Breaking up Long Sedentary Periods of Office Workers through a Virtual Coach using Activity Data

Jasmijn Franke, Christiane Grünloh*, Dennis Hofs, Boris van Schooten, Andreea Bondrea, Miriam Cabrita

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

11 Downloads (Pure)

Abstract

Office workers often lead sedentary lifestyles, a lifestyle responsible for higher risks of cardiovascular disease, stroke, diabetes and premature mortality. Improvements towards a more active lifestyle reduce cardiovascular risks and thus changing the sedentary lifestyle might prevent chronic illness. The Recurring Sedentary Period Detection (RSPD) algorithm described in this paper was designed to identify recurring sedentary periods using data from an activity tracker, summarise the sedentary periods and pinpoint notification times at which the user should be motivated to get some movement. The outcome of the RSPD algorithm was validated using data from a 10-week period of one typical office worker. Our results show that the RSPD algorithm could correctly identify the recurring sedentary periods, compute fitting daily summaries and pinpoint the notification times correctly. With minor differences, the RSPD algorithm was successfully implemented in the healthyMe smartphone applicati
Original languageEnglish
Title of host publicationProceedings of the 13th International Joint Conference on Computational Intelligence
Pages389-397
Volume1
DOIs
Publication statusPublished - 2021
Event13th International Joint Conference on Computational Intelligence, IJCCI 2021 - Virtual Conference
Duration: 25 Oct 202127 Oct 2021
Conference number: 13

Conference

Conference13th International Joint Conference on Computational Intelligence, IJCCI 2021
Abbreviated titleIJCCI 2021
CityVirtual Conference
Period25/10/2127/10/21

Fingerprint

Dive into the research topics of 'Breaking up Long Sedentary Periods of Office Workers through a Virtual Coach using Activity Data'. Together they form a unique fingerprint.

Cite this