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Publicly Available Published by De Gruyter January 1, 2017

Treatment success in neck pain: The added predictive value of psychosocial variables in addition to clinical variables

  • Ruud Groeneweg EMAIL logo , Tsjitske Haanstra , Catherine A.W. Bolman , Rob A.B. Oostendorp , Maurits W. van Tulder and Raymond W.J.G. Ostelo

Abstract

Background and aims

Identification of psychosocial variables may influence treatment outcome. The objective of this study was to prospectively examine whether psychosocial variables, in addition to clinical variables (pain, functioning, general health, previous neck pain, comorbidity), are predictive factors for treatment outcome (i.e. global perceived effect, functioning and pain) in patients with sub-acute and chronic non-specific neck pain undergoing physical therapy or manual therapy. Psychosocial factors included treatment outcome expectancy and treatment credibility, health locus of control, and fear avoidance beliefs.

Methods

This study reports a secondary analysis of a primary care-based pragmatic randomized controlled trial. Potential predictors were measured at baseline and outcomes, in 181 patients, at 7 weeks and 26 weeks.

Results

Hierarchical logistic regression models showed that treatment outcome expectancy predicted outcome

Conclusions

Based on the results of this study we conclude that outcome expectancy, in patients with non-specific sub-acute and chronic neck pain, has additional predictive value for treatment success above and beyond clinical and demographic variables.

Implications

Psychological processes, health perceptions and how these factors relate to clinical variables may be important for treatment decision making regarding therapeutic options for individual patients.

1 Introduction

Neck pain is an important individual, social and economic health problem. As neck pain ranks fourth world wide as a cause of Years Lived with Disability [1], it is clinically and economically desirable to prevent complaints becoming chronic. However, if complaints persist for more than six months the average severity of neck pain remains fairly stable [2].

While there are several interventions for neck pain, it is unclear which interventions are most effective and whether particular subgroups of neck pain patients benefit more from specific interventions [3, 4, 5]. Besides intervention-specific factors (biomechanical and neurophysiological effects [6, 7]), the effectiveness of treatment and outcomes related to neck pain may also be influenced by psychosocial factors. One model that takes all of these factors into account is the biopsychosocial disease model [8]. Psychological and social factors have been consistently associated with the onset and persistence of neck pain [9, 10, 11], indicating that these factors could be used as predictors of outcome. Importantly, (some) psychosocial factors can potentially be modified [12, 13, 14], implying that early identification of patients at risk for poor outcomes or maintenance of chronic symptoms might potentially aid treatment [9, 15, 16, 17, 18]. Psychosocial factors hypothesized to predict the effects of neck pain treatment include treatment outcome expectancy, treatment credibility, locus of control and fear avoidance beliefs.

Numerous studies in the fields of rehabilitation [19, 20, 21, 22, 23, 24, 25], psychotherapy [26, 27, 28, 29] and placebo research [30, 31] have shown that expectations of treatment outcome may affect the prognosis of (neck) pain. The ‘response expectancy theory’ attempts to explain the relationship between expectations and outcomes. This theory states that a person’s expectations will affect their experiences, a process that may (possibly) underlie the placebo and nocebo effect. This conclusion is supported by research showing that influencing expectations can change subjective and physiological responses [30, 32], effects confirmed in functional magnetic resonance imaging (fMRI) studies [33]. The Credibility and Expectancy scale [34] was developed on the premise that credibility is influenced by a patient’s logical thought, while expectation is functionally related to affective processes similar to hope and confidence.

According to Delsignore et al. [35], locus of control includes explicit prognostic beliefs, meaning the degree to which individuals attribute their health to their own behavior (internal locus of control) or to factors beyond their personal control (external locus of control). Several studies have reported an association between low back pain and locus of control [36, 37, 38, 39, 40]. Overall, patients with higher internal LOC exercised more frequently, had better outcomes and were more likely to return to work. In cervicogenic headache [41], high internal LOC was associated with a reduction in headache frequency in patients treated with manipulative therapy in combination with exercises, compared to manipulative therapy alone.

