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In Journal of minimally invasive gynecology ; h5-index 40.0

STUDY OBJECTIVE : To assess the feasibility of a non-contact radio sensor as an objective measurement tool to study postoperative recovery from endometriosis surgery.

DESIGN : Prospective cohort pilot study.

SETTING : Center for minimally-invasive gynecologic surgery at an academically-affiliated community hospital in conjunction with in-home monitoring.

PATIENTS : Patients over 18 years old who sleep independently and are scheduled to have laparoscopy for the diagnosis and treatment of suspected endometriosis.

INTERVENTIONS : A wireless, non-contact sensor, Emerald, was installed in the subjects' home and used to capture physiological signals without body contact. The device captured objective data about the patients' movement and sleep in their home for 5 weeks prior to surgery and approximately 5 weeks postoperatively. Subjects were concurrently asked to complete a daily pain assessment using a Numerical Rating System (NRS) and a free-text survey about their daily symptoms.

MEASUREMENTS AND MAIN RESULTS : Three women, aged 23-39, with mild to moderate endometriosis participated in the study. Emerald-derived sleep and wake times were contextualized and corroborated by select participant comments from retrospective surveys. Additionally, self-reported pain levels and one sleep variable, sleep onset to deep sleep time, showed a significant (p<0.01), positive correlation with next day pain scores in all three subjects: r=0.45, 0.50, and 0.55. In other words, the longer it took the subject to go from sleep onset to deep sleep, the higher their pain score the following day.

CONCLUSION : A patient's experience with pain is challenging to meaningfully quantify. This study highlights Emerald's unique ability to capture objective data in both pre-operative functioning and post-operative recovery in an endometriosis population. The utility of this uniquely objective data for the clinician - patient relationship is just beginning to be explored.

Loring Megan, Kabelac Zachary, Munir Usman, Yue Shichao, Ephraim Hannah Y, Rahul Hariharan, Isaacson Keith B, Griffith Linda G, Katabi Dina


Digital, Machine Learning, Pain, Remote Sensing, Sleep