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BMC Health Services Research

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Exploring physiotherapists’ personality traits that may influence treatment outcome in patients with chronic diseases: a cohort study

  • Elisah Margretha Buining1, 2Email author,
  • Margit K. Kooijman2,
  • Ilse C. S. Swinkels2,
  • Martijn F. Pisters1, 3 and
  • Cindy Veenhof4
BMC Health Services Research201515:558

https://doi.org/10.1186/s12913-015-1225-1

Received: 14 April 2015

Accepted: 10 December 2015

Published: 16 December 2015

Abstract

Background

During treatment of patients with Chronic Diseases (CD) the therapist-patient interaction is often intense, and the strategies used during treatment require physiotherapists to assume a coaching role. Uncovering therapist factors that explain inter-therapist variation might provide tools to improve treatment outcome and to train future therapists. The purpose of this study was to explore the so-called ‘therapist-effect’, by looking at the influence of intrinsic therapist factors, specifically personality traits, on treatment outcome in patients with CD.

Methods

A cohort study was performed using data from the NIVEL Primary Care Database (NPCD) in 2011–2012 and an additional questionnaire. Patients with CD (n = 393) treated by Dutch physiotherapists working in outpatient practices (n = 39) were included. Patient and treatment outcome variables were extracted from NPCD. The course of complaint was measured using the Numeric Rating Scale. Therapist variables were measured using a questionnaire consisting of demographics and the Big Five traits: Extraversion, Neuroticism, Agreeableness, Conscientiousness and Openness to experiences. Data were analysed using multilevel linear regression.

Results

Only Neuroticism was found to be significant (Neuroticism F = 0.71, P = 0.01; therapist gender F = 0.72, P = 0.03; life events F = −0.54, P = 0.09; patient gender F = −0.43, P = 0.10; patient age F = 0.01, P = 0.27). Subgroup analyses of 180 patients with Osteoarthritis and 30 therapists showed similar results.

Conclusions

There are indications that patients with CD who are treated by therapists who tend to be calmer, more relaxed, secure and resilient have a greater reduction in severity of complaints compared to patients treated by therapists who show less of these traits. Being a male therapist and having experienced life events influence outcome positively. However, more extensive research is needed to validate the current findings.

Keywords

Therapist effectsChronic diseasesPersonalityNeuroticismBig fivePhysiotherapyOsteoarthritis

Background

Chronic diseases (CDs) are a growing health problem worldwide, causing 89 % of all mortality in the Dutch population in 2014 [1]. As CDs, such as cardiovascular diseases, cancers, chronic respiratory diseases, arthritis and diabetes, are generally of long duration and low progression, patients need ongoing management over a period of months, years or decades. Besides this, patients with CD generally need more healthcare than patients with non-CD [2]. In daily physiotherapy practice, treatment sessions are often prolonged compared to patients with non-CD [3]. Considerable research has gone into how to treat patients with CD in daily physiotherapy practice. This information forms the basis of Dutch physiotherapy evidence-based statements and guidelines regarding these diseases [49]. In these guidelines the core components of treatment are similar: (1) patients learn to manage and live with their disease in daily life and (2) they learn how to become and stay physically fit [10]. Both cases require a change in the patients’ behaviour and a need to adopt the skill of self-management.

Research by Lewis and colleagues [11] shows that physiotherapists can influence treatment outcome. In their study comparing two randomized clinical trials (RCTs) therapists accounted for around 3–7 % of the overall effect in patient disability outcome scores. The use of strategies to direct behavioural change and self-management within treatment requires physiotherapist to adopt a coaching role [410]. In addition, the prolonged therapy sessions lead to more contact with the treating party. Lewis et al. [11] hypothesized that an approach focusing on coaching may contribute to the effect of therapists on treatment outcomes. Based on these considerations, we assume that therapist-patient interaction is more intense in the treatment of patients with CD and therefore treatment outcome might be subject to greater influence by therapist related factors: the so-called ‘therapist effect’.

Identifying therapist related factors that affect treatment outcome could provide tools to improve treatment outcome in patients with CD. Some research has gone into extrinsic therapist related factors such as physiotherapists’ experience and education, [1118] showing no consistent influence on patient outcome. Only organizational related stress was associated with better physical patient outcomes. Unfortunately, the study’s conclusions are limited due to it being a cross-sectional analysis - time and influences at different hierarchical level were not taken into account [19]. Although proposed, [12, 15, 18] rather less attention has been paid to exploring intrinsic therapist factors such as personal beliefs, calmness or empathy.

The influence of intrinsic healthcare professionals’ characteristics on treatment outcome has been studied in related professional fields. Boerebach et al. [20] conducted a systematic review in which they examined the influence of clinicians’ personality and interpersonal behaviour on the quality of patient care. However, based on the low number of studies found, they could give no conclusion regarding the effect of personality on patient care. In their study, four articles were found showing a small effect of ‘Openness to experience’ [21], no effect of ‘Agreeableness’ , Openness to experiences’ [22, 23] or ‘Extraversion’ [24], and inconsistent findings for ‘Neuroticism’ and ‘Conscientiousness’ [2224]. In a sample of patients with anxiety and mood disorders, Heinonen et al. [25] showed that active, engaging and extrovert psychotherapists achieved a faster symptom reduction in short-term treatment while more cautious, non-intrusive therapists realized greater benefits during long-term treatment. Also, treatments by psychotherapists who had lower confidence and did not enjoy their work predicted poorer outcomes on the short- and long-term [25]. In four studies, [2629] more empathic psychotherapists and general practitioners affected treatment outcome in a positive manner.

A systematic approach to examining intrinsic physiotherapist factors is to look at personality traits, as contained in the Big Five personality theory [30, 31]. The Big Five is a widely used and accepted approach to examining the structure of inter-individual differences, using five personality dimensions. Based on prior theoretical research such as psycholexical theory [32], these personality dimensions have been shown to closely reflect actual behaviour traits [33]. Greater understanding of the influence of personality traits may contribute to general understanding of the physiotherapist effect and might be useful for general training of therapists. To our knowledge, no study has investigated the influence of physiotherapists’ personality traits on treatment outcome in patients with CD. Therefore, the objective of this study is to explore the influence of physiotherapists’ personality traits, using the Big Five, on treatment outcome in patients with CD in primary care.

