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      Mobile Assessments of Mood, Cognition, Smartphone-Based Sensor Activity, and Variability in Craving and Substance Use in Patients With Substance Use Disorders in Norway: Prospective Observational Feasibility Study

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          Abstract

          Background

          Patients with substance use disorders (SUDs) are at increased risk for symptom deterioration following treatment, with up to 60% resuming substance use within the first year posttreatment. Substance use craving together with cognitive and mental health variables play important roles in the understanding of the trajectories from abstinence to substance use.

          Objective

          This prospective observational feasibility study aims to improve our understanding of specific profiles of variables explaining SUD symptom deterioration, in particular, how individual variability in mental health, cognitive functioning, and smartphone use is associated with craving and substance use in a young adult clinical population.

          Methods

          In this pilot study, 26 patients with SUDs were included at about 2 weeks prior to discharge from inpatient SUD treatment from 3 different treatment facilities in Norway. Patients underwent baseline neuropsychological and mental health assessments; they were equipped with smartwatches and they downloaded an app for mobile sensor data collection in their smartphones. Every 2 days for up to 8 weeks, the patients were administered mobile ecological momentary assessments (EMAs) to evaluate substance use, craving, mental health, cognition, and a mobile Go/NoGo performance task. Repeated EMAs as well as the smartphone’s battery use data were averaged across all days per individual and used as candidate input variables together with the baseline measures in models of craving intensity and the occurrence of any substance use episodes.

          Results

          A total of 455 momentary assessments were completed out of a potential maximum of 728 assessments. Using EMA and baseline data as candidate input variables and craving and substance use as responses, model selection identified mean craving intensity as the most important predictor of having one or more substance use episodes and with variabilities in self-reported impulsivity, mental health, and battery use as significant explanatory variables of craving intensity.

          Conclusions

          This prospective observational feasibility study adds novelty by collecting high-intensity data for a considerable period of time, including mental health data, mobile cognitive assessments, and mobile sensor data. Our study also contributes to our knowledge about a clinical population with the most severe SUD presentations in a vulnerable period during and after discharge from inpatient treatment. We confirmed the importance of variability in cognitive function and mood in explaining variability in craving and that smartphone usage may possibly add to this understanding. Further, we found that craving intensity is an important explanatory variable in understanding substance use episodes.

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          Most cited references51

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          The hospital anxiety and depression scale.

          A self-assessment scale has been developed and found to be a reliable instrument for detecting states of depression and anxiety in the setting of an hospital medical outpatient clinic. The anxiety and depressive subscales are also valid measures of severity of the emotional disorder. It is suggested that the introduction of the scales into general hospital practice would facilitate the large task of detection and management of emotional disorder in patients under investigation and treatment in medical and surgical departments.
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            mice: Multivariate Imputation by Chained Equations inR

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              Repeated Measures Correlation

              Repeated measures correlation (rmcorr) is a statistical technique for determining the common within-individual association for paired measures assessed on two or more occasions for multiple individuals. Simple regression/correlation is often applied to non-independent observations or aggregated data; this may produce biased, specious results due to violation of independence and/or differing patterns between-participants versus within-participants. Unlike simple regression/correlation, rmcorr does not violate the assumption of independence of observations. Also, rmcorr tends to have much greater statistical power because neither averaging nor aggregation is necessary for an intra-individual research question. Rmcorr estimates the common regression slope, the association shared among individuals. To make rmcorr accessible, we provide background information for its assumptions and equations, visualization, power, and tradeoffs with rmcorr compared to multilevel modeling. We introduce the R package (rmcorr) and demonstrate its use for inferential statistics and visualization with two example datasets. The examples are used to illustrate research questions at different levels of analysis, intra-individual, and inter-individual. Rmcorr is well-suited for research questions regarding the common linear association in paired repeated measures data. All results are fully reproducible.
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                Author and article information

                Contributors
                Journal
                JMIR Form Res
                JMIR Form Res
                JFR
                JMIR Formative Research
                JMIR Publications (Toronto, Canada )
                2561-326X
                2023
                23 June 2023
                : 7
                : e45254
                Affiliations
                [1 ] Department of Mental Health Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway
                [2 ] Clinic of Substance Use and Addiction Medicine St Olavs University Hospital Trondheim Norway
                [3 ] Department of Neuromedicine and Movement Science Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway
                [4 ] Department of Mathematical Sciences Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway
                [5 ] Human-Computer Interaction and Human-Centered AI Systems Lab, AI for Healthcare Lab School of Systems and Enterprises Stevens Institute of Technology Hoboken, NJ United States
                [6 ] Department of Research and Development Division of Psychiatry St Olavs University Hospital Trondheim Norway
                [7 ] Black Dog Institute Sydney Australia
                [8 ] Faculty of Medicine and Health University of New South Wales Sydney Australia
                Author notes
                Corresponding Author: Anders Dahlen Forsmo Lauvsnes anders.d.f.lauvsnes@ 123456ntnu.no
                Author information
                https://orcid.org/0000-0002-2484-9821
                https://orcid.org/0000-0003-2503-6205
                https://orcid.org/0000-0003-0751-6926
                https://orcid.org/0000-0002-2047-1358
                https://orcid.org/0000-0001-8879-9213
                https://orcid.org/0000-0001-8408-9070
                https://orcid.org/0000-0002-0272-2053
                https://orcid.org/0000-0002-5714-0288
                Article
                v7i1e45254
                10.2196/45254
                10337471
                37351934
                ae4fddbf-7d51-4f74-8dbc-d5c433d9111e
                ©Anders Dahlen Forsmo Lauvsnes, Tor Ivar Hansen, Sebastian Øiungen Ankill, Sang Won Bae, Rolf W Gråwe, Taylor A Braund, Mark Larsen, Mette Langaas. Originally published in JMIR Formative Research (https://formative.jmir.org), 23.06.2023.

                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 Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

                History
                : 25 December 2022
                : 23 February 2023
                : 22 March 2023
                : 30 March 2023
                Categories
                Original Paper
                Original Paper

                executive functioning,substance use disorder,ecological momentary assessment,clinical inference,substance use,pilot study,mood,mental health,neurocognitive functioning,smartphone use,mobile sensor,sensor,decision support,mobile phone

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