Stress in Crohn's (SinC)

@article{Health Data Nexus-sinc-1.0.0,
  author = {Yang, Robin and Karlin, Emma and Goodday, Sarah and Friend, Stephen},
  title = {{Stress in Crohn's (SinC)}},
  journal = {{Health Data Nexus}},
  year = {2026},
  month = nov,
  note = {Version 1.0.0},
  doi = {10.57764/rhmn-rv79},
  url = {https://doi.org/10.57764/rhmn-rv79}
}

Abstract

The Stress in Crohn’s study was designed to explore the feasibility of following stress using wearable devices to predict the risk of upcoming symptom changes. Day to day acute changes, as well as the longer-term progression of Crohn’s disease, are known to be somehow affected by stress, but the details of this relationship is poorly understood. The goal of this study is to empower those with Crohn’s disease by enabling them to monitor their own stress responses and early warning signs of impending illness worsening in between clinic visits and to provide the means to navigate their lives in ways that mitigate the consequences of stress on their disease.

Background

Crohn’s disease is a chronic inflammatory bowel disease characterized by periods of remission and worsening symptoms. Although both acute day-to-day changes and longer-term disease progression are thought to be influenced by stress, the timing, magnitude, and mechanisms of this relationship remain poorly understood.

The Stress in Crohn’s study was created to investigate whether continuous digital measures can help characterize stress responses and identify early warning signs of impending symptom worsening. By combining wearable physiology, smartphone sensing, cognitive tasks, connected-device measurements, and patient-reported outcomes, the study aims to better understand how stress relates to Crohn’s disease symptoms in real-world settings.

Methods

Participants were followed for approximately 6 to 9 months depending on study timing and available data. During the study, participants contributed both passive and active data streams.

Data were collected through a study companion app installed on each participant’s own smartphone. The app collected participant-reported outcomes, stress-related measures, Crohn’s disease symptom reports, app engagement data, passive phone sensor measures, and active cognitive task data.

Participants were also provided with connected devices, including:

• A smart wristwatch for continuous or near-continuous physiological and activity-related measures.

• A smart ring for sleep, activity, heart rate, heart rate variability, temperature-related measures, and other physiological signals.

• A smart scale for weight and related body measurements.

These data streams were intended to capture real-world stress-related physiology and behavior over time. The high-dimensional device and app data were linked with self-reported Crohn’s disease symptoms and stress levels to support analyses of symptom trajectories, stress responses, and early indicators of worsening health.

Data Description

The SinC dataset is structured as a longitudinal, participant-level digital health dataset containing multiple tables or files representing different data streams. Most data are organized around a participant identifier and a timestamp, date, or study-day variable, allowing records to be linked across modalities over time.

Depending on the released subset, the dataset may include the following categories of data:

Participant and study metadata.

Includes participant identifiers, enrollment information, clinical site, study timeline variables, and participation status. These fields allow researchers to define the observation window for each participant and align data across devices and surveys.

Patient-reported Crohn’s disease symptom data

Includes self-reported symptoms related to Crohn’s disease collected through the study companion app. These may include measures of gastrointestinal symptoms, abdominal pain, stool frequency or consistency, disease activity, perceived worsening, and related daily or periodic symptom reports.

Stress and psychological self-report data

Includes participant-reported stress levels and related psychological or emotional measures. These data can be used to examine acute stress, longer-term stress patterns, and associations between perceived stress and symptom changes.

Smartphone app and active task data

Includes data collected through the study app, such as survey responses, app engagement, passive phone sensor measures, and cognitive active task results. These data provide behavioral, cognitive, and contextual measures that may relate to stress and disease activity.

Wearable device data

Includes passively collected data from a smart wristwatch and smart ring. These data may include heart rate, heart rate variability, sleep, activity, movement, temperature-related measures, respiratory-related measures, and other physiological or behavioral signals. Data may be available at multiple temporal resolutions, including minute-level, session-level, daily summary, or nightly summary formats.

Smart scale data

Includes weight and related body measures collected from a connected scale over time.

Usage Notes

The SinC dataset can be used to study the relationship between stress, physiology, behavior, and Crohn’s disease symptom changes in daily life. It is particularly suited for longitudinal analyses that examine within-person patterns, early warning signs, and individualized risk signals.

Ethics

IRAS project ID: 269286
Protocol number: N/A
REC reference: 19/SC/0556
Sponsor 4YouandMe

Acknowledgements

Funder:

Helmsley Trust

Clinical Partner(s):

Mount Sinai School of Medicine and Oxford University

Coalition and Collaboration Partners:

Bodyport, Cambridge Cognition, Empatica, Evidation, Healthmode, and Vector Institute.

Conflicts of Interest

No conflicts of interest