Fear of movement can best be defined as ‘fear that arises when stimuli related to pain are seen as a major threat’ [42] Considered important mediators of the development and maintenance of chronic pain [43]. Investigations of neck pain [44, 45, 46, 47, 48, 49] have identified fear avoidance beliefs as risk factors for neck pain and disability [45, 46, 47, 48, 49], working capacity [45, 47] and poor outcome [44].

The objective of this study was to determine whether treatment outcome expectancy, treatment credibility, locus of control and fear avoidance beliefs predict treatment success of manual therapy and physical therapy for patients with non-specific neck pain. In order to investigate the relative importance of these variables, the predictive value and cut-off points of treatment success were evaluated in addition to known predictive demographic and clinical variables commonly used in clinical practice. A secondary aim was to investigate whether type of treatment, specifically physical therapy (active exercise) versus manual therapy (passive mobilization), is an effect modifier in the relationship between the psychosocial factors and treatment outcome.

2 Methods

2.1 Design and setting

This study is a secondary analysis of a pragmatic randomized controlled trial conducted in primary care practices in the Netherlands.

2.2 Study population

Patients included were aged 18–70, with non-specific subacute and chronic neck pain, with or without radiation to the shoulder region or the upper extremities, and with or without headache. Exclusion criteria were presence of red flags [50], pregnancy, whiplash trauma as cause, and treatment for neck pain in the previous three months. All patients gave written informed consent.

2.3 Interventions

Patients participating in the randomized controlled trial received either manual therapy or physical therapy. In the manual therapy arm, the manual therapist performed a number of protocol-based patient assessments that included recording the natural asymmetry of shape, posture and movement. This also allows the direction and position of movement axes in the joints of the patient to be determined. Passive mobilization techniques were performed very gently and were (generally) pain-free. Manual therapists also commonly offer advice on activities of daily living and lifestyle, and recommend home exercise and exercises. In the physical therapy arm, treatment consisted of active exercises aimed at improving strength, mobility and movement coordination, which included exercises to improve posture and to promote relaxation, manual traction for pain reduction, and massage therapy for relaxation. Specific manual mobilization techniques, known as manual therapy techniques, were not part of the physical therapy. The physical therapist spent at least two-thirds of treatment time on active exercise. Giving advice on activities of daily living and lifestyle, and recommending home exercise is common and was therefore equal in both conditions.

2.4 Study overview (measurement at baseline and at follow-up at 7 and 26 weeks)

At baseline, a range of demographic and clinical variables commonly queried in daily clinical practice were measured, including age, gender, previous symptoms, pain, functioning and general physical and mental health, and comorbidity. The psychosocial variables that are the main focus of this study were also measured at baseline with the exception of expectancy and credibility, which were measured after the first treatment session. The reasoning behind this choice was that we wished to measure ‘well-informed’ expectancies and credibility, and therefore preferred to perform this measurement only after the treatment rationale had been explained to the patient. Follow-up assessments were carried out at 7 (short-term) and 26 weeks (long-term) after baseline (as ‘standard’ measurement moments in the randomized controlled trial primarily focused on effectiveness), with follow-up questionnaires that contained a measure of general perceived effect, the neck disability index and the numerical rating scale pain.

2.5 Psychosocial factors

2.5.1 Credibility Expectancy Questionnaire (CEQ)

The CEQ uses two subscales (each scale has three questions, scored 1–9) that measure the credibility and outcome expectancies regarding a proposed treatment.The questionnaire shows good internal consistency and test-retest reliability [34]. Sum scores for each scale range from 3 to 27; the higher the score, the higher the expected outcome or the more credible the treatment is to the patient. Smeets et al. [25] translated this questionnaire into Dutch and validated and confirmed the two factor structure (credibility/expectancy) [25].

