Influence of Incentive Design and Organizational Characteristics on Wellness Participation and Health Outcomes.

OBJECTIVE: To explore how changing incentive designs influence wellness participation and health outcomes. METHODS: Aggregated retrospective data were evaluated using cluster analysis to group 174 companies into incentive design types. Numerous statistical models assessed between-group differences in wellness participation, earning incentives, and over-time differences in health outcomes. RESULTS: Four incentive design groups based on requirements for earning incentives were identified. The groups varied in support for and participation in wellness initiatives within each company. All four design types were associated with improved low density lipoprotein (LDL) (P < 0.01), three with improved blood pressure (P < 0.001), and two with improved fasting glucose (P < 0.03). No incentive plan types were associated with improved body mass index (BMI), but designs predominantly focused on health outcomes (eg, Outcomes-Focused) exhibited a significant increase over time in BMI risk. CONCLUSION: Incentive design and organizational characteristics impact population-level participation and health outcomes.

Workplace Well-Being Factors That Predict Employee Participation, Health and Medical Cost Impact, and Perceived Support.

PURPOSE: This study tested relationships between health and well-being best practices and 3 types of outcomes. DESIGN: A cross-sectional design used data from the HERO Scorecard Benchmark Database. SETTING: Data were voluntarily provided by employers who submitted web-based survey responses. SAMPLE: Analyses were limited to 812 organizations that completed the HERO Scorecard between January 12, 2015 and October 2, 2017. MEASURES: Independent variables included organizational and leadership support, program comprehensiveness, program integration, and incentives. Dependent variables included participation rates, health and medical cost impact, and perceptions of organizational support. ANALYSIS: Three structural equation models were developed to investigate the relationships among study variables. RESULTS: Model sample size varied based on organizationally reported outcomes. All models fit the data well (comparative fit index > 0.96). Organizational and leadership support was the strongest predictor (P < .05) of participation (n = 276 organizations), impact (n = 160 organizations), and perceived organizational support (n = 143 organizations). Incentives predicted participation in health assessment and biometric screening (P < .05). Program comprehensiveness and program integration were not significant predictors (P > .05) in any of the models. CONCLUSION: Organizational and leadership support practices are essential to produce participation, health and medical cost impact, and perceptions of organizational support. While incentives influence participation, they are likely insufficient to yield downstream outcomes. The overall study design limits the ability to make causal inferences from the data.

Development and Validity of a Workplace Health Promotion Best Practices Assessment.

OBJECTIVE: To explore the factor structure of the HERO Health and Well-being Best Practices Scorecard in Collaboration with Mercer (HERO Scorecard) to develop a reduced version and examine the reliability and validity of that version. METHODS: A reduced version of the HERO Scorecard was developed through formal statistical analyses on data collected from 845 organizations that completed the original HERO Scorecard. RESULTS: The final factors in the reduced Scorecard represented content pertaining to organizational and leadership support, program comprehensiveness, program integration, and incentives. All four implemented practices were found to have a strong, statistically significant effect on perceived effectiveness. Organizational and leadership support had the strongest effect (ß = 0.56), followed by incentives (ß = 0.23). CONCLUSION: The condensed version of the HERO Scorecard has the potential to be a promising tool for future research on the extent to which employers are adopting best practices in their health and well-being (HWB) initiatives.

Measuring Participation in Employer-Sponsored Health and Well-Being Programs: A Participation Index and Its Association With Health Risk Change.

PURPOSE: To develop an index of participation in workplace health and well-being programs and assess its relationship with health risk status. DESIGN: Study design comprised a retrospective longitudinal analysis of employee health risk assessment (HRA) and program participation data. SETTING: Data from 6 companies that implemented health and well-being programs from 2014 to 2016. PARTICIPANTS: Employee participants (n = 95 318) from 6 companies who completed an HRA in 2014 to 2016. After matching those who completed the HRA in all 3 years, the longitudinal file included 38 789 respondents. MEASURES: Participation indicators were created for 9 different program components. The sum of these 9 components established the total participation index. ANALYSIS: Descriptive and correlation analyses were conducted on all participation measures. Repeated-measures analysis of variance was used to assess the impact of participation level on health risk over time. RESULTS: Higher levels of participation were associated with a greater reduction in risks, with each participation dose yielding a reduction of 0.038 risks (P < .001). CONCLUSION: Results suggest that employees who participate more in workplace health and well-being programs experience more health risk improvement. The study also supports a more granular definition of participation based on the number of interactions and type of program.

Health risk change as a predictor of productivity change.

OBJECTIVE: To assess how health risk change influences concurrent and subsequent change in absenteeism and presenteeism. METHODS: A retrospective, longitudinal study design analyzed repeated health assessment survey data using maximum likelihood structural equation modeling. RESULTS: A statistically significant relationship was detected between self-reported health risks at one point in time and lower productivity (absenteeism and presenteeism) at the same point in time as well as a longitudinal effect of increasing risks at one point in time associated with decreased productivity at subsequent measurement periods. CONCLUSIONS: Health is a predictor of productivity, and the benefits of improved health on improved productivity are cumulative over time.

Impact of a comprehensive population health management program on health care costs.

OBJECTIVE: Assess the influence of participation in a population health management (PHM) program on health care costs. METHODS: A quasi-experimental study relied on logistic and ordinary least squares regression models to compare the costs of program participants with those of nonparticipants, while controlling for differences in health care costs and utilization, demographics, and health status. Propensity score models were developed and analyses were weighted by inverse propensity scores to control for selection bias. RESULTS: Study models yielded an estimated savings of $60.65 per wellness participant per month and $214.66 per disease management participant per month. Program savings were combined to yield an integrated return-on-investment of $3 in savings for every dollar invested. CONCLUSIONS: A PHM program yielded a positive return on investment after 2 years of wellness program and 1 year of integrated disease management program launch.

Analyzing best practices in employee health management: how age, sex, and program components relate to employee engagement and health outcomes.

OBJECTIVE: Examine the influence of employee health management (EHM) best practices on registration, participation, and health behavior change in telephone-based coaching programs. METHODS: Individual health assessment data, EHM program data, and health coaching participation data were analyzed for associations with coaching program enrollment, active participation, and risk reduction. Multivariate analyses occurred at the individual (n = 205,672) and company levels (n = 55). RESULTS: Considerable differences were found in how age and sex impacted typical EHM evaluation metrics. Cash incentives for the health assessment were associated with more risk reduction for men than for women. Providing either a noncash or a benefits-integrated incentive for completing the health assessment, or a noncash incentive for lifestyle management, strengthened the relationship between age and risk reduction. CONCLUSIONS: In EHM programs, one size does not fit all. These results can help employers tailor engagement strategies for their specific population.

Comparing alternative methods of targeting potential high-cost individuals for chronic condition management.

OBJECTIVE: To compare the performance of predictive models based on health care claims, health risk assessment (HRA), or both in prospectively identifying high-cost individuals with chronic conditions. METHODS: Participants included 8056 employees who completed an HRA and were enrolled in a health plan for at least 6 months during the 36-month study period. Two-stage multivariate regression identified predictors of claims cost in four age-by-gender groups. RESULTS: All models predicted costs effectively, but the combined model performed better (R2 = 0.198 to 0.309) than either the claims-based (R2 = 0.168 to 0.263) or HRA-based model (R2 = 0.125 to 0.205). CONCLUSION: An HRA-based predictive model appears to be a reasonable alternative to claims-based predictive models for identifying individuals for chronic condition management outreach. Where practical, a combined model offers advantages of both approaches and meaningfully increases accuracy.