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Work-Health Management Interference for Workers with Chronic Health Conditions: Construct Development and Scale Validation

Abstract

A large and growing percentage of working adults has one or more chronic health conditions (CHCs). One under-appreciated issue for workers with CHCs is experiencing competing, incompatible pressures from the need to manage one’s health condition and the need to manage one’s work responsibilities. We refer to this as work-health management interference (WHMI). Despite its potential significance to the working lives of many people, scarce research has addressed WHMI. In this study, we explained the construct of WHMI, developed and evaluated a WHMI measure, and tested its relationships with work-related outcomes. We found support for time- and energy-based WHMI in workers with CHCs using qualitative (N = 35) and quantitative (including lagged) data samples (N = 204, N = 250, and N = 158). As expected, time- and energy-based WHMI positively related to work-family conflict and health condition severity, and negatively related to boundary flexibility. Energy-based WHMI predicted variance in work burnout beyond time-based WHMI and work-family conflict in all three samples, and energy-based WHMI predicted variance in work withdrawal beyond time-based WHMI and work-family conflict in two of three samples. Energy-based WHMI also predicted variance in perceived work ability beyond time-based WHMI and work-family conflict. A high level of WHMI signals a need for intervention for the individual (through education, coaching, job accommodations, etc.) and/or the organization (through supervisor training, implementing flexibility, etc.) to promote healthier and more sustainable employment for affected individuals.

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The primary study author will post the anonymous datasets on the Open Science Framework (OSF) at https://osf.io/dashboard.

Notes

  1. Additionally, the health domain by Keeney et al. (2013) is one of several dimensions of a broader life domains scale that has parallel items with different life domains at the end of each. Because of our specific interest in providing solutions for those with CHCs, we created an entire measure for health management starting with input from workers with CHCs rather than using a common set of items and varying the domain.

  2. Importantly, although we take an “interference” approach, we do not mean to imply that work is solely or mainly detrimental to workers with CHCs. To the contrary, we acknowledge that work is important to well-being for workers with CHCs (Gignac et al. 2014; Nilsen and Anderssen 2014). Yet, at the same time, we argue that understanding WHMI is critical to efforts to promote sustainable work for workers with CHCs.

  3. These were selected from a larger survey based on relevance to the aforementioned definition of WHMI. This initially included more general items, e.g., “How has work interfered or affected your illness?” These were excluded in this paper because they yielded responses that were not aligned with the definition of WHMI as interference stemming from the management of work and the management of a health condition. For example, these responses included work tasks causing one’s symptoms to worsen.

  4. Although the survey software restricted duplicate IP address access, participants could get around that by clearing cookies from their browsers or using a virtual private network (VPN).

  5. In addition, when determining whether to remove E6 or E7, we looked at item wording. E6 contained the term “drained” as opposed to “tired” in E7. The word “tired was also used in E1, E2, and E5, so we chose to retain E6 to include diversity in wording for the energy-based WHMI items.

  6. The RMSEA exceeds recommended cutoff values (.06 from Hu and Bentler 1999; .08 from MacCallum et al. 1996). Recently, Kenny et al. (2015) demonstrated that RMSEA is not a reliable measure of model fit for models with small degrees of freedom such as this one unless the sample size is very large. Our sample size (N = 158) was modest, therefore we relied on CFI and SRMR to determine adequacy.

  7. We tested an alternative model using Sample 4 in which all items loaded on a common factor. The model fit was significantly worse: X2(20) = 196.03, CFI = .849, RMSEA = .236, SRMR = .061; ΔX2(1) = 108.07 (p < .001), which supports the discriminant validity of energy and time dimensions in Sample 4.

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Acknowledgments

The authors thank Adam Roebuck (Roosevelt University) for assistance with coding the qualitative data and developing scale items and Zachary Fragoso (Wayne State University) for other assistance with this manuscript.

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Correspondence to Alyssa K. McGonagle.

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McGonagle, A.K., Schmidt, S. & Speights, S.L. Work-Health Management Interference for Workers with Chronic Health Conditions: Construct Development and Scale Validation. Occup Health Sci 4, 445–470 (2020). https://doi.org/10.1007/s41542-020-00073-2

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  • DOI: https://doi.org/10.1007/s41542-020-00073-2

Keywords

  • Chronic illness
  • Chronic health conditions
  • Work-family conflict
  • Burnout
  • Work ability