2.5.2 Multidimensional Health Locus of Control (MHLC)

This questionnaire contains 18 items concerning beliefs about responsibility for health. Items are scored on a six-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’. The three subscales of the questionnaire (6 items each, scoring between 6 and 36 for each subscale) are: (1) Internal health Locus Of Control (ILOC), where individuals feel that events are more under their own control; (2) External health Locus Of Control (ELOC), where other people such as caregivers and family play a major role in determining patients’ health; and (3) the Chance Locus Of Control (CLOC), where people believe that their health is affected by chance or luck [51]. Higher scores represent greater control. This questionnaire has been tested in different patient populations and has shown a satisfactory internal consistency [52]. The three dimensions are relatively independent of each other [51].

2.5.3 Fear Avoidance Belief Questionnaire – Dutch version (FABQ-DV)

This questionnaire focuses on pain-related fear in patients using fear avoidance components. The FABQ includes 16 items, which are answered on a 7-point Likert scale. Besides a total score, there are also two (Physical Activity and Work) subscale scores. Summary scores can be a calculated score for the entire questionnaire (range: 0–96), or for each of the separate subscales Physical Activity (5 items, range 0–30) and Work (11 items, range 0–66). Higher scores represent higher fear avoidance beliefs. Cronbach’s alpha (0.90–0.97) and test-retest reliability (0.81–0.93) were good [53, 54].

2.6 Clinical variables

2.6.1 Neck Disability Index – Dutch version (NDI-DV)

The NDI-DV consists of 10 items related to daily functioning, each with six possible answers (scored 0–5). The maximum score is 50 and a higher score indicates more restrictions. The reliability, validity and responsiveness [55, 56] of the NDI are good. The Minimal Clinically Important Change (MCIC) on NDI-DV is 3.5 [56, 57] or 30% [58].

2.6.2 Numeric Rating Scale on Pain (NRS-P)

The NRS-P is a single item scale (11 points, from 0 for no pain to 10 for maximum pain) with good psychometric properties that measures the intensity of pain that the patient has experienced over the past week [59, 60]. The NRS-P is a sensitive instrument and is similar to the VAS [61, 62]. The Minimal Clinically Important Change (MCIC) of the NRS-P in patients with neck pain is 2.5 points [56] or 30% [58].

2.6.3 Short Form-36 (SF-36)

The general health questionnaire (SF36) was used to obtain a detailed health profile. The eight domains of the SF36 can be summarized into physical and mental component scores.The Dutch translation showed satisfactory validity and reproducibility [63].

2.7 Dependent (outcome) variables measured at 7 and 26 weeks

2.7.1 Global Perceived Effect (GPE)

Global Perceived Effect was measured with one item, scored on a 7-point ordinal scale (‘To what extent are your complaints changed when compared with the situation just before you stared treatment?’; answers ranging from “much worse” to “full recovery”). Test-retest reliability of the GPE is excellent [64]. The GPE was dichotomized in success and absence of success. On the 7-point scale, the answers ‘fully recovered’ and ‘much improved’ were, in line with previous studies [58,65,66,67], recoded as ‘success’, whereas ‘somewhat improved’ ‘no change’, ‘slightly worse’, ‘much worse’, and ‘worse than ever’ indicated absence of treatment success. This dichotomization seems to reflect the concept of clinical relevance, as “slightly improved” can reflect a socially desirable answer [65, 68, 69].

2.7.2 Neck Disability Index – Dutch version (NDI-DV)

For follow-up measurements we dichotomized the NDI based on a clinically relevant change whereby a 30% improvement in scores was defined as a cut-off point to indicate success.

2.7.3 Numeric Rating Scale on Pain (NRS-P)

Similar to the NDI-DV and the literature, the NRS-P was dichotomized by defining a 30% improvement as a cutoff point to indicate success.

2.8 Statistical analyses

Demographic variables such as gender and age, clinical variables such as duration and severity of symptoms, and comorbidity were described as percentage or mean (standard deviation – SD). As clustering by treatment center or intervention was not present, as demonstrated in the primary analyses of these data (Groeneweg, submitted), a multi-level approach was not employed in the current analyses.

To determine the added value of the psychosocial variables, a series of hierarchical logistic regression analyses were performed on the dependent variables ‘global perceived effect’, ‘function’ and ‘pain’ at 7 and 26 weeks.

