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

doi:10.5220/0010721100003063

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

Biomedical Signals and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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 language	English
Title of host publication	Proceedings of the 13th International Joint Conference on Computational Intelligence
Pages	389-397
Volume	1
DOIs	https://doi.org/10.5220/0010721100003063
Publication status	Published - 2021
Event	13th International Joint Conference on Computational Intelligence, IJCCI 2021 - Virtual Conference Duration: 25 Oct 2021 → 27 Oct 2021 Conference number: 13

Conference

Conference	13th International Joint Conference on Computational Intelligence, IJCCI 2021
Abbreviated title	IJCCI 2021
City	Virtual Conference
Period	25/10/21 → 27/10/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.5220/0010721100003063Licence: CC BY-NC-ND

ArticleFinal published version, 1.42 MBLicence: CC BY-NC-ND

Cite this

@inproceedings{4db7fcba4de7475d9f05049d1486eacb,

title = "Breaking up Long Sedentary Periods of Office Workers through a Virtual Coach using Activity Data",

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 ",

author = "Jasmijn Franke and Christiane Gr{\"u}nloh and Dennis Hofs and {van Schooten}, Boris and Andreea Bondrea and Miriam Cabrita",

year = "2021",

doi = "10.5220/0010721100003063",

language = "English",

volume = "1",

pages = "389--397",

booktitle = "Proceedings of the 13th International Joint Conference on Computational Intelligence",

note = "13th International Joint Conference on Computational Intelligence, IJCCI 2021, IJCCI 2021 ; Conference date: 25-10-2021 Through 27-10-2021",

}

Franke, J, Grünloh, C, Hofs, D, van Schooten, B, Bondrea, A & Cabrita, M 2021, Breaking up Long Sedentary Periods of Office Workers through a Virtual Coach using Activity Data. in Proceedings of the 13th International Joint Conference on Computational Intelligence. vol. 1, pp. 389-397, 13th International Joint Conference on Computational Intelligence, IJCCI 2021, Virtual Conference, 25/10/21. https://doi.org/10.5220/0010721100003063

Breaking up Long Sedentary Periods of Office Workers through a Virtual Coach using Activity Data. / Franke, Jasmijn; Grünloh, Christiane; Hofs, Dennis et al.
Proceedings of the 13th International Joint Conference on Computational Intelligence. Vol. 1 2021. p. 389-397.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Breaking up Long Sedentary Periods of Office Workers through a Virtual Coach using Activity Data

AU - Franke, Jasmijn

AU - Grünloh, Christiane

AU - Hofs, Dennis

AU - van Schooten, Boris

AU - Bondrea, Andreea

AU - Cabrita, Miriam

N1 - Conference code: 13

PY - 2021

Y1 - 2021

N2 - 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

AB - 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

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DO - 10.5220/0010721100003063

M3 - Conference contribution

VL - 1

SP - 389

EP - 397

BT - Proceedings of the 13th International Joint Conference on Computational Intelligence

T2 - 13th International Joint Conference on Computational Intelligence, IJCCI 2021

Y2 - 25 October 2021 through 27 October 2021

ER -

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

Abstract

Conference

UN SDGs

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