Using Data Science To Center Patient Perspectives in Mechanism Discovery (CPP)
Using Data Science To Center Patient Perspectives In Psychological Mechanism Discovery and Intervention Development
Study Overview
Status
Detailed Description
Demand for cost-effective, novel, scalable trauma-focused interventions is high. Prevalence rates of posttraumatic stress disorder (PTSD) and Complex PTSD in US community mental health clinics are estimated to be as high as 50%. Yet response heterogeneity to PTSD interventions remains high, with non-response rates reaching 50-60% and dropout rates for traditional interventions (i.e., cognitive behavioral, exposure therapies) at 30-40%. Moreover, research populations in typical stepwise, efficacy-driven clinical research trials are often characterized by strict exclusion criteria and low representation from underrepresented communities. The homogeneous nature of efficacy-based research populations creates an incomplete picture, especially in public sector and community-based mental health facilities. Studies have suggested not only does this homogeneity limit effectiveness across diverse populations, but may contribute to exacerbating health disparities.
Engaging patient perspectives is crucial to research because it can provide insight into response heterogeneity and engagement, ultimately leading to an understanding of mechanisms and creating more patient-centered interventions. One way to center the patient's voice and increase the potential of identifying unique mechanisms of change for a novel therapy, is to use qualitative interviews because it directly accesses the lived experience and its context. Despite the potential benefits of utilizing qualitative data in stepwise randomized control trials, several obstacles persist, including resource constraints, the inability to quantify interactive elements, and concerns regarding the practical value of the gathered information. Innovative methods that reliably and rapidly extract value-laden, relevant themes, and discern non-verbal conversational elements may facilitate the integration of patient experience and inclusion of their perspectives in clinical intervention trials.
This single-arm study aims to evaluate the feasibility of using natural language processing (NLP) and deep learning to identify potential mechanisms of PTSD symptom change from patient interviews. The study will utilize ongoing cohorts from a clinical service that offers a 16-week, live-online group therapy model (and adjunct individual therapy sessions) called Program For Alleviating And Resolving Trauma and Stress (PARTS) that uses the IFS model. The investigators will use a convergent mixed methods approach applying machine learning and natural language processing to develop models that identify potential mechanisms of change.
Analysis: The investigators will use several different methods to develop our models including Latent Dirichlet Allocation, pre-trained language models, transfer learning (recurrent neural networks, generative adversarial network), and penalized regression-based models. These models will use data derived from patient perspectives of psychological change, as expressed in interviews, and will be compared to models derived from clinical measures. The study will use standard performance metrics and cross-validation scores to evaluate comparative performance of the models. As an exploratory aim, the study will evaluate the feasibility of using features derived from language processing models and clinical measures to predict individual therapy visits post-intervention. The exploratory data will also include structured clinical data, social determinants of health, and therapy-based utilization (dates, provider type, length).
Anticipated results: The development of two validated models: one derived from patient interview data and the other based on clinical measures to comprehensively identify mechanisms of change from group-based therapy models of IFS for PTSD.
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
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Massachusetts
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Malden, Massachusetts, United States, 02148
- Cambridge Health Alliance
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
Must be enrolled in the clinical service offering online PARTS group and approved and confirmed to start by the clinical team.
Have sufficient English fluency and literacy skills to understand the consent process, procedures and questionnaires and have the ability to provide written informed consent.
Have access to the internet and an electronic device with adequate data capacity; to complete questionnaires online and participate in two online video interviews.
Must be willing to complete online computerized assessments both at baseline and post-intervention; and participate in two, one-hour videotaped interviews one at baseline and one 2-4 weeks post-intervention.
Exclusion Criteria:
Inability to complete an informed consent assessment AND/OR inability to complete baseline study assessment procedures (due to cognitive deficit, non-proficiency in English literacy, or any other reason).
Expected medical hospitalization in 24 weeks from the date of enrollment.
Expected incarceration in 24 weeks from the date of enrollment.
Individuals who are pregnant with a due date within 24 weeks after study consent.
Insufficient internet connection to conduct online interviews or computerized assessments.
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Similarity of topics and mechanism identification
Time Frame: 24 weeks
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Similarity of topics obtained from interview data using language processing tools to those classified by human coders and identification of mechanisms from predictive models with language processing derived features.
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24 weeks
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Comparable mechanism identification and performance
Time Frame: 24 weeks
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Models using features derived from interview data will find comparable mechanisms to ones using clinical survey data and exhibit moderate predictive power.
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24 weeks
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Other Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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PTSD Symptom Severity Changes
Time Frame: 20 weeks
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The PTSD Checklist for DSM-5 (PCL-5) is a 20-item self-report measure designed to assess PTSD symptom severity (scores above 30-31 indicate more severe symptoms).
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20 weeks
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PTSD and Complex PTSD Symptom Severity Changes
Time Frame: 20 weeks
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The 18-item International Trauma Questionnaire (ITQ) captures PTSD symptom severity, disturbances in self-organization (DSO) (characteristic of Complex PTSD), and impact on functioning.
For the ITQ, PTSD and DSO scores range from 0 to 24, while Complex PTSD scores range from 0 to 48.
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20 weeks
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Collaborators and Investigators
Sponsor
Collaborators
Investigators
- Principal Investigator: Zev Schuman-Olivier, MD, Center for Mindfulness and Compassion, Cambridge Health Alliance
- Principal Investigator: Dilara Ally, PhD, Center for Mindfulness and Compassion, Cambridge Health Alliance
Publications and helpful links
General Publications
- Schottenbauer MA, Glass CR, Arnkoff DB, Tendick V, Gray SH. Nonresponse and dropout rates in outcome studies on PTSD: review and methodological considerations. Psychiatry. 2008 Summer;71(2):134-68. doi: 10.1521/psyc.2008.71.2.134.
- Spoont MR, Murdoch M, Hodges J, Nugent S. Treatment receipt by veterans after a PTSD diagnosis in PTSD, mental health, or general medical clinics. Psychiatr Serv. 2010 Jan;61(1):58-63. doi: 10.1176/ps.2010.61.1.58.
- Adams-Campbell LL, Ahaghotu C, Gaskins M, Dawkins FW, Smoot D, Polk OD, Gooding R, DeWitty RL. Enrollment of African Americans onto clinical treatment trials: study design barriers. J Clin Oncol. 2004 Feb 15;22(4):730-4. doi: 10.1200/JCO.2004.03.160.
- Erves JC, Mayo-Gamble TL, Malin-Fair A, Boyer A, Joosten Y, Vaughn YC, Sherden L, Luther P, Miller S, Wilkins CH. Needs, Priorities, and Recommendations for Engaging Underrepresented Populations in Clinical Research: A Community Perspective. J Community Health. 2017 Jun;42(3):472-480. doi: 10.1007/s10900-016-0279-2.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 13664
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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