The journal of pain : official journal of the American Pain Society
-
Few studies have examined whether maintaining moderate or vigorous physical activity (PA) reduces the risk of low back pain in older people. This study aimed to examine the magnitude of the associations of changes in PA on the risk of low back pain at 4 years of follow-up. We analyzed 4,882 participants in the English Longitudinal Study of Ageing who were initially free from low back pain (mean age, 65.6 ± 8.9 years at baseline). ⋯ Interventions for maintaining either moderate or vigorous PA might be beneficial in preventing the incidence of low back pain in the older population. PERSPECTIVE: This study examined the magnitude of the association between changes in physical activity over time and the risk of low back pain. The findings suggest that encouraging people to maintain at least moderate physical activity over 2 years is useful for reducing the risk of low back pain at 4 years of follow-up.
-
This is a cross-sectional study that analysed the association between workplace bullying and LBP. The participants were 894 judicial civil servants from Porto Alegre, southern Brazil. Workplace Bullying was measured by the Negative Acts Questionnaire (NAQ-r) and Low Back Pain by the Nordic Questionnaire for Musculoskeletal Symptoms (NQMS). ⋯ PERSPECTIVES: As a psychosocial risk, workplace bullying may play a role in low back pain and can be focus of interventions to prevent LBP. Dose-response patterns on the association between workplace bullying and low back pain are discussed and hypotheses are raised. The paper addresses different ways of measuring and categorising bullying at work, in order to study the relationship between bullying and pain.
-
Little is known about the mechanisms by which pain catastrophizing may be associated with opioid use outcomes. This study aimed to investigate the potential mediating role of beliefs about the appropriateness of pain medicines for pain treatment on the association between pain catastrophizing and prescription opioid use in a community chronic non-cancer pain (CNCP) sample. Individuals (N = 420) diagnosed with CNCP participated in a cross-sectional online self-report study with validated measures of pain medication beliefs, pain catastrophizing, and current prescription opioid use. ⋯ A similar pattern of findings was found for high-dose opioid use, with pain medication beliefs significantly mediating the pain catastrophizing-high-dose use association (CI = 0.006, 0.050). Pain medication beliefs are a potentially modifiable psychological mechanism by which pain catastrophizing is associated with opioid use, including high-dose use. These findings have important implications for personalizing prevention and treatment programs.
-
Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. The primary aim of this review was to evaluate the effectiveness of ML algorithms for predicting pain intensity, phenotypes or treatment response from EEG. Electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO and The Cochrane Library were searched. ⋯ PERSPECTIVE: This systematic review explores the state-of-the-art machine learning methods for predicting pain intensity, phenotype or treatment response from EEG data. Results suggest that machine learning may demonstrate clinical utility, pending further research and development. Areas for improvement, including standardized processing, reporting and the need for better methodological assessment tools, are discussed.
-
The Helping to End Addiction Long-term Initiative (NIH HEAL Initiative) is an aggressive trans-NIH effort to speed solutions to stem the national opioid public health crisis, including through improved pain management. Toward this end, the NIH HEAL Initiative launched a common data element (CDE) program to ensure that NIH-funded clinical pain research studies would collect data in a standardized way. NIH HEAL Initiative staff launched a process to determine which pain-related core domains should be assessed by every clinical pain study and what questionnaires are required to ensure that the data is collected uniformly. ⋯ The selection of core domains will ensure that valuable clinical pain data generated by the initiative is standardized, useable for secondary data analysis, and useful for guiding future research, clinical practice decisions, and policymaking. PERSPECTIVE: The NIH HEAL Initiative launched a common data element program to ensure that NIH-funded clinical pain research studies would collect data in a standardized way. Nine core pain domains and questionnaires to measure them were chosen for studies examining acute pain and chronic pain in adults and pediatric populations.