Abstract
Cognitive flexibility – the ability to restructure one’s knowledge, incorporate new facts, widen perspective, and adapt to the demands of new and unexpected conditions - can help one adapt to illness. The aim of this study was to assess the relationship between cognitive flexibility and hand and upper extremity specific disability in patients presenting to a hand surgeon. Secondarily, we determined predictors of cognitive flexibility and pain. Eighty-nine consecutive outpatients completed the Cognitive flexibility questionnaire (CFS), Short Health Anxiety Inventory-5 (SHAI-5), Pain Self-Efficacy Questionnaire (PSEQ), Disabilities of Arm, Shoulder and Hand, short form (QuickDASH), and Patient Health Questionnaire for Depression-2 (PHQ-2) in a cross-sectional study. CFS did not correlate with disability or pain intensity. Disability correlated with PSEQ (r = −0.66, p < 0.01), PHQ-2 (r = 0.38, p = <0.01), and SHAI-5 (r = 0.33, p < 0.01). Pain intensity correlated with PSEQ (r = −0.51 p < 0.01) and PHQ-2 (r = 0.41 p < 0.01). There was a small correlation between the CFS and PSEQ (r = 0.25, p = 0.02). The best multivariable models for QuickDASH and pain intensity included PSEQ and PHQ and explained 35 % and 28 % of the variability respectively. Upper extremity specific disability and pain intensity are limited more by self-efficacy than cognitive flexibility. Interventions to improve self-efficacy might help patients with upper extremity illness.
Keywords: Cognitive flexibility scale, Pain intensity, Disability, Upper extremity, Self-efficacy
Introduction
Symptoms of depression and coping strategies are known correlates of pain intensity, magnitude of disability, and patient satisfaction.[1–5] More adaptive patients report less pain and disability irrespective of the diagnosis or impairment.[1, 5–9] Recent developments from the field of positive psychology emphasize the importance of cognitive flexibility– the ability to restructure one’s knowledge, incorporate new facts, widen perspective, and adapt to the demands of new and unexpected conditions–[10, 11] for problem solving and adaptation to life’s demands. Martin and Rubin describe adaptation as a process of social cognition during which one becomes aware of options, becomes flexible, and experiences increased self-efficacy.[12–14] This implies that adaptation and resilience—key aspects of good health—require cognitive flexibility. As such, we reasoned that cognitive flexibility may be important in adapting to medical conditions including hand and upper extremity illness.
Previous work showed that cognitive flexibility correlates with some types of psychopathology.[15–17] For instance, low cognitive flexibility according to the Wisconsin Card Sorting Task correlated with severity of eating disorder.[16, 18] Studies of cognitive flexibility have been conducted with neuropsychological assessments and primarily in school-aged children.[19, 20] To our knowledge, no prior studies assessed the relationship between self-reported cognitive flexibility and medical illness.
Our interest in cognitive flexibility arose from our observation that patients who are cognitively inflexible and tend to hold strongly to their first impressions (intuitions, gut feelings, cognitive errors) in response to symptoms and impairment--in spite of expert advise to the contrary--have greater disability and pain intensity and can take longer to recover from upper extremity musculoskeletal illness. [5, 21–24] It appears that these patients are unable to negotiate and integrate the information presented to them. Rather, they seem prone to confirmatory bias, disregarding information that does not confirm their beliefs, and get stuck in a pattern of negative thinking. This is manifested in the strong relationship of catastrophic thinking and low self-efficacy to pain intensity and magnitude of disability in patients with arm illness.[5, 21–24].
Kahneman’s simplification of these aspects of human thought into system 1 (human heuristics or the intuitive system) and system 2 (analytical calculation or reconsideration of first impressions) is useful.[25] System 1 searches for causality; it creates coherent interpretations quickly and unconsciously based on emotions, previous experiences/information and memories. System 2 is much slower and has to be activated. It involves conscious judgment based on critical thinking and examination; it is rational and analytical. System 1 has a larger capacity and is generally more effective in day-to-day living. Employing system 2 in simple activities of daily living would be tiring.[25] However, in many situations that involve decisions with important consequences, system 2 is much better equipped to give the best solutions.
