Health Enhancement Research Organization, MN, USA.
Pro-Change Behavior Systems, Inc., South Kingstown, RI, USA.
Am J Health Promot. 2020 May;34(4):349-358. doi: 10.1177/0890117119898613. Epub 2020 Jan 27.
This study tested relationships between health and well-being best practices and 3 types of outcomes.
A cross-sectional design used data from the HERO Scorecard Benchmark Database.
Data were voluntarily provided by employers who submitted web-based survey responses.
Analyses were limited to 812 organizations that completed the HERO Scorecard between January 12, 2015 and October 2, 2017.
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.
Three structural equation models were developed to investigate the relationships among study variables.
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 ( < .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 ( < .05). Program comprehensiveness and program integration were not significant predictors ( > .05) in any of the models.
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.
本研究检验了健康和福利最佳实践与 3 种结果类型之间的关系。
使用 HERO 记分卡基准数据库中的横断面设计数据。
数据由提交基于网络的调查回复的雇主自愿提供。
分析仅限于在 2015 年 1 月 12 日至 2017 年 10 月 2 日期间完成 HERO 记分卡的 812 个组织。
自变量包括组织和领导力支持、计划全面性、计划整合和激励措施。因变量包括参与率、健康和医疗成本影响以及对组织支持的看法。
开发了 3 个结构方程模型来研究研究变量之间的关系。
基于组织报告的结果,模型样本量有所不同。所有模型都很好地拟合了数据(比较拟合指数>0.96)。组织和领导力支持是参与度(n=276 个组织)、影响(n=160 个组织)和感知组织支持(n=143 个组织)的最强预测因素(<0.05)。激励措施预测了健康评估和生物识别筛查的参与度(<0.05)。综合计划和计划整合在任何模型中都不是显著的预测因素(>0.05)。
组织和领导力支持实践对于产生参与度、健康和医疗成本影响以及对组织支持的看法至关重要。虽然激励措施会影响参与度,但它们可能不足以产生下游结果。总体研究设计限制了从数据中得出因果推断的能力。