Evidence map›Paper›PMID 42253709›Full record

Article... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks2025

Predicting Craving-Related Emotions among Opioid Use Disorder Patients: Preliminary Results.

Zachary King, Zoe Setiadi, Liana Hamdan, Hajar Ahmed, Bishal Lamichhane, Ashutosh Sabharwal, Ramiro Salas, Nidal Moukaddam, Akane Sano

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In one paragraph

Article in ... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Zachary KingDepartment of Electrical and Computer Engineering, Rice University, Houston, TX.
Zoe SetiadiDepartments of Statistics and Psychological Sciences, Rice University, Houston, TX.
Liana HamdanDepartment of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX.
Hajar AhmedDepartment of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX.
Bishal LamichhaneDepartment of Electrical and Computer Engineering, Rice University, Houston, TX.
Ashutosh SabharwalDepartment of Electrical and Computer Engineering, Rice University, Houston, TX.
Ramiro SalasDepartment of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX.
Nidal MoukaddamDepartment of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX.
Akane SanoDepartment of Electrical and Computer Engineering, Rice University, Houston, TX.

Funding

SCH: Multidimensional Data Science Approach: Measuring and Characterizing Craving and Affective Profiles in Substance Use DisordersR01DA059925 · NIDA · RICE UNIVERSITY · PI Nidal Moukaddam, Akane Sano · 2023 to 2026
$1.2M
NIDA NIH HHS R01 DA059925
6 · The paper itself

Abstract

Individuals with Opioid Use Disorder (OUD) often struggle to maintain sobriety, with many experiencing relapse within the first year. While medication-assisted treatment (MAT) is among the most effective approaches, access to intensive care is often limited by financial barriers. Mobile health (mHealth) technologies offer a promising, cost-effective alternative by enabling continuous monitoring and timely intervention through tools such as ecological momentary assessments (EMAs), wearable sensors, and smartphone data. In this study, we explore the feasibility of using mHealth data to predict emotions that align with cravings in OUD patients undergoing MAT. Using data collected from EMAs, wearables, smartphone tracking, and surveys, we demonstrate that machine learning models can accurately predict emotional states associated with cravings. These findings highlight the potential of mHealth systems to support individuals with OUD through timely and scalable interventions.

Identifiers

PMID42253709
PMCPMC13237597

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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.