Kahneman’s theory has many applications to illnesses and the decisions patients make with regard to their medical care. In patients with pain, it can be theorized that those who are cognitively inflexible are employing the intuitive system 1 rather than the analytical system 2 when faced with musculoskeletal conditions. In other words, they go with their first impressions and are not able to look at the evidence presented and analytically assess the information presented to them. Because system 1 is unable to process complex information, patients get stuck in the normal protective response to pain (where we tend to prepare for the worse) and are unable to adapt. In contrast, patients who are cognitively flexible may be more capable of adaptation and may employ the analytical system 2, “rethink” the normal protective response to pain, and regain trust in their body, which is the essence of good health.
The purpose of this study was to assess the relationship between cognitive flexibility and hand and upper extremity specific disability in patients presenting to an orthopedics hand and arm practice. We also studied psychosocial factors associated with cognitive flexibility and pain intensity that could potentially be used to eventually develop mind body interventions for hand surgery patients.
Material and Methods
After approval of our institutional review board, all non-pregnant, English speaking new and follow-up patients aged 18 years or greater were asked to participate in this cross-sectional study at a tertiary care institution. The patients were recruited from an office with three hand surgeons (the majority from just one of the surgeons—the one who more consistently allows his patients to participate in research) in a tertiary care urban hospital in the United States where most patients are referred directly from a primary care network. The enrollment was random and based on the availability of researchers working on this project and competition with other active projects. The doctor and study staff described the study details and informed consent was obtained.
Eighty-nine patients were enrolled, but 5 decided not to participate while completing questionnaires, due either to time constraints (4 patients) and or lack of interest (1 patient). [25] Patients completed a survey of demographics and the following questionnaires: Cognitive Flexibility Scale (CFS), Short Health Anxiety Inventory-5 (SHAI-5), Pain Self-Efficacy Questionnaire (PSEQ), Disabilities of Arm, Shoulder and Hand, short form (QuickDASH) and Patient Health Questionnaire for Depression-2 (PHQ-2).
Measurement Tools
The CFS is a validated tool to measure patients’ ability to adapt to new situations, awareness of different alternatives, and readiness to adapt to different alternatives and be flexible. [26–28] The questionnaire consists of 12 questions answered on 6-point Likert scales ranging from strongly agree to strongly disagree. Scores range from 12 to 72, with a higher score implying higher cognitive flexibility.[14] In one case, individual mean imputation for a singular missing item was used to calculate that individual’s CFS. We had one missing values on the CFS. We used mean imputation to complete this missing value.
The SHAI-5 questionnaire is a validated shortened 5-item version of the SHAI-18.[29, 2] Scores range from 0 to 15, with a higher score indicating greater health anxiety [29, 2].
The PSEQ is a 10-item patient-reported outcome inventory. The PSEQ assesses a patient’s confidence and ability to accomplish their daily activities despite the pain.[28, 29] The questions are scored on a 7-point Likert scale ranging from 0 (“not at all confident”) to 6 (“completely confident”). The outcome score is calculated by adding up the items on a scale ranging from 0 to 70, with a higher score indicating greater self-efficacy. For missing values mean imputation was used.
The QuickDASH was used to measure upper extremity specific disability.[30] This questionnaire is the shortened version of the DASH.[30] The original DASH questionnaire is a thirty-item questionnaire.[31] The QuickDASH is comprised of 11 questions, which each are answered on a 5-point Likert scale. The score is scaled to a value between 0 (no disability) to 100 (most severe disability).[32] The QuickDASH is not valid if more than one question is missing.
The PHQ-2 was used to assess symptoms of depression.[33, 34] The PHQ-2 is a shortened 2-item questionnaire and is comprised of the first 2 questions of the PHQ-9. It has been validated in prior studies. It consists of two questions on a 4-point Likert scale between 0 “not at all” and 3 “nearly every day” assessing depression and anhedonia. The overall score ranges from 0 to 6 [35].
Patients rated their pain using the Numeric Rating Scale (an 11-point ordinal scale from 0, no pain to 10, the worst imaginable pain) [36].
Statistical Analyses
A priori power analysis for our primary study question determined that 84 patients would provide 80 % power to detect a 0.30 (medium) correlation between the CFS and QuickDASH. The data was not normally distributed according to the Kolomogorov-Smirnov test and therefore non-parametric tests were used. The Spearman correlation was used to assess the relationship between continuous variables, the Mann–Whitney U test was performed to test the relationship between continuous and dichotomous variables, and the Kruskal-Wallis test was done to determine the relationship between categorical variables with more than two categories and continuous variables.
