Using Experience Sampling Methodology Data to Characterize the Substance Use of Youth With or At-Risk of Psychosis

Abstract

ObjectivesPsychotic-spectrum disorders emerge during adolescence and early adulthood, which corresponds with the peak period for substance use initiation. Clinical and epidemiological data provide support that substance use is associated with psychotic symptom onset and severity. Experience-sampling methodology (ESM) data may provide additional insight into dynamic associations between substance use and psychotic symptoms. This is one of the first efforts to characterize substance use frequency and dynamic associations with psychotic symptoms and negative affect from ESM data in both clinical high risk (CHR) and early psychosis (EP) individuals.MethodsUsing ESM, 33 individuals, including 17 with CHR and 16 EP (age range: 15–24), provided information on substance use, negative affect, and psychotic symptoms 6 times a day across a 21-day data collection window. Psychotic symptoms and negative affect included multi-item variables rated on a seven-point Likert Scale. Participants reported recent substance use for 4 drug classes (nicotine, cannabis, depressants, stimulants) via a yes/no item. Descriptive information included data on substance use frequency, and momentary negative affect and psychotic symptoms. Exploratory analyses included multi-level and person-level dynamic structural equation models, which assessed contemporaneous and lagged associations between substance use and symptoms.ResultsTwenty-seven individuals (82%) reported recurrent substance use including stimulants (n = 12, 46%), nicotine (n = 9, 27%), cannabis (n = 6, 18%), and depressants (n = 4, 12%). Individuals with any recurrent substance use indicated usage at 47.7% of answered prompts; stimulants at 23.6%; nicotine at 74.2%; cannabis at 39.1%; and depressants at 20.1%. A multi-level dynamic structural equation model reflected that substance use (any class) was associated with lagged negative affect (β = −0.02, CI: −0.06, < -0.00) but no significant contemporaneous or lagged associations between substance use and psychotic symptoms. Person-level models suggest potentially meaningful inter-individual variability.ConclusionsCHR and EP individuals use a range of substances that may both reflect and influence other experiences in daily life experiences. Data reflected moderate to high rates of recurrent substance use with more consistent use within nicotine and cannabis classes. ESM data have the potential to increase our understanding of the dynamic relationships between substance use and symptoms and to inform treatment for individuals in early course psychosis.

The interdependence of substance use, satisfaction with life, and psychological distress: a dynamic structural equation model analysis

“…Our findings suggest that higher-than-average psychological distress at a three-month lag credibly predicts higher-than-average drug use at the concurrent time point t, suggesting that experiencing higher psychological distress at the previous measurement occasion leads to higher substance use at the concurrent time point. This is in line with previous multi-level dynamic structural equation model indicating an association between substance use and lagged negative affect (39). Furthermore, previous research suggests that reduced substance use is associated with lowered psychological distress over three years (12) and that psychological distress may decline across several years following abstinence (13,40).…”

Digitalising mental health care: Practical recommendations from the European Psychiatric Association

“…Nonetheless, first evidence suggests that EMAs could increase precision in the characterisation of at-risk populations [ 50 ], in detecting stressful experiences at the individual level [ 51 ] and in managing major psychiatric disorders, such as depression [ 44 ] or schizophrenic psychoses [ 52 ]. An especially promising approach could be to utilise digital phenotypes for machine-learning-based prediction models in order to define group and individual prognostic trajectories better, to take into account a wider range of influencing factors, to optimise treatments, and to more reliably predict clinical outcomes [ 53 ].…”