Jonathan P Stange1, Evan M Kleiman2, Robin J Mermelstein3, Timothy J Trull4. 1. University of Illinois at Chicago, Department of Psychiatry, 1601 W Taylor St., Chicago, IL, 60612, USA. Electronic address: jstange@uic.edu. 2. Rutgers, The State University of New Jersey, Department of Psychology, Tillett Hall, 53 Avenue E, Piscataway, NJ, 08854, USA. 3. University of Illinois at Chicago, Department of Psychology and Institute for Health Research and Policy, 1747 W Roosevelt Rd., Chicago, IL, 60608, USA. 4. University of Missouri, Department of Psychological Sciences, 210 McAlester Hall, Columbia, MO, 65211, USA.
Abstract
BACKGROUND: Rapid advances in the capability and affordability of digital technology have begun to allow for the intensive monitoring of psychological and physiological processes associated with affective disorders in daily life. This technology may enable researchers to overcome some limitations of traditional methods for studying risk in affective disorders, which often (implicitly) assume that risk factors are distal and static - that they do not change over time. In contrast, ambulatory assessment (AA) is particularly suited to measure dynamic "real-world" processes and to detect fluctuations in proximal risk for outcomes of interest. METHOD: We highlight key questions about proximal and distal risk for affective disorders that AA methods (with multilevel modeling, or fully-idiographic methods) allow researchers to evaluate. RESULTS: Key questions include between-subject questions to understand who is at risk (e.g., are people with more affective instability at greater risk than others?) and within-subject questions to understand when risk is most acute among those who are at risk (e.g., does suicidal ideation increase when people show more sympathetic activation than usual?). We discuss practical study design and analytic strategy considerations for evaluating questions of risk in context, and the benefits and limitations of self-reported vs. passively-collected AA. LIMITATIONS: Measurements may only be as accurate as the observation period is representative of individuals' usual life contexts. Active measurement techniques are limited by the ability and willingness to self-report. CONCLUSIONS: We conclude by discussing how monitoring proximal risk with AA may be leveraged for translation into personalized, real-time interventions to reduce risk.
BACKGROUND: Rapid advances in the capability and affordability of digital technology have begun to allow for the intensive monitoring of psychological and physiological processes associated with affective disorders in daily life. This technology may enable researchers to overcome some limitations of traditional methods for studying risk in affective disorders, which often (implicitly) assume that risk factors are distal and static - that they do not change over time. In contrast, ambulatory assessment (AA) is particularly suited to measure dynamic "real-world" processes and to detect fluctuations in proximal risk for outcomes of interest. METHOD: We highlight key questions about proximal and distal risk for affective disorders that AA methods (with multilevel modeling, or fully-idiographic methods) allow researchers to evaluate. RESULTS: Key questions include between-subject questions to understand who is at risk (e.g., are people with more affective instability at greater risk than others?) and within-subject questions to understand when risk is most acute among those who are at risk (e.g., does suicidal ideation increase when people show more sympathetic activation than usual?). We discuss practical study design and analytic strategy considerations for evaluating questions of risk in context, and the benefits and limitations of self-reported vs. passively-collected AA. LIMITATIONS: Measurements may only be as accurate as the observation period is representative of individuals' usual life contexts. Active measurement techniques are limited by the ability and willingness to self-report. CONCLUSIONS: We conclude by discussing how monitoring proximal risk with AA may be leveraged for translation into personalized, real-time interventions to reduce risk.
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