Support for Chronic Pain Management for Breast Cancer Survivors Through Novel Digital Health Ecosystems: Pilot Usability Study of the PainRELife Mobile App.

BACKGROUND: Chronic pain is one of the most common and critical long-term effects of breast cancer. Digital health technologies enhance the management of chronic pain by monitoring physical and psychological health status and supporting pain self-management and patient treatment decisions throughout the clinical pathway. OBJECTIVE: This pilot study aims to evaluate patients' experiences, including usability, with a novel digital integrated health ecosystem for chronic pain named PainRELife. The sample included patients with breast cancer during survivorship. The PainRELife ecosystem comprises a cloud technology platform interconnected with electronic health records and patients' devices to gather integrated health care data. METHODS: We enrolled 25 patients with breast cancer (mean age 47.12 years) experiencing pain. They were instructed to use the PainRELife