Search

BackgroundApproximately 70% of mental health disorders appear prior to 25 years of age and can become chronic when ineffectively treated. Individuals between 18 and 25 years old are significantly more likely to experience mental health disorders, substance dependencies, and suicidality. Treatment progress, capitalizing on the tendencies of youth to communicate online, can strategically address depressive disorders. ObjectiveWe performed a randomized controlled trial (RCT) that compared online mindfulness-based cognitive behavioral therapy (CBT-M) combined with standard psychiatric care to standard psychiatric care alone in youth (18-30 years old) diagnosed with major depressive disorder. MethodsForty-five participants were randomly assigned to CBT-M and standard care (n=22) or to standard psychiatric care alone (n=23). All participants were provided standard psychiatric care (ie, 1 session per month), while participants in the experimental group received an additional intervention consisting of the CBT-M online software program. Interaction with online workbooks was combined with navigation coaching delivered by phone and secure text messaging. ResultsIn a two-level linear mixed-effects model intention-to-treat analysis, significant between-group differences were found for the Beck Depression Inventory-II score (difference –8.54, P=.01), Quick Inventory of Depressive Symptoms score (difference –4.94, P=.001), Beck Anxiety Inventory score (difference –11.29, P

BackgroundChronic pain imposes a large burden on individuals and society. A patient-centric digital chronic pain management app called Manage My Pain (MMP) can be used to enhance communication between providers and patients and promote self-management. ObjectiveThe purpose of this study was to evaluate the real-world engagement of patients in urban and rural settings in Ontario, Canada with the MMP app alongside their standard of care and assess the impact of its usage on clinical outcomes of pain and related mental health. MethodsA total of 246 participants with chronic pain at a rural and 2 urban pain clinics were recruited into this prospective, open-label, exploratory study that compared the use of MMP, a digital health app for pain that incorporates validated questionnaires and provides patients with summarized reports of their progress in combination with standard care (app group), against data entered on paper-based questionnaires (nonapp group). Participants completed validated questionnaires on anxiety, depression, pain catastrophizing, satisfaction, and daily opioid consumption up to 4.5 months after the initial visit (short-term follow-up) and between 4.5 and 7 months after the initial visit (long-term follow-up). Engagement and clinical outcomes were compared between participants in the two groups. ResultsA total of 73.6% (181/246) of the participants agreed to use the app, with 63.4% (111/175) of them using it for at least one month. Individuals who used the app rated lower anxiety (reduction in Generalized Anxiety Disorder 7-item questionnaire score by 2.10 points, 95% CI –3.96 to –0.24) at short-term follow-up and had a greater reduction in pain catastrophizing (reduction in Pain Catastrophizing Scale score by 5.23 points, 95% CI –9.55 to –0.91) at long-term follow-up relative to patients with pain who did not engage with the MMP app. ConclusionsThe use of MMP by patients with chronic pain is associated with engagement and improvements in self-reported anxiety and pain catastrophizing. Further research is required to understand factors that impact continued engagement and clinical outcomes in patients with chronic pain. Trial RegistrationClinicalTrials.gov NCT04762329; https://clinicaltrials.gov/ct2/show/NCT04762329

BackgroundNumerical anchoring occurs when exposure to a numeric quantity influences a person’s subsequent judgment involving other quantities. This could be applicable to the evaluation of pain, where exposure to an unrelated number before the evaluation of pain could influence pain ratings. ObjectiveThis study aimed to determine whether exposure to a random numeric anchor influences subsequent pain intensity ratings of a hypothetical patient. MethodsIn this study, 385 participants read a vignette describing a patient with chronic pain before being randomly assigned to one of four groups. Groups 1 and 2 spun an 11-wedge number wheel (0-10), which was, unbeknown to the participants, programmed to stop on a high number (8) or a low number (2), respectively. Group 3 spun a similar letter wheel (A-K), which was programmed to stop on either the letter C or I (control 1). Group 4 did not spin a wheel (control 2). Participants were then asked to rate the patient’s pain intensity using a 0 to 10 numeric rating scale. ResultsThe high-number group rated the patient’s pain (median 8, IQR 2) significantly higher than the letter wheel control (median 7, IQR 2; P=.02) and the low-number group (median 6, IQR 2; P

