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
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
111353Informations 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.