Investigating Employees’ Concerns and Wishes for Digital Stress Management Interventions with Value Sensitive Design: Mixed Methods Study (Preprint)

BACKGROUND Work stress places a large economic and disease burden on society. Recent technological advances include digital health interventions to help employees prevent and manage 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 framework to 1) identify relevant values concerning a digital stress management intervention (dSMI) at the workplace, to 2) assess how users comprehend these values, and to 3) derive specific requirements for an ethics-informed design of dSMIs. Value Sensitive Design is a theoretically grounded framework that frontloads ethics by accounting for values throughout the design process of a technology. METHODS We conducted a literature search to identify the relevant values of dSMIs at the workplace. To understand how potential users comprehend these values and derive design requirements, we ran an online study with employees of a Swiss company that contained closed and open questions, allowing both quantitative and qualitative analyses. RESULTS The values health and well-being, privacy, autonomy, accountability, and identity were identified from our literature search. The statistical analysis of 170 responses from the online study revealed that 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 even amplify their stress levels. Privacy concerns were also rated on the higher end of the score range, while concerns regarding autonomy, accountability and identity were rated lower. Moreover, a personalised dSMI that included a monitoring system involving a machine learning-based analysis of data led to significantly higher privacy (P=.009) and accountability concerns (P=.04) compared to one without. In addition, integrability, user friendliness and digital independence emerged as novel values from the qualitative analysis of 85 text responses. CONCLUSIONS While most surveyed employees are willing to use a dSMI at the workplace, there are considerable health and well-being concerns with regard to effectiveness and problem perpetuation. For a minority of employees, who value digital independence, a non-digital offer might even 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 value-sensitive design 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.