Investigating resilience patterns based on within-subject changes in sleep and resting heart rate variability

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

Onderzoeksoutput: AbstractAcademic

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Occupational stress can cause all kinds of health problems. Resilience interventions that help employees deal with and adapt to adverse events can prevent these negative consequences. 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 own context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. The Wearables and app-based resilience Modelling in employees (WearMe) project 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 included concepts are operationalized and measured using sleep tracking (Fitbit Charge 2), heart rate variability measurements (Elite HRV + Polar H7) and Ecological Momentary Assessment (mobile app), administered in the morning (7 questions) and evening (12 questions). The first (ongoing) study within the WearMe project investigates the feasibility of the developed measurement cycle and explores the development of such models in social studies students that are on their first major internship. Analyses will target the development of both within-subject (n=1) models, as well as between-subjects models. The first results will be shared at the Health By Tech 2019 conference in Groningen. If successful, future work will focus on further developing these models and eventually exploring the effectiveness of the envisioned personalized resilience system.
Originele taal-2English
StatusAccepted/In press - 17 mei 2019
EvenementSupporting Health by Tech - Martiniplaza, Groningen, Netherlands
Duur: 16 mei 201917 mei 2019
http://healthbytech.com/

Conference

ConferenceSupporting Health by Tech
LandNetherlands
StadGroningen
Periode16/05/1917/05/19
Internet adres

Keywords

  • resilience
  • veerkracht
  • werknemers
  • sensortechnologie
  • modellering
  • slaap
  • hartritmevariabiliteit

Citeer dit

de Vries, H., Oldenhuis, H., Kamphuis, W., Sanderman, R., & van der Schans, C. (Geaccepteerd/In druk). Investigating resilience patterns based on within-subject changes in sleep and resting heart rate variability. Abstract van Supporting Health by Tech, Groningen, Netherlands.
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de Vries, H, Oldenhuis, H, Kamphuis, W, Sanderman, R & van der Schans, C 2019, 'Investigating resilience patterns based on within-subject changes in sleep and resting heart rate variability' Supporting Health by Tech, Groningen, Netherlands, 16/05/19 - 17/05/19, .

Investigating resilience patterns based on within-subject changes in sleep and resting heart rate variability. / de Vries, Herman; Oldenhuis, Hilbrand; Kamphuis, Wim; Sanderman, Robbert; van der Schans, Cees.

2019. Abstract van Supporting Health by Tech, Groningen, Netherlands.

Onderzoeksoutput: AbstractAcademic

TY - CONF

T1 - Investigating resilience patterns based on within-subject changes in sleep and resting heart rate variability

AU - de Vries, Herman

AU - Oldenhuis, Hilbrand

AU - Kamphuis, Wim

AU - Sanderman, Robbert

AU - van der Schans, Cees

PY - 2019/5/17

Y1 - 2019/5/17

N2 - Occupational stress can cause all kinds of health problems. Resilience interventions that help employees deal with and adapt to adverse events can prevent these negative consequences. 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 own context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. The Wearables and app-based resilience Modelling in employees (WearMe) project 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 included concepts are operationalized and measured using sleep tracking (Fitbit Charge 2), heart rate variability measurements (Elite HRV + Polar H7) and Ecological Momentary Assessment (mobile app), administered in the morning (7 questions) and evening (12 questions). The first (ongoing) study within the WearMe project investigates the feasibility of the developed measurement cycle and explores the development of such models in social studies students that are on their first major internship. Analyses will target the development of both within-subject (n=1) models, as well as between-subjects models. The first results will be shared at the Health By Tech 2019 conference in Groningen. If successful, future work will focus on further developing these models and eventually exploring the effectiveness of the envisioned personalized resilience system.

AB - Occupational stress can cause all kinds of health problems. Resilience interventions that help employees deal with and adapt to adverse events can prevent these negative consequences. 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 own context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. The Wearables and app-based resilience Modelling in employees (WearMe) project 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 included concepts are operationalized and measured using sleep tracking (Fitbit Charge 2), heart rate variability measurements (Elite HRV + Polar H7) and Ecological Momentary Assessment (mobile app), administered in the morning (7 questions) and evening (12 questions). The first (ongoing) study within the WearMe project investigates the feasibility of the developed measurement cycle and explores the development of such models in social studies students that are on their first major internship. Analyses will target the development of both within-subject (n=1) models, as well as between-subjects models. The first results will be shared at the Health By Tech 2019 conference in Groningen. If successful, future work will focus on further developing these models and eventually exploring the effectiveness of the envisioned personalized resilience system.

KW - resilience

KW - veerkracht

KW - werknemers

KW - sensortechnologie

KW - modellering

KW - slaap

KW - hartritmevariabiliteit

KW - resilience

KW - modelling

KW - wearables

KW - sleep

KW - heart rate

KW - heart rate variability

M3 - Abstract

ER -