Effectiveness of physical activity prescription by primary care nurses using health assets: A randomized controlled trial

Are physical activity referral scheme components associated with increased physical activity, scheme uptake, and adherence rate? A meta-analysis and meta-regression

BACKGROUND

Physical activity referral schemes (PARS) are composed of various components, such as a written prescription or a person-centered approach. The role of these components in their effectiveness is yet to be understood. Therefore, we aimed to explore the relationships between PARS components and physical activity, scheme uptake, and adherence rate; and to estimate the effect of PARS.

METHODS

We searched Scopus, PubMed, Web of Science, CINAHL, ScienceDirect, SpringerLink, HTA, Wiley Online Library, SAGE Journals, Taylor & Francis, Google Scholar, OpenGrey, and CORE. Eligible studies were published between 1990 and November 2023 in English or German, investigated PARS with participants aged ≥ 16 years, and reported physical activity, scheme uptake, or scheme adherence. Separate random-effects meta-analysis by comparison group were conducted for physical activity. Scheme uptake and adherence rates were pooled using proportional meta-analysis. The components were analyzed via univariate meta-regression. We rated the risk of bias using RoB2 and ROBINS-I, and the certainty of evidence using GRADE.

RESULTS

Fifty-two studies were included. PARS were more effective in increasing physical activity than usual care (k = 11, n = 5046, Hedges' g = 0.18, 95%CI 0.12 to 0.25; high certainty of evidence). When PARS were compared with physical activity advice or enhanced scheme versions, the pooled Hedges' g values for physical activity were -0.06 (k = 5, n = 1082, 95%CI -0.21 to 0.10; low certainty of evidence), and 0.07 (k = 9, n = 2647, 95%CI -0.03 to 0.18; low certainty of evidence) respectively. Scheme uptake was 87% (95%CI 77% to 94%, k = 14, n = 5000) across experimental studies and 68% (95%CI 51% to 83%, k = 14, n = 25,048) across non-experimental studies. Pooled scheme adherence was 68% (95%CI 55% to 80%, k = 16, n = 3939) and 53% (95%CI 42% to 63%, k = 18, n = 14,605). The meta-regression did not detect any significant relationships between components and physical activity or scheme uptake. A person-centered approach, screening, and brief advice were positively associated with scheme adherence, while physical activity sessions were negatively associated.

CONCLUSION

PARS are more effective in increasing physical activity than usual care only. We did not identify any components as significant predictors of physical activity and scheme uptake. Four components predicted scheme adherence, indicating that the component-effectiveness relationship warrants further research.

A systematic review and narrative synthesis of physical activity referral schemes' components

BACKGROUND

Physical activity referral schemes (PARS) are complex multicomponent interventions that represent a promising healthcare-based concept for physical activity (PA) promotion. This systematic review and narrative synthesis aimed to identify the constitutive components of PARS and provide an overview of their effectiveness.

METHODS

Following a published protocol, we conducted a systematic search of PubMed, Scopus, Web of Science, CINAHL, ScienceDirect, SpringerLink, HTA, Wiley Online Library, SAGE Journals, Taylor & Francis, Google Scholar, OpenGrey, and CORE from 1990 to January 2023. We included experimental, quasi-experimental, and observational studies that targeted adults participating in PARS and reported PA outcomes, scheme uptake, or adherence rates. We performed an intervention components analysis using the PARS taxonomy to identify scheme components and extracted data related to uptake, adherence, and PA behavior change. We combined these to provide a narrative summary of PARS effectiveness.

RESULTS

We included 57 studies reporting on 36 PARS models from twelve countries. We identified 19 PARS components: a patient-centered approach, individualized content, behavior change theory and techniques, screening, brief advice, written materials, a written prescription, referral, baseline and exit consultation, counselling support session(s), PA sessions, education session(s), action for non-attendance, structured follow-up, a PA network, feedback for the referrer, and exit strategies/routes. The PARS models contained a mean of 7 ± 2.9 components (range = 2-13). Forty-five studies reported PA outcome data, 28 reported uptake, and 34 reported adherence rates. Of these, approximately two-thirds of studies reported a positive effect on participant PA levels, with a wide range of uptake (5.7-100.0%) and adherence rates (8.5-95.0%).

CONCLUSIONS

Physical activity referral scheme components are an important source of complexity. Despite the heterogeneous nature of scheme designs, our synthesis was able to identify 19 components. Further research is required to determine the influence of these components on PARS uptake, adherence, and PA behavior change. To facilitate this, researchers and scheme providers must report PARS designs in more detail. Process evaluations are also needed to examine implementation and increase our understanding of what components lead to which outcomes. This will facilitate future comparisons between PARS and enable the development of models to maximize impact.

