Cheng Abby L, Dwivedi Mollie E, Martin Adriana, Leslie Christina G, Pashos Madeline M, Donahue Viola B, Huecker Julia B, Salerno Elizabeth A, Steger-May Karen, Hunt Devyani M
Department of Orthopaedic Surgery, Division of Physical Medicine and Rehabilitation, Washington University in St. Louis School of Medicine, St Louis, MO, USA (AC, MD, AM, CL, DH).
Department of Surgery, Division of Public Health Sciences, Washington University in St. Louis School of Medicine, St Louis, MO, USA (AC, ES).
Am J Lifestyle Med. 2023 Dec 23:15598276231222877. doi: 10.1177/15598276231222877.
Changes in lifestyle habits can reduce morbidity and mortality, but not everyone who can benefit from lifestyle intervention is ready to do so.
To describe characteristics of patients who did and did not engage with a lifestyle medicine program, and to identify predictors of engagement.
This was a single-center, retrospective cohort study of 276 adult patients who presented for consultation to a goal-directed, individualized, interprofessional lifestyle medicine program. The primary outcome was patients' extent of engagement. Candidate predictors considered in multivariable multinomial logistic regression models included baseline sociodemographic, psychological, and health-related variables.
A predictor of full engagement over no engagement was having private or Medicare insurance (rather than Medicaid, other, or no insurance) (OR 4.2 [95% CI 1.3-14.2], = .021). A predictor of partial engagement over no engagement was having a primary goal to lose weight (OR 3.1 [1.1-8.4], = .026).
System-level efforts to support coverage of lifestyle medicine services by all insurers may improve equitable engagement with lifestyle medicine programs. Furthermore, when assessing patients' readiness to engage with a lifestyle medicine program, clinicians should consider and address their goals of participation.
生活方式习惯的改变可降低发病率和死亡率,但并非所有能从生活方式干预中获益的人都愿意这样做。
描述参与和未参与生活方式医学项目的患者特征,并确定参与的预测因素。
这是一项单中心回顾性队列研究,研究对象为276名成年患者,他们前来咨询一个目标导向、个性化、跨专业的生活方式医学项目。主要结局是患者的参与程度。多变量多项逻辑回归模型中考虑的候选预测因素包括基线社会人口统计学、心理和健康相关变量。
与未完全参与相比,完全参与的一个预测因素是拥有私人保险或医疗保险(而非医疗补助、其他保险或无保险)(优势比4.2 [95%置信区间1.3 - 14.2],P = 0.021)。与未部分参与相比,部分参与的一个预测因素是首要目标是减肥(优势比3.1 [1.1 - 8.4],P = 0.026)。
支持所有保险公司覆盖生活方式医学服务的系统层面努力可能会改善对生活方式医学项目的公平参与。此外,在评估患者参与生活方式医学项目的意愿时,临床医生应考虑并解决他们的参与目标。