Clinical and evoked pain, personality traits, and emotional states: can familial confounding explain the associations?

Unpacking the relationship between Big Five personality traits and experimental pain: A systematic review and meta-analysis

Pain is essential for survival, but individual responses to painful stimuli vary, representing a complex interplay between sensory, cognitive, and affective factors. Individual differences in personality traits and in pain perception covary but it is unclear which traits play the most significant role in understanding the pain experience and whether this depends on pain modality. A systematic search identified 1534 records (CINAHL, MEDLINE, PsycInfo, PubMed and Web of Science), of which 22 were retained and included in a systematic review. Only studies from the pressure pain domain (n=6) could be compared in a formal meta-analysis to evaluate the relationship between Big Five traits and experimental pain. Pressure pain tolerance correlated positively with Extraversion and negatively with Neuroticism with a trivial effect size (<0.1). While these findings suggest personality might be only weakly related to pain in healthy individuals, we emphasize the need to consider standardization, biases, and adequate sample sizes in future research, as well as additional factors that might affect experimental pain sensitivity.

Are We Any Closer to Understanding How Chronic Pain Develops? A Systematic Search and Critical Narrative Review of Existing Chronic Pain Vulnerability Models

Identifying biopsychosocial factors underlying chronic pain vulnerability is essential for the design of preventative efforts. Multiple chronic pain vulnerability models exist, however, there is a lack of comprehensive evaluation of these models in the literature, potentially due to the lack of guidelines that specify the criteria by which these types of work should be assessed. In this work, we created evaluation criteria (based on the general goals of conceptual models), and we then used them to critically review the chronic pain vulnerability models available in the current peer-reviewed literature (identified through a systematic search). Particularly, we evaluated the models on the basis of conceptual clarity/specificity of measures, depth of description of aetiological and mechanistic factors, use of a whole system approach, and quality of the evidence associated with the models. We found nine conceptual models that have been explored in detail (eg, fear avoidance model, diathesis-stress model). These models excel at clarity and are supported mostly by self-report evidence of a psychological nature (anxiety sensitivity, pain catastrophizing, etc.), but provide little explanation of mechanistic and aetiological factors. In the future, models could be improved by complementing them with proposals from other models and exploring potential causal factors and mechanisms maintaining the condition. This task could be carried out through prospective cohort studies, and computational approaches, amongst others.

Accuracy of Pain Tolerance Self-assessment Versus Objective Pressure Sensitivity

INTRODUCTION

Effective treatment of postoperative pain after elective surgery remains elusive, and the experience of pain can be variable for patients. The patient's intrinsic pain tolerance may contribute to this variability. We sought to identify whether there was a correlation between subjective report of intrinsic pain tolerance and objective measurement of pressure dolorimetry (PD). We also sought to identify whether a correlation existed between PD and Patient Reported Outcome Measurement Information System (PROMIS) scores of pain intensity, physical function, and mood. PD is a validated, objective method to assess pain tolerance. Markers of general mental and physical health are correlated with pain sensitization and may also be linked to pain tolerance.

METHODS

PROMIS scores, dolorimetry measurements, and survey data were collected on 40 consecutive orthopaedic foot and ankle surgery patients at the initial clinic visit. Patients were included if they had normal sensation on the plantar foot and no prior surgery or plantar heel source of pain.

RESULTS

Objective dolorimetry data reflecting 5/10 pain for the patients were 24 N/cm2 (±8.9). Patients estimated their pain threshold as 7.3/10 (±2.1). No correlation was found between objective and subjective pain threshold identified. A moderate negative correlation of R = -0.44 was observed regarding PROMIS-M with dolorimetry data (P < -0.05). PROMIS-M score >60 had a significant decrease in pain threshold to 15.9 ± 8.5 N/cm2 compared with 25.7 ± 8.9 N/cm2 for those who were less depressed with a PROMIS<60 (P < 0.05).

CONCLUSION

Subjective pain tolerance is not correlated with the patient's own objective pain threshold or markers of mental health and should not be used to assist clinical decision making. PROMIS-M is inversely correlated with objective pain. Higher PROMIS-M scores are associated with a lower objective pain threshold.

LEVEL OF EVIDENCE

Level II-Lesser Quality Randomized Controlled Trial or Prospective Comparative Study.

Machine-learned identification of psychological subgroups with relation to pain interference in patients after breast cancer treatments

Background

Persistent pain in breast cancer survivors is common. Psychological and sleep-related factors modulate perception, interpretation and coping with pain and may contribute to the clinical phenotype. The present analysis pursued the hypothesis that breast cancer survivors form subgroups, based on psychological and sleep-related parameters that are relevant to the impact of pain on the patients’ life.