Methods

Design overview

For this study, data were used from the NIVEL Primary Care Database (NPCD). This longitudinal registration database holds data of several primary care health care providers, including physiotherapists. NPCD contains information on the domains patients’ demographics, treatment plan, treatment and evaluation [34]. Data are continuously collected in a representative network of 73 therapists working in 40 primary care physical therapy practices. The therapists included worked at least 50 % of their hours as a general physiotherapist in primary care practices. Patients were recruited using a convenience sample. All patients treated by therapists who participated in NPCD were eligible to participate and were registered in the database, with the exception of those who declined to participate. However, this rarely occurred. Data were extracted monthly from the electronic medical records used to reimburse treatment costs. In addition, the therapists completed an online questionnaire annually. Informed consent was not applicable, as the study does not fall within the scope of the Medical Research Involving Subjects Act. However, the study did adhere to the Declaration of Helsinki [35]. Specifics regarding the method are reported by Swinkels et al. [3638].

Study setting and design

Data related to physiotherapists who participated in the NPCD period 2009–2011 were obtained by entering additional questions on the annual NPCD-physical therapy questionnaire. The additional questions concerned therapists’ experience of a life-event and their personality traits, using the Big Five Inventory (BFI) [3941]. The questionnaire was sent digitally to 73 therapists in February 2012. To reduce non-response, two reminders were sent digitally to non-responding therapists 10 and 20 days after the questionnaire was provided.

This study used patient data from the NPCD period 2009–2011. The registration period of three years was chosen for practical reasons related to sample size and treatment duration of CD patients. Physiotherapists collected patients’ demographics at the start of treatment. Information regarding the course of complaints was collected at the start and end of therapy.

Sample

All therapists who participated in NPCD were included, with the exception of those who had stopped participating by 2011. NPCD registered patients were eligible if they were adults (≥18 years) who started treatment in the period 2009–2011, with CDs defined as non-reversible, non-communicable, diseases [42]. The patient’s diagnosis was registered by the physiotherapist according to the general practitioners’ referral letter. Using the International Classification of Primary Care (ICPC) NPCD researchers recoded the registered diagnosis to an ICPC code [43]. If a patient entered through direct access (no referrer), the physical therapist registered the complaints and this physiotherapist’s diagnostic record was used and recoded by the researchers to an ICPC code. Patients were excluded if there was a possibility of recovery in the long term (e.g., fractures, ruptures, acute organ diseases, post-operative or pre-/post-partum diagnoses). To avoid the inclusion of non-chronic patients, the following diagnostic areas were excluded: symptom-related diagnoses (e.g., pain, stiffness, etc.), skin diseases, and physical deformities. Patients were excluded if no ICPC code was available. The sample selection is stated in Fig. 1.
Fig. 1

Flow diagram of therapist and patient selection

Variables

Therapists’ personality traits were measured using the Dutch version [40] of the BFI [39, 41] – a 41-item questionnaire using a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) [40]. The BFI comprises five scales based on and named after the universally accepted personality trait dimensions Neuroticism, Extraversion, Agreeableness, Conscientiousness and Openness to experiences. These traits are known together as the Big Five [32, 44]. The term Big Five indicates that each domain represents a wide range of personality traits [39]. A higher score on Extraversion implied an ‘energetic approach towards the social and material world’; this includes being sociable, assertive, positive emotionality, active and talkative [39]. Higher scores on Agreeableness indicated a ‘pro-social and communal orientation towards others’ , including being sympathetic, forgiving, good-natured and polite. Conscientiousness indicated a ‘socially prescribed impulse control that facilitates task- and goal-directed behavior’. A higher score implied being reliable, well organized, self-disciplined and cautious. Neuroticism indicated ‘emotional stability and even-temperedness with negative–emotionality’. A lower score indicated being more calm, relaxed, secure and hardy. A higher score on Openness to experience indicated being more innovative, creative, curious and complex mentally and experientially [39]. The internal consistency of the BFI was high – Cronbach’s α ranged from 0.73 (Agreeableness) to 0.86 (Neuroticism) - and inter-scale correlation was relatively low (Fisher r-to-z transformation 0.24) [30, 45]. Convergent validity with the Big Five dimensions of Goldberg and the Neuroticism-Extroversion-Openness Five-Factor Inventory (NEO-FFI) was good [45]. The therapists’ life-changing event was seen as a possible confounder if the event appeared during the measuring period [46]. Therapists’ encounter with a life-changing event, either positive or negative, was answered with ‘yes’ or ‘no’ (e.g., getting married, bereavement, retirement, etc.) [46]. Other variables measured on therapist level were age, gender, education, and years of working experience.

The outcome of therapy was measured using the Numeric Rating Scale (NRS). The NRS is a widely used Dutch outpatient practice tool for evaluating treatment effect by looking at the course of complaints during treatment. Therapists recorded the NRS at the start and end of therapy. The NRS score ranged from 0.0–10.0, with a higher score indicating more severe complaints. Based on the NRS scores at the start and end of therapy, a difference score for the course of a patient’s complaint was calculated. A score of −10 to −1 indicated a decrease, a score of 0 indicated no difference and a score of 1 to 10 indicated an increase in the course of complaints. The test-retest reliability of the NRS is moderate in measuring pain [47] and high in measuring spasticity [48]. The validity is moderate to good in measuring a variety of patient-specific complaints [4852]. A minimum clinically important difference was found to be 1.39 (SD 1.05) in measuring pain [47]. Other variables on patient level included patient’s age, gender, education, recurrence of complaint, duration of treatment and diagnosis.

Sample size

The sample size was calculated per level, as different hierarchical levels (therapists and patients) were distinguished in the data [53]. The calculation was constructed using the following estimates: An Intraclass Correlation Coefficient (ICC) of 0.059 was estimated based on an average between-practitioners difference of 5.9 % [11, 54]. An average of six patients per therapist was estimated, based on LiPZ registrations of 2009. The variance was derived from a Z-score, as influences of personality traits on treatment outcome were unclear. A coefficient of 0.3 (conservative) was estimated, as previous research revealed diverse therapists’ effects (3–7 %) [11]. Based on these estimates, a power of 0.8 and significance level of 0.5, [54] the study needed to include 25 therapists and 152 patients.

