OBJECTIVES: Guidelines advocate goal setting for promoting lifestyle changes in cardiovascular disease (CVD) patients. This study investigates 1) preferences in health and life goal domains in CVD patients, 2) the impact of linking life goals to health goals on intention-to-change-lifestyle and explores 3) socio-demographic and health-related variables influencing intention-to-change-lifestyle. DESIGN: Online experimental study. METHODS: Patients (N = 629, mean age 66.6; 39% female) were randomized to health-goal-group (HG) or life-and-health-goal-group (LHG). HG set a health goal, and LHG first established a life goal and then set a supporting health goal. Directly after goal setting, the primary outcome, intention-to-change-lifestyle, was measured and analysed using logistic regression (high: 9-10 vs. lower: ≤8.5), as were the secondary outcomes. RESULTS: Exercise goals were most frequently selected in LHG (66.0%) and HG (66.9%). Preference for selecting stress management was significantly higher in LHG (17.3%) than HG (9.3%), χ2(1) = 8.85, p = .003; OR = 2.05, 95%CI [1.27-3.30]. The direct effect of goal-setting condition on intention-to-change-lifestyle was non-significant (OR = .98, 95%CI [.71-1.34], p = .88). In exploratory analyses, lower- and medium-educated patients showed significantly higher intention when life and health goals were linked (OR = 2.55, 95%CI [1.03-6.27], p = .04, and OR = 2.47, 95%CI [1.15-5.30], p = .02, respectively). Perceived meaning in life was positively associated with intention. CONCLUSIONS: No main effect of goal-setting condition on intention-to-change-lifestyle was found. Linking life goals to health goals increased preference for stress management and, in exploratory analyses, was associated with higher intention-to-change-lifestyle among lower- and medium-educated patients. Findings emphasize the relevance of personalized, value-based goal setting within cardiac rehabilitation.

Objective
Communicating risk information and offering lifestyle advice are important goals in cardiac rehabilitation. However, the most effective way and the most effective source to communicate this information are not yet known. Therefore, we examined the effect of source (cardiologist, physiotherapist) and framing (gain, loss) of brief lifestyle advice on patients’ intention-to-change-lifestyle.

Methods
In an online experimental study, 636 cardiac patients (40% female, 67 (10) yrs.) were randomly assigned to one of four textual vignettes. Effect of source and framing on intention-to-change-lifestyle (assessed using a 5-point Likert scale) was analysed using analysis of covariance (ANCOVA).

Results
Patients expressed positive intention-to-change-lifestyle after receiving advice from the cardiologist (M = 4.1) and physiotherapist (M = 3.9). However, patients showed significantly higher intention-to-change-lifestyle after receiving advice from the cardiologist (0.58 [0.54–0.61]) when compared with the physiotherapist (0.52 [0.48–0.56]), (F[1,609] = 7.06, P = 0.01). Gain-framed and loss-framed advice appeared equally effective. However, communicating risks (loss) was remembered by only 9% of patients, whereas 89% remembered benefits (gain).

Conclusions
Our study shows the value of cardiologists and physiotherapists communicating brief lifestyle advice, as cardiac patients expressed positive intention for lifestyle change after receiving advice, irrespective of framing. Lifestyle advice should include benefits due to better recall.

Objective Up to 10% of the general population experiences persistent somatic symptoms (PSS). Numerous studies in a variety of health domains are dedicated to identifying factors that are associated with PSS onset. The present study aimed to provide an overview of predictors for PSS onset in the general population and the related health domains. Methods A systematic search was performed identifying longitudinal cohort studies that examined factors associated with PSS onset in the general population. Included studies measured potential predictors before PSS onset and were categorized according to the dynamic biopsychosocial model. Four levels of evidence were discerned for predictors, based on the number of studies and percentage of consistent findings. Results In the 154 articles eligible for analysis, 27 PSS subtypes were studied, with primary focus on fibromyalgia (25.0%) and irritable bowel syndrome (23.3%). Of the >250 predictors of PSS onset, 46 were investigated more than once and showed consistent results. Strong evidence identifies biological (e.g., infections, body weight-related metrics), psychological (e.g., sleep problems, psychopathology), interpersonal (life events, childhood/interpersonal stress), contextual (employment), and health behavioral (health care utilization) predictors. Conclusions The results provide strong evidence for factors from all dynamic biopsychosocial domains, although interpersonal and health behavioral factors are relatively under investigated. Thus, evidence suggests that reduction of predictors of PSS onset to a specific factor/domain may be too restrictive. There is no evidence that this differs per PSS subtype. Exploring all domains and measuring common factors across subtypes are essential to improve the clinical course of PSS.

Objective Persistent somatic symptoms (PSSs) are defined as symptoms not fully explained by well-established pathophysiological mechanisms and are prevalent in up to 10% of patients in primary care. The present study aimed to explore methods to identify patients with a recognisable risk of having PSS in routine primary care data. Design A cross-sectional study to explore four identification methods that each cover part of the broad spectrum of PSS was performed. Cases were selected based on (1) PSS-related syndrome codes, (2) PSS-related symptom codes, (3) PSS-related terminology and (4) Four-Dimensional Symptom Questionnaire scores and all methods combined. Setting Coded electronic health record data were extracted from 76 general practices in the Netherlands. Participants Patients who were registered for at least 1 year during 2014-2018, were included (n=169 138). Outcome measures Identification methods were explored based on (1) PSS sample sizes and demographics, (2) presence of chronic conditions and (3) healthcare utilisation (HCU) variables. Overlap between methods and practice specific differences were examined. Results The percentage of cases identified varied between 0.3% and 7.0% across the methods. Over 58.1% of cases had chronic physical condition(s) and over 33.8% had chronic mental condition(s). HCU was generally higher for cases selected by any method compared with the total cohort. HCU was higher for method B compared with the other methods. In 26.7% of cases, cases were selected by multiple methods. Overlap between methods was low. Conclusions Different methods yielded different patient samples which were general practice specific. Therefore, for the most comprehensive data-based selection of PSS cases, a combination of methods A, C and D would be recommended. Advanced (data-driven) methods are needed to create a more sensitive algorithm for identifying the full spectrum of PSS. For clinical purposes, method B could possibly support screening of patients who are currently missed in daily practice.