Perioperative medicine training resources: U1M2 Promotion of physical and mental wellbeing

Australia’s physical activity and sedentary behaviour guidelines outline how much physical activity you should do, the importance of reducing the time you spend sitting or lying down, and how much sleep children and young people should get. Needs vary depending on your age.

Abstract
Background: Primary health professionals are well positioned to support the delivery of patient self-management in an evidence-based, structured capacity. A need exists to better understand the active components required for effective self-management support, how these might be delivered within primary care, and the training and system changes that would subsequently be needed.

Objectives: (1) To examine self-management support interventions in primary care on health outcomes for a wide range of diseases compared to usual standard of care; and (2) To identify the effective strategies that facilitate positive clinical and humanistic outcomes in this setting.

Method: A systematic review of randomized controlled trials evaluating self-management support interventions was conducted following the Cochrane handbook & PRISMA guidelines. Published literature was systematically searched from inception to June 2019 in PubMed, Scopus and Web of Science. Eligible studies assessed the effectiveness of individualized interventions with follow-up, delivered face-to-face to adult patients with any condition in primary care, compared with usual standard of care. Matrices were developed that mapped the evidence and components for each intervention. The methodological quality of included studies were appraised.

Results: 6,510 records were retrieved. 58 studies were included in the final qualitative synthesis. Findings reveal a structured patient-provider exchange is required in primary care (including a one-on-one patient-provider consultation, ongoing follow up and provision of self-help materials). Interventions should be tailored to patient needs and may include combinations of strategies to improve a patient's disease or treatment knowledge; independent monitoring of symptoms, encouraging self-treatment through a personalized action plan in response worsening symptoms or exacerbations, psychological coping and stress management strategies, and enhancing responsibility in medication adherence and lifestyle choices. Follow-up may include tailored feedback, monitoring of progress with respect to patient set healthcare goals, or honing problem-solving and decision-making skills. Theoretical models provided a strong base for effective SMS interventions. Positive outcomes for effective SMS included improvements in clinical indicators, health-related quality of life, self-efficacy (confidence to self-manage), disease knowledge or control. An SMS model has been developed which sets the foundation for the design and evaluation of practical strategies for the construct of self-management support interventions in primary healthcare practice.

Conclusions: These findings provide primary care professionals with evidence-based strategies and structure to deliver SMS in practice. For this collaborative partnership approach to be more widely applied, future research should build on these findings for optimal SMS service design and upskilling healthcare providers to effectively support patients in this collaborative process.

Abstract
In this article, we review the evidence underpinning the broader prehabilitation concept and the target behavioural and lifestyle risk factors including their perioperative impact and evidence for prehabilitation intervention. We also identify principles for delivering prehabilitation in practice, alongside lessons for the perioperative setting from well-established allied interventions; cardiac and pulmonary rehabilitation.

Abstract
Objective: The Patient Activation Measure (PAM) is a 22-item measure that assesses patient knowledge, skill, and confidence for self-management. The measure was developed using Rasch analyses and is an interval level, unidimensional, Guttman-like measure. The current analysis is aimed at reducing the number of items in the measure while maintaining adequate precision.

Study methods: We relied on an iterative use of Rasch analysis to identify items that could be eliminated without loss of significant precision and reliability. With each item deletion, the item scale locations were recalibrated and the person reliability evaluated to check if and how much of a decline in precision of measurement resulted from the deletion of the item.

Data sources: The data used in the analysis were the same data used in the development of the original 22-item measure. These data were collected in 2003 via a telephone survey of 1,515 randomly selected adults. Principal Findings. The analysis yielded a 13-item measure that has psychometric properties similar to the original 22-item version. The scores for the 13-item measure range in value from 38.6 to 53.0 (on a theoretical 0-100 point scale). The range of values is essentially unchanged from the original 22-item version. Subgroup analysis suggests that there is a slight loss of precision with some subgroups.

Conclusions: The results of the analysis indicate that the shortened 13-item version is both reliable and valid.

Abstract
Patient activation is an important predictor of health outcomes and health care usage, yet we do not know how enduring the benefits of greater patient activation are. This study uses a large panel survey of people with chronic conditions (n = 4,865) to examine whether a baseline patient activation measure predicts outcomes 4 years later, and whether changes in patient activation measure scores are associated with changes in outcomes. The findings indicate that the benefits of health activation are enduring, yielding benefits in the form of better self-management, improved functioning, and lower use of costly health care services over time. Furthermore, the findings indicate that when activation levels change, many outcomes change in the same direction. Patient activation seems to be an important and modifiable factor for influencing chronic disease outcomes; health care delivery systems can use this information to personalize and improve care.

Abstract
Objective: To determine whether an approach that assesses patient capabilities for self-management and then tailors coaching support based on this assessment would be more effective in improving outcomes than the usual disease management approach.

Study design: A quasi-experimental pre-post design was used, with an intervention group coached with a tailored approach and a control group coached in the usual way.

Methods: Data derived from telephonic coaching and from archival utilization data were used in the analysis. Differences in activation scores, clinical indicators, and utilization rates between intervention and control group members were assessed. Propensity scores were used to weigh the data and equalize baseline differences between the intervention and control groups. Analysis of variance repeated measures were used to examine changes over time. This analytic approach assessed whether individual changes over time in the intervention group were significantly different from individual changes over time in the control group.

Results: Overall, the findings showed a consistent picture that indicated a positive impact of the tailored intervention. Activation scores increased, clinical indicators improved, and utilization rates declined to a greater extent in the intervention group than in the control group.

Conclusion: The findings suggest that tailoring coaching to patients' activation level and using the same metric to track progress improves the outcomes of disease management.