In the first step, associations between baseline characteristics (i.e. demographic and clinical variables as well as the psychosocial baseline characteristics) and outcome (GPE, functioning and pain) were explored using Pearson’s correlation coefficients for continuous variables and Spearman correlation coefficients for categorical or dichotomous variables, whereby the variables with p ≤ 0.15 were included in the model. In the second step, candidate demographic and baseline clinical variables were selected and entered as fixed block 1 in the regression models based on the P-value (P ≤ 0.15) of their correlation with the outcome variables. The rationale for starting with these variables is that they are common factors included in history taking in daily clinical practice. The third step involved separately entering the subscales of the psychosocial variables in block 2 of the model; respectively, treatment credibility, treatment outcome expectancy, internal locus of control, external locus of control, chance locus of control, fear avoidance beliefs subscale work and subscale physical activity. Thus, seven hierarchical logistic regression analyses were performed for three outcome variables, for both follow-up measurements, resulting in a total of 42 regression models.

In the fourth step, psychosocial factors that showed statistically significant predictive value in the hierarchical regression analyses were explored in further analyses. These were performed to investigate the importance of these factors relative to the other psychosocial factors from the same measurement tool. This was done by adding the psychosocial variables in block 2 and 3 (and if necessary further blocks) to the hierarchical logistic regression model. The order of input of the respective psychosocial variables in the blocks was performed separately. The change in the R2 value between the blocks indicates the relative contribution of the variables in each block to the variance in outcome.

Finally, all procedures were performed with adjustment for intervention type. Possible interactions between the psychosocial variables and intervention type (MT vs. PT) were explored in all analyses by adding an interaction term. If significant interaction occurred, further analyses were carried out, stratified for type of intervention.

Finally, for individual subscales with relevant predictive value, the sensitivity and specificity of the subscales were calculated and plotted on a Receiver Operating Characteristic (ROC) curve. An area under the curve (AUC) of at least 0.7 was seen as an acceptable level at which to determine a cut-off point. The point on the curve closest to the upper left corner represents the value with the best diagnostic accuracy, and this point was selected as the cut-off point for a positive test [70].

The missing values of this study were treated with listwise deletion. All statistical analyses with treatment as an interaction term were performed according to the intention-to-treat principle: all patients, including dropouts from treatment, remained in the group to which they were randomized. Data were analyzed with IBM Statistics SPSS 21.

3 Results

Sixteen primary care practices participated in the trial, and a total of 181 patients were included. The mean age was 49 years (SD = 12.5), and approximately 62% were women. Most patients (67%) had previous episodes of neck pain and the majority had multiple musculoskeletal complaints (see Table 1). No differences were found between the two intervention groups at baseline. The scores on the treatment credibility and treatment outcome expectancy subscale of the CEQ were high (around 22 out of a maximum of 27). At 7 weeks, 3.9% of responses were missing, while 12.2% were missing at 26 weeks. There were no relevant differences on baseline measurements between responders and non-responders.

Table 1

Baseline characteristics of the study population (N = 181).

Gender, female % (N) 61.9 (112)
Age, years, mean (SD) 49.0 (l2.5)
Complaints thoracic spine (yes, %) 23.2
Complaint lumbar spine (yes, %) 16.6
First time neck complaints (yes, %) 66.9
Last Year visit any therapist for neck (yes, %) 40.3
Intervention (MTU, %) 49.7
Measurements, mean (SD)
 NDI 12.1 (6.2)
 NPRS 5.7 (1.9)
 CEQ-Expectancy 20.8 (3.9)
 CEQ-Credibility 22.2 (3.3)
 FABQ-Total 29.8 (15.7)
 FABQ-Physical Activity 15.1 (5.5)
 FABQ-Work 14.7 (12.8)
 MHLC-Intern 19.8 (4.7)
 MHLC-extern 25.6 (5.0)
 MHLC-chance 24.2 (4.2)
 SF-36-PCS 44.6 (7.5)
 SF-36-MCS 46.8 (11.3)
 Complain intensity main complaint (NRS) 6.9 (1.3)
  1. N, number; SD, standard deviation; MTU, Manual Therapy Utrecht; NDI, Neck Disability Index; NPRS, Numeric Pain Rating Scale; CEQ, Credibility Expectancy Questionnaire; FABQ, Fear Avoidance Questionnaire; MHLC, Multidimensional Health Locus of Control; SF, Short Form; PCS, Physical Component Scale; MCS, Mental Component Scale.

