Which patients benefit from physical activity on prescription (PAP)? A prospective observational analysis of factors that predict increased physical activity

Physical inactivity is a global health concern [1] that increases the risk of lifestyle-related disorders, including metabolic syndrome (MetS) and premature death [2–4]. MetS typically includes various combinations of overweight, abdominal obesity, insulin resistance, dyslipidaemia, and hypertension [3, 5]. There is a dose-response relationship between physical activity (PA) and health, with the most sedentary and physically inactive patients who increase their PA levels showing more pronounced improvements in health [6–8]. The internationally recommended minimum PA level is 150 min per week of moderate-intensity PA or 75 min per week of vigorous-intensity PA [9].

While the evidence supporting the positive health effects of regular PA is robust [7], many people do not achieve the recommended levels of PA. The best way to promote PA and the factors affecting long-term adherence to a PA program remain unclear [10, 11], and there is a need in clinical practice for studies that evaluate strategies that aim to increase PA [12, 13] and the mechanisms underlying PA behaviour [14]. Specifically, the reasons why PA interventions are effective are not fully understood [10] and the factors or possible correlates that favour successful changes in PA behaviour [14] are not fully elucidated. Sherwood and Jeffery studied the factors associated with increased exercise in PA interventions [15] and found that higher motivation for PA, improved self-efficacy for exercise, enhanced social and environmental support, and tailored interventions for subgroups all helped increase PA.

Such correlates of changes in PA are defined as intervening causal variables that are necessary for creating a cause-effect pathway between different interventions and PA [14, 16, 17]. The mediating variable model proposed by Baranowski et al. [18] suggests that changes in correlates may lead to changes in PA [16, 18, 19], thus identifying why and how treatments exert their effects [20]. Effect modifiers (e.g. age, sex), should also be taken into account. They affect the direction and/or strength of the relationship between an intervention, such as PA, and the outcome [17, 21, 22] and can identify who will be affected by treatments and under what circumstances the treatment will have an effect [20]. Notably, few studies have examined the roles of correlates of PA in experimental PA studies [19], and more research is needed to better understand these possible correlates and PA-related behaviour [23, 24].

Previous studies have identified a number of possible correlates that may affect the outcomes of a PA intervention. These include readiness to change based on the principles of the transtheoretical model and the stages of change [25, 26]. The patients readiness to change PA behaviour cannot be taken for granted and the focus will be on that part the patient is most ready to change. Self-efficacy is based on social cognitive theory including self-efficacy expectations; the belief in capability to perform a behaviour, and outcome expectations; the belief that a specific behaviour will lead to a desired outcome [27–29]. Enjoyment defines as a positive affective state that reflects feelings such as pleasure and fun [30]. To enhance enjoyment of PA would increase the possibilities for PA changes [10]. Social support, based on social cognitive theory [31], is defined as activities that help the individual move toward goals and includes dimensions as social relationships, structure of relationships and functional content of relationships [32]. Body mass index (BMI), and health-related quality of life (HRQOL) may also affect the outcome of a PA intervention. Previous studies has revealed an inverse association between obesity and PA as well as an impact of HRQOL on PA [22, 33]. Sociodemographic factors such as age, sex, social situation, economic status, education, and smoking may also be important. Studies has shown inversely associations between age in adulthood and PA level, males to be more active than females and that lower levels of education and socio-economic status were associated with less PA [22, 34, 35].

The aim of physical activity on prescription (PAP) is to increase the motivation for PA and the PA level. Swedish PAP studies that are based on an individualized methodology have shown positive effects on PA level, metabolic health, and HRQOL [36–40]. A recent systematic review found that there is a high level of evidence that Swedish PAP increases the PA level [41]. PAP treatment is part of patient-centred care, which takes into account the patient’s knowledge, experience, and needs [42, 43]. PAP treatment includes individual counseling, an individually-tailored PA recommendation with a written prescription, and individualised, structured follow-up [44]. All licensed Swedish professionals in healthcare may use PAP treatment [45]. Further studies are needed to illuminate the effects of Swedish PAP-treatment and the underlying correlates influencing behaviour change in terms of increased PA.

The aim of this study was to explore possible predictive baseline correlates associated with changes in the PA level over a 6-month period of PAP treatment in order to identify the primary care patients most likely to benefit from a PAP intervention.

A total of 368 of the 444 patients, with a mean age of 57 years and 54% female, were retested at the 6-month follow-up (Table 1). Of these patients, 73% (n = 270) increased their PA level to some extent, and 42% (n = 153) moved from having an inadequate PA level to having a sufficient level according to public health recommendations, as described previously [40]. The results derives from an analysis using per-protocol. An additional ITT analysis (mixed linear regression) including random baseline covariates, was done revealing results that did not make any difference to the conclusions of the article (Additional file 1).

This study’s main finding was that a number of baseline factors were associated with the PA level at the 6-month follow-up after the PAP intervention. These predictive factors included greater self-efficacy expectations, more preparedness and confidence in terms of one’s readiness to change, a BMI < 30 and a positive value for a measure of physical health at baseline. The proportion of patients with these predictors were higher in the group that increased PA after PAP. In addition, if 2 to 4 of these factors were present, the likelihood of reaching a sufficient PA level (according to public health recommendations) increased significantly. These findings highlight potential predictors of PAP that can lead to an increased PA level at a 6-month follow-up. Considering these predictors at an early stage of the intervention could improve individualised support of the patient during the behavioural change process.

