The aims of this project are two-fold. We seek to: 1) better understand the associations of discrimination with sleep and cardiovascular health among LGBTQ+ adults, and 2) compare various unsupervised learning techniques on their performance on clustering sleep patterns using actigraphy data. We have multiple datasets for conducting our analyses to delineate the pattern of associations between sleep and other variables. We will also explore various modeling techniques to quantify the within- and between-person variability in sleep patterns. We aim to complete multiple analyses in the Fall and Spring semesters to develop conference abstracts and manuscripts to be submitted in Spring 2022. Since our DSI seed grant is still ongoing, during the Fall semester the Scholar will work on data wrangling and analyses, in addition to data management from existing data collected from our Precision in Symptom Self-Management pilot studies. In January 2022, we will begin analyzing data from our DSI seed grant. We hope to collaborate with a student who is interested in machine learning, sleep, and/or health disparities. The Scholar will have the opportunity to contribute to all of these aspects of the project. The Scholar will also be able to propose and lead additional analyses.

This is an UNPAID research project.

Faculty Advisor

  • Professor: Billy Caceres
  • Center/Lab: Precision in Symptom Self-Management Center
  • Location: 560 West 168th Street New York, NY 10032
  • Our team investigates how discrimination and other psychosocial factors (e.g., violence) are associated with sleep and cardiovascular health disparities among marginalized adults. We use a variety of subjective and objective measures (e.g., data from wearables/sensors, text messages) to conduct analyses using both supervised and unsupervised learning models. Our ongoing study, funded by the Data Science Institute, uses ecological momentary assessments collected over 30 days to understand psychosocial factors that contribute to sleep disturbances in Black and Latinx LGBTQ+ adults. We use data from daily electronic diaries, sleep and activity trackers, and home blood pressure monitoring to link discrimination and other psychosocial stressors with sleep health and blood pressure.

Project Timeline

  • Earliest starting date: 10/1/21
  • End date: 5/1/22
  • Number of hours per week of research expected during Fall 2021: ~10
  • Number of hours per week of research expected during Summer 2022: ~10

Candidate requirements

  • Skill sets: The Scholar should have experience with processing and analyzing data from sensors and wearables. We hope to work with a Scholar that has an interest in the research topic. Although not required, the project would benefit from the Scholar with working knowledge of techniques for repeated-measures and multi-level data with uneven sampling rates.
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes
  • Additional comments: Not applicable