BackgroundPain volatility is an important factor in chronic pain experience and adaptation. Previously, we employed machine-learning methods to define and predict pain volatility levels from users of the Manage My Pain app. Reducing the number of features is important to help increase interpretability of such prediction models. Prediction results also need to be consolidated from multiple random subsamples to address the class imbalance issue. ObjectiveThis study aimed to: (1) increase the interpretability of previously developed pain volatility models by identifying the most important features that distinguish high from low volatility users; and (2) consolidate prediction results from models derived from multiple random subsamples while addressing the class imbalance issue. MethodsA total of 132 features were extracted from the first month of app use to develop machine learning–based models for predicting pain volatility at the sixth month of app use. Three feature selection methods were applied to identify features that were significantly better predictors than other members of the large features set used for developing the prediction models: (1) Gini impurity criterion; (2) information gain criterion; and (3) Boruta. We then combined the three groups of important features determined by these algorithms to produce the final list of important features. Three machine learning methods were then employed to conduct prediction experiments using the selected important features: (1) logistic regression with ridge estimators; (2) logistic regression with least absolute shrinkage and selection operator; and (3) random forests. Multiple random under-sampling of the majority class was conducted to address class imbalance in the dataset. Subsequently, a majority voting approach was employed to consolidate prediction results from these multiple subsamples. The total number of users included in this study was 879, with a total number of 391,255 pain records. ResultsA threshold of 1.6 was established using clustering methods to differentiate between 2 classes: low volatility (n=694) and high volatility (n=185). The overall prediction accuracy is approximately 70% for both random forests and logistic regression models when using 132 features. Overall, 9 important features were identified using 3 feature selection methods. Of these 9 features, 2 are from the app use category and the other 7 are related to pain statistics. After consolidating models that were developed using random subsamples by majority voting, logistic regression models performed equally well using 132 or 9 features. Random forests performed better than logistic regression methods in predicting the high volatility class. The consolidated accuracy of random forests does not drop significantly (601/879; 68.4% vs 618/879; 70.3%) when only 9 important features are included in the prediction model. ConclusionsWe employed feature selection methods to identify important features in predicting future pain volatility. To address class imbalance, we consolidated models that were developed using multiple random subsamples by majority voting. Reducing the number of features did not result in a significant decrease in the consolidated prediction accuracy.

BackgroundPain is one of the most prevalent health-related concerns and is among the top 3 most common reasons for seeking medical help. Scientific publications of data collected from pain tracking and monitoring apps are important to help consumers and healthcare professionals select the right app for their use. ObjectiveThe main objectives of this paper were to (1) discover user engagement patterns of the pain management app, Manage My Pain, using data mining methods; and (2) identify the association between several attributes characterizing individual users and their levels of engagement. MethodsUser engagement was defined by 2 key features of the app: longevity (number of days between the first and last pain record) and number of records. Users were divided into 5 user engagement clusters employing the k-means clustering algorithm. Each cluster was characterized by 6 attributes: gender, age, number of pain conditions, number of medications, pain severity, and opioid use. Z tests and chi-square tests were used for analyzing categorical attributes. Effects of gender and cluster on numerical attributes were analyzed using 2-way analysis of variances (ANOVAs) followed up by pairwise comparisons using Tukey honest significant difference (HSD). ResultsThe clustering process produced 5 clusters representing different levels of user engagement. The proportion of males and females was significantly different in 4 of the 5 clusters (all P ≤.03). The proportion of males was higher than females in users with relatively high longevity. Mean ages of users in 2 clusters with high longevity were higher than users from other 3 clusters (all P

INTRODUCTION:Approximately 400 000 Americans and 36 000 Canadians undergo cardiac surgery annually, and up to 56% will develop chronic postsurgical pain (CPSP). The primary aim of this study is to explore the association of pain-related beliefs and gender-based pain expectations on the development of CPSP. Secondary goals are to: (A) explore risk factors for poor functional status and patient-level cost of illness from a societal perspective up to 12 months following cardiac surgery; and (B) determine the impact of CPSP on quality-adjusted life years (QALYs) borne by cardiac surgery, in addition to the incremental cost for one additional QALY gained, among those who develop CPSP compared with those who do not. METHODS AND ANALYSES:In this prospective cohort study, 1250 adults undergoing cardiac surgery, including coronary artery bypass grafting and open-heart procedures, will be recruited over a 3-year period. Putative risk factors for CPSP will be captured prior to surgery, at postoperative day 3 (in hospital) and day 30 (at home). Outcome data will be collected via telephone interview at 6-month and 12-month follow-up. We will employ generalised estimating equations to model the primary (CPSP) and secondary outcomes (function and cost) while adjusting for prespecified model covariates. QALYs will be estimated by converting data from the Short Form-12 (version 2) to a utility score. ETHICS AND DISSEMINATION:This protocol has been approved by the responsible bodies at each of the hospital sites, and study enrolment began May 2015. We will disseminate our results through CardiacPain.Net, a web-based knowledge dissemination platform, presentation at international conferences and publications in scientific journals. TRIAL REGISTRATION NUMBER:NCT01842568.

The spacing effect refers to the improvement in memory retention for materials learned in a series of sessions, as opposed to massing learning in a single session. It has been extensively studied in the domain of verbal learning using word lists. Less evidence is available for connected discourse or tasks requiring the complex coordination of verbal and other domains. In particular, the effect of spacing on the retention of words and music in song has yet to be determined. In this study, university students were taught an unaccompanied two-verse song based on traditional materials to a criterion of 95% correct memory for sung words. Subsequent training sessions were either massed or spaced by two days or one week and tested at a retention interval of three weeks. Performances were evaluated for number of correct and incorrect syllables, number of correctly and incorrectly pitched notes, degree notes were off-pitch, and number of hesitations while singing. The data revealed strong evidence for a spacing effect for song between the massed and spaced conditions at a retention interval of three weeks, and evidence of no difference between the two spaced conditions. These findings suggest that the ongoing cues offered by surface features in the song are strong enough to enable verbatim recall across spaced conditions, as long as the spacing interval reaches a critical threshold.

