Personalized support for well-being at work: an overview of the SWELL project

Kraaij, Wessel; Verberne, Suzan; Koldijk, Saskia; de Korte, E.M.; van Dantzig, Saskia; Sappelli, Maya; Hulsebosch, Bob; Wabeke, Thymen; Neerincx, M.A.

doi:10.1007/s11257-019-09238-3

Personalized support for well-being at work

Title

Personalized support for well-being at work: an overview of the SWELL project

Author

Kraaij, Wessel (Universiteit Leiden; TNO; Radboud Universiteit Nijmegen)
Verberne, Suzan (Universiteit Leiden; Radboud Universiteit Nijmegen)
Koldijk, Saskia (TNO; Radboud Universiteit Nijmegen)
de Korte, E.M. (TU Delft Applied Ergonomics and Design; TNO)
van Dantzig, Saskia (Philips Research)
Sappelli, Maya (TNO; Radboud Universiteit Nijmegen)
Hulsebosch, Bob (Innovalor BV)
Wabeke, Thymen (TNO)
Neerincx, M.A. (TU Delft Interactive Intelligence; TNO)

Date

2019

Abstract

Recent advances in wearable sensor technology and smartphones enable simple and affordable collection of personal analytics. This paper reflects on the lessons learned in the SWELL project that addressed the design of user-centered ICT applications for self-management of vitality in the domain of knowledge workers. These workers often have a sedentary lifestyle and are susceptible to mental health effects due to a high workload. We present the sense–reason–act framework that is the basis of the SWELL approach and we provide an overview of the individual studies carried out in SWELL. In this paper, we revisit our work on reasoning: interpreting raw heterogeneous sensor data, and acting: providing personalized feedback to support behavioural change. We conclude that simple affordable sensors can be used to classify user behaviour and heath status in a physically non-intrusive way. The interpreted data can be used to inform personalized feedback strategies. Further longitudinal studies can now be initiated to assess the effectiveness of m-Health interventions using the SWELL methods.

Subject

Behavioural change
Context aware systems
m-Health
Personalization
Privacy
Quantified self

To reference this document use:

http://resolver.tudelft.nl/uuid:0c8763bb-3873-4b4c-8470-722b1d56cdcd

DOI

https://doi.org/10.1007/s11257-019-09238-3

ISSN

0924-1868

Source

User Modeling and User-Adapted Interaction: the journal of personalization research, 30 (3), 413-446

Part of collection

Institutional Repository