The etiology underlying chronic widespread pain (CWP) remains largely unknown. An integrative biopsychosocial model seems to yield the most promising explanations for the pathogenesis of the condition, with genetic factors also contributing to disease development and maintenance. Here, we conducted a search of studies investigating the genetic and epigenetic epidemiology of CWP through electronic databases including Web of Science, Medline, PubMed, EMBASE, and Google Scholar. Combinations of keywords including CWP, chronic pain, musculoskeletal pain, genetics, epigenetics, gene, twins, single-nucleotide polymorphism, genotype, and alleles were used. In the end, a total of 15 publications were considered relevant to be included in this review: eight were twin studies on CWP, six were molecular genetic studies on CWP, and one was an epigenetic study on CWP. The findings suggest genetic and unique environmental factors to contribute to CWP. Various candidates such as serotonin-related pathway genes were found to be associated with CWP and somatoform symptoms. However, studies show some limitations and need replication. The presented results for CWP could serve as a template for genetic studies on other chronic pain conditions. Ultimately, a more in-depth understanding of disease mechanisms will help with the development of more effective treatment, inform nosology, and reduce the stigma still lingering on this diagnosis.

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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.