Experience Sampling for Healthy Subjects

Overseen By Tiara Bounyarith, BA

Age: 18 - 65

Sex: Any

Trial Phase: Academic

Sponsor: Drexel University

Disqualifiers: Psychiatric, Neurological disorders, MRI contraindication

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 2

60 Participants Needed

Trial Summary

Do I need to stop taking my current medications for this trial?

The trial protocol does not specify whether you need to stop taking your current medications. However, since the trial involves MRI scanning, you should check with the study team if your medications might affect the MRI results.

What data supports the effectiveness of the treatment Experience Sampling?

The research suggests that measuring patient experiences, like with the experience-based design approach, can improve healthcare services by identifying and reducing negative feelings, such as anxiety, during patient consultations. This implies that Experience Sampling, which also focuses on patient experiences, might be effective in enhancing patient care by addressing emotional and personal aspects of their healthcare journey. ^{1 2 3 4 5}

Is Experience Sampling safe for healthy humans?

The research articles provided do not contain specific safety data for Experience Sampling in healthy humans. ^{6 7 8 9 10}

How does the Experience Sampling treatment differ from other treatments for this condition?

Experience Sampling is unique because it involves participants reporting their momentary experiences multiple times a day, which provides real-time data on their well-being and health. Unlike traditional treatments, it focuses on collecting data about daily life experiences rather than administering medication or therapy. ^{11 12 13 14 15}

What is the purpose of this trial?

In a neuroimaging session, study participants will repeatedly and intermittently report the content of their ongoing, self-generated experiences based on an experience-sampling protocol in which self-report ratings will be triggered based on real-time analysis of the participant's current brain state. The protocol will be conducted while participants are undergoing MRI scanning.

Research Team

Aaron Kucyi, PhD

Principal Investigator

Drexel University

Eligibility Criteria

This trial is for healthy individuals aged between 18-35 years old. It's designed to understand how people's thoughts and feelings relate to their brain activity, which will be monitored through MRI scans.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Neuroimaging Session

Participants undergo MRI scanning while reporting self-generated experiences based on real-time fMRI analysis

Two sessions over one month

2 visits (in-person)

Follow-up

Participants are monitored for any delayed effects or additional data collection post-neuroimaging

4 weeks

Treatment Details

Interventions

• Experience Sampling

Trial Overview The study tests a new way of sampling experiences by asking participants to report their thoughts and feelings when prompted during an MRI scan. This happens as the scanner detects certain brain states in real-time.

Participant Groups

Group I: Real-time fMRI Experimental Treatment 1 Intervention

Participants will repeatedly and intermittently report the content of their ongoing, self-generated experiences based on an experience-sampling protocol in which self-report ratings will be triggered based on real-time analysis of the participant's current brain state. The protocol will be conducted while participants are undergoing MRI scanning.

Who Is Running the Clinical Trial?

Drexel University

Lead Sponsor

Trials

160

Recruited

48,600+

National Institute of Mental Health (NIMH)

Collaborator

Trials

3,007

Recruited

2,852,000+

Findings from Research

The experience-based design (ebd) approach effectively measures the subjective and emotional experiences of teenage patients during orthodontic consultations, helping to identify key moments of anxiety.

Implementing service improvements based on ebd findings has successfully reduced negative feelings among teenagers during their initial consultations, enhancing their overall experience.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using the experience-based design approach to improve orthodontic care. Ellis, PE., Silverton, S.[2015]

Analyzing data from 2.2 million patients in the 2009 General Practice Patient Survey revealed that 26 out of 45 patient experience questions had excellent reliability (≥0.90), indicating they are strong indicators for assessing organizational performance in healthcare.

The study highlights the importance of using reliability and intraclass correlation coefficients to select effective measures for comparing healthcare organizations, ensuring that surveys are both cost-effective and capable of providing high-quality data.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

How can health care organizations be reliably compared?: Lessons from a national survey of patient experience. Lyratzopoulos, G., Elliott, MN., Barbiere, JM., et al.[2015]

Investigating patient and staff experiences is an emerging field that offers valuable insights into the quality of care provided in healthcare settings.

