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Short-term Detection of Job Strain in Knowledge Workers with a Scalable, Low-cost and Minimal-invasive Information System Service: Instrument Development and Evaluation
Type
fundamental research project
Start Date
01 March 2015
End Date
31 December 2015
Status
ongoing
Keywords
health-is
job strain
neuro-is
gff
Description
The increasingly demanding work environments today make job strain a timely and relevant subject of investigation.
Early detection and tailored treatment of job strain is important because it negatively affects the health condition of employees, the performance of organizations, and the overall costs of the health care system likewise. Although there exist several self-report instruments for measuring job strain, one major limitation is the low number of measurements and, related to it, high-effort and high-costs associated with each wave of data collection. That is, measurements are usually conducted only two times per health intervention with several weeks or even months in between. As a result and significant shortcoming, short-term episodes of high job strain cannot be identified reliably.
The current research aims therefore to design, implement and evaluate a scalable, low-cost and minimal-invasive Job Strain Information System Service (JSISS) that continuously senses the degree of job strain in knowledge workers. Based on recent findings, which relate variations in mouse interactions to the degree of arousal and because arousal and job strain are hypothesized to be associated, two research questions are addressed in this project:
(1) Which features of an employee's motor activity measured by mouse interactions are significantly related to the degree of job strain?
(2) Under which technological, organizational and legal conditions are JSISS accepted by employees and organizations?
One lab experiment and one longitudinal field study are conducted to identify the relevant features of mouse interactions and to develop and validate machine learning models that infer job strain, and thus, answer the first research question. By contrast, a literature review, workshops, focus groups and two cross-sectional online surveys in the educational and engineering sector are conducted to answer the second research question.
The result of the current investigation will be a validated JSISS that paves the way for a novel class of individually tailored interventions that are expected to positively influence health and well being of employees, the performance of organizations, and the serious development of today's increasing health care costs.
Early detection and tailored treatment of job strain is important because it negatively affects the health condition of employees, the performance of organizations, and the overall costs of the health care system likewise. Although there exist several self-report instruments for measuring job strain, one major limitation is the low number of measurements and, related to it, high-effort and high-costs associated with each wave of data collection. That is, measurements are usually conducted only two times per health intervention with several weeks or even months in between. As a result and significant shortcoming, short-term episodes of high job strain cannot be identified reliably.
The current research aims therefore to design, implement and evaluate a scalable, low-cost and minimal-invasive Job Strain Information System Service (JSISS) that continuously senses the degree of job strain in knowledge workers. Based on recent findings, which relate variations in mouse interactions to the degree of arousal and because arousal and job strain are hypothesized to be associated, two research questions are addressed in this project:
(1) Which features of an employee's motor activity measured by mouse interactions are significantly related to the degree of job strain?
(2) Under which technological, organizational and legal conditions are JSISS accepted by employees and organizations?
One lab experiment and one longitudinal field study are conducted to identify the relevant features of mouse interactions and to develop and validate machine learning models that infer job strain, and thus, answer the first research question. By contrast, a literature review, workshops, focus groups and two cross-sectional online surveys in the educational and engineering sector are conducted to answer the second research question.
The result of the current investigation will be a validated JSISS that paves the way for a novel class of individually tailored interventions that are expected to positively influence health and well being of employees, the performance of organizations, and the serious development of today's increasing health care costs.
Leader contributor(s)
Member contributor(s)
Funder(s)
Topic(s)
Health-IS
Job Strain
Neuro-IS
Method(s)
One lab experiment and one longitudinal field study are conducted to identify the relevant features of mouse interactions and to develop and validate machine learning models that infer job strain
and thus
answer the first research question. By contrast
a literature review
workshops
focus groups and two cross-sectional online surveys in the educational and engineering sector are conducted to answer the second research question.
Range
Institute/School
Range (De)
Institut/School
Division(s)
Eprints ID
239929
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PublicationMobileCoach: A Novel Open Source Platform for the Design of Evidence-based, Scalable and Low-Cost Behavioral Health Interventions - Overview and Preliminary Evaluation in the Public Health Context(IEEE, 2015-04-15)
;Haug, Severin ;Wahle, Fabian ;Staake, ThorstenEffective and efficient behavioral interventions are important and of high interest today. Due to shortcomings of related approaches, we introduce MobileCoach (mobile-coach.eu) as novel open source behavioral intervention platform. With its modular architecture, its rule-based engine that monitors behavioral states and triggers state transitions, we assume MobileCoach to lay a fruitful ground for evidence-based, scalable and low-cost behavioral interventions in various application domains. The code basis is made open source and thus, MobileCoach can be used and revised not only by interdisciplinary research teams but also by public bodies or business organizations without any legal constraints. Technical details of the platform are presented as well as preliminary empirical findings regarding the acceptance of one particular intervention in the public health context. Future work will integrate Internet of Things services that sense and process data streams in a way that MobileCoach interventions can be further tailored to the needs and characteristics of individual participants.Type: conference paperScopus© Citations 34