Data analysis

The computer software Stata 11 was used to analyse the data [55]. Categorical variables were presented as number and percentages. Continuous variables were presented as mean values with standard deviations or median values for non-normally distributed variables. Analyses of non-responders and missing data were performed using the Pearson’s Contingency coefficient Chi2, Independent T-test or Mann–Whitney U test. Unanswered BFI items (maximum of six per case per scale) were left out and the scale score was based on the remaining filled-in items [56, 57]. Differences between scale scores were checked using Cronbach’s α. Comparing Alphas between 1) scale scores including the remaining filled-in scores of the item with missing values and 2) the scale scores without the item that had missing values [58], the following was found: The Alpha of the scales stayed about the same – changing from 0.73 to 0.71 (Extraversion), 0.745 to 0.748 (Neuroticism), 0.76 to 0.77 (Conscientiousness), 0.6575 to 0.6581 (Agreeableness) and 0.723 to 0.718 (Openness to experiences). Based on the missing data analysis a full case analysis was performed [5961].

Due to different hierarchical levels a two-level linear regression was performed. Multicollinearity was found to exist: therapist’s age was highly correlated to years of working experience (r = 0.94) [54, 58]. Therefore, only therapist’s age was included as more cases were available [62]. Not normally distributed variables were transformed into dummies. As the research question aimed at studying differences between therapists, a random intercept was used [63]. Regression was tested using the Wald test. Significant personality traits were entered with a fixed coefficient (Likelihood-ratio test = 0.58, P = 0.45) and regression coefficients were estimated using the Maximum Likelihood [63]. To avoid over-identification, the maximum number of variables included in the model was set to one variable per 10 therapists, and regression coefficients and significance levels were observed when entering a variable. The variables tested in the multilevel analysis are shown in Table 1.
Table 1

Variables used in analyses

Patient level

  

Therapist level

  

Gender

Female

Categorical

Gender

Female

Categorical

(Female = 0)a

Male

(Female = 0)a

Male

Age

Years

Continuous

Age

31–45 years

Categorical

   

(31–45 years = 0)a

46–59 years

 
    

60–75 years

 

Reoccurrence

No

Categorical

Life event

No

Categorical

(No = 0)a

Yes

 

(No = 0)a

Yes

 

Education

Low

Categorical

Extraversion

Scale 1–5

Continuous

(Low = 0)a

Middle

 

Openness to experiences

Scale 1–5

 
 

High

 

Neuroticism

Scale 1–5

 
 

Other

 

Agreeableness

Scale 1–5

 
   

Conscientiousness

Scale 1–5

 

Course of complaints

-10 − 10

Continuous

   

aReference value for dummies of ordinal or categorical variables

Low Primary School, Medium Secondary- or higher education, High University, Other not specified, yrs. Years

Variables were entered into the model using the forward method based on their univariate p-values (p = <0.10). First an empty model with the difference in course of complaint as dependent variable was calculated (Model 0). Next, patient variables were added in turn, to correct for effects on patient level (level 1) (Model I). Afterwards, personality traits (level 2) were added in turn (Model II). Next, therapist gender, age, life event, and remaining BFI variables, which were not significant in model II, were entered in turn. If the independent variable’s regression coefficient changed ≥10 % compared to model II the particular therapist variable was seen as a confounder and was included in the final model (Model III) [63]. Finally, the unexplained variance between therapists (Intraclass Correlation Coefficient, ICC) and the amount of variance that was explained by the therapist variables entered (R2) were calculated [63]. Subgroup analyses were performed to check the construct of the patient group used.

Results

Non-responding therapists and missing cases

Fifty-six therapists (77 %) completed the BFI questionnaire. The 17 non-responding therapists (23 %) did not significantly differ from the responding therapists with regard to gender (Chi2 = 0.30, P = 0.59), age (Z = 1.59, P = 0.11) but significantly for years working experience (Z = 2.03, P = 0.043). A total of thirteen BFI items (0.7 %) were not filled in; items were not mentioned twice. There were no significant differences between therapists who omitted an item and those who did not, regarding gender and age for Extraversion (respectively Z = −1.02, P = 0.31 and Z = −0.86, P = 0.39), Neuroticism (respectively Chi2 = 1.07, P = 0.30 and Z = 0.12, P = 0.90), Conscientiousness (respectively Chi2 = 0.01, P = 0.98 and Z = 0.87, P = 0.38), Agreeableness (Chi2 = 0.24, P = 0.63) and Openness to experiences (respectively Chi2 = 0.49, P = 0.48 and Z = −0.53, P = 0.59).

In the patient cases without an ICPC code there was no difference between missing and completed patient cases with regard to patient’s gender (Chi2 = 1.93, P = 0.17), age (Z = 0.34, P = 0.73) and significant difference in education (Z = −3.17, P = 0.002).

Characteristics

Thirty-nine therapists and 393 patients were included in the analysis. Therapists had an average age of 53 years (SD 1.6, range 28–69) and were mainly male. They had worked on average 27 years (SD 1.4, range 4–40). Besides being a general physiotherapist, therapists were specialized in the pelvis (n = 2, 5 %), paediatrics (n = 2, 5 %), manual therapy (n = 10, 26 %) oedema (n = 1, 3 %), sport (n = 4, 10 %) and/or other specializations (n = 4, 10 %). The therapists treated an average of 10 patients with CD within the three-year period (range 1–51). The BFI scores were generally higher on Openness to experiences (mean 3.42, SD 0.09), Extraversion (mean 3.49, SD 0.07), Conscientiousness (mean 3.69, SD 0.08) and Agreeableness (mean 3.75, SD 0.06) and lower on Neuroticism (2.39, SD 0.09). The range of all but one trait (Neuroticism) was limited. Therapists’ characteristics are shown in Table 2.
Table 2

Descriptive statistics of the physiotherapists (n = 39)

Therapist variables

 

Outcome

Gender, n (%)

Female/Male

10 (26)/29 (74)

Age (yrs.), n (%)

≤30

1 (2.5)

 

31–45

6 (17)

 

46–59

23 (57.5)

 

60+

9 (22.5)

Educationa, n (%)

Specialization

9 (23)

 

Academic Education (MSc.)