Based on the correlations of demographic and clinical variables with outcomes (step one), the variables age, baseline functioning, and pain were selected for block 1 of the hierarchical regression analyses.

Tables 2a and 2b show the results of the hierarchical logistic regression models on treatment outcome success. At both followup measurements, treatment credibility added 3.4% to the variance explained by GPE as an outcome, in addition to contributions from demographic and clinical variables. ‘Functioning’ added 5.9% to the explained variance at 7 weeks and 11.2% at 26 weeks, while ‘pain’ added 1.8% at 7 weeks and 2.7% at 26 weeks.

Table 2a

Summary hierarchical logistic regression analysis of added values of the psychosocial variables at 7 weeks on treatment outcome success.

Model steps and entered variables Stand. β S.E. Sig. Nagelk. R2 Added values
GPE 7 weeks
 Block 1, N = 146 .031
  Age −.010 .013 .468
  NRS-P baseline .112 .104 .276
  NDI baseline −.056 .032 .078
 Block 2, respectively
  A. Credibility .103 .052 .047[**] .065 .034
  B. Expectancy .206 .055 .000[**] .166 .135
 Block 1, N = 157 .030
  Age −.008 .013 .521
  NRS-P baseline .118 .099 .233
  NDI baseline −.057 .031 .071
 Block 2, respectively
  C. Internal locus of control −.037 .033 .261 .036
  D. External locus of control .040 .031 .194 .040
  E. Chance locus of control .024 .037 .518 .033
  F. FAB – Physical Activity −.026 .028 .354 .029
  G. FAB – Work −.007 .012 .541 .026
NDI 7 weeks
 Block 1, N = 146 .061
  Age −.026 .015 .080
  NRS-P baseline .188 .113 .099
  NDI baseline −.021 .033 .530
 Block 2
  A. Credibility .140 .015 .013[**] .120 .059
  B. Expectancy .241 .061 .000[**] .234 .173
 Block 1, N = 155 .028
  Age −.019 .014 .197
  NRS-P baseline .114 .107 .287
  NDI baseline −.011 .032 .732
 Block 2, respectively
  C. Internal locus of control −.044 .035 .219 .044
  D. External locus of control −.023 .034 .497 .035
  E. Chance locus of control −.019 .040 .642 .033
  F. FAB – Physical Activity −.025 .032 .441 .033
  G. FAB – Work −.017 .016 .289 .038
NRS-P 7 weeks
 Block 1, N = 146 .060
  Age .001 .014 .964
  NRS-P baseline .264 .107 .964
  NDI baseline −.066 .032 .039
 Block 2
  A. Credibility .075 .051 .142 .078 .018
  B. Expectancy .127 .049 .009[**] .120 .060
 Block 1, N = 157 .052
  Age .002 .013 .857
  NRS-P baseline .242 .120 .017
  NDI baseline −.062 .031 .048
 Block 2
  C. Internal locus of control −.036 .034 .284 .060
  D. External locus of control .063 .033 .057 .079
  E. Chance locus of control .031 .038 .637 .057
  F. FAB – Physical Activity −.033 .032 .293 .055
  G. FAB – Work −.017 .015 .983 .057
  1. Stand. β, standardized beta; S.E., standard error; Sign., significant; Nageik. R2, Nageikerke R square; N, number; NDI, Neck Disability Index; NRS-P, Numeric Rating Scale for Pain; FAB, Fear Avoidance Beliefs. A. Hierarchical logistic regression analysis with age, NRS-P baseline and NDI baseline in block 1, and credibility in block 2; B. Hierarchical logistic regression analysis with age, NRS-P baseline and NDI baseline in block 1, and expectancy in block 2; C. Same block 1, and Internal locus of control in block 2; D. Same block 1, and external locus of control in block 2; E. Same block 1, and chance locus of control in block 2; F. Same block 1, and FAB-Physical Activity in block 2; G. Same block 1, and FAB-Work in block 2.

