Modelling employee resilience using wearables and apps: a conceptual framework and research design

Herman de Vries, Wim Kamphuis, Hilbrand Oldenhuis, Cees van der Schans, Robbert Sanderman

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Occupational stress can cause health problems, productivity loss or absenteeism. Resilience interventions that help employees positively adapt to adversity can help prevent the negative consequences of occupational stress. Due to advances in sensor technology and smartphone applications, relatively unobtrusive self-monitoring of resilience-related outcomes is possible. With models that can recognize intra-individual changes in these outcomes and relate them to causal factors within the employee's context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. This paper presents the conceptual framework and methods behind the WearMe project, which aims to develop such models. A cyclical conceptual framework based on existing theories of stress and resilience is presented as the basis for the WearMe project. The operationalization of the concepts and the daily measurement cycle are described, including the use of wearable sensor technology (e.g., sleep tracking and heart rate variability measurements) and Ecological Momentary Assessment (mobile app). Analyses target the development of within-subject (n=1) and between-subjects models and include repeated measures correlation, multilevel modelling, time series analysis and Bayesian network statistics. Future work will focus on further developing these models and eventually explore the effectiveness of the envisioned personalized resilience system.
Original languageEnglish
Pages (from-to)110-118
JournalInternational journal on advances in life sciences
Volume11
Issue number3-4
Publication statusPublished - 30 Dec 2019

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Application programs
resilience
research planning
Research Design
employee
Mobile Applications
Personnel
Technology
Absenteeism
Bayes Theorem
occupational stress
Sleep
Heart Rate
Efficiency
Time series analysis
Smartphones
Health
Bayesian networks
Medical problems
absenteeism

Keywords

  • employees
  • health

Cite this

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title = "Modelling employee resilience using wearables and apps:: a conceptual framework and research design",
abstract = "Occupational stress can cause health problems, productivity loss or absenteeism. Resilience interventions that help employees positively adapt to adversity can help prevent the negative consequences of occupational stress. Due to advances in sensor technology and smartphone applications, relatively unobtrusive self-monitoring of resilience-related outcomes is possible. With models that can recognize intra-individual changes in these outcomes and relate them to causal factors within the employee's context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. This paper presents the conceptual framework and methods behind the WearMe project, which aims to develop such models. A cyclical conceptual framework based on existing theories of stress and resilience is presented as the basis for the WearMe project. The operationalization of the concepts and the daily measurement cycle are described, including the use of wearable sensor technology (e.g., sleep tracking and heart rate variability measurements) and Ecological Momentary Assessment (mobile app). Analyses target the development of within-subject (n=1) and between-subjects models and include repeated measures correlation, multilevel modelling, time series analysis and Bayesian network statistics. Future work will focus on further developing these models and eventually explore the effectiveness of the envisioned personalized resilience system.",
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Modelling employee resilience using wearables and apps: a conceptual framework and research design. / de Vries, Herman; Kamphuis, Wim; Oldenhuis, Hilbrand; van der Schans, Cees; Sanderman, Robbert.

In: International journal on advances in life sciences, Vol. 11, No. 3-4, 30.12.2019, p. 110-118.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Kamphuis, Wim

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AU - Sanderman, Robbert

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