Variables with p-value < 0.10, were inserted in a backward, stepwise, multivariable linear regression analysis to find predictors of the QuickDASH score. When categorical variables were inserted in multivariable analysis dummy codes were generated when there were more than two categories.
We accounted for potential confounders such as demographics, anxiety, self-efficacy, physical function and depression.
Results
The mean age of the 84 patients that completed the study was 45 years (SD = 16, range 19–94 years) and 43 patients (51 %) were men. Forty-eight (57 %) patients had acute injuries, 10 (12 %) carpal tunnel syndrome, and 26 (31 %) other discrete diagnoses (Table 1).
Table 1.
Demographics
| n = 84 | |||
|---|---|---|---|
| Parameter | |||
| Mean | SD | Range | |
| Age (y) | 45 | 16 | 19–94 |
| Education (y of School) | 15 | 2.9 | 9–22 |
| Number | % | ||
| Sex | |||
| Male | 41 | 49 | |
| Female | 43 | 51 | |
| Marital status | |||
| Single | 27 | 32 | |
| Living with partner | 1 | 1.2 | |
| Married | 46 | 55 | |
| Separated/Divorced | 7 | 8.3 | |
| Widowed | 3 | 3.6 | |
| Work status | |||
| Working full time | 51 | 61 | |
| Working part time | 8 | 10 | |
| Homemaker | 1 | 1.2 | |
| Retired | 10 | 12 | |
| Unemployed, able to work | 3 | 3.4 | |
| Unemployed, unable to work | 10 | 12 | |
| Workers compensation | 1 | 1.2 | |
| Physician | |||
| Surgeon 01 | 2 | 2.4 | |
| Surgeon 02 | 5 | 5.9 | |
| Surgeon 03 | 71 | 85 | |
| Other | 6 | 7.1 | |
| Diagnosis Group | |||
| Acute injuries | 46 | 55 | |
| Closed tendon injury | 8 | 17 | |
| Fractures | 28 | 61 | |
| Laceration | 10 | 22 | |
| Non-specific arm pain | 1 | 1.2 | |
| Trigger finger | 4 | 4.8 | |
| Carpal Tunnel Syndrome | 10 | 12 | |
| Ganglion | 6 | 7.1 | |
| Osteoarthritis | 4 | 4.8 | |
| Tendinitis | 2 | 2.4 | |
| Lateral epicondylosis | 3 | 3.6 | |
| Rotator cuff tendinosis | 1 | 1.2 | |
| Giant cell tumor | 2 | 2.4 | |
| Ligament deficiency | 1 | 1.2 | |
| Other | 4 | 4.8 | |
| Type of patient | |||
| New patient | 35 | 42 | |
| Follow-up | 33 | 39 | |
| Post operative followup | 16 | 19 | |
| Sougth treatment for this condition before | |||
| Yes | 28 | 33 | |
| No | 56 | 67 | |
Patients had moderate hand and upper extremity disability on average as measured by the QuickDASH (Table 2). QuickDASH correlated with PSEQ, PHQ, SHAI-5 and marital status, but not with cognitive flexibility. (Table 3) The best multivariable model included PSEQ alone and explained 35 % of the variability in QuickDASH (adjusted R-squared: 0.35, p < 0.01).
Table 2.
Health related parameters at enrollment
| n = 84 | |||
|---|---|---|---|
| Parameter | Initial enrollment | ||
| Mean | (±SD) | Range | |
| CFS | 63 | 6.3 | 45–72 |
| Quick DASH | 32 | 20 | 0–84 |
| Pain | 3.6 | 1.3 | 0–10 |
| PSEQ | 50 | 11 | 6–60 |
| PHQ | 0.7 | 1.2 | 0–4.0 |
| SHAI-5 | 4.0 | 2.3 | 0–12 |
CFS, Cognitive Flexibility Scale; SHAI-5, Short Health Anxiety Inventory-5; PSEQ, Pain Self-Efficacy Questionnaire; QuickDASH, Disabilities of Arm, Shoulder and Hand, short form; PHQ-2, Patient Health Questionnaire for Depression-2
Table 3.
