Deriving Requirements for Pervasive Well-Being Technology From Work Stress and Intervention Theory: Framework and Case Study

Background Stress in office environments is a big concern, often leading to burn-out. New technologies are emerging, such as easily available sensors, contextual reasoning, and electronic coaching (e-coaching) apps. In the Smart Reasoning for Well-being at Home and at Work (SWELL) project, we explore the potential of using such new pervasive technologies to provide support for the self-management of well-being, with a focus on individuals' stress-coping. Ideally, these new pervasive systems should be grounded in existing work stress and intervention theory. However, there is a large diversity of theories and they hardly provide explicit directions for technology design. Objective The aim of this paper is to present a comprehensive and concise framework that can be used to design pervasive technologies that support knowledge workers to decrease stress. Methods Based on a literature study we identify concepts relevant to well-being at work and select different work stress models to find causes of work stress that can be addressed. From a technical perspective, we then describe how sensors can be used to infer stress and the context in which it appears, and use intervention theory to further specify interventions that can be provided by means of pervasive technology. Results The resulting general framework relates several relevant theories: we relate “engagement and burn-out” to “stress”, and describe how relevant aspects can be quantified by means of sensors. We also outline underlying causes of work stress and how these can be addressed with interventions, in particular utilizing new technologies integrating behavioral change theory. Based upon this framework we were able to derive requirements for our case study, the pervasive SWELL system, and we implemented two prototypes. Small-scale user studies proved the value of the derived technology-supported interventions. Conclusions The presented framework can be used to systematically develop theory-based technology-supported interventions to address work stress. In the area of pervasive systems for well-being, we identified the following six key research challenges and opportunities: (1) performing multi-disciplinary research, (2) interpreting personal sensor data, (3) relating measurable aspects to burn-out, (4) combining strengths of human and technology, (5) privacy, and (6) ethics.

[1]  R. Yerkes,et al.  The relation of strength of stimulus to rapidity of habit‐formation , 1908 .

[2]  H SELYE,et al.  Stress and the General Adaptation Syndrome* , 1950, British medical journal.

[3]  F. Braceland.  THE STRESS OF LIFE , 1976 .

[4]  H. Selye,et al.  [Stress without distress]. , 1976, Bruxelles medical.

[5]  Robert Karasek,et al.  Job decision latitude and mental strain: Implications for job redesign , 1979 .

[6]  R. Caplan.  Person-environment fit theory and organizations: Commensurate dimensions, time perspectives, and mechanisms , 1987 .

[7]  R W Bohannon,et al.  Objective measures. , 1989, Physical therapy.

[8]  J. S. Phillips,et al.  Worksite stress management interventions. , 1990, The American psychologist.

[9]  D. Carroll,et al.  The effects of physical activity and exercise training on psychological stress and well-being in an adolescent population. , 1992, Journal of psychosomatic research.

[10]  T. Meijman.  Psychological Aspects of Workload , 1998 .

[11]  Wilmar B. Schaufeli,et al.  Psychische vermoeidheid en werk , 2000 .

[12]  P Hassmén,et al.  Physical exercise and psychological well-being: a population study in Finland. , 2000, Preventive medicine.

[13]  W. Schaufeli,et al.  Job burnout. , 2001, Annual review of psychology.

[14]  A. Bakker,et al.  The job demands-resources model of burnout. , 2001, The Journal of applied psychology.

[15]  A. Bakker,et al.  The Measurement of Engagement and Burnout: A Two Sample Confirmatory Factor Analytic Approach , 2002 .

[16]  B. J. Fogg,et al.  Persuasive technology: using computers to change what we think and do , 2002, UBIQ.

[17]  S. Michie.  CAUSES AND MANAGEMENT OF STRESS AT WORK , 2002, Occupational and environmental medicine.

[18]  Michael P. Leiter,et al.  AREAS OF WORKLIFE: A STRUCTURED APPROACH TO ORGANIZATIONAL PREDICTORS OF JOB BURNOUT , 2003 .