Methods

We analysed 337 women treated for breast cancer, in whom psychological and sleep-related parameters as well as parameters related to pain intensity and interference had been acquired. Data were analysed by using supervised and unsupervised machine-learning techniques (i) to detect patient subgroups based on the pattern of psychological or sleep-related parameters, (ii) to interpret the detected cluster structure and (iii) to relate this data structure to pain interference and impact on life.

Results

Artificial intelligence-based detection of data structure, implemented as self-organizing neuronal maps, identified two different clusters of patients. A smaller cluster (11.5% of the patients) had comparatively lower resilience, more depressive symptoms and lower extraversion than the other patients. In these patients, life-satisfaction, mood, and life in general were comparatively more impeded by persistent pain.

Conclusions

The results support the initial hypothesis that psychological and sleep-related parameter patterns are meaningful for subgrouping patients with respect to how persistent pain after breast cancer treatments interferes with their life. This indicates that management of pain should address more complex features than just pain intensity. Artificial intelligence is a useful tool in the identification of subgroups of patients based on psychological factors.

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Pain interference with daily life is relevant for post-surgery breast cancer patients.

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11.5% of 337 patients belonged to a subgroup with high life interference of pain.

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Life interference was not a function of pain intensity.

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Risk factors were lower resilience, more depressive symptoms and lower extraversion.

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Life-satisfaction, mood, and life in general were impeded by persistent pain.

Understanding Monitoring Technologies for Adults With Pain: Systematic Literature Review

BACKGROUND

Monitoring of patients may decrease treatment costs and improve quality of care. Pain is the most common health problem that people seek help for in hospitals. Therefore, monitoring patients with pain may have significant impact in improving treatment. Several studies have studied factors affecting pain; however, no previous study has reviewed the contextual information that a monitoring system may capture to characterize a patient's situation.

OBJECTIVE

The objective of this study was to conduct a systematic review to (1) determine what types of technologies have been used to monitor adults with pain, and (2) construct a model of the context information that may be used to implement apps and devices aimed at monitoring adults with pain.

METHODS

A literature search (2005-2015) was conducted in electronic databases pertaining to medical and computer science literature (PubMed, Science Direct, ACM Digital Library, and IEEE Xplore) using a defined search string. Article selection was done through a process of removing duplicates, analyzing title and abstract, and then reviewing the full text of the article.

RESULTS

In the final analysis, 87 articles were included and 53 of them (61%) used technologies to collect contextual information. A total of 49 types of context information were found and a five-dimension (activity, identity, wellness, environment, physiological) model of context information to monitor adults with pain was proposed, expanding on a previous model. Most technological interfaces for pain monitoring were wearable, possibly because they can be used in more realistic contexts. Few studies focused on older adults, creating a relevant avenue of research on how to create devices for users that may have impaired cognitive skills or low digital literacy.

CONCLUSIONS

The design of monitoring devices and interfaces for adults with pain must deal with the challenge of selecting relevant contextual information to understand the user's situation, and not overburdening or inconveniencing users with information requests. A model of contextual information may be used by researchers to choose possible contextual information that may be monitored during studies on adults with pain.

Familial Contributions to Self-Reported Sleep and Pain in Female Twins

OBJECTIVE

The relationship between sleep quality and pain has been studied in populations with chronic pain and in nonclinical populations using experimental paradigms. Little is known about the familial contributions to this relationship. This study examines self-reported sleep quality and pain in a nonclinical sample and to explore familial (i.e., shared genetic and common family environment) confounding in those relationships.

DESIGN

Cross-sectional.

SUBJECTS

Ninety nine community-based female twin pairs (N = 198) with a mean age of 29 years; 72% monozygotic.

METHODS

The short form McGill Pain Questionnaire (McGill), a visual analog scale (VAS), a body map, and the Pittsburgh Sleep Quality Index (PSQI) measured self-reported pain and sleep quality. Mixed model regression adjusted for age was used to examine relationships between the pain indices and PSQI in overall and within-pair models.

RESULTS

Higher PSQI total scores were significantly associated with higher scores across the McGill sensory (B = 0.37, p < 0.001), affective (B = 0.16, p < 0.001), total scores (B = 0.54, p < 0.001), the VAS (B = 2.41, p < 0.001), and number of sites with any pain on the body map (B = 0.42, p = 0.001). All of these associations were diminished and rendered nonsignificant in within-pair analyses that accounted for genetic and familial factors (all p's ≥ 0.01; Bonferroni α = 0.01).

CONCLUSIONS

These findings support an association between poor sleep quality and pain and suggest that this relationship may be confounded by shared genetic and environmental factors, which could elucidate biological mechanisms that underlie the development and maintenance of pain and sleep problems.