Crafting Wellness Programs That Engage Employees Requires Self-Reported Data
With nearly half of the U.S. population in the work force,¹ employers have an opportunity to significantly improve population health by focusing on the health and wellness of their employees. Workplace wellness programs are on the rise with almost half of all employers offering some type of health promotion or wellness program in 2017.² And these programs work. Numerous systematic reviews evaluating workplace wellness programs concluded they are effective at changing behavior and improving health outcomes.³⁻⁶ They have also shown to increase work productivity, employee retention, and reduce costs.⁷⁻⁹ However, arbitrarily implementing a workplace wellness program cannot ensure engagement nor successful outcomes.
Self-reported Data is Key to Success
Health and wellness professionals use self-reported health and lifestyle data collected via health risk assessments (HRAs) to identify individuals with existing health conditions, individuals whose lifestyle behaviors pose a significant risk to their health and, ultimately, individuals at risk for future health conditions such as cancer, cardiovascular disease, and diabetes. Self-reported health and lifestyle data also reveal which unhealthy habits individuals have and are interested in changing, enabling health and wellness professionals to target interventions that will be most effective and most successful in their employee population base.
Evidence shows that assessing health risks is useful as a gateway to workplace wellness programs that offer health promotion, education, and other interventions, and are effective at changing unhealthy behaviors (e.g., smoking, drinking), improving biometrics, and reducing the number of works days lost.¹⁰ According to the Kaiser Family Foundation, 37% of small employers (3-199 workers ) and 62% of large employers (200+ workers) provide employees with the opportunity to complete a HRA.¹¹ One randomized control trial, however, found that employees with the highest healthcare costs were least likely to participate in a corporate wellness program.¹² Self-reported data such as an employee interest survey and change readiness questions in an HRA can assist in the development of strategies to engage many of these employees in wellness program interventions.
Take diabetes for example. It is estimated that the number of U.S. adults diagnosed with diabetes will nearly triple by 2060.¹³ Some modifiable lifestyle risk factors contributing to diabetes include having a diet consisting of refined grains and added sugars, being mostly sedentary, and being overweight or obese. Health and wellness professionals can use the self-reported health, lifestyle, and change readiness data to not only implement system-level changes, such as removing sugar-sweetened beverages from workplace vending machines or offering gym membership reimbursement to promote physical activity, but also to target interventions to individuals with or at risk for diabetes.
The Diabetes Prevention Program (DPP) is one of the most effective evidence-based lifestyle interventions among individuals at risk for diabetes.¹⁴ It provides year-long online training from lifestyle coaches and is accessible via in-person or online classes to accommodate roadblocks such as time constraints or mobility issues. An online intervention can appeal to employees who don’t like group challenges. And because an employee can take the course without co-workers or managers knowing, it helps address workplace privacy concerns. But what if the employees who most need the DPP aren’t ready to address the behaviors that put them at risk for diabetes? Self-reported data can reveal underlying triggers, such as stress or psychological distress, that lessen the likelihood that an individual will be able to cut out foods with added sugars or start a fitness regimen. Other interventions, such as personalized stress management, mindfulness therapy, and financial wellness coaching might need to occur as well.
Every piece of self-reported health and lifestyle information is important—even if it is just coming from the individual—because it can be acted upon. Using self-reported health and lifestyle data, however, has received its share of criticism due to the subjective nature of the information and associated biases. Employees may be answering the questions in a certain way to appear healthier (social desirability bias), they might not be able to accurately remember how many fruits and vegetables they ate last week (recall bias), and they might not want to tell their employer about their personal information. Health researchers are looking for more objective measurements to replace self-reported health and lifestyle data, but these measurements are not always valid and reliable either,¹⁵⁻¹⁶ and subject to different biases and issues (e.g., observer bias, miscalibration).
Even if fully reliable objective measurements could be created, the fact remains that not all aspects of an employee’s health and wellness can be evaluated using objective measurements such as a biometric screen or a wearable device. Clinicians ask patients to measure pain on a subjective self-reported scale. And mental health can only be measured by self-report. The two-question Physician Health Question (PHQ-2) is a commonly used questionnaire to identify those at risk for depression that can only be completed by the individual. Using this self-reported information, health and wellness professionals can identify employees at risk for depression and offer or provide depression prevention interventions or other employee assistance programs to them.
As mentioned previously, a key piece of self-reported data that health and wellness professional can leverage to maximize health outcomes is an employee’s readiness to change (based on Prochaska’s transtheoretical model of behavior change). Change is hard. And an employer’s resources dedicated to employee health and wellness are best spent on health and lifestyle behaviors that their workers are willing to change. But knowing what behaviors employees are willing to change can only be discovered by asking them.
