Evidence map›Paper›PMID 37040162›Full record

ArticleJMIR research protocols2023

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

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Interpol review of forensic drug chemistry, 2022-2025.Forensic science international. Synergy · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 1 country.

Ayumi TakanoDepartment of Mental Health and Psychiatric Nursing, Tokyo Medical and Dental University, Tokyo, Japan.ORCID https://orcid.org/0000-0001-8363-1235
Koki OnoDepartment of Clinical Information Engineering, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0002-3296-8038
Kyosuke NozawaDepartment of Mental Health and Psychiatric Nursing, Osaka University, Osaka, Japan.ORCID https://orcid.org/0000-0001-6913-0426
Makito SatoHumanome Lab, Inc, Tokyo, Japan.ORCID https://orcid.org/0009-0004-1932-6868
Masaki OnukiHumanome Lab, Inc, Tokyo, Japan.ORCID https://orcid.org/0000-0001-7275-0191
Jun SeseHumanome Lab, Inc, Tokyo, Japan.ORCID https://orcid.org/0000-0003-3495-4382
Yosuke YumotoNational Hospital Organization Kurihama Medical and Addiction Center, Yokosuka, Japan.ORCID https://orcid.org/0000-0002-4837-9174
Sachio MatsushitaNational Hospital Organization Kurihama Medical and Addiction Center, Yokosuka, Japan.ORCID https://orcid.org/0000-0002-2439-7984
Toshihiko MatsumotoDepartment of Drug Dependence Research, National Center of Neurology and Psychiatry, Tokyo, Japan.ORCID https://orcid.org/0000-0002-7076-2161
Kurihama Medical and Addiction Center · JPNational Center of Neurology and Psychiatry · JPThe University of Osaka · JPTokyo Medical and Dental University · JPTokyo University of Information Sciences · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigital 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.

objectiveWe 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.

methodsThis 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.

resultsEnrollment 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.

conclusionsReal-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.

Indexed as

alcohol and drug usealcoholismdigital healthdrug useecological momentary assessmentecological momentary interventionelectronic health recordFitbitmachine learningmHealthmobile appself-monitoringwearables devices

Identifiers

PMID37040162
PMCPMC10131735
OpenAlexW4323789396

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.