research-article

Modeling Biobehavioral Rhythms with Passive Sensing in the Wild: A Case Study to Predict Readmission Risk after Pancreatic Surgery

Authors:

Afsaneh Doryab,

Carissa LowAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 1

Article No.: 8, Pages 1 - 21

https://doi.org/10.1145/3314395

Published: 29 March 2019 Publication History

Abstract

Biobehavioral rhythms are associated with numerous health and life outcomes. We study the feasibility of detecting rhythms in data that is passively collected from Fitbit devices and using the obtained model parameters to predict readmission risk after pancreatic surgery. We analyze data from 49 patients who were tracked before surgery, in hospital, and after discharge. Our analysis produces a model of individual patients' rhythms for each stage of treatment that is predictive of readmission. All of the rhythm-based models outperform the traditional approaches to readmission risk stratification that uses administrative data.

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Index Terms

Modeling Biobehavioral Rhythms with Passive Sensing in the Wild: A Case Study to Predict Readmission Risk after Pancreatic Surgery
1. Applied computing
  1. Life and medical sciences
2. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 1

March 2019

786 pages

EISSN:2474-9567

DOI:10.1145/3323054

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 29 March 2019

Accepted: 01 January 2019

Revised: 01 November 2018

Received: 01 August 2018

Published in IMWUT Volume 3, Issue 1

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Cited By

Syversen ADosis AJayne DZhang Z(2024)Wearable Sensors as a Preoperative Assessment Tool: A ReviewSensors10.3390/s2402048224:2(482)Online publication date: 12-Jan-2024
https://doi.org/10.3390/s24020482
Ahmed MHasan TIslam SAhmed N(2024)Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide StudyJMIR Research Protocols10.2196/5154013(e51540)Online publication date: 24-Apr-2024
https://doi.org/10.2196/51540
Yan RLiu XDutcher JTumminia MVillalba DCohen SCreswell JCreswell KMankoff JDey ADoryab A(2024)Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative StudyJMIR AI10.2196/471943(e47194)Online publication date: 18-Apr-2024
https://doi.org/10.2196/47194
Zhang JLi DDai RCos HWilliams GRaper LHammill CLu C(2024)Predicting Surgical Outcomes Using WearablesGetMobile: Mobile Computing and Communications10.1145/3686138.368614628:2(17-22)Online publication date: 31-Jul-2024
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Ahmed MRony RHadi MHossain EAhmed N(2023)A Minimalistic Approach to Predict and Understand the Relation of App Usage with Students' Academic PerformanceProceedings of the ACM on Human-Computer Interaction10.1145/36042407:MHCI(1-28)Online publication date: 13-Sep-2023
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Sarwar AAgu EAlmadani A(2023)CovidRhythm: A Deep Learning Model for Passive Prediction of Covid-19 Using Biobehavioral Rhythms Derived From Wearable Physiological DataIEEE Open Journal of Engineering in Medicine and Biology10.1109/OJEMB.2023.32612234(21-30)Online publication date: 2023
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Zarate Rodriguez JCos HSrivastava RBewley ARaper LLi DDai RWilliams GFields RHawkins WLu CSanford DHammill C(2023)Preoperative levels of physical activity can be increased in pancreatectomy patients via a remotely monitored, telephone-based intervention: A randomized trialSurgery in Practice and Science10.1016/j.sipas.2023.10021215(100212)Online publication date: Dec-2023
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