General discussion 169 to use in practice, a calculator (a web-based prognostic tool: https://derzis.nu/Calculator/) to predict the probability of adherence was developed. Users can enter the individual patient variables into the calculator to obtain adherence probabilities (Figure 1). Figure 1 Example of the PATCH calculator Note: Logistic regression formula of the PATCH tool; Ln(odds) = 4.108 – (0.203 x Intention) + (0.147 x Depression) – (0.055 x Alliance) + (0.950 x MRC) In chapter 7 a supplement on the prediction model, described in chapter 6, examined predictors that could potentially had a causal relationship with exercise adherence in patients with COPD following pulmonary rehabilitation in a primary physiotherapy practice. The results showed that the strongest predictors of exercise adherence were education (b = 1.39; p = 0.01), MRC-score (b = -1.23; p = 0.001), intention (b = 0.63; p = 0.000), depression (b = -0.53; p = 0.000), and alliance (b = 0.21; p = 0.000). Variance in exercise intention was explained by Perceived Behavioral Control (PBC) (b = 0.662; p = 0.000), attitude (b = 0.086; p = 0.01), and alliance (b = -0.080, p = 0.000). Findings of this analysis suggest that healthcare providers should obtain information about their patients’ attitudes, PBC, depressive symptoms, alliance, MRC-score, and education level, when their patients with COPD are following PR. In doing so, they can target the specific constructs to increase their patients’ exercise intention and exercise adherence during PR. Since self-management interventions may become a key strategy for addressing chronic disease burden and healthcare costs [11], also in pulmonary rehabilitation
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