Psychometric Properties of a Scale Measuring Patients' Perceptions Regarding Physical Activity Counseling by Physicians

This study developed a scale measuring patients' perceptions regarding physical activity (PA) counseling by physicians. Confirmatory factor analysis and multiple linear regression analysis were used to assess psychometric properties. The first factor extracted was "PA recommendation," which accounted for a variance of 45.1% out of total variance of 76.3% and comprises 5 items. The final scale yielded satisfactory psychometric properties including internal consistency (Cronbach's α = 0.75), convergent validity, discriminant validity, and predictive validity. Nine (9) items that makeup two factors (i.e., PA recommendation and follow-up) are potential measures of patients' perceptions regarding PA counseling by physicians in healthcare.

[Self-management in people with multimorbility: Contributions from salutogenesis and health assets model]

Different models of care in context of chronicity and multimorbidity include community, health system, clinical practice, health policies, prevention, and health promotion. Among these, the role of the health team as a facilitator of self-management is pointed out, being people the protagonists of their process. Multimorbidity approach is mostly carried out from a risk and disease focused point of view, which limits the exploration of resources of people and their environment. Incorporating a positive health approach can contribute to a greater comprehensiveness. The purpose of this article is to propose an approach from the synergy model of health, integrating salutogenesis and health assets model, to help facilitate self-management promoting people's agency capacity. Potential areas of application of these models are presented to work in the context of multimorbidity, promoting health and well-being conditions in people and their families.

Effects of Self-Monitoring Physical Activity Using Technology in Primary Care: A Pilot Intervention

BACKGROUND

Physical activity (PA) is a primary factor in reducing the risk of chronic diseases, yet only half of U.S. adults meet recommended levels.

OBJECTIVE

To evaluate a PA self-monitoring pilot intervention using technology in obese adult primary care patients.

METHODS

The project had a prospective, single group, pretest/posttest design using an accelerometer with a smartphone app. Obese adult primary care patients (N = 31) were followed over 6 months. Demographic (age, race, sex, marital status, educational level) and PA-relevant (PA, body mass index [BMI], self-efficacy for exercise [SEE]) data were collected at enrollment. PA and BMI were recorded monthly for 6 months. SEE was reassessed at 6 months.

RESULTS

PA and BMI showed gradual improvement; however, changes in PA (p = .130), BMI (p = .326), and SEE (p = .877) at 6 months were not statistically significant. A strong, negative relationship was found between PA and BMI (r = -.727, p < .01). Anecdotal data indicated the smartphone app was acceptable to patients.

CONCLUSIONS

Data-supported clinician-initiated PA self-monitoring with a smartphone app was acceptable and showed favorable trends in improving PA and BMI in obese adult patients.

IMPLICATIONS FOR NURSING

Prescribing PA self-monitoring using technology may be easily implemented.

Association between Depression, Lifestyles, Sleep Quality and Sense of Coherence in a Population with Cardiovascular Risk

People with cardiovascular risk have more depression than the general population. Depression and cardiovascular risk have been commonly linked to lower sense of coherence (SOC) values, unhealthy lifestyles, and poor sleep quality. The aim of this study was to analyze the association between depression, health-related lifestyles, sleep quality, and SOC in a population with cardiovascular risk. A cross-sectional study was conducted in 310 participants (aged 35–75 years) with cardiovascular risk. Sociodemographic and anthropometric characteristics, cardiovascular risk, SOC score, depression levels, sleep quality, and lifestyles (physical activity, diet quality (measured as the adherence to the Mediterranean diet), and tobacco and alcohol consumption) were determined. The regression analysis showed significant associations between depression levels and sex (odds ratio (OR): 2.29; 95% CI: 1.29, 4.07), diet (OR: 0.85; 95% CI: 0.73, 0.99), body mass index (BMI) (OR: 1.06; 95% CI: 1.01, 1.12), cardiovascular disease (CVD) (OR: 2.55; 95% CI: 1.18, 5.48), sleep quality (OR: 0.26; 95% CI: 0.15, 0.46), and SOC (OR: 0.96; 95% CI: 0.94, 0.98). Protective effects of male sex, a lower BMI, no CVD, a higher adherence to the Mediterranean diet, a high sleep quality, and a higher SOC were found. In conclusion, among lifestyles determined, only diet was associated with depression levels. SOC and sleep quality were also found as significant predictors for depression levels.