % respectively. All models performed close to previously validated models from a research grade sensor. Participants used the app for a mean of 14.2 days (SD 10.1) and 11.7 h per day (SD 8.2). Anxiety disorders were associated with higher mean hours per day connected, and return to drug use events were associated with lower mean hours per day connected. Future work should explore the effect of similar digital health systems on treatment outcomes and the optimal dose of digital interventions needed to make a clinically significant impact.

Sections du résumé

BACKGROUND BACKGROUND
Digital health interventions offer opportunities to expand access to substance use disorder (SUD) treatment, collect objective real-time data, and deliver just-in-time interventions: however implementation has been limited. RAE (Realize, Analyze, Engage) Health is a digital tool which uses continuous physiologic data to detect high risk behavioral states (stress and craving) during SUD recovery.
METHODS METHODS
This was an observational study to evaluate the digital stress and craving detection during outpatient SUD treatment. Participants were asked to use the RAE Health app, wear a commercial-grade wrist sensor over a 30-day period. They were asked to self-report stress and craving, at which time were offered brief in-app de-escalation tools. Supervised machine learning algorithms were applied retrospectively to wearable sensor data obtained to develop group-based digital biomarkers for stress and craving. Engagement was assessed by number of days of utilization, and number of hours in a given day of connection.
RESULTS RESULTS
Sixty percent of participants (N=30) completed the 30-day protocol. The model detected stress and craving correctly 76 % and 69 % of the time, respectively, but with false positive rates of 33 % and 28 % respectively. All models performed close to previously validated models from a research grade sensor. Participants used the app for a mean of 14.2 days (SD 10.1) and 11.7 h per day (SD 8.2). Anxiety disorders were associated with higher mean hours per day connected, and return to drug use events were associated with lower mean hours per day connected.
CONCLUSIONS CONCLUSIONS
Future work should explore the effect of similar digital health systems on treatment outcomes and the optimal dose of digital interventions needed to make a clinically significant impact.

Identifiants

pubmed: 38917718
pii: S0376-8716(24)00275-8
doi: 10.1016/j.drugalcdep.2024.111353
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

111353

Informations de copyright

Copyright © 2024 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest JS is employed by RAE Health. SC and PI and are academic partners with RAE Health on two Small Business Innovation Research awards.

Auteurs

Stephanie Carreiro (S)

Department of Emergency Medicine, Division of Medical Toxicology, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA. Electronic address: stephanie.carreiro@umassmed.edu.

Pravitha Ramanand (P)

Department of Electrical and Computer Engineering, University of Texas at Tyler, 3900 University Blvd, Tyler, TX 75799, USA.

Melissa Taylor (M)

Department of Emergency Medicine, Division of Medical Toxicology, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA.

Rebecca Leach (R)

Department of Emergency Medicine, Division of Medical Toxicology, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA.

Joshua Stapp (J)

Department of Electrical and Computer Engineering, University of Texas at Tyler, 3900 University Blvd, Tyler, TX 75799, USA; RAE Health, 13 Devoe Raod, Bristol, ME 04539, USA.

Sloke Sherestha (S)

Department of Electrical and Computer Engineering, University of Texas at Tyler, 3900 University Blvd, Tyler, TX 75799, USA.

David Smelson (D)

Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA.

Premananda Indic (P)

Department of Electrical and Computer Engineering, University of Texas at Tyler, 3900 University Blvd, Tyler, TX 75799, USA.

Classifications MeSH