National Institutes of Health Clinical Center
Bethesda, Maryland, 20892, United States
NCT Number: NCT07801079
Background:
Life experiences could impact how one feel, and in turn alter how decisions are made. For example, an argument with a loved one might lead to frustration that drives one to seek alcohol or engage in other riskier decision-making. By tracking not only how life experiences might change feelings but also the decision-making tendencies assessed by behavioral tasks, researcher aimed to better understand naturalistically how life experiences might shape behaviors through feelings. This could lead to a better understanding, identification and treatment for people with mental disorders.
Objective:
Naturalistically track how life experiences change self-reported mental states, such as emotions or mood, as well as decision-making tendencies.
Eligibility:
Individuals aged 18 to 55 years, who are either healthy or with varying degrees of depressive, anxious or addictive symptom severity.
Design:
Participants in the study will complete the following activities: 1) complete an initial assessment of their medical, mental health and personal history; 2) complete a set of behavior tasks; 3) for 12 weeks, complete surveys, tasks and audiovisual diary on a smart phone while they go about their life; 4) for 12 weeks, continuously wear an smartwatch-like device to track physiological signals; 5) every 2 weeks for 12 weeks, complete a set of surveys on mental health conditions; and 6) complete an end-of-study debriefing session to discuss their experience in the study.
Trial opening soon.
Get Notified18 year–55 year
All sexes
Observational
Bethesda, Maryland, 20892, United States
Study Description:
This observational study employs an Ecological Momentary Assessment (EMA) methodology to investigate how naturalistic changes in internal states (ranging from emotional to physiological and interoceptive states) lead to changes in value-based decision-making. The study will include a "dimensional cohort" of participants that range from healthy to subclinical to clinical levels of psychopathology, with a focus on substance use, depression, and anxiety. Using a mobile application, participants will remotely respond to multiple daily prompts for a total participation time of 12 weeks. In addition to this, the study will incorporate multimodal biosensor recording collected from wearable technology to capture passive physiological metrics. It aims to understand the dynamics of decision making, metacognition, and how their variability may be causally related to changes in emotional internal states, captured in real-world settings.
Objectives:
Primary Objective:
To measure the influence of naturally occurring fluctuations in emotional internal states on value-based decision-making.
Secondary Objectives:
Endpoints:
Primary Endpoint:
Through EMA, repeated measures of self-reported internal states, significant life events, and behaviors in established and novel cognitive-behavioral decision-making tasks.
Secondary Endpoints:
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
In order to be eligible to participate in this study, an individual must meet all of the following criteria:
Exclusion criteria
An individual who meets any of the following criteria will be excluded from participation:
Time frame: Throughout the study, transdiagnostic measures of trait- and state-level symptom severity and clinical diagnosis determined by Structured Clinical Interview for DSM Disorders (SCID).
Dimensional approach allows for examination of the moderation effect of symptom-level severity across psychiatric diagnosis. The state-level symptom severity can also be used to infer the state dynamic.However, clinical diagnosis still serves an important role in understanding how decision making and internal state dynamics might be different for patients with psychopathology. Further, collecting clinical diagnosis can better connect knowledge gained from this protocol to existing literature.
Time frame: In 12 weeks of EMA, measure of metacognition derived from questionnaires and confidence ratings in decision-making tasks.
Confidence is a standard proxy measure of metacognitive ability.
Time frame: In 12 weeks of EMA, self-report of internal states in different time scale and following significant life event.
Time difference between a measurable change in decision-making characteristics and the most recent change in internal state can be calculated. State dynamics (acute versus sustained changes in internal state) can be identified by analyzing internal state fluctuation measures collected at different time scales and temporal proximity to significant life events.Life events of different types or characteristics might be more likely to elicit acute versus sustained changes and vice versa.
Time frame: In 12 weeks of EMA, self-report of internal states and significant life events as well as behaviors in established and novel cognitive-behavioral decision-making tasks.
EMA allows for measures of changes in naturalistic settings. Collecting self-reported internal states and significant life events can establish an internal state timeline for modeling naturally occurring fluctuations in internal states.Cognitive-behavioral decision-making tasks are informative of value-based decision-making processes.Behaviors in these tasks can also be used for computational modeling of the (sub)processes and mechanisms of value-based decision-making, such as risk tolerance, ambiguity tolerance, impulsive choice, approach/avoidance behavior, planning, learning, goal progress, pleasure related to goal progress, and decision confidence.
Time frame: Possible decision points jointly estimated with self-reported internal state, model predicted internal states, decision-making behavior, and symptom severity.
EMA data and analysis will aid in identifying some of the key elements for a future JITAI study aimed at reducing emotional reactivity.
Time frame: Throughout the study, physiological signals collected from biosensors, speech and facial features from audiovisual recording, and lexical and semantical features derived from free responses.
Physiological signals such as heart rate and electrodermal activity is linked to dimensions of internal state, such as arousal. Speech and facial features are used to infer internal states such as emotions (voice intonation and facial expression).Language use features, lexicon and semantic content, are sensitive to internal states.
Contact information is provided by the study sponsor or research team.
National Institute of Mental Health (NIMH)
Nih
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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