Understanding and measuring these experiences can lead to practical changes in healthcare practices, ultimately improving patient care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

How patient and staff experiences affect outcomes. Churchill, N., Warden, R.[2016]

Better patient care experiences are linked to improved adherence to treatment recommendations, leading to better clinical outcomes and enhanced patient safety in hospitals.

Patient experience measures, when collected using reliable methods and appropriate sample sizes, are valuable for assessing health care quality and should be included in public reporting and pay-for-performance programs.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Examining the role of patient experience surveys in measuring health care quality. Anhang Price, R., Elliott, MN., Zaslavsky, AM., et al.[2022]

In a study of nearly 70,000 patients from the 2009 English national inpatient survey, it was found that those who responded to the survey after receiving reminders tended to give less positive evaluations of their care.

The results suggest that early responders are more likely to provide favorable feedback, indicating that the timing of responses can introduce bias in patient experience surveys, which is important for ensuring accurate quality improvement efforts.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

It takes patience and persistence to get negative feedback about patients' experiences: a secondary analysis of national inpatient survey data. Barron, DN., West, E., Reeves, R., et al.[2022]

The study analyzed adverse event (AE) data from six randomized controlled trials involving schizophrenia treatments, highlighting that the absolute prevalence and expected duration of AEs provide a more comprehensive understanding of a drug's safety compared to just incidence rates.

Using a new metric to assess the drug-placebo difference in AE prevalence, the research found that some AEs not listed in the standard drug label significantly impacted drug tolerability, suggesting that including these metrics in drug labels could enhance safety signal detection and inform better treatment choices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Novel Method for Deriving Adverse Event Prevalence in Randomized Controlled Trials: Potential for Improved Understanding of Benefit-Risk Ratio and Application to Drug Labels. Piacentino, D., Ogirala, A., Lew, R., et al.[2023]

In a meta-analysis of 11,028 healthy participants across 394 non-oncology phase I studies, 63.7% experienced adverse events, but 85% of these were classified as mild, indicating a relatively safe profile for the study drugs.

Only 0.31% of participants experienced serious adverse events, with no deaths or life-threatening incidents reported, suggesting that while adverse events are common, they are mostly not severe.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Quantifying the risks of non-oncology phase I research in healthy volunteers: meta-analysis of phase I studies. Emanuel, EJ., Bedarida, G., Macci, K., et al.[2018]

In a study reviewing 170 patient records, 27.1% showed evidence of harm related to medication use, highlighting a significant safety concern in primary care practices.

The research identified 8 refined triggers that effectively measure patient harm, with a higher odds ratio of 6.3 for harm occurrence, indicating their potential to improve patient safety monitoring.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using triggers in primary care patient records to flag increased adverse event risk and measure patient safety at clinic level. Eggleton, KS., Dovey, SM.[2014]

The proposed three-part framework for analyzing adverse events includes phases for documenting incidents, conducting in-depth reviews, and assessing the association between exposure and adverse events, which can enhance product safety.

By standardizing the evaluation of adverse events through this framework, companies can better manage risks and improve consumer products based on reliable data.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Post-market surveillance of consumer products: Framework for adverse event management. Kingston, R., Sioris, K., Gualtieri, J., et al.[2022]

The new safety monitoring procedure introduced for two-arm blinded clinical trials effectively addresses the FDA's requirements for expedited safety reporting by using a Bayesian hierarchical model to analyze adverse event rates.

This procedure has demonstrated good performance in detecting unexpected rates of adverse events in the treatment group, making it suitable for use in multi-armed clinical trials with blinded data.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Bayesian Exposure-Time Method for Clinical Trial Safety Monitoring With Blinded Data. Schnell, PM., Ball, G.[2020]

In a study involving 228 university students using experience sampling method (ESM) over a week, researchers found that missing ESM reports was not significantly correlated with common psychological constructs, suggesting that the reasons for missing reports are not related to the participants' mental states.

The use of the Electronically Activated Recorder (EAR) provided valuable insights into participants' activities during missed reports, reinforcing the validity of ESM research in healthy university student populations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Eavesdropping on Missing Data: What Are University Students Doing When They Miss Experience Sampling Reports? Sun, J., Rhemtulla, M., Vazire, S.[2021]

A study involving 7016 individuals found that recruiting 200 participants and collecting 20 affect reports per person is sufficient to detect medium-sized associations in affect dynamics, which are linked to well-being.

