CS2c: Panel: Data at Full Tilt: Accelerating Discovery with Wearables and Smartphones

Conference: Women in Statistics and Data Science 2025
11/13/2025: 10:00 AM - 11:30 AM EST
Panel 

Description

The rise of wearable devices and smartphones has ushered in a new era of rich, real-time, and context-aware data. These technologies hold tremendous promise for advancing population health, behavioral science, and precision medicine-yet their integration into rigorous statistical workflows presents both opportunities and challenges. Sponsored by the Caucus for Women in Statistics, this session brings together three researchers leveraging mobile data to drive discovery. The talks will span hospital settings, smartphone-based mobility tracking, and fertility research-each highlighting how statisticians are adapting methods to make sense of high-volume, high-frequency, and sometimes incomplete data from personal devices.

Keywords

Fitbit

Accelerometer

Missing data

Machine learning

Fertility

Cancer 

Organizer

Sarah Lotspeich, Wake Forest University

Target Audience

Mid-Level

Tracks

Knowledge
Women in Statistics and Data Science 2025

Presentations

Using ViSi Mobile Monitoring for Analyzing Posture of Hospitalized Patients

ViSi Mobile is a wearable device used in inpatient settings to continuously monitor patient posture during hospitalization. Analysis of ViSi data can enable researchers and healthcare providers to quantify an individual patient's movement and investigate collective patterns of many patients. However, erroneous values can exist in routinely-collected ViSi data because these data may accrue unexpected errors during data collection or storage. I will discuss our study of identifying the types of errors that can occur and understanding how they arise. I will also describe our methodology to improve the quality of routinely-collected ViSi data for reliable inference, which can have broad application to other monitoring systems in hospital use.
 

Speaker

Emily Huang, Wake Forest University

Functional Accelerated Failure Time Models for Detecting Acute Cannabis Impairment from Wearable Pupillometer Data

The widespread legalization of cannabis has created a pressing need for objective markers of recent use that remain valid even in frequent users with high tolerance. Pupil light response curves, measured using wearable pupillometers and analyzed with functional data methods, offer a promising solution. We collected pupil response curves from individuals who had smoked cannabis 25–60 minutes prior, and from controls with no use in the past 8 hours. We treat time since cannabis use as a right-censored survival outcome and model it using novel functional linear and additive accelerated failure time (AFT) models. In cross-validation, our functional AFT models outperform functional Cox models in predictive accuracy. While the Cox model offers flexibility, the AFT model's parametric formulation may better reflect the underlying biological process. These results support the potential utility of functional AFT models for detecting recent cannabis use in occupational and roadside settings. 

Speaker

Julia Wrobel, Emory University

Integrating Actigraphy and Ecological Momentary Assessment Data to Assess Discrimination and Cardiovascular Disease

In this presentation I will focus on the development of a few assessable methods we have employed to integrate and visualize Actigraphy data with ecological momentary assessment data to understand the association between when and where young adults experience discrimination and a subsequent CVD risk behavior (e.g., substance use, seep disruptions, dietary changes).
 

Speaker

Stephanie Cook, New York University