Investigating Employees' Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study

Abstract

Background: Work stress places a heavy economic and disease burden on society. Recent technological advances include digital health interventions for helping employees prevent and manage their stress at work effectively. Although such digital solutions come with an array of ethical risks, especially if they involve biomedical big data, the incorporation of employees' values in their design and deployment has been widely overlooked.

Objective: To bridge this gap, we used the value sensitive design (VSD) framework to identify relevant values concerning a digital stress management intervention (dSMI) at the workplace, assess how users comprehend these values, and derive specific requirements for an ethics-informed design of dSMIs. VSD is a theoretically grounded framework that front-loads ethics by accounting for values throughout the design process of a technology.

Methods: We conducted a literature search to identify relevant values of dSMIs at the workplace. To understand how potential users comprehend these values and derive design requirements, we conducted a web-based study that contained closed and open questions with employees of a Swiss company, allowing both quantitative and qualitative analyses.

Results: The values health and well-being, privacy, autonomy, accountability, and identity were identified through our literature search. Statistical analysis of 170 responses from the web-based study revealed that the intention to use and perceived usefulness of a dSMI were moderate to high. Employees' moderate to high health and well-being concerns included worries that a dSMI would not be effective or would even amplify their stress levels. Privacy concerns were also rated on the higher end of the score range, whereas concerns regarding autonomy, accountability, and identity were rated lower. Moreover, a personalized dSMI with a monitoring system involving a machine learning-based analysis of data led to significantly higher privacy (P=.009) and accountability concerns (P=.04) than a dSMI without a monitoring system. In addition, integrability, user-friendliness, and digital independence emerged as novel values from the qualitative analysis of 85 text responses.

Conclusions: Although most surveyed employees were willing to use a dSMI at the workplace, there were considerable health and well-being concerns with regard to effectiveness and problem perpetuation. For a minority of employees who value digital independence, a nondigital offer might be more suitable. In terms of the type of dSMI, privacy and accountability concerns must be particularly well addressed if a machine learning-based monitoring component is included. To help mitigate these concerns, we propose specific requirements to support the VSD of a dSMI at the workplace. The results of this work and our research protocol will inform future research on VSD-based interventions and further advance the integration of ethics in digital health.

Keywords: digital health intervention; employee well-being; ethics; machine learning; mobile phone; monitoring; stress; value sensitive design.

Figure 1

Value-related wishes. Responses in percentages to individual items reflecting a specific value. dSMI: digital stress management intervention.

Figure 2

Just-in-time adaptive specific wishes. Responses in percentages to individual items regarding a digital stress management intervention (dSMI) with a just-in-time adaptive component.

Figure 3

Percentages of willingness to trade the values (1) privacy, (2) accountability, (3) identity, and (4) autonomy for the value health and well-being. dSMI: digital stress management intervention.

Figure 4

Percentages of willingness to share specific data sources for a digital stress management intervention (dSMI) with a just-in-time adaptive component.

Figure 5

Box plots and distribution plots of value-related concerns.

Figure 6

Grouped box plots and distribution plots for value-related concerns that shows significant differences along sample characteristics. DASS-S-21: stress subscale of the Depression Anxiety and Stress Scales; JITAI: just-in-time adaptive intervention.