Protocol for a feasibility randomized controlled trial of gentle yoga in older patients discharged from phase II cardiac rehabilitation.

Background: Physical activity (PA) is essential following an acute cardiac event. Cardiac rehabilitation (CR) is commonly prescribed, and PA after CR is recommended. Because of age-related changes in functional ability and multi-comorbidity, many older cardiac patients struggle to continue performing PA at home after CR. Depressive symptoms and anxiety are prevalent in cardiac patients and associated with poor self-care, including lack of daily PA. Yoga has been demonstrated to improve psychological and physical health outcomes in cardiac patients, but it is unknown whether yoga, modified for older CR patients - Gentle Yoga - is beneficial in managing psychological distress and maintaining PA following phase II CR. Our specific aims are to:1) determine the feasibility and acceptability of a modified gentle yoga intervention delivered via video conferencing for older cardiac patients; 2) compare, at 3-month follow-up, the effects and determine effect sizes of a gentle yoga intervention versus control on psychological health and physical health. Methods: We are conducting a 2-group (intervention versus control) randomized controlled pilot study. The intervention is a 12-week gentle yoga program delivered via video conference. Short-term effects will be evaluated at 3-month. Conclusion: This study is designed to be suited for older cardiac patients who would not have access to supervised PA opportunities after facility-based CR to enhance PA. This study will provide data about the feasibility and acceptability of the protocol for older cardiac patients and will offer effect sizes to determine sample size for a fully powered randomized controlled trial.

The relationship of health activation with risk of future cardiovascular disease among rural family caregivers of patients with chronic illnesses.

BACKGROUND: Family caregivers are at higher risk for developing cardiovascular disease (CVD) than non-caregivers. This risk is worse for those who live in rural compared to urban areas. Health activation, an indicator of engagement in self-care, is predictive of health outcomes and CVD risk in several populations. However, it is not known whether health activation is associated with CVD risk in rural caregivers of patients with chronic illnesses nor is it clear whether sex moderates any association. OBJECTIVES: Our aims were to determine (1) whether health activation independently predicts 10-year CVD risk; and (2) whether sex interacts with health activation in the prediction of 10-year CVD risk among rural family caregivers (N = 247) of patients with chronic illnesses. METHODS: Health activation was measured using the Patient Activation Measure. The predicted 10-year risk of CVD was assessed using the Framingham Risk Score. Data were analyzed using nonlinear regression analysis. RESULTS: Higher levels of health activation were significantly associated with decreased risk of developing CVD (p < 0.028). There was no interaction of sex with health activation on future CVD risk. However, male caregivers had greater risk of developing CVD in the next 10 years than female caregivers (p < 0.001). CONCLUSIONS: We demonstrated the importance of health activation to future CVD risk in rural family caregivers of patients with chronic illnesses. We also demonstrated that despite the higher risk of future CVD among male, the degree of association between health activation and CVD risk did not differ by sex.

Identifying subgroups: Part 2: Trajectories of change over time.

Methods to identify multiple trajectories of change over time are of great interest in nursing and in related health research. Latent growth mixture modeling is a data-centered analytic strategy that allows us to study questions about distinct trajectories of change in key measures or outcomes of interest. In this article, a worked example of latent growth mixture modeling is presented to help expose researchers to the use and appeal of this analytic strategy.

Identifying subgroups: Part 1: Patterns among cross-sectional data.

Non-experimental designs are common in nursing and allied health research wherein study participants often represent more than a single population or interest. Hence, methods used to identify subgroups and explore heterogeneity have become popular. Latent class mixture modeling is a versatile and person-centered analytic strategy that allows us to study questions about subgroups within samples. In this article, a worked example of latent class mixture modeling is presented to help expose researchers to the nuances of this analytic strategy.