The Role of Wearable Devices in Predicting and Detecting Complications and Adverse Events

The overarching goal of this research is to use machine learning analysis of high-resolution data-collected by wearable technology-to predict complications and poor recovery in patients undergoing treatment for benign or malignant conditions.

Study Overview

• Status: Recruiting
• Conditions: Recovery; Treatment Complication
• Intervention / Treatment: Device: Device: Wearable Device

Detailed Description

This is a multi-center non-randomized prospective cohort study using wearable devices and machine learning to predict complications and poor recovery in patients undergoing treatment for benign or malignant conditions.

Patients who meet the inclusion and exclusion criteria will be enrolled consecutively with verbal informed consent from the time this protocol is approved by the IRB until 2,400 subjects are enrolled. At ~30 days before treatment the subjects will have a wearable device (such as a Fitbit) placed on their wrist and will wear the device for up to 5 years following treatment. This device will wirelessly transmit data regarding activity and sleep quality to a smartphone application for the duration of wear and data will be analyzed by our collaborators at Case Western Reserve University.

• Study Type: Observational
• Enrollment (Estimated): 2400

Contacts and Locations

• Study Contact: Name: Chi-Fu Jeffrey Yang, MD; Phone Number: 617-726-5200; Email: cjyang@mgh.harvard.edu
• Study Contact Backup: Name: Isha Mehta Warikoo, MD; Phone Number: 857-250-1355; Email: imehtawarikoo@mgh.harvard.edu

Study Locations

• United States
  • Massachusetts
    • Boston, Massachusetts, United States, 02114
      • Recruiting
      • Massachusetts General Hospital
      • Contact: Chi-Fu J Yang, M.D.; Phone Number: 814-574-8695; Email: cjyang@mgh.harvard.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Patients undergoing treatment for benign or malignant conditions that are amenable to wearing a device of interest and meet the inclusion and exclusion criteria above will be approached for informed consent.

Description

Inclusion Criteria:

1. Age 18 years or older
2. Individuals scheduled to undergo one of the following surgical or non-surgical treatments: cardiothoracic surgery, orthopedic surgery, vascular surgery, colorectal surgery, pancreatic surgery, other major abdominal surgeries, treatment for chronic disease, or systemic therapy (i.e., chemotherapy, immunotherapy, or targeted therapy), radiotherapy, or ablation.
3. Amenable to using one of the wearable devices of interest (Fitbit, iWatch, Biostrap).
4. Individuals willing to provide informed consent and who have capacity for all study procedures

Exclusion Criteria:

1. Individuals with mental incapacity and/or cognitive impairment that would preclude adequate understanding of, or cooperation with the study protocol.
2. Any pregnant participant.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Treatment Group; Adults patients who are scheduled to undergo treatment for a benign or malignant condition and meet the inclusion and exclusion criteria.	Device: Device: Wearable Device; A Wearable Device will be placed on the wrist of the patient ~30 days prior to the patient's scheduled treatment and for up to 5 years following treatment. The device will record activity in terms of steps, sleep quality, heart rate, etc.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Early detection of complications and adverse events using machine learning analysis of patient biometric data.	Proportion of complications detected by the machine learning algorithm.	Five Years
Prediction of the quality of recovery after treatment using patient biometric data.	Proportion of patients whose quality of recovery is correctly predicted by the machine learning algorithm.	Four Years

Collaborators and Investigators

• Sponsor: Massachusetts General Hospital
• Collaborators: Case Western Reserve University
• Investigators: Principal Investigator: Chi-Fu Jeffrey Yang, Massachusetts General Hospital

Publications and helpful links

General Publications

• Wang D, Fang Z, Zhu A, Rettner B, Potter AL, McCarthy M, Zhang L, Kim J, Zhang Y, Powell J, Pope A, Beqari J, Cranor J, Smock G, Warikoo IM, Aaron A, Guo Q, Hanna G, Mitri J, Zarif M, Melki A, Wilkins I, Lin MW, Lee H, Costantino C, Furlow PW, Sachdeva UM, Auchincloss HG, Wright C, Lanuti M, Li X, Jeffrey Yang CF. Changes in patient-reported quality of life after lobectomy versus sublobar resection. J Thorac Cardiovasc Surg. 2026 Apr;171(4):826-835.e5. doi: 10.1016/j.jtcvs.2025.10.040. Epub 2025 Nov 8.

Study record dates

Study Major Dates

• Study Start (Actual): July 10, 2021
• Primary Completion (Estimated): January 1, 2029
• Study Completion (Estimated): June 30, 2029

Study Registration Dates

• First Submitted: March 11, 2021
• First Submitted That Met QC Criteria: March 26, 2021
• First Posted (Actual): April 1, 2021

Study Record Updates

• Last Update Posted (Actual): May 28, 2026
• Last Update Submitted That Met QC Criteria: May 24, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Postoperative Complications; Equipment and Supplies; Electrical Equipment and Supplies; Wearable Electronic Devices
• Other Study ID Numbers: 2020P002984

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No