[19]  Gregory S. Kolt,et al.  Eustress, distress, and interpretation in occupational stress , 2003 .

[20]  Mary Czerwinski,et al.  A diary study of task switching and interruptions , 2004, CHI.

[21]  Neerincx,et al.  Situated cognitive engineering for complex task environments , 2005 .

[22]  Ashish Kapoor,et al.  Multimodal affect recognition in learning environments , 2005, ACM Multimedia.

[23]  Dimitris N. Metaxas,et al.  Optical computer recognition of facial expressions associated with stress induced by performance demands. , 2005, Aviation, space, and environmental medicine.

[24]  Yvonne de Kort,et al.  Persuasive Technology for Human Well-Being: Setting the Scene , 2006, PERSUASIVE.

[25]  S. Sonnentag,et al.  The Recovery Experience Questionnaire: development and validation of a measure for assessing recuperation and unwinding from work. , 2007, Journal of occupational health psychology.

[26]  M. Johnston,et al.  From Theory to Intervention: Mapping Theoretically Derived Behavioural Determinants to Behaviour Change Techniques , 2008 .

[27]  Christina Maslach,et al.  Early predictors of job burnout and engagement. , 2008, The Journal of applied psychology.

[28]  Arthur C. Graesser,et al.  Emote aloud during learning with AutoTutor: Applying the Facial Action Coding System to cognitive–affective states during learning , 2008 .

[29]  Katherine M. Richardson,et al.  Effects of occupational stress management intervention programs: a meta-analysis. , 2008, Journal of occupational health psychology.

[30]  A. Bakker,et al.  DAILY RECOVERY FROM WORK-RELATED EFFORT DURING NONWORK TIME , 2009 .

[31]  Andrew Sears,et al.  Automated stress detection using keystroke and linguistic features: An exploratory study , 2009, Int. J. Hum. Comput. Stud..

[32]  Lando L. J. Koppes,et al.  Nationale Enquête Arbeidsomstandigheden 2011. Methodologie en globale resultaten , 2010 .

[33]  Teresa M. Amabile,et al.  The Progress Principle: Using Small Wins to Ignite Joy, Engagement, and Creativity at Work , 2011 .

[34]  Mykola Pechenizkiy,et al.  Stess@Work: from measuring stress to its understanding, prediction and handling with personalized coaching , 2012, IHI '12.

[35]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[36]  Wessel Kraaij,et al.  Real-time task recognition based on knowledge workers' computer activities , 2012, ECCE.

[37]  Saskia Koldijk.  Automatic recognition of context and stress to support knowledge workers , 2012, ECCE.

[38]  Tamás D. Gedeon,et al.  Objective measures, sensors and computational techniques for stress recognition and classification: A survey , 2012, Comput. Methods Programs Biomed..

[39]  Maarten A. Hogervorst,et al.  Physiological Correlates of Stress in Individuals about to Undergo Eye Laser Surgery , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[40]  Alexis A. Fink.  The Progress Principle: Using Small Wins to Ignite Joy, Engagement, and Creativity at Work by Teresa Amabile and Steven Kramer , 2013 .

[41]  Wessel Kraaij,et al.  Unobtrusive Monitoring of Knowledge Workers for Stress Self-regulation , 2013, UMAP.

[42]  T. R. Wabeke,et al.  Recommending tips that support well-being at work to knowledge workers , 2014 .

[43]  Suzan Verberne,et al.  Fishualization: a group feedback display , 2014 .

[44]  Kenneth Jay,et al.  Effect of Individually Tailored Biopsychosocial Workplace Interventions on Chronic Musculoskeletal Pain and Stress Among Laboratory Technicians: Randomized Controlled Trial. , 2015, Pain physician.

[45]  Vanessa Schendel,et al.  A matter of time:the influence of context-based timing on compliance with well-being triggers , 2015 .