Bivariate analyses
| Quick DASH | Pain | |||
|---|---|---|---|---|
| Parameters at enrollment | r | p-value | r | p-value |
| CFS | −0.053 | 0.63 | −0.11 | 0.32 |
| Age | 0.078 | 0.48 | 0.13 | 0.24 |
| Education | −0.083 | 0.45 | −0.18 | 0.095 |
| PSEQ | −0.66 | <0.01 | −0.52 | <0.01 |
| PHQ | 0.38 | <0.01 | 0.41 | <0.01 |
| SHAI-5 | 0.33 | <0.01 | 0.19 | 0.09 |
| Duration of injury | −0.022 | 0.84 | 0.05 | 0.63 |
| Sex | Mean (SD) | p-value | Mean (SD) | p-value |
| Male | 34 (±20) | 0.45 | 3.7 (±2.5) | 0.50 |
| Female | 31 (±21) | 3.4 (±2.6) | ||
| Marital status | ||||
| Single | 32 (±20) | 0.06 | 3.7 (±2.6) | 0.017 |
| Living with partner | 11 (±0.0) | 1.0 (0.0) | ||
| Married | 29 (±19) | 3.0 (±2.3) | ||
| Separated/Divorced | 46 (±26) | 6.4 (±2.2) | ||
| Widowed | 54 (±8.0) | 5.3 (±2.3) | ||
| Work status | ||||
| Working full time | 30 (±19) | 0.25 | 3.1 (±2.4) | 0.47 |
| Working part time | 46 (±23) | 4.5 (±2.3) | ||
| Homemaker | 39 (±0.0) | 2.0 (±0.0) | ||
| Retired | 36 (±25) | 4.5 (±2.5) | ||
| Unemployed, able to work | 22 (±15) | 4.3 (±2.5) | ||
| Unemployed, unable to work | 37 (±17) | 4.2 (±3.1) | ||
| Others | 9.0 (±0.0) | 2.0 (±0.0) | ||
| Diagnosis | ||||
| Acute injuries | 31 (±21) | 0.86 | 3.0 (±2.5) | 0.63 |
| Closed tendon injury | 29 (±18) | 4.4 (±2.4) | ||
| Fractures | 34 (±22) | 3.1 (±2.4) | ||
| Laceration | 25 (±24) | 1.7 (±2.3) | ||
| Non-specific arm pain | 18 (±0.0) | 3.0 (±0.0) | ||
| Trigger finger | 29 (±26) | 4.3 (±3.1) | ||
| Carpal Tunnel Syndrome | 39 (±18) | 4.2 (±3.4) | ||
| Ganglion | 30 (±18) | 4.2 (±2.1) | ||
| Osteoarthritis | 26 (±28) | 4.0 (±2.4) | ||
| Tendinitis | 41 (±13) | 4.5 (±2.2) | ||
| Epicondylitis lateralis | 44 (±14) | 5.7 (±2.1) | ||
| Rotator cuff tendinosis | 36 (±0.0) | 5.0 (±0.0) | ||
| Giant cell tumor | 28 (±1.6) | 3.0 (±2.8) | ||
| Ligament deficiency | 36 (±0.0) | 1.0 (±(0.0) | ||
| Other | 39 (±25) | 5.0 (±2.2) | ||
| Doctor | ||||
| Surgeon 1 | 36 (±22) | 0.40 | 2.0 (±2.8) | 0.73 |
| Surgeon 2 | 20 (±18) | 3.0 (±3.0) | ||
| Surgeon 3 | 33 (±18) | 3.6 (±2.5) | ||
| Other | 38 (±18) | 3.8 (±2.8) | ||
| Type of patient | ||||
| New patient | 33 (±22) | 0.58 | 3.9 (±2.4) | 0.14 |
| Follow-up | 30 (±20) | 2.9 (±2.5) | ||
| Post operative followup | 37 (±16) | 4.2 (±2.7) | ||
| Sougth treatment for this condition before | ||||
| Yes | 34 (±21) | 0.58 | 3.5 (±2.7) | 0.46 |
| No | 30 (±18) | 3.8 (±2.1) | ||
CFS, Cognitive Flexibility Scale; SHAI-5, Short Health Anxiety Inventory-5; PSEQ, Pain Self-Efficacy Questionnaire; QuickDASH, Disabilities of Arm, Shoulder and Hand, short form; PHQ-2, Patient Health Questionnaire for Depression-2
Pain intensity correlated with PSEQ, PHQ-2 and SHAI-5, but not with CFS. (Table 3) The best multivariable model included PSEQ and PHQ and explained 29 % of the variability in pain intensity (adjusted R-squared: 0.27, p < 0.01).