2 (5)

 

Course aimed at chronic patients

12 (30)

 

Course aimed at communication & coaching

15 (38)

 

Course aimed at self-management

7 (18)

 

None of above

13 (33)

Life-changing event ≤3 years., n (%)

Yes/No

19 (52)/17 (47)

Big Five, mean (min – max)

Neuroticism

2.38 (1.25–3.88)

 

Extraversion

3.49 (2.63–4.63)

 

Agreeableness

3.75 (3.00–4.78)

 

Conscientiousness

3.69 (2.89–4.89)

 

Openness to experiences

3.42 (2.70–4.80)

% Percentage, n number, min minimum, max maximum, SD standard deviation, Yrs. Years

amore than one answer possible

Patients’ average age was 67 years (SD 15, range 18–98) and they were mostly female. Overall, the patients experienced a clinically important reduction in their complaint (Mean −3.66, SD 2.5, −9 min – -2 max). The most frequent diagnosis was Osteoarthritis disorders (n = 180, 46 %), followed by Rheumatoid Arthritis (n = 40, 10 %) and Cerebral Vascular Accident (n = 39, 10 %). Patients’ characteristics are shown in Table 3.
Table 3

Descriptive statistics of patients (n = 393)

Patient variable

Outcome

Gender, n (%)

Female/Male

240 (61)/153 (39)

Age yrs., n (%)

≤30

9 (2.3)

 

31–45

22 (5.6)

 

46–59

81 (20.6)

 

60–75

154 (39.2)

 

76–85

99 (25.2)

 

≥86

28 (3.1)

Education, n (%)

Lower

143 (36.3)

 

Middle

83 (21.1)

 

Higher

46 (11.7)

 

Othera

121 (31)

Recurrence of the complaint, n (%)

Yes

139 (36)

 

No

250 (64)

Severity, mean (SD, 95 % C.I.)

Start therapy

6.84 (0.1, 6.6–7.0)

 

End therapy

3.19 (0.1, 2.9–3.4)

Disease, n

Cancer

Neoplasm or lymphatic system

1

  

Esophageal malignancy

1

Nervous system

1

Neoplasm bronchus/lung

1

 

Cardiovascular

Heart failure

2

Heart valve disease

2

Cerebral ischemia

1

Cerebrovascular accident

39

Claudicatio intermittent

18

 

Rheumatic disorders

Fibromyalgia

15

Rheumatoid arthritisb

40

Other arthritis

26

Tietze syndrome

4

 

Degenerative bone and joint disorders

Osteoarthritis of the Spine

76

Osteoarthritis of the Hip

34

Osteoarthritis of the Knee

70

Osteoporosis

16

 

Disorder (central) nervous system

Multiple sclerosis

6

Parkinson

15

Alzheimer disease

2

 

Lung diseases

Chronic bronchitis

2

Emphysema/COPD

17

Asthma

2

 

Metabolic disorders

Cystic fibrosis

1

Diabetes Mellitus

1

aFilled in by therapist as other, % percentage, n number, SD standard deviation, yrs. Years

b(incl. rheumatic polymyalgia), CI Convenience Interval

% percentage, n number, SD standard deviation, X mean, yrs. Years

Multilevel analysis

The analysis is shown in Table 4.
Table 4

Steps to prediction model for the course of complaints

 

Coef.

S.E.

Z

P

95 % CI

ICC

Model 0

 Intercept

 

−3.66

0.19

  

−4.02 – -3.30

 

 Total Model

       

 Var. Th. level

 

0.47

0.31

  

0.13–1.73

0.076

 Var. Pt. level

 

5.75

0.43

  

4.96–6.66

 

Model I

Patients

 

Gender

−0.47

0.25

−1.88

0.060

−0.96–0.02

 
 

Age

0.01

0.01

1.76

0.079

−0.002–0.03

 

 Intercept

 

−2.90

0.44

  

−3.76 – -2.03

 

 Total Model

       

 Var. Th. level

 

0.41

0.29

  

0.11–1.63

0.067

 Var. Pt. level

 

5.68

0.43

  

4.90–6.58

 

Model II

Patients

 

Gender

−0.48

0.25

−1.94

0.053

−0.97–0.006

 
 

Age

0.01

0.01

1.63

0.103

−0.003–0.03

 

Therapists

      
 

Neuroticism

0.59

0.32

1.81

0.070

−0.048–1.22

 

 Intercept

 

−4.27

0.88

  

−5.99 – -2.56

 

 Total Model

      

 Var. Th. level

0.36

0.26

  

0.09–1.46

0.060

 Var. Pt. level

5.65

0.42

  

4.88–6.54

 

Model III

Patients

 

Gender

−0.43

0.25

−1.66

0.098

−0.92–0.08

 
 

Age

0.01

0.01

1.11

0.269

−0.01–0.03

 

Therapists

      
 

Neuroticism

0.71

0.29

2.47

0.014*

0.15–1.28

 
 

Gender

0.72

0.32

2.21

0.027*

0.08–1.35

 
 

Life events

−0.54

0.32

−1.68

0.092

−1.16–0.09

 

 Intercept

 

−5.42

0.94

  

−7.27 − -3.57

 

 Total Model

       

 Var. Th. level

 

0.12

0.19

  

0.01–2.57

0.021

 Var. Pt. level

 

5.60

0.43

  

4.82–6.52

 

*Significant variables ≤0.05, CI convenience interval, coef. Regression coefficient, ICC Intraclass Correlation Coefficient, P significant level, Pt Patient, R2 percentage of variance explained by model, SE. standard error, Th Therapist, Z z-score

Of the initial model 7.6 % (ICC 0.076) was ascribed to inter-therapists variation (Model 0, Table 4). The patients’ gender (P = 0.06) and age (P = 0.08) were found to be eligible and were entered into the model (Model I, Wald Chi2 = 6.71, P = 0.03). The ICC was reduced to 6.7 %, meaning that a small part of the variance (9 %) between therapists was explained by these patient variables.

Of the Big Five variables, only Neuroticism was found to be eligible (Model II, Wald Chi2 = 10.11, P = 0.02). Therapist gender and experienced life events were added as confounders (Model III). Neuroticism was found to be significant (Wald Chi2 = 16.82, P = 0.005). Table 5 describes how the R2 was calculated. 70 % of the variation between therapists could be explained by Neuroticism, therapist gender and experienced life events.
Table 5

Amount of explained variance per model

Model

  

R2

Total R2

(0→I)

\( \frac{\left(0.47-5.75\right)-\left(0.41+5.68\right)}{\left(0.47+5.75\right)} \)

=0.13

Therapist variablesR2

(I→III)

\( \frac{0.41-0.12}{0.41} \)

=0.71

Patient variablesR2

(0→I)

\( \frac{5.75-5.68}{5.75} \)

=0.01

The subgroup analysis using only patients with Osteoarthritis (n = 180) treated by 30 therapists showed similar results to the main model, with Neuroticism as the independent variable and Conscientiousness and therapists’ gender as confounders: constant F = −10.18, Neuroticism F = 1.15, p = 0.003 (0,40–1.91 95 % CI), Conscientiousness F = 0.68, p = 0,07 (−0,04–1.41 95 % CI), therapists’ gender F = 0.76, p = 0.55 (−0.02–1.54 95 % CI). This might give an indication that the kind of chronic disease is unrelated to the influence of therapist on treatment outcome.