Introduction: Individuals with limb loss frequently report post-amputation phenomena, including nonpainful phantom sensations, phantom limb pain (PLP), and residual limb pain (RLP). Although post-amputation pain is common, not all patients benefit from widely accepted treatments. A greater understanding of phantom limb "telescoping", the experience of one's phantom hand or foot gradually approaching the residual limb, may assist in developing more effective interventions for reducing post-amputation pain. This case report explores the relationships between PLP, RLP, telescoping, and psychosocial experience in one person with a lower limb amputation. The aim of this case is to illustrate one possible relationship between telescoping and PLP as the mechanisms linking the two remain equivocal. Methods: The participant is a 35-year-old male who underwent a transfemoral amputation due to a traumatic injury to his right leg approximately 4 years prior. He responded to questionnaires evaluating demographic and healthrelated information (e.g., age, sex, marital status, reason for amputation), pain and psychological variables via the Brief Pain Inventory (BPI-SF), ID Pain Questionnaire (IDPQ), Pain Catastrophizing Scale (PSC-4), Patient Health Questionnaire-4 (PHQ-4), Life Orientation Test-Revised (LOT-R), Connor-Davidson Resilience Scale (CD-RISC2), and Chronic Pain Acceptance Questionnaire (CPAQ-8) and telescoping, measured by a newly developed app. The participant completed a semi-structured interview that was designed to ascertain patterns in the overlapping experience of phantom limb telescoping and post-amputation pain. Results: The participant rated his average PLP as 10 on a Numeric Rating Scale (NRS) from 0 ("no pain") to 10 ("worst pain imaginable") shortly after amputation. Approximately 12 months later, the participant noticed a shortening of his phantom limb, with a concurrent decrease in PLP. At present, his average NRS pain intensity is a 5/10. The participant described how the daily, debilitating PLP intensity diminished to weekly, manageable pain over time. Most notably, his responses on questionnaires were consistent with neuropathic PLP, mild to moderate levels of pain interference, a high level of catastrophic thinking about pain, low optimism, and mild symptoms of anxiety and depression. Discussion: In this report, telescoping appeared to be preceded by an initial reduction in PLP intensity but these findings are based on a single case report and must be replicated with a large sample size before we have a clearer idea of the relationship between telescoping and PLP. This study provides insight into factors that may maintain PLP, generating targets for further investigation. [ABSTRACT FROM AUTHOR]

The article discusses the patterns of opioid use after surgical discharge and the need for guidelines on opioid prescribing practices. The authors conducted a study of postsurgical patients from 25 countries to evaluate opioid prescribing practices and patient consumption patterns. They found that opioid overprescribing at the time of hospital discharge is more prevalent than previously thought. The article emphasizes the importance of specialized multidisciplinary programs, such as transitional pain services, to manage postsurgical pain and appropriately wean patients off opioids. The authors also suggest implementing a part-fill prescription system to reduce the quantity of opioids dispensed initially. Overall, the article highlights the need for improved opioid stewardship practices and increased support for patients struggling with postsurgical pain. [Extracted from the article]

Introduction It has been well described that a small but significant proportion of patients continue to use opioids months after surgical discharge. We sought to evaluate postdischarge opioid use of patients who were seen by a Transitional Pain Service compared with controls. Methods We conducted a retrospective cohort study using administrative data of individuals who underwent surgery in Ontario, Canada from 2014 to 2018. Matched cohort pairs were created by matching Transitional Pain Service patients to patients of other academic hospitals in Ontario who were not enrolled in a Transitional Pain Service. Segmented regression was performed to assess changes in monthly mean daily opioid dosage. Results A total of 209 Transitional Pain Service patients were matched to 209 patients who underwent surgery at other academic centers. Over the 12 months after surgery, the mean daily dose decreased by an estimated 3.53 morphine milligram equivalents (95% CI 2.67 to 4.39, p<0.001) per month for the Transitional Pain Service group, compared with a decline of only 1.05 morphine milligram equivalents (95% CI 0.43 to 1.66, p<0.001) for the controls. The difference-in-difference change in opioid use for the Transitional Pain Service group versus the control group was -2.48 morphine milligram equivalents per month (95% CI -3.54 to -1.43, p=0.003). Discussion Patients enrolled in the Transitional Pain Service were able to achieve opioid dose reduction faster than in the control cohorts. The difficulty in finding an appropriate control group for this retrospective study highlights the need for future randomized controlled trials to determine efficacy. [ABSTRACT FROM AUTHOR]

Although chronic pain is one of the most important medical problems facing society, there has been very limited progress in the development of novel therapies for this condition. Here, we discuss high-impact research priorities to reduce the number of people transitioning from acute to chronic intractable pain.

Although chronic pain is one of the most important medical problems facing society, there has been very limited progress in the development of novel therapies for this condition. Here, we discuss high-impact research priorities to reduce the number of people transitioning from acute to chronic intractable pain.