Table 2b

Summary hierarchical logistic regression analysis of added values of the psychosocial variables at 26 weeks on treatment outcome success.

Model steps and entered variables Stand. β S.E. Sig. Nagelk. R2 Added values
GPE 26 weeks
 Block 1, N = 146 .116
  Age −.046 .015 .003
  NRS-P baseline .028 .114 .806
  NDI baseline −.071 .037 .055
 Block 2, respectively
  A. Credibility .115 .058 .046[**] .150 .034
  B. Expectancy .187 .057 .001[**] .217 .101
 Block 1, N = 157 .100
  Age −.041 .015 .005
  NRS-P baseline .010 .108 .0926
  NDI baseline −.069 .036 .054
 Block 2, respectively
  C. Internal locus of control −.042 .036 .241 .111
  D. External locus of control .006 .034 .855 .101
  E. Chance locus of control −.035 .041 .399 .106
  F. FAB – Physical Activity −.011 .033 .740 .098
  G. FAB – Work .009 .017 .579 .100
NDI 26 weeks
 Block 1, N = 146 .008
  Age .002 .016 .902
  NRS-P baseline .095 .122 .434
  NDI baseline −.031 .039 .428
 Block 2, respectively
  A. Credibility .210 .064 .001[**] .120 .112
  B. Expectancy .239 .064 .000[**] .170 .162
 Block 1, N = 157 .013
  Age .000 .015 .994
  NRS-P baseline .110 .118 .354
  NDI baseline −.044 .039 .258
 Block 2, respectively
  C. Internal locus of control −.032 .038 .411 .020
  D. External locus of control −.009 .038 .812 .014
  E. Chance locus of control .012 .044 .793 .014
  F. FAB – Physical Activity −.027 .037 .462 .017
  G. FAB – Work −.012 .018 .497 .016
NRS-P 26 weeks
 Block 1, N = 146 .087
  Age .006 .016 .713
  NRS-P baseline .321 .125 .010
  NDI baseline −.104 .040 .009
 Block 2, respectively
  A. Credibility .103 .061 .091 .114 .027
  B. Expectancy .158 .057 .006[**] .162 .075
 Block 1, N = 157 .085
  Age .003 .015 .824
  NRS-P baseline .311 .119 .009
  NDI baseline −.106 .039 .007
 Block 2, respectively
  C. Internal locus of control −.036 .038 .340 .093
  D. External locus of control .030 .037 .418 .091
  E. Chance locus of control .006 .045 .900 .086
  F. FAB – Physical Activity −.070 .038 .061 .112
  G. FAB – Work −.026 .017 .125 .102
  1. Stand. β, standardized beta; S.E., standard error; Sign., significant; Nageik. R2, Nageikerke R square; N, number; NDI, Neck Disability Index; NRS-P, Numeric Rating Scale for Pain; FAB, Fear Avoidance Beliefs. A. Hierarchical logistic regression analysis with age, NRS-P baseline and NDI baseline in block 1, and credibility in block 2; B. Hierarchical logistic regression analysis with age, NRS-P baseline and NDI baseline in block 1, and expectancy in block 2; C. Same block 1, and Internal locus of control in block 2; D. Same block 1, and external locus of control in block 2; E. Same block 1, and chance locus of control in block 2; F. Same block 1, and FAB-Physical Activity in block 2; G. Same block 1, and FAB-Work in block 2.

Treatment outcome expectancy added 13.2% (7 weeks) and 10.0% (26 weeks) to the explained variance in GPE. In functioning treatment outcome expectancy added either 17.7% (7 weeks) or 16.2% (26 weeks) to the explained variance, and in pain added 6.0% (7 weeks) and 7.5% (26 weeks). When treatment outcome expectancy was added to the models in block 2 and credibility in block 3, further analyses showed that credibility did not significantly add to the explained variance in any of the outcomes. Thus, treatment outcome expectancy is the variable with the highest additional predictive value when added to demographic and clinical variables. Health locus of control and fear avoidance beliefs did not add to the explained variance in any of the outcomes at 7 or 26 weeks.

