SEARCHCROSS-SECTIONALoccupational_health, digital_health

cross-sectional·occupational_health, digital_health, psychiatry, public_health, preventive_medicine·PMC10141316

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

Journal of Medical Internet Research · 7 authors, 3 centres

AI SUMMARY

FIDELITY 80%

POPULATIONEmployees (aged 18–65) of a large Swiss insurance company who work on a computer for most hours (n=170 analyzed; 71 female, 99 male; 78 managers, 92 employees)

INTERVENTIONPresentation of a digital stress management intervention (dSMI) with or without a just-in-time adaptive intervention (JITAI) component involving machine learning-based monitoring of physiological/behavioral data

COMPARISONJITAI dSMI (n=100) vs non-JITAI dSMI (n=70); also comparisons by propensity to trust health technologies (low vs high), prior experience with health technologies (yes vs no), and stress level (normal vs severe/extremely severe)

This summary was generated by AI from a single paper. It has not been reviewed by a clinician and is not clinical advice. Verify against the source before acting on it.

AGENT 01 · STRUCTURED SUMMARY

TRACE TO SOURCE

“

TL;DR

This mixed methods study applied the Value Sensitive Design (VSD) framework to identify and assess employee values regarding digital stress management interventions (dSMIs) at the workplace. Based on 170 survey responses from a Swiss insurance company, employees showed moderate-to-high willingness to use dSMIs but expressed significant concerns about effectiveness, privacy (especially with machine learning-based monitoring), and accountability. The authors propose specific design requirements—including data minimization, user control, and optional non-digital alternatives—to align dSMI development with employee values.

Full summary

3,334 CHARS

**Background:** Work stress imposes a heavy economic and disease burden, and digital stress management interventions (dSMIs) offer scalable solutions. However, ethical risks—especially those involving biomedical big data and machine learning—are often overlooked. Value Sensitive Design (VSD) is a framework that front-loads ethics by systematically accounting for human values throughout technology design. This study aimed to identify relevant values for dSMIs at the workplace, assess how employees comprehend those values, and derive ethics-informed design requirements.

**Methods:** A narrative literature search identified relevant values. A web-based study was then conducted with employees of a large Swiss insurance company. Participants watched a 3-minute video introducing either a dSMI with a JITAI component (using ML-based monitoring of physiological/behavioral data) or a dSMI without monitoring. They then completed closed-ended scales measuring value-related concerns, wishes, user acceptance, and health trade-offs, plus open-ended questions. Quantitative analysis used nonparametric tests (Mann-Whitney U, Kruskal-Wallis) with Cliff delta effect sizes; qualitative data were analyzed using framework method analysis (FMA).

**Key Results:** Of 241 participants, 170 were included in the final analysis (70 non-JITAI, 100 JITAI). Mean intention to use was 4.60 (SD 1.66) and perceived usefulness was 4.12 (SD 1.40) on a 7-point scale. Health and well-being concerns were moderate (beneficence: mean 3.63, SD 1.51; nonmaleficence: mean 4.19, SD 1.62). Privacy concerns were moderate-to-high (mean 3.69, SD 1.81), while autonomy (mean 2.63, SD 1.32), identity (mean 2.14, SD 1.37), and accountability (mean 2.91, SD 1.24) concerns were lower. The JITAI dSMI led to significantly higher privacy concerns (median 4.33 vs 2.83; U=4322.5, P=.009, δ=0.24) and accountability concerns (median 2.67 vs 2.33; U=4145, P=.04, δ=0.18) compared to the non-JITAI dSMI. Employees with low propensity to trust health technologies had significantly higher accountability concerns (median 3.00 vs 2.33; U=4359, P=.02, δ=0.22) and beneficence concerns (median 4.00 vs 3.25; U=4576.5, P=.002, δ=0.28). Employees with severe/extremely severe stress had significantly higher nonmaleficence concerns than those with normal stress (median 5.33 vs 4.00; P=.02, δ=0.46). Qualitative analysis of 85 text responses from 70 participants confirmed all five identified values and revealed three novel values: integrability, digital independence, and user-friendliness. Over 40% of participants were completely unwilling to share eye movement or body posture data.

**Clinical Implications:** The findings highlight that while employees are generally open to dSMIs, significant ethical concerns must be addressed—particularly regarding privacy and accountability when ML-based monitoring is involved. The authors recommend data minimization, client-side processing, transparent consent, adjustable privacy settings, and the option to deactivate monitoring. For a minority valuing digital independence, non-digital stress management alternatives should be offered. These VSD-informed requirements aim to align dSMI design with employee values, potentially improving uptake, adherence, and ethical integrity of workplace digital health interventions.

PICO

PPOPULATION

Employees (aged 18–65) of a large Swiss insurance company who work on a computer for most hours (n=170 analyzed; 71 female, 99 male; 78 managers, 92 employees)

IINTERVENTION

Presentation of a digital stress management intervention (dSMI) with or without a just-in-time adaptive intervention (JITAI) component involving machine learning-based monitoring of physiological/behavioral data

OOUTCOME

Value-related concerns (health & well-being, privacy, autonomy, accountability, identity), value-related wishes, user acceptance (intention to use, perceived usefulness, trust, distrust), willingness to share data, and willingness to trade values for health benefits

STUDY TYPE

cross-sectional

SPECIALTY

occupational_health

SUMMARISED BY

AI pipeline

FIDELITY CHECK

80% · B

ORIGINAL PAPER

PMC10141316

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

Journal of Medical Internet Research

READ THE FULL PAPER

JUMP TO SECTION

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.

CITATION EXPORT

Jasmine I Kerr, Mara Naegelin, Michaela Benk et al.. Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study. Journal of Medical Internet Research. (2023). https://doi.org/10.2196/44131

AUTHORS

7 · 3 CENTRES

Jasmine I Kerr, Mara Naegelin, Michaela Benk, Florian v Wangenheim, Erika Meins, Eleonora Viganò, Andrea Ferrario

1 Mobiliar Lab for Analytics at ETH Zurich Department of Management, Technology, and Economics ETH Zurich Zürich Switzerland
2 Chair of Technology Marketing Department of Management, Technology, and Economics ETH Zurich Zurich Switzerland
3 Institute of Biomedical Ethics and History of Medicine University of Zurich Zurich Switzerland

EDITOR / REVIEWER

Gunther Eysenbach, Sander Hermsen