Discussion

The purpose of this study was to explore the influence of therapists’ personality traits on treatment outcome in patients with CD. Specht et al. [46] indicated that personality can change not only change due to maturation, [64] but also due to social demands and experiences. These changes are more pronounced at younger and older ages, but occur throughout a person’s lifetime [46]. As personality traits might be accounted for, knowledge of traits that influence treatment outcome might be useful for general training of therapists and specifically for patients with CD. Generally, the results indicate that Neuroticism might have an influence on treatment outcome in patients with CD. A higher score on Neuroticism was associated with worse treatment outcomes. The current variables Neuroticism, gender and life events, explained approximately 71 % of the total variance between therapists. Therefore future research looking at the differences between therapists in treatment outcome should include the identified variables. Of the Big Five trait, Neuroticism was the only personality trait that was associated with better treatment outcomes. This suggests that treatment by therapists who tend to be calmer, more relaxed, secure and hardy, may produce better treatment outcomes in patients with CD.

To the author’s knowledge, this is the first study that looks systematically at physiotherapists’ personality traits in relation to treatment outcome. The indication of the possible relevance of Neuroticism corresponds with evidence found in the field of psychotherapy, showing that being treated by secure therapists predicts a better outcome [25]. Moreover, the overall ICC of 0.075 found in this study is similar to previous research showing an ICC of 0.03–0.07 on therapist level [11]. The results are based on a sample of predominantly older women with chronic diseases, treated by older male therapists. Therefore caution should be exercised when generalizing the current results. More research into the influence of these traits on treatment outcome in a more heterogeneous sample is needed. Evidently, this study supports prior research that a physiotherapist effect does exist [11].

Contrary to expectations, no evidence was found for the four other personality traits. This finding contradicts previous research in psychotherapy suggesting that traits including being empathic, [2527, 29] cautious, non-intrusive, [25] respectful, being able to adjust and exuding warmth [29] (as a psychotherapist or general practitioner) improve treatment outcome. The contradiction with earlier research might be due to limited distribution of the personality traits and the difference in professions and diagnosis being examined. Further research with a sample of therapists with a wider range of Big Five scores is needed to obtain a better understanding of the influence of all Big Five traits. The influence of therapists’ gender confirmed the results of another physiotherapy study that investigated the placebo effect and its relation to personality [28]. The study indicated that a female therapist was associated with better outcomes in patients with an irritable bowel syndrome.

While little is known about the influence of being more neurotic as a therapist on patient outcome in research, more is known of the influence on the therapist himself. Studies in the fields of psychotherapy and general practitioners underline that being less neurotic reduces the practitioner’s chances of emotional exhaustion (a form of burn-out) [65] and increases their sense of satisfaction with life [66]. If a therapist does not feel mentally stable, it is reasonable to assume that this might have consequences for his or her attitude when interacting with the patient. Further research is needed to clarify these assumptions.

Reflecting on ones personality as a physiotherapist could yield information on the existence of negative influencers, like Neuroticism. In the fields of psychotherapy and general practice, training has been advised as part of the professional education [67]. Tools like communication skills training might be used as supplement to reflection, [68] but the authors believe that self-awareness and reflection training during the early stages of study are needed, before these tools can be used effectively.

Other mechanisms such as patient personality traits, health beliefs, moral compass, placebo effects and other interaction mechanisms might affect both the patient and the therapist and therefore treatment outcome [69]. For example, the patients’ beliefs regarding the effect of treatment or previous experiences with their goal of ‘getting physically active’ might influence their motivation towards adopting a more active role in the self-management process, which could influence treatment outcome [69]. In the same way, a therapist who experienced negative results when engaged in physical exercise may have created a different conceptualization of the goal ‘getting physically active’. This, combined with having a certain personality trait, like being more neurotic, might increase the chance of a negative outcome when getting others to be physically active. Future studies that focus on the physiotherapist’s effect on treatment outcome ought therefore to not only look at the personality domains as such, but also take other mechanisms like experiences, health beliefs, etc. into consideration.

There are implications that CDs influence patients’ wellbeing differently [70, 71]. For example, it is known that anxiety and depression are common in patients with Chronic Obstructive Pulmonary Diseases [72]. Consequently, knowledge of personality traits that influence treatment outcome in specific CD groups would support therapists during treatment as they could adjust their approach accordingly. Therefore, analysis of specific CD groups might be of interest. In the current study, the outcome in the subgroup analysis points to patients with Osteoarthritis, showing that both Neuroticism and Conscientiousness are possible influencing factors. The association between Conscientiousness and Neuroticism has been described in previous studies [22, 23].

When investigating the therapist’s effect, interdependency of the cases have to be taken into account as this can change the outcome considerably [63]. A multilevel analysis, especially including subgroup analysis, requires large sample sizes. This can be a hindrance when performing this type of analysis. The current study gives an example of the use of longitudinal electronic patient record data for multilevel research into the physiotherapist effect. The use of the NPCD database reduced the organizational burden considerably, particularly in view of the number of therapists and patients needed. Furthermore, the database provided standard patient care data. Accordingly, missing patients were not study-specific and therapists were not aware of the patient data researched for this study.

Limitations

Unfortunately, in the NPCD database, around 60 % of the outcome variable was missing, causing a loss in the number of patients and therapists that could be studied. The missing data in the patient database was due to the fact that the study was based on voluntary registration of some of the variables in the NPCD. The authors did compare the missing data with the existing data. The demographic data did not differ significantly between missing and non-missing patients and therapists’ cases. Despite the amount of missing data, there were enough patients and therapists included to perform the analysis and there was a higher average of patients treated per therapist than estimated (ten vs. six) for the patient sample size. For the therapist data, the authors did try to reduce non-responsiveness by sending two reminders. It could be that a specific group of therapists, with specific personality traits, did not respond. However, there was variation in the BFI scales, albeit low. Therefore no large effect of missing a subgroup is expected.

Although the authors tried to account for the influence of a life event on personality traits [46], it was not specified if the experience was positive or negative. As the effect can be the opposite depending on the experience, no judgement can be made on the kind of influence the item life events has on Neuroticism [46]. Further research is needed to study this in greater depth.

