Mobile Data Collection of Cognitive-Behavioral Tasks in Substance Use Disorders: Where Are We Now?

Abstract

<b><i>Introduction:</i></b> Over the last decades, our understanding of the cognitive, motivational, and neural processes involved in addictive behavior has increased enormously. A plethora of laboratory-based and cross-sectional studies has linked cognitive-behavioral measures to between-subject differences in drinking behavior. However, such laboratory-based studies inevitably suffer from small sample sizes and the inability to link temporal fluctuations in task measures to fluctuations in real-life substance use. To overcome these problems, several existing behavioral tasks have been transferred to smartphones to allow studying cognition in the field. <b><i>Method:</i></b> In this narrative review, we first summarize studies that used existing behavioral tasks in the laboratory and self-reports of substance use with ecological momentary assessment (EMA) in the field. Next, we review studies on psychometric properties of smartphone-based behavioral tasks. Finally, we review studies that used both smartphone-based tasks and self-reports with EMA in the field. <b><i>Results:</i></b> Overall, studies were scarce and heterogenous both in tasks and in study outcomes. Nevertheless, existing findings are promising and point toward several methodological recommendations: concerning psychometrics, studies show that – although more systematic studies are necessary – task validity and reliability can be improved, for example, by analyzing several measurement sessions at once rather than analyzing sessions separately. Studies that use tasks in the field, moreover, show that power can be improved by choosing sampling schemes that combine time-based with event-based sampling, rather than relying on time-based sampling alone. Increasing sampling frequency can further increase power. However, as this also increases the burden to participants, more research is necessary to determine the ideal sampling frequency for each task. <b><i>Conclusion:</i></b> Although more research is necessary to systematically study both the psychometrics of smartphone-based tasks and the frequency at which task measures fluctuate, existing studies are promising and reveal important methodological recommendations useful for researchers interested in implementing behavioral tasks in EMA studies.

Individual Differences in Computational Psychiatry: A Review of Current Challenges

“…For example, monitoring disease course, treatment response, or characterizing dynamic computational phenotypes requires longitudinal testing, likely involving batteries of tasks. To make frequent longitudinal testing feasible, it might be necessary to move exclusively to remote testing strategies such as smartphone-based tasks (Gillan and Rutledge, 2021;Zech et al, 2022;Pronk et al, 2022;Howlett et al, 2022). Another underappreciated challenge is engagement.…”

Measuring self-regulation in everyday life: reliability and validity of smartphone-based experiments in alcohol use disorder

“…Our smartphone tasks are very short (less than 5 min per task) and the observed improvement rests on data from more than one session and more than one person. Here, smartphone-based tasks have a major advantage over laboratory-based tasks by allowing researchers to collect data more efficiently from several shorter sessions in the field (Miller, 2012;Zech et al, 2020;Zech et al, 2022). Future research could aim at moving tasks to smartphone to obtain reliable scores from two or potentially even more sessions.…”

“…This indicates that at least part of the unexplained variance in experimental task measures might be driven by state-dependent changes in cognitive and motivational processes (Hedge et al, 2018). As most tasks measuring such processes use cross-sectional designs, little is known about whether and on what frequency cognitive and motivational processes fluctuate (Zech et al, 2022).…”

“…Unlike questionnaires, tasks are more difficult to deploy outside the laboratory in real-life scenarios (Zech et al, 2022). Most experimental tasks have been designed to run on laboratory computers, requiring specialized software and, in some cases, specialized hardware.…”

Targeting Learning Mechanisms in Addictive Disorders

“…New digital tools may revolutionize addiction research and assessment. Zech et al [14] critically review the use of smartphone-based health tools to measure cognitive and behavioral tasks in real life known as ecologically momentary assessment. Finally, Rosenthal et al [15] give an overview on novel cognitive-behavioral approaches in the treatment of addictive behaviors comprising virtual reality, memory-focused, and pharmacological interventions.…”