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

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.

Including all baseline or repeated measures aggregated variables in our regression analyses is not possible due to our small sample size and large number of highly correlated covariates, since this would lead poor estimation of effects. In this appendix we show how the candidate explanatory variables were selected based on studying correlation plots. These candidate explanatory variables were then the starting point for the stepwise AIC model selection, as reported in the main article.  Table 1 of the main article) and CPTHRTSD (referred to as Reaction time consistency SD of hit reaction time in Table 1 of the main article) are included as candidate explanatory variables.

ASRS: Adult ADHD Self-Report Scale (2 variables)
• ASRSIA: ASRS Inattentiveness • ASRSHI: ASRS Hyperactivity/Impulsivity Both variables ASRSIA and ASRSHI are included as candidate explanatory variables (referred to as Adult Attention-Deficit/Hyperactivity Disorder Self-Report Scale screener, Factor 1 and Factor 2, in Table 1 of the main article).

HADS: Hospital Anxiety and Depression Scale (3 variables)
• HADS_A: HADS Anxiety • HADS_D: HADS Depression • HADS_TOTAL: HADS Total score Only hads_total (referred to as Hospital Anxiety and Depression Scale, Total, in Table 1 of the main article) is included as a candidate explanatory variable.
In total 7 candidate explanatory variables are chosen. The strongest correlation within this set is between ASRS AI and CPTCOM, with value 0.43.

Section 2: Repeated measures covariates (12 variables)
Remark: In our regression models either craving mean or binary substance use episodes is used as response. The craving mean is also included as an explanatory variable for the binary substance use episodes. The craving mean (repeated) measure is not included in the correlation plot below and is a candidate explanatory variable in addition to the variables identified below. Based on studying the correlation plot only 2 (of the 6) complex reaction time variables were chosen as candidate explanatory variables: crt_allRT_mean (referred to as Complex reaction time (mean across sessions) Reaction time in Table 1 of the main article) and crt_commissions_mean (referred to as Commission errors in Table 1 of the main article).

Impulsivity (Item 12 and 17 from Barrat Impulsiveness scale, BIS-11) (2 variables)
• Impulsivity_mean: Mean of all impulsivity scores across sessions • Impulsivity_sd: Standard deviation of all impulsivity scores across sessions Based on the analysis of the correlation plot the impulsivity_mean (referred to as Impulsivity mean in Table 1 of the main article) is labelled as candidate explanatory variable.

SCL: Symptom Check List 5-item version (2 variables)
• SCL5TOT_mean: SCL-5 Total score Mean • SCL5TOT_sd: SCL-5 Total score Sd SCL5TOT_mean (referred to as Symptom Check List 5-item version in Table 1 of the main article) is chosen candidate explanatory variable.

Mobile sensors (2 variables)
• battery_use_mean: first the battery percent points used during a day is calculated and then this is averaged across sessions • battery_use_sd: first the battery percent points used during a day is calculated and then the standard deviation this is calculated across sessions The battery_use_mean (referred to as Mobile sensors: battery use (battery percentage points used during a day, mean across sessions)in Table 1 of the main article) is chosen as candidate explanatory variable.
In total 5 candidate explanatory variables are chosen. The strongest correlation within this set is between battery_use_mean and SCL5TOT_mean (-0.32).
For the logistic regression analysis (where the binary substance use episodes is the response) the craving mean is also included as a candidate explanatory variable, and we have strong correlation between the craving mean and SLC5TOT_mean (0.52), craving mean and battery_use_mean (-0.53) and craving mean and impulsivity_mean (0.44). The correlation between craving mean and the last two chosen candidate explanatory variables are -0.23 with crt_commissions_mean and -0.1 with crt_allRT_mean.