User Engagement and Clinical Impact of the Manage My Pain App in Patients With Chronic Pain: A Real-World, Multi-site Trial.

BACKGROUND: Chronic 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. OBJECTIVE: The 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. METHODS: A 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. RESULTS: A 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. CONCLUSIONS: The 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 REGISTRATION: ClinicalTrials.gov NCT04762329; https://clinicaltrials.gov/ct2/show/NCT04762329.

Engagement with Manage My Pain mobile health application among patients at the Transitional Pain Service.

OBJECTIVE: Mobile health platforms have become an important component of pain self-management programs and hundreds of mobile applications are commercially available for patients to monitor pain. However, few of these applications have been developed in collaboration with healthcare professionals or have been critically evaluated. Manage My Pain is a user-driven mobile health platform developed by ManagingLife in collaboration with clinician researchers. Manage My Pain allows patients to keep a "pain record" and supports communication of this information with clinicians. The current report describes a user engagement study of Manage My Pain among patients at the Transitional Pain Service (TPS) at Toronto General Hospital, a multidisciplinary clinic for patients at high risk of developing postsurgical pain. METHODS: Patients at the TPS were encouraged to register on Manage My Pain as one component of a larger, non-randomized prospective study of treatment predictors and treatment enhancement. Uptake of the application and rates of registration, use, and retention were tracked for 90 days. RESULTS: Of the 196 patients who consented to the larger study, 132 (67%) also provided consent to the Manage My Pain component, indicating that they found this to be an acceptable treatment adjunct, and 119 (61%) completed registration. Of those who used the app, 67.9% and 43.2% continued to use Manage My Pain beyond 30 and 90 days, respectively. On average, users engaged with the app for 93.14 days (SD = 151.9 days) logged an average of 47.39 total records (SD = 136.1). CONCLUSIONS: Manage My Pain was found acceptable by a majority of patients at an academic pain management program. Rates of user registration and retention were favorable compared to those reported by other applications. Further research is needed to develop strategies to retain users and maximize patient benefit.

Interpretability and Class Imbalance in Prediction Models for Pain Volatility in Manage My Pain App Users: Analysis Using Feature Selection and Majority Voting Methods.

BACKGROUND: Pain 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. OBJECTIVE: This 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. METHODS: A 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. RESULTS: A 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. CONCLUSIONS: We 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.

Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods.

BACKGROUND: Measuring and predicting pain volatility (fluctuation or variability in pain scores over time) can help improve pain management. Perceptions of pain and its consequent disabling effects are often heightened under the conditions of greater uncertainty and unpredictability associated with pain volatility. OBJECTIVE: This study aimed to use data mining and machine learning methods to (1) define a new measure of pain volatility and (2) predict future pain volatility levels from users of the pain management app, Manage My Pain, based on demographic, clinical, and app use features. METHODS: Pain volatility was defined as the mean of absolute changes between 2 consecutive self-reported pain severity scores within the observation periods. The k-means clustering algorithm was applied to users' pain volatility scores at the first and sixth month of app use to establish a threshold discriminating low from high volatility classes. Subsequently, we extracted 130 demographic, clinical, and app usage features from the first month of app use to predict these 2 volatility classes at the sixth month of app use. Prediction models were developed using 4 methods: (1) logistic regression with ridge estimators; (2) logistic regression with Least Absolute Shrinkage and Selection Operator; (3) Random Forests; and (4) Support Vector Machines. Overall prediction accuracy and accuracy for both classes were calculated to compare the performance of the prediction models. Training and testing were conducted using 5-fold cross validation. A class imbalance issue was addressed using a random subsampling of the training dataset. Users with at least five pain records in both the predictor and outcome periods (N=782 users) are included in the analysis. RESULTS: k-means clustering algorithm was applied to pain volatility scores to establish a threshold of 1.6 to differentiate between low and high volatility classes. After validating the threshold using random subsamples, 2 classes were created: low volatility (n=611) and high volatility (n=171). In this class-imbalanced dataset, all 4 prediction models achieved 78.1% (611/782) to 79.0% (618/782) in overall accuracy. However, all models have a prediction accuracy of less than 18.7% (32/171) for the high volatility class. After addressing the class imbalance issue using random subsampling, results improved across all models for the high volatility class to greater than 59.6% (102/171). The prediction model based on Random Forests performs the best as it consistently achieves approximately 70% accuracy for both classes across 3 random subsamples. CONCLUSIONS: We propose a novel method for measuring pain volatility. Cluster analysis was applied to divide users into subsets of low and high volatility classes. These classes were then predicted at the sixth month of app use with an acceptable degree of accuracy using machine learning methods based on the features extracted from demographic, clinical, and app use information from the first month.

Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation.

BACKGROUND: Pain 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. OBJECTIVE: The 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. METHODS: User 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). RESULTS: The 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 <.001). Overall, males were significantly older than females (P <.001). Across clusters, females reported more pain conditions than males (all P <.001). Users from highly engaged clusters reported taking more medication than less engaged users (all P <.001). Females reported taking a greater number of medications than males (P =.04). In 4 of 5 clusters, the percentage of males taking an opioid was significantly greater (all P ≤.05) than that of females. The proportion of males with mild pain was significantly higher than that of females in 3 clusters (all P ≤.008). CONCLUSIONS: Although most users of the app reported being female, male users were more likely to be highly engaged in the app. Users in the most engaged clusters self-reported a higher number of pain conditions, a higher number of current medications, and a higher incidence of opioid usage. The high engagement by males in these clusters does not appear to be driven by pain severity which may, in part, be the case for females. Use of a mobile pain app may be relatively more attractive to highly-engaged males than highly-engaged females, and to those with relatively more complex chronic pain problems.

The Toronto General Hospital Transitional Pain Service: development and implementation of a multidisciplinary program to prevent chronic postsurgical pain.

Chronic postsurgical pain (CPSP), an often unanticipated result of necessary and even life-saving procedures, develops in 5-10% of patients one-year after major surgery. Substantial advances have been made in identifying patients at elevated risk of developing CPSP based on perioperative pain, opioid use, and negative affect, including depression, anxiety, pain catastrophizing, and posttraumatic stress disorder-like symptoms. The Transitional Pain Service (TPS) at Toronto General Hospital (TGH) is the first to comprehensively address the problem of CPSP at three stages: 1) preoperatively, 2) postoperatively in hospital, and 3) postoperatively in an outpatient setting for up to 6 months after surgery. Patients at high risk for CPSP are identified early and offered coordinated and comprehensive care by the multidisciplinary team consisting of pain physicians, advanced practice nurses, psychologists, and physiotherapists. Access to expert intervention through the Transitional Pain Service bypasses typically long wait times for surgical patients to be referred and seen in chronic pain clinics. This affords the opportunity to impact patients' pain trajectories, preventing the transition from acute to chronic pain, and reducing suffering, disability, and health care costs. In this report, we describe the workings of the Transitional Pain Service at Toronto General Hospital, including the clinical algorithm used to identify patients, and clinical services offered to patients as they transition through the stages of surgical recovery. We describe the role of the psychological treatment, which draws on innovations in Acceptance and Commitment Therapy that allow for brief and effective behavioral interventions to be applied transdiagnostically and preventatively. Finally, we describe our vision for future growth.