Background: Making decisions during daily life is a complicated behavior. Even simple choices can be affected by a range of emotions, stresses, and moods. Mental health issues such as depression and anxiety can lead people to make decisions that have negative effects on their lives. Learning how changes in feeling affect a person's decisions may help researchers find ways to help them make more positive choices. Objective: To learn how changes in a person's feelings and emotions affect their decisions. Eligibility: People aged 18 to 55 years who are healthy or who have some level of depressive, anxiety, substance-related, or addictive disorders. Design: All study activities can be done remotely; participants may use a smartphone app, a website, or telehealth. Participants will have a baseline visit. They will complete tasks that require decisions; these tasks are like games they play on their phone. They will also answer questions about their habits, how they feel, their sleep quality, and any problem behaviors. They may wear a smartwatch during the study; it will record data such as their movements and heart rate. For 12 weeks, a study app will prompt participants to respond. They will be asked to answer questions about how they are feeling, any problematic behaviors, or big life events; they will be asked to complete tasks that require making decisions. They may record a video or audio clip describing their day and how they feel. For 3 of the 12 weeks, participants will get prompts up to 6 times per day. For 9 weeks, they will get up to 3 prompts per day. They will have an end-of-study visit to discuss their experiences.
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: 1. To estimate the temporal profile of internal state; the lag between changes in internal state and measurable changes in decision-making and if different aspects of decision-makings might be sensitive to whether a change in internal state is acute or sustained (state dynamic). 2. To identify how individual differences in metacognition might moderate the influence of internal states on decision-making. 3. To establish the relationship between psychiatric symptom severity and the reactivity of decision-making to changes in internal state. 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: 1. Time of recording of ratings, responses, and completed tasks; repeated measures of self-reported internal states at different temporal scales (hours, days or weeks); repeated measures on internal states followed by reporting of significant life events (Microbursts); and repeated reports on the time of occurrence, description, and characteristics of significant life events. 2. Measures of metacognition derived from questionnaires and confidence ratings in decision-making tasks, such as confidence bias and confidence sensitivity. 3. Transdiagnostic dimensional measures of individual-level and state-level symptom severity, collected throughout the study; clinical diagnosis on psychopathology as determined by Structured Clinical Interview for DSM Disorders (SCID).
Study Type
OBSERVATIONAL
Enrollment
420
National Institutes of Health Clinical Center
Bethesda, Maryland, United States
To establish the relationship between psychiatric symptom severity and the reactivity of decision-making to changes in internal state.
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: Throughout the study, transdiagnostic measures of trait- and state-level symptom severity and clinical diagnosis determined by Structured Clinical Interview for DSM Disorders (SCID).
To identify how individual differences in metacognition might moderate the influence of internal states on decision-making.
Confidence is a standard proxy measure of metacognitive ability.
Time frame: In 12 weeks of EMA, measure of metacognition derived from questionnaires and confidence ratings in decision-making tasks.
To estimate the temporal profile of internal state; the lag between changes in internal state and measurable changes in decision-making and to assess whether different aspects of decision-makings are sensitive to acute versus sustained changes i...
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 in different time scale and following significant life event.
To assess the influence of naturally occurring fluctuations in internal states on value-based decision-making.
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: 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.
To inform key elements of a just-in-time adaptive intervention (JITAI)
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: Possible decision points jointly estimated with self-reported internal state, model predicted internal states, decision-making behavior, and symptom severity.
To predict the presence of emotional state changes using multimodal feature data
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.
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.
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.