Wearable Sensor and Mobile App-Based mHealth Approach for Investigating Substance Use and Related Factors in Daily Life: Protocol for an Ecological Momentary Assessment Study

Ayumi Takano; Koki Ono; Kyosuke Nozawa; Makito Sato; Masaki Onuki; Jun Sese; Yosuke Yumoto; Sachio Matsushita; Toshihiko Matsumoto

doi:10.2196/44275

Wearable Sensor and Mobile App-Based mHealth Approach for Investigating Substance Use and Related Factors in Daily Life: Protocol for an Ecological Momentary Assessment Study

JMIR Res Protoc. 2023 Apr 11:12:e44275. doi: 10.2196/44275.

Authors

Ayumi Takano¹, Koki Ono², Kyosuke Nozawa³, Makito Sato⁴, Masaki Onuki⁴, Jun Sese⁴, Yosuke Yumoto⁵, Sachio Matsushita⁵, Toshihiko Matsumoto⁶

Affiliations

¹ Department of Mental Health and Psychiatric Nursing, Tokyo Medical and Dental University, Tokyo, Japan.
² Department of Clinical Information Engineering, The University of Tokyo, Tokyo, Japan.
³ Department of Mental Health and Psychiatric Nursing, Osaka University, Osaka, Japan.
⁴ Humanome Lab, Inc, Tokyo, Japan.
⁵ National Hospital Organization Kurihama Medical and Addiction Center, Yokosuka, Japan.
⁶ Department of Drug Dependence Research, National Center of Neurology and Psychiatry, Tokyo, Japan.

PMID: 37040162
PMCID: PMC10131735
DOI: 10.2196/44275

Abstract

Background: Digital health technologies using mobile apps and wearable devices are a promising approach to the investigation of substance use in the real world and for the analysis of predictive factors or harms from substance use. Moreover, consecutive repeated data collection enables the development of predictive algorithms for substance use by machine learning methods.

Objective: We developed a new self-monitoring mobile app to record daily substance use, triggers, and cravings. Additionally, a wearable activity tracker (Fitbit) was used to collect objective biological and behavioral data before, during, and after substance use. This study aims to describe a model using machine learning methods to determine substance use.

Methods: This study is an ongoing observational study using a Fitbit and a self-monitoring app. Participants of this study were people with health risks due to alcohol or methamphetamine use. They were required to record their daily substance use and related factors on the self-monitoring app and to always wear a Fitbit for 8 weeks, which collected the following data: (1) heart rate per minute, (2) sleep duration per day, (3) sleep stages per day, (4) the number of steps per day, and (5) the amount of physical activity per day. Fitbit data will first be visualized for data analysis to confirm typical Fitbit data patterns for individual users. Next, machine learning and statistical analysis methods will be performed to create a detection model for substance use based on the combined Fitbit and self-monitoring data. The model will be tested based on 5-fold cross-validation, and further preprocessing and machine learning methods will be conducted based on the preliminary results. The usability and feasibility of this approach will also be evaluated.

Results: Enrollment for the trial began in September 2020, and the data collection finished in April 2021. In total, 13 people with methamphetamine use disorder and 36 with alcohol problems participated in this study. The severity of methamphetamine or alcohol use disorder assessed by the Drug Abuse Screening Test-10 or the Alcohol Use Disorders Identification Test-10 was moderate to severe. The anticipated results of this study include understanding the physiological and behavioral data before, during, and after alcohol or methamphetamine use and identifying individual patterns of behavior.

Conclusions: Real-time data on daily life among people with substance use problems were collected in this study. This new approach to data collection might be helpful because of its high confidentiality and convenience. The findings of this study will provide data to support the development of interventions to reduce alcohol and methamphetamine use and associated negative consequences.

International registered report identifier (irrid): DERR1-10.2196/44275.

Keywords: Fitbit; alcohol and drug use; alcoholism; digital health; drug use; ecological momentary assessment; ecological momentary intervention; electronic health record; mHealth; machine learning; mobile app; self-monitoring; wearables devices.

©Ayumi Takano, Koki Ono, Kyosuke Nozawa, Makito Sato, Masaki Onuki, Jun Sese, Yosuke Yumoto, Sachio Matsushita, Toshihiko Matsumoto. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 11.04.2023.