Personality inventories like the NEO-FFI might possibly have been more precise for measure personality traits [45]. That said, the BFI was chosen for practical reasons, since it does not take too long for therapist to fill out. Besides, the BFI provides a general view on personality, which was the purpose of the study.

Conclusion

There are indications that patients with CD who are treated by therapists who tend to be calmer, more relaxed, secure and hardy have a greater reduction in severity of complaints compared to patients treated by therapists who show less of these traits. Being a male therapist and having experienced life events influence the outcome positively. However, more extensive research is needed to validate the current findings.

Declarations

Acknowledgments

The authors thank Peter Spreeuwenberg for his guidance during the statistical analysis.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Physiotherapy Science, Program in Clinical Health Sciences & Department of Rehabilitation, Nursing Science and Sport, Brain Center Rudolf Magnus, University Medical Center Utrecht
(2)
NIVEL, Netherlands Institute for Health Services Research
(3)
Center for Physical Therapy Research and Innovation in Primary Care, Julius Health Care Centers
(4)
Department of Rehabilitation, Nursing Science & Sport, University Medical Center Utrecht, Brain Center Rudolf Magnus

References

  1. World Health Organization. Country profiles. In: Noncommunicable diseases country profiles 2014. World Health Organization. 2014. http://apps.who.int/iris/bitstream/10665/128038/1/9789241507509_eng.pdf. Accessed 30 Oct. 2015.
  2. Heijmans M, Spreeuwenberg P, Rijken M. Zorggebruik. In: Ontwikkelingen in de zorg voor chronisch zieken. Netherlands Institute for Health Services Research. 2010. http://www.nivel.nl/sites/default/files/bestanden/Rapport-ontwikkelingen-in-de-zorg-voor-chronisch-zieken.pdf. Accessed 30 Oct. 15.
  3. Kooijman MK, Swinkels IC, Barten JA, Veenhof C. Fysiotherapeutisch zorggebruik door patiënten met een chronische aandoening in de periode 2006–2009. Factsheet Landelijke Informatievoorziening Paramedische Zorg. Netherlands Institute for Health Services Research 2011. http://www.nivel.nl/pdf/Factsheet-fysiotherapeutisch-zorggebruik.pdf. Accessed 30 Oct. 15
  4. Köke AJA, van den Ende CMH, Jansen MJ, Steultjens MPM, Veenhof C. KNGF-standaard Beweeginterventie artrose. Royal Dutch Society for Physical Therapy. 2011. https://www.fysionet-evidencebased.nl/images/pdfs/beweeginterventies/standaard_bi_artrose_2011.pdf. Accessed 30 Oct. 15.
  5. Troosters T, Jongert MWA, de Bie RA, Toereppel K, de Gruijter EEMH. KNGF-standaard Beweeginterventie chronisch obstructieve longziekten. Royal Dutch Society for Physical Therapy. 2009. https://www.fysionet-evidencebased.nl/images/pdfs/beweeginterventies/standaard_bi_copd_2009.pdf. Accessed 30 Oct. 15.
  6. Verhagen SJM, Jongert MWA, Koers H, Toereppel K, Walhout R, Staal JB. KNGF-standaard Beweeginterventie coronaire hartziekten. Royal Dutch Society for Physical Therapy. 2009. https://www.fysionet-evidencebased.nl/images/pdfs/beweeginterventies/standaard_bi_corn_hartziekten_2009_update3.pdf. Accessed 30 Oct. 15.
  7. Praet SFE, van Uden C, Hartgens F, Savelberg HHCM, Toereppel K, de Bie RA. KNGF-standaard Beweeginterventie diabetes mellitis type 2. Royal Dutch Society for Physical Therapy. 2009. https://www.fysionet-evidencebased.nl/images/pdfs/beweeginterventies/standaard_bi_dm2_2009.pdf. Accessed 30 Oct. 15.
  8. Stuiver MM, Wittink MH, Velthuis MJ, Kool N, Jongert MWA. KNGF-standaard Beweeginterventie oncologie. Royal Dutch Society for Physical Therapy. 2011. https://www.fysionet-evidencebased.nl/images/pdfs/beweeginterventies/standaard_bi_oncologie_2011.pdf. Accessed 30 Oct. 15.
  9. Hurkmans EJ, van der Giesen FJ, Bloo H, Boonman DCG, van der Esch M, Fluit M, et al. Supplement: KNGF-richtlijn Reumatoïde artritis. Nederlands Tijdschrift voor Fysiotherapie. 2008;5:1–33.Google Scholar
  10. Crajé MC, Hodselmans AP, van Ittersum MW, van Heeringen-de Groot D, Verhoef J, van der Schans CP. Inleiding bij de KNGF-standaarden Beweeginterventies. Royal Dutch Society for Physical Therapy. 2013. https://www.fysionet-evidencebased.nl/images/pdfs/beweeginterventies/inleiding_bij_de_kngf-standaarden_beweeginterventies.pdf. Accessed 30 Oct. 15.
  11. Lewis M, Morley S, Van Der Windt D, Hay E, Jellema P, Dziedzic K, Main C. Measuring practitioner/therapist effects in randomised trials of low back pain and neck pain interventions in primary care settings. European Journal of Pain. 2010; doi:10.1016/j.ejpain.2010.04.002
  12. Simon CB, Stryker SE, George SZ. Assessing the influence of treating therapist and patient prognostic factors on recovery from axial pain. J Man Manip Ther. 2013; doi:10.1179/2042618613Y.0000000035.
  13. Whitman JM, Fritz JM, Childs JD. The influence of experience and specialty certifications on clinical outcomes for patients with low back pain treated within a standardized physical therapy management program. J Orthop Sports Phys Ther. 2004;34:662–72. discussion 672–5.View ArticlePubMedGoogle Scholar
  14. Resnik L, Hart D. Using clinical outcomes to identify expert physical therapists. Phys Ther. 2003;83:990–1002.PubMedGoogle Scholar
  15. Resnik L, Jensen G. Using clinical outcomes to explore the theory of expert practice in physical therapy. Phys Ther. 2003;83:1090–106.PubMedGoogle Scholar
  16. Shepard K, Hack L, Gwyer J, Jensen G. Describing expert practice in physical therapy. Qual Health Res. 1999;9:746–58.View ArticlePubMedGoogle Scholar
