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      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

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          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

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          Most cited references56

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          Ecological Momentary Assessment

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            The drug abuse screening test

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              Assessment of client/patient satisfaction: Development of a general scale

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                Author and article information

                Contributors
                Journal
                JMIR Res Protoc
                JMIR Res Protoc
                ResProt
                JMIR Research Protocols
                JMIR Publications (Toronto, Canada )
                1929-0748
                2023
                11 April 2023
                : 12
                : e44275
                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
                Author notes
                Corresponding Author: Ayumi Takano ayumi-takano@ 123456umin.ac.jp
                Author information
                https://orcid.org/0000-0001-8363-1235
                https://orcid.org/0000-0002-3296-8038
                https://orcid.org/0000-0001-6913-0426
                https://orcid.org/0009-0004-1932-6868
                https://orcid.org/0000-0001-7275-0191
                https://orcid.org/0000-0003-3495-4382
                https://orcid.org/0000-0002-4837-9174
                https://orcid.org/0000-0002-2439-7984
                https://orcid.org/0000-0002-7076-2161
                Article
                v12i1e44275
                10.2196/44275
                10131735
                37040162
                5f946e8a-51b9-40d0-ba8f-7c5a9edf990c
                ©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.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.

                History
                : 19 November 2022
                : 25 January 2023
                : 5 March 2023
                : 9 March 2023
                Categories
                Protocol
                Protocol

                alcohol and drug use,alcoholism,digital health,drug use,ecological momentary assessment,ecological momentary intervention,electronic health record,fitbit,machine learning,mhealth,mobile app,self-monitoring,wearables devices

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