The research suggests that distant sampling designs (few daily measurements over weeks) provide more accurate estimates of affect variability than close sampling designs (many measurements over a few days), although the differences in accuracy are often minor.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Measuring affect dynamics: An empirical framework. Pirla, S., Taquet, M., Quoidbach, J.[2023]

mobileQ is a free, open-source platform designed for experience sampling studies, making it accessible for researchers without programming skills.

The platform includes a web interface and an Android app, allowing for controlled studies on dedicated research phones, which helps eliminate selection bias and improve data quality.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

mobileQ: A free user-friendly application for collecting experience sampling data. Meers, K., Dejonckheere, E., Kalokerinos, EK., et al.[2021]

The Experience Sampling Method (ESM) collects valuable psychological data but often results in unequally spaced measurements, which can lead to biases when analyzed using discrete-time (DT) models like autoregressive models.

Simulations showed that the bias in DT models varies significantly based on the true parameters and data characteristics, suggesting that continuous-time (CT) models should be preferred for analyzing ESM data to improve accuracy, especially with new software tools available.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data. de Haan-Rietdijk, S., Voelkle, MC., Keijsers, L., et al.[2020]

In a study involving 163 students, longer experience sampling method (ESM) questionnaires (30 vs. 60 items) increased participant burden and negatively impacted data quality and quantity, while higher sampling frequencies (3, 6, or 9 times per day) did not have adverse effects.

The results suggest that researchers should avoid using long questionnaires in ESM studies to maintain data integrity, as increased burden from lengthy surveys can lead to careless responses.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Effects of Sampling Frequency and Questionnaire Length on Perceived Burden, Compliance, and Careless Responding in Experience Sampling Data in a Student Population. Eisele, G., Vachon, H., Lafit, G., et al.[2022]

References

1. England pubmed.ncbi.nlm.nih.gov

Using the experience-based design approach to improve orthodontic care. [2015]

2. United States pubmed.ncbi.nlm.nih.gov

How can health care organizations be reliably compared?: Lessons from a national survey of patient experience. [2015]

3. England pubmed.ncbi.nlm.nih.gov

How patient and staff experiences affect outcomes. [2016]

4. United States pubmed.ncbi.nlm.nih.gov

Examining the role of patient experience surveys in measuring health care quality. [2022]

5. England pubmed.ncbi.nlm.nih.gov

It takes patience and persistence to get negative feedback about patients' experiences: a secondary analysis of national inpatient survey data. [2022]

6. United States pubmed.ncbi.nlm.nih.gov

A Novel Method for Deriving Adverse Event Prevalence in Randomized Controlled Trials: Potential for Improved Understanding of Benefit-Risk Ratio and Application to Drug Labels. [2023]

7. England pubmed.ncbi.nlm.nih.gov

Quantifying the risks of non-oncology phase I research in healthy volunteers: meta-analysis of phase I studies. [2018]

8. New Zealand pubmed.ncbi.nlm.nih.gov

Using triggers in primary care patient records to flag increased adverse event risk and measure patient safety at clinic level. [2014]

9. Netherlands pubmed.ncbi.nlm.nih.gov

Post-market surveillance of consumer products: Framework for adverse event management. [2022]

10. Switzerland pubmed.ncbi.nlm.nih.gov

A Bayesian Exposure-Time Method for Clinical Trial Safety Monitoring With Blinded Data. [2020]

11. United States pubmed.ncbi.nlm.nih.gov

Eavesdropping on Missing Data: What Are University Students Doing When They Miss Experience Sampling Reports? [2021]

12. United States pubmed.ncbi.nlm.nih.gov

Measuring affect dynamics: An empirical framework. [2023]

13. United States pubmed.ncbi.nlm.nih.gov

mobileQ: A free user-friendly application for collecting experience sampling data. [2021]

14. Switzerland pubmed.ncbi.nlm.nih.gov

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data. [2020]

15. United States pubmed.ncbi.nlm.nih.gov

The Effects of Sampling Frequency and Questionnaire Length on Perceived Burden, Compliance, and Careless Responding in Experience Sampling Data in a Student Population. [2022]