  17. Jensen GM, Shepard KF, Gwyer J, Hack LM. Attribute dimensions that distinguish master and novice physical therapy clinicians in orthopedic settings. Phys Ther. 1992;72:711–22.PubMedGoogle Scholar
  18. Jensen GM, Shepard KF, Hack LM. The novice versus the experienced clinician: insights into the work of the physical therapist. Phys Ther. 1990;70:314–23.PubMedGoogle Scholar
  19. Alesii A, Damiani C, Pernice D. The physical therapist-patient relationship. Does physical therapists‘occupational stress affect patients’ quality of life? Funct Neurol. 2005;20:121–6.PubMedGoogle Scholar
  20. Boerebach BC, Scheepers RA, van der Leeuw RM, Heineman MJ, Arah OA, Lombarts KM. The impact of clinicians’ personality and their interpersonal behaviors on the quality of patient care: a systematic review. Int J Qual Health Care. 2014; doi:10.1093/intqhc/mzu055.
  21. Duberstein PR, Meldrum S, Fiscella K, Shields CG, Epstein RM. Influences on patients’ ratings of physicians: physicians demographics and personality. Patient Educ Couns. 2007;65:270–4.View ArticlePubMedGoogle Scholar
  22. Chapman BP, Duberstein PR, Epstein RM, Fiscella K, Kravitz RL. Patient-centered communication during primary care visits for depressive symptoms: what is the role of physician personality? Med Care. 2008; doi:10.1097/MLR.0b013e31817924e4.
  23. Duberstein PR, Chapman BP, Epstein RM, McCollumn KR, Kravitz RL. Physician personality characteristics and inquiry about mood symptoms in primary care. J Gen Intern Med. 2008; doi:10.1007/s11606-008-0780-0.
  24. Nash L, Daly M, Johnson M, Coulston C, Tennant C, van Ekert E, et al. Personality, gender and medico-legal matters in medical practice. Australas Psychiatry. 2009; doi:10.1080/10398560802085359.
  25. Heinonen E, Lindfors O, Laaksonen M, Knekt P. Therapists’ professional and personal characteristics as predictors of outcome in short- and long-term psychotherapy. J Affect Disord. 2012; doi:10.1016/j.jad.2012.01.023.
  26. Neumann M, Scheffer C, Tauschel D, Lutz G, Wirtz M, Edelhauser F. Physician empathy: definition, outcome-relevance and its measurement in patient care and medical education. GMS Z Med Ausbild. 2012; doi:10.3205/zma000781.
  27. Elliott R, Bohart A, Watson J, Greenberg L. Empathy. Psychotherapy (Chic) 2011; doi:10.1037/a0022187.
  28. Kelley JM, Lembo AJ, Ablon JS, Villanueva JJ, Conboy LA, Levy R, et al. Patient and practitioner influences on the placebo effect in irritable bowel syndrome. Psychosom Med. 2009; doi:10.1097/PSY.0b013e3181acee12.
  29. Beutler LE, Machado P, Allstetter Neufeldt SR. Therapist variables. In: Bergin A, Garfield S, editors. Handbook of Psychotherapy and Behavior Change. 4th ed. New York: Wiley; 1994.Google Scholar
  30. John OP, Srivastava S. The Big Five trait taxonomy: History, measurement and theoretical perspectives. In: Pervin L, John O, editors. Handbook of personality: Theory and research. New York: Guilford; 1999. p. 102–38.Google Scholar
  31. McCrae RR, Costa Jr PT. Personality trait structure as a human universal. Am Psychol. 1997;52:509–16.View ArticlePubMedGoogle Scholar
  32. Goldberg LR. An alternative “description of personality”: the Big-Five factor structure. J Pers Soc Psychol. 1990;59:1216–29.View ArticlePubMedGoogle Scholar
  33. Fleeson W, Gallagher MP. The Implications of Big-Five Standing for the Distribution of Trait Manifestation in Behavior: Fifteen Experience-Sampling Studies and a Meta-Analysis. J Pers Soc Psychol. 2009; doi:10.1037/a0016786.
  34. Netherlands institute for health services research (NIVEL). Primary Care Database, Utrecht. 2015. http://www.nivel.nl/en/dossier/nivel-primary-care-database. Accessed 30 Oct. 2015.
  35. World Medical Association. WMA Declaration of Helsinki - Ethical Principles for Medical Research Involving Human Subjects. Helsinki 1964. http://www.wma.net/en/30publications/10policies/b3/. Accessed 1 Nov. 2015.
  36. Swinkels IC, Kooijman MK, Spreeuwenberg PM, Bossen D, Leemrijse CJ, Dijk CEv, et al. An Overview of 5 Years of Patient Self-Referral for Physical Therapy in the Netherlands. Phys Ther. 2014; doi:10.2522/ptj.20130309.
  37. Swinkels IC, Hart DL, Deutscher D, van den Bosch WJ, Dekker J, de Bakker D, et al. Comparing patient characteristics and treatment processes in patients receiving physical therapy in the United States, Israel and the Netherlands: Cross sectional analyses of data from three clinical databases. BMC Health Serv Res. 2008; doi:10.1186/1472-6963-8-163.
  38. Swinkels IC, van den Ende CH, de Bakker D, Van der Wees PJ, Hart DL, Deutscher D, et al. Clinical databases in physical therapy. Physiother Theory Pract. 2007;23:153–67.View ArticlePubMedGoogle Scholar
  39. John OP, Naumann LP, Soto CJ. Paradigm shift to the integrative Big Five trait taxonomy: History, measurement, and conceptual issues. In: John O, Robins R, Pervin L, editors. Handbook of personality psychology: Theory and research. 3rd ed. New York: Guilford Press; 2008. p. 114–58.Google Scholar
  40. Denissen JJ, Geenen R, van Aken MAG, Gosling SD, Potter J. Development and validation of a Dutch translation of the Big Five Inventory (BFI). J Pers Assess. 2008; doi:10.1080/00223890701845229.
  41. Benet-Martínez V, John OP. Los Cinco Grandes across cultures and ethnic groups: Multitrait-multimethod analyses of the Big Five in Spanish and English. J Pers Soc Psychol. 1998;75:729–50.View ArticlePubMedGoogle Scholar
  42. Hoeymans N, Schellevis FC, Oostrom SH van, Gijsen R. Wat is een chronische ziekte en wat is multimorbiditeit? In: Volksgezondheid Toekomst Verkenning, Nationaal Kompas Volksgezondheid. RIVM. 2008. http://www.nationaalkompas.nl/gezondheid-en-ziekte/ziekten-en-aandoeningen/chronische-ziekten-en-multimorbiditeit/beschrijving/. Accessed 14 November 2013.