CFS correlated with PSEQ (r = 0.25, p = 0.02), but not PHQ-2 or SHAI-5. (Table 4).
Table 4.
Bivariate relationships between CFS and main study variables
| CFS | ||
|---|---|---|
| Parameters | r | p-value |
| QDASH | −0.05 | NS |
| PSEQ | 0.25 | 0.02 |
| PHQ | −0.12 | NS |
| SHAI-5 | −0.19 | 0.08 |
| Pain | −0.11 | NS |
CFS, Cognitive Flexibility Scale; SHAI-5, Short Health Anxiety Inventory-5, PSEQ, Pain Self-Efficacy Questionnaire; QuickDASH, Disabilities of Arm, Shoulder and Hand, short form; PHQ-2, Patient Health Questionnaire for Depression-2
Discussion
We found no correlation between cognitive flexibility and pain intensity or hand and upper extremity disability. Consistent with prior work, pain self-efficacy was strongly associated with hand and upper extremity disability and pain intensity.[22, 24] Cognitive flexibility was significantly associated with pain self-efficacy, but not depression and health anxiety. These findings suggest that the specific coping tactic of interpreting nociception in the most optimistic and adaptive way (pain self-efficacy) might be the only part of the general construct of cognitive flexibility that has an impact on hand and upper extremity illness. For clinical care, this may mean that focusing on improving patients’ sense of self-efficacy about their pain may be more effective in decreasing hand specific disability than encouraging open mindedness. It is not uncommon for surgeons in clinical practice to find themselves in the position of attempting to convince patients that it is safe to engage in activities that cause pain. Perhaps a better strategy for surgeons is to focus on providing encouragement, communicating confidence in patients ability to be successful in managing their pain condition (as they undoubtedly have successfully managed other difficult times in their lives), and foster an environment of hope and positivity. In addition, cognitive behavioral therapy may be of addition value, coaching patients with ineffective coping skills to better physical outcomes.
A prospective study might identify an association between cognitive flexibility and greater reduction in disability after reassurance or treatment that could not be demonstrated in this cross-sectional study. On the other hand, cognitive flexibility may have limited correlation with disability due to cognitive errors at both ends of the spectrum: either failing to incorporate new ideas that are more adaptive, or being too receptive resulting in a tendency to be influenced by maladaptive concepts.
This study should be considered in light of its shortcomings. We might find different results in subsets of patients with more uniform demographics and disease. Most of the patients were from the practice of one surgeon and different surgeon styles might alter the results. Stepwise regressions may be prone to spurious results. Finally, the reliability and validity of the CFS among elderly population with cognitive impairment is debated.
The results of this study suggest that the best strategy is to help patients limit pain and disability to improve their mood and self-efficacy. It may be that before a patient can shift their thinking and engage in cognitive flexibility and other helpful coping strategies, he or she needs to be confident that they are able to make changes. Patients that have difficulty gaining confidence that they can accomplish their goals in spite of pain might benefit from psychosocial interventions such as Cognitive Behavioral Therapy.
Acknowledgment
M.G. Hageman is supported by Dutch research grants from Marti-Keunig Eckhart Stichting and Anna Foundation.
Conflict of Interest
None of the authors has a conflict of interest related directly to the subject matter.
Ethical Statement
This research was approved by our human research committee and was performed in accordance with the Helsinki Declaration. Informed consent was obtained from each subject.
Disclosures
No benefits in any form have been received or will be received related directly or indirectly to the subject of this article. The authors report no proprietary or commercial interest in any product mentioned or concept discussed in this article.
Contributor Information
Michiel G.J.S. Hageman, Email: mghageman@partners.org
Jan Paul Briet, Email: jp.briet@gmail.com.
Thijs C.H. Oosterhoff, Email: tch.oosterhoff@gmail.com
Arjan G. Bot, Email: a.g.j.bot@gmail.com
David Ring, Email: dring@partners.org.
Ana-Maria Vranceanu, Phone: +1-617-6437996, Email: avranceanu@partners.org.
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