There were no significant interactions between the intervention and psychosocial variables in any of the models of the outcome measures (GPE, functioning and pain) at either measurement moment (7 and 26 weeks), other than an interaction between intervention and credibility in the model in which functioning at 7 weeks was the dependent variable. MT credibility showed an explained variance of 32.9%, compared to 6.5% in the PT group.

A Receiver Operating Characteristics (ROC) curve was created to assess the most optimal cut-off point (based on the lowest overall misclassification) to predict treatment success on the psychosocial subscales that showed relevant predictive value in the hierarchical logistic regression analysis. Only the CEQ subscale expectancy showed an area under the curve (AUC) of at least 0.7 on GPE and functioning (NDI) at both 7 and 26 weeks. The cut-off value on this scale was 22.5 points (out of 27). For GPE and functioning, the optimal cut-off point for sensitivity was 0.6 at 7 and 26 weeks, while for specificity at 7 and 26 weeks it was 0.6 for GPE and 0.7 for functioning. For all other (sub) scales and time points the AUC was less than 0.7.

4 Discussion

A patient’s expectancy regarding treatment outcome appears to be of added predictive value in treatment success (measured at 7 and 26 weeks follow-up in terms of global perceived effect, functioning and pain), in addition to demographic and clinical variables in patients with non-specific sub-acute and chronic neck pain. Treatment credibility showed no additional predictive value above and beyond expectancy. Health locus of control and fear avoidance beliefs at baseline were not predictive for treatment success.

To the best of our knowledge this is the first time that these factors have been analyzed in combination with demographic and clinical variables. The results of this study are in line with previous literature on other musculoskeletal conditions, which indicated that expectancy, in particular, shows a positive relationship with treatment outcomes [19, 20, 22, 23]. It should be noted that, in contrast to the present study, these earlier studies examined the primary treatment outcome without the addition of other variables. In the few studies that have investigated the predictive value of treatment outcome expectations fortreatment of neck pain [23, 71], in agreement with our findings, treatment outcome expectancy was also found to have greater predictive value than credibility.

Reports on the predictive value of locus of control on treatment outcome have varied, with some studies reporting a significant relationship with treatment outcome [36, 38, 39, 40, 41, 72], while others found no relationship [73]. However, none of these studies examined these variables as added values. The differences in results may be partly explained by the fact that we did not categorize subscales scores for the MHLC into high and low scores, as suggested by others [40, 74].

Although fear avoidance beliefs are frequently reported to predict treatment outcome [44, 48, 75, 76, 77], this was not confirmed by our study. This may be attributable to the fact that our study was not limited to patients with chronic neck pain, which is the patient category on which the fear avoidance belief model is based [78]. We also found relatively low baseline scores for fear avoidance, possibly explaining the limited predictive value for outcome. Furthermore, the fact that in this study we focused on the additional value of fear avoidance beliefs over and above demographic and clinical variables may explain why no predictive value was found in our study.

There were also no interaction effects between the intervention and psychosocial variables.

4.1 Limitations

Some aspects of this study may somewhat hamper generalizability of the results. As the participants in the study agreed to volunteer in a randomized controlled trial, the characteristics and outcome expectancy of these patients may differ from those that declined to participate. Indeed we observed, compared to other studies [79], that patients in our study had a relatively high treatment expectancy (22 out of 27, with a relatively small standard deviation). Furthermore, our study results have to be interpreted in light of the specific interventions employed in the study. The predictive value of treatment outcome expectancy may be different for other interventions (and possibly for other musculoskeletal complaints).

The moment at which treatment outcome expectancy should be measured is a matter of debate. Measurement can take place ‘naïve’, before the start of the intervention, or after the first treatment session when the rationale has been explained to the patient. Measurement may also take place after the intervention, to evaluate whether outcome expectancy has been met. Although the impact of these various measurement moments on expectancy is not understood, research in primary care patients with low back pain has shown that outcome expectancies do not change over time for the majority of patients, at least during the first 3 months of their treatment [80].