There are ways to minimize bias when collecting self-reported data, such as:
- Using a well-validated, reliable, and evidence-based assessment as vetted by a third party such as the National Committee of Quality Assurance (NCQA);
- Creating a safe environment for the employee to complete the assessment by ensuring all privacy and confidentiality laws are followed and no retaliation for poor health or behaviors occurs; and
- Educating employees about the importance of addressing health and lifestyle behaviors to improve their health as part of creating a culture of health and wellness.
Answering questions about their health and lifestyle can give employees a good look at their current and future health, in many cases exposing harmful habits and inspiring behavior change. Subsequent assessments can help individuals track the effectiveness of their actions. Self-reported health and lifestyle data can also be used to set baseline values and create benchmarks from to which to measure the success of workplace wellness programs. By collecting self-reported health and lifestyle data at least annually, health and wellness professionals can see if the interventions implemented are leading to behavior changes and improved health outcomes. It provides an opportunity to modify interventions and/or refocus efforts on another modifiable lifestyle risk factor that have risen to the surface.
Employers who collect, analyze, and utilize self-reported health and lifestyle information to identify at risk employees and target interventions will have the most successful workplace wellness programs and greatest effect on population health. Meeting employees where they are at with regards to their health and lifestyle behaviors will help efficiently engage them in their health improvement efforts by offering interventions most applicable to them.
- U.S. Department of Labor. Labor force statistics from the current population survey. https://data.bls.gov/pdq/SurveyOutputServlet?graph_name=LN_cpsbref1&request_action=wh. Published 2019. Accessed June 18, 2019.
- Linnan LA, Cluff L, Lang JE, Penne M, Leff MS. Results of the Workplace Health in America Survey. Am J Health Promot. 2019;33(5):652-665.
- Wan Mohd Yunus WMA, Musiat P, Brown JSL. Systematic review of universal and targeted workplace interventions for depression. Occup Environ Med. 2018;75(1):66-75.
- Cahill K, Lancaster T. Workplace interventions for smoking cessation. Cochrane Database Syst Rev. 2014(2):CD003440.
- Robbins R, Jackson CL, Underwood P, Vieira D, Jean-Louis G, Buxton OM. Employee Sleep and Workplace Health Promotion: A Systematic Review. Am J Health Promot. 2019:890117119841407.
- Proper KI, van Oostrom SH. The effectiveness of workplace health promotion interventions on physical and mental health outcomes - a systematic review of reviews. Scand J Work Environ Health. 2019.
- Cancelliere C, Cassidy JD, Ammendolia C, Cote P. Are workplace health promotion programs effective at improving presenteeism in workers? A systematic review and best evidence synthesis of the literature. BMC Public Health. 2011;11:395.
- Astrella JA. Return on Investment: Evaluating the Evidence Regarding Financial Outcomes of Workplace Wellness Programs. J Nurs Adm. 2017;47(7-8):379-383.
- Ott-Holland CJ, Shepherd WJ, Ryan AM. Examining wellness programs over time: Predicting participation and workplace outcomes. J Occup Health Psychol. 2019;24(1):163-179.
- Community Preventive Services Task Force. Assessment of health risks with feedback plus health education with or without other interventions. Atlanta: Centers for Disease Control and Prevention;2010.
- Claxton G, Rae M, Long M, Damico A, Whitmore H. 2018 employer health benefits survey. San Francisco: Henry J. Kaiser Family Foundation;2018.
- Jones D, Molitor D, Reif J. What do workplace wellness programs do? Evidence from the Illinois Workplace Wellness Study. 2018.
- Lin J, Thompson TJ, Cheng YJ, et al. Projection of the future diabetes burden in the United States through 2060. Popul Health Metr. 2018;16(1):9.
- Galaviz KI, Weber MB, Straus A, Haw JS, Narayan KMV, Ali MK. Global Diabetes Prevention Interventions: A Systematic Review and Network Meta-analysis of the Real-World Impact on Incidence, Weight, and Glucose. Diabetes Care. 2018;41(7):1526-1534.
- Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act. 2015;12:159.
- Maringer M, Wisse-Voorwinden N, Veer PV, Geelen A. Food identification by barcode scanning in the Netherlands: a quality assessment of labelled food product databases underlying popular nutrition applications. Public Health Nutr. 2019;22(7):1215-1222.