This study investigated which primary care patients are most likely to benefit from PAP treatment and found that the proportion of patients with low PA at baseline increased their PA level in a higher extent compared to patients with high PA at baseline, especially in combination with positive valued predictors (maximum of 95% vs. 65%). These results suggest that patients with the lowest PA levels benefit most from PAP. Considering the dose-response relationship between sedentary time, PA and health and the more pronounced health gain for those changing from sedentary/low PA level [7, 8, 62], this could have important clinical implications.

One strength of this study was its size. The included patients represent a major patient group that may be suitable for receiving PAP, and the PAP treatment was executed in a daily clinical primary care setting. Even if a majority of patients had two components of MetS and were treated with medication, showing higher complexity, the majority of the patients increased their PA level and health parameters during the 6-month PAP treatment [40]. These results indicate that the PAP treatment may be suitable for use in broader groups of patients with medical conditions that range from less severe to more severe, but this awaits further study. Another strength of the study is that the individualised PAP intervention commonly resulted in a PA recommendation to be carried out in everyday life (e.g. walking), which is suitable for the most physically inactive patients. These patients are, from a health perspective, the most vulnerable and have the most to gain from an increased PA level [6–8].

PA is a complex behaviour, and it is complicated to measure how factors such as PA category, type, frequency, duration, intensity, and purpose affect PA measurement and outcome [8, 63]. PA behaviour is also affected by diverse factors at the individual, social, psychological, behavioural, environmental, and policy levels [22, 64]. Barnett et al. [34] found that adults increased their PA in order to improve their health and well-being and to establish new daily routines with the opportunity for social interactions and personal challenges. PA behaviour is also affected by genetics and controlled by neural and situational processes which are difficult to measure and control [18, 65]. In light of the fact that PA behaviour is affected by many variables and is complicated to measure, it is not surprising that there are small correlation (r²) values between the correlates of PA and the PA level [18, 21, 50].

It has been argued that there are likely to be errors when correlates of PA are measured using multiple regression analyses because the correlates are often internal, psychological variables [21]. Thus, the effect of the correlate tends to be underestimated while the effect of the independent variable on the dependent variable tends to be overestimated. Hansen et al. [65] argue that behavioural interventions only have indirect effects i.e. they may change the predisposing and enabling factors that lead to behaviour but not the behaviour itself, resulting in lower correlation values. Although the present study showed overall small r² values, it could take into consideration potential predictors of increased PA level at the 6-month follow-up of the PAP treatment.

In the dichotomised predictor analysis, a positive value for confidence in readiness to change and BMI < 30 were associated with an increased PA level and with a higher degree of fulfilment of the PA recommendations, respectively. Confidence in readiness to change emanates from the concept of self-efficacy that, in previous research, has been shown to be one of the most important correlates of PA for adults [16, 22]. This study found significant values for VAS-estimated confidence/self-efficacy but not for the Self-Efficacy for Exercise (SEES) questionnaire in the dichotomised analysis. The reason that the SEES questionnaire did not show significant values could be due to the use of the word exercise instead of physical activity and the predetermined level of exercise as 20 min, three times per week. This could have complicated estimation of PA by the patient. In contrast, marking readiness to change PA on a VAS seems more feasible.

It is well known that BMI has an inverse association with PA, and while PA may be a determinant of obesity, obesity may also be a determinant of PA [22, 66]. In a previous analysis of this study group [40], the study group showed a decrease in BMI at the 6-month follow-up of the PAP treatment. Here, in the dichotomised analysis, an increased PA level and fulfilment of PA recommendations at 6 months could be predicted based on a baseline BMI < 30. These results support both determinant theories i.e. that PA is a determinant of obesity and vice versa. In this study the patients had a mean BMI value of 32 and 73% of the patients increased their PA level in some extent indicating that patients with BMI > 30 benefits from PAP-treatment. However, further subgroup analysis is needed to investigate the content and amount of PAP-support for patients with a BMI > 30. As in previous research on HRQOL, our results indicate that a positive value for physical health as measured on the SF-36 plays a role in predicting an increased PA level at the 6-month follow-up [67, 68].

The simultaneous use of 2 to 4 baseline predictors in the analysis (with fewer patients in the 4-predictor analysis) showed a clearer PA trend for patients with positive vs. negative values, especially regarding the fulfilment of PA recommendations. In the study group, 73% of the patients increased their PA level to some extent, and 42% achieved the PA recommendations at the 6-month follow-up. For many patients, reaching a PA level ≥ 5 points was a major step, and the results showed that having 2 to 4 predictors with positive values at baseline increased the possibility of predicting achievement of the PA recommendation. Sherwood et al. [15] stated that we have to incorporate multiple determinants of PA that reflect the complexity of predicting exercise behaviour, and Trost et al. [69] argued that longitudinal and intervention studies are needed to infer causal relationships. To our knowledge, there is no research that assesses multiple predictive correlates with increased PA level in PAP interventions. The present study thus contributes to the knowledge about predictors of increased PA at a 6-month follow-up of PAP treatment. Further studies are, however, needed in the research field of predicting correlates.

This study identified potential predictive correlates of an increased PA level after a 6-month PAP intervention. This may help improve the individualisation of PAP support.

The results indicated that the proportion of patients with the lowest PA levels increased their PA level in a higher extent (84%), primarily benefiting from the PAP intervention. These results have clinical implications for professionals who work to promote behavioural changes in patients who can potentially improve their health by increasing their PA level.