There were no significant interactions between psychosocial variables and the interventions on any of the outcome measures or measurement moments. This may have been (partly) due to the possibility that, as a secondary analysis, the study was underpowered to detect interaction. The only exception was credibility at 7 weeks on the outcome functioning, with a greater predictive value of credibility for manual therapy in comparison with physical therapy. The explanation for this difference in predictive value may be that the rationale of manual therapy, as explained to the patient, is aimed at optimizing (arthrogenic) function. Consequently, the patient may, hypothetically, focus on the function aspect of treatment outcome and the rationale as explained may have a greater impact on credibility than on outcome expectancy. The response expectancy theory states that a person’s expectations will affect their experiences, which could explain why treatment credibility, closely related to expectancy, is a predictive variable specifically for outcome functioning in patients treated with MT. This link may weaken by 26 weeks, perhaps explaining why credibility is no longer a predictive variable for MT.

4.2 Practical implications and suggestions for further research

Our finding that a patient’s treatment outcome expectancy is a predictor of outcome when added to clinical and demographic variables is in line with explanations of treatment effects based on the biopsychosocial model. Psychological processes, health perceptions and how these factors relate to clinical variables may be important for treatment decision making regarding therapeutic options for individual patients. It has been demonstrated in experiments with healthy subjects that expectancy is modifiable and can be used as a potential non-specific/placebo component of an intervention [26, 31, 32]. On the basis of our results it is of interest to investigate this potential modifiability in clinical practice and its influence on outcome success.

In this study the success/no success cut-off point for the CEQ subscale treatment outcome expectancy was found to be 22.5 points for the outcomes perceived recovery and functioning. Patients with a score of 22 points or less on the CEQ subscale outcome expectancy have a greater chance of worse outcomes. In such a case, the practitioner could focus specific attention on a patients’ outcome expectancy. However, these figures should be interpreted with caution given the limited power of the study and the specific nature of expectations regarding interventions that have been applied.

In future studies on the predictive value of psychosocial aspects, in addition to clinical symptoms, a broader set of patient characteristics should be taken into account (e.g. referral or self-referral, patient’s socioeconomic status). Therapist characteristics must also be considered in any analysis, in order to clarify the parameters determining treatment outcome expectancy and credibility. Explaining the treatment rationale may also affect credibility and outcome expectancy, for example through cooperation with the patient and therapist empathy. Another suggestion for future research is to query expectancy in a more outcome-specific (e.g. on pain or functioning) way, in order to achieve better predictions for these outcomes.

5 Conclusion

Based on the results of this study we conclude that outcome expectancy, in patients with non-specific sub-acute and chronic neck pain, has additional predictive value for treatment success above and beyond clinical and demographic variables.

Highlights

  • Psychosocial variables may influence outcome of treatment of neck pain.

  • Treatment expectancy predicted success in neck pain added to clinical variables.

  • Health locus of control and fear avoidance beliefs had no additional prediction.


DOI of refers to article: http://dx.doi.org/10.1016/j.sjpain.2016.10.006.



De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.

  1. Ethical issues: Ethical approval was obtained from the Medical Ethics committee CMO Arnhem-Nijmegen (NL21128.091.08). The clinical trial was registered: ClinicalTrials.gov Identifier: NCT00713843. All patients signed informed consent.

  2. Conflict of interest: There are no conflicts of interests.

Acknowledgements

We would like to thank Luite van Assen, Hans Kropman and Huco Leopold for their contribution to setting up the effectiveness study (the NECKproject), Joke Laphor for her help collecting data and Lilian Lechner for reading and correcting the manuscript. The initial effectiveness study was partially funded by Stichting Gezondheidszorg Spaarneland (Healthcare Foundation Spaarneland), The Netherlands. They had no role in designing, collecting or analyzing data, or in drafting the article.

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Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.sjpain.2016.10.003.

Received: 2016-09-01
Accepted: 2016-10-07
Published Online: 2017-01-01
Published in Print: 2017-01-01

© 2016 Scandinavian Association for the Study of Pain

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