  43. International Classification Committee of WONCA. International Classification of Primary care 2. 2nd ed. Oxford: Oxford University Press; 1998.Google Scholar
  44. Goldberg LR. The development of markers for the Big-Five factor structure. Psychol Assess. 1992; doi:10.1037/1040-3590.4.1.26.
  45. McCrae RR, Costa Jr PT. Validation of the five-factor model of personality across instruments and observers. J Pers Soc Psychol. 1987;52:81–90.View ArticlePubMedGoogle Scholar
  46. Specht J, Egloff B, Schmukle SC. Stability and change of personality across the life course: The impact of age and major life events on mean-level and rank-order stability of the Big Five. J Pers Soc Psychol. 2011; doi:10.1037/a0024950.
  47. Kendrick DB, Strout TD. The minimum clinically significant difference in patient-assigned numeric scores for pain. Am J Emerg Med. 2005;23:828–32.View ArticlePubMedGoogle Scholar
  48. Anwar K, Barnes MP. A pilot study of a comparison between a patient scored numeric rating scale and clinician scored measures of spasticity in multiple sclerosis. NeuroRehabilitation. 2009; doi:10.3233/NRE-2009-0487.
  49. Gift AG, Narsavage G. Validity of the numeric rating scale as a measure of dyspnea. Am J Crit Care. 1998;7:200–4.PubMedGoogle Scholar
  50. Ruyssen-Witrand A, Fernandez-Lopez CJ, Gossec L, Anract P, Courpied JP, Dougados M. Psychometric properties of the OARSI/OMERACT osteoarthritis pain and functional impairment scales: ICOAP, KOOS-PS and HOOS-PS. Clin Exp Rheumatol. 2011;29:231–7.PubMedGoogle Scholar
  51. Ferreira-Valente MA, Pais-Ribeiro JL, Jensen MP. Validity of four pain intensity rating scales. Pain. 2011; doi:10.1016/j.pain.2011.07.005.
  52. Paice JA, Cohen FL. Validity of a verbally administered numeric rating scale to measure cancer pain intensity. Cancer Nurs. 1997;20:88–93.View ArticlePubMedGoogle Scholar
  53. Snijders TAB. ‘Power and Sample Size in Multilevel Linear Models’. In: Everitt B, Howell D, editors. Encyclopedia of Statistics in Behavioral Science. Volume 3. 3rd ed. Chicester: Wiley; 2005. p. 1570–3.Google Scholar
  54. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale: Lawrence Erlbaum; 1988.Google Scholar
  55. StataCorp. Stata Statistical Software: Release 11. College Station: StataCorp LP; 2009.Google Scholar
  56. Enders CK. Averaging the available items. In: Little T, editor. Applied Missing Data Analysis (Methodology In The Social Sciences). 1st ed. New York: The Guilfort press; 2010. p. 50–1.Google Scholar
  57. John OP. How do you handle missing items? In: The Big Five Inventory Frequently Asked Questions. Berkeley Personality Lab. 2007–9. https://www.ocf.berkeley.edu/~johnlab/bfi.htm. Accessed 15 Oct. 2015.
  58. Portney LG, Watkins MP. Statistical Measures of Reliability. In: Foundations of Clinical Research: Applications to Practice. 3rd ed. Upper Saddle River: Prentice-Hall; 2009. p. 588–607.Google Scholar
  59. Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009; doi:10.1136/bmj.b2393.
  60. Graham JW. Missing data analysis: making it work in the real world. Annu Rev Psychol. 2009; doi:10.1146/annurev.psych.58.110405.085530.
  61. Duffy ME. Handling missing data: a commonly encountered problem in quantitative research. Clin Nurse Spec. 2006;20:273–6.View ArticlePubMedGoogle Scholar
  62. Shieh YY, Fouladi RT. The Effect of Multicollinearity on Multilevel Modeling Parameter Estimates and Standard Errors. Educational and Psychological Measurement. 2003; doi:10.1177/0013164403258402.
  63. Twisk JRW. Applied Multilevel Analysis, practical guides to biostatistics and epidemiology. 1st ed. New York: Cambridge University Press; 2006.View ArticleGoogle Scholar
  64. McCrae RR, Costa Jr PT, Ostendorf F, Angleitner A, Hrebickova M, Avia MD, et al. Nature over nurture: temperament, personality, and life span development. J Pers Soc Psychol. 2000;78:173–86.View ArticlePubMedGoogle Scholar
  65. Hochwalder J. Burnout among Torgersen’s eight personality types. Social Behavior and Personality. 2009;37:467–80.View ArticleGoogle Scholar
  66. Tyssen R, Hem E, Gude T, Grønvold NT, Ekeberg O, Vaglum P. Lower life satisfaction in physicians compared with a general population sample: A 10-year longitudinal, nationwide study of course and predictors. Soc Psychiatry Psychiatr epidemiol. 2009; doi:10.1007/s00127-008-0403-4.
  67. DasGupta S, Charon R. Personal Illness narratives: using reflective writing to teach empathy. Acad Med. 2004;79:351–6.View ArticlePubMedGoogle Scholar
  68. de la Croix A, Rose C, Wildig E, Willson S. Arts-based learning in medical education: the students’ perspective. Med Educ. 2011; doi:10.1111/j.1365-2923.2011.04060.x.
  69. Bensing JM, Verheul W. The silent healer: The role of communication in placebo effects. Patient Educ Couns. 2010; doi:10.1016/j.pec.2010.05.033.
  70. Hidaka BH. Depression as a disease of modernity: Explanations for increasing prevalence. J Affect Disord. 2012; doi:10.1016/j.jad.2011.12.036.
  71. Alonso J, Ferrer M, Gandek B, Ware Jr JE, Aaronson NK, Mosconi P, et al. IQOLA Project Group: Health-related quality of life associated with chronic conditions in eight countries: results from the International Quality of Life Assessment (IQOLA) Project. Qual Life Res. 2004;13:283–98.View ArticlePubMedGoogle Scholar
  72. Maurer J, Rebbapragada V, Borson S, Goldstein R, Kunik ME, Yohannes AM, et al. Anxiety and depression in COPD: current understanding, unanswered questions, and research needs. Chest. 2008;134 Suppl 4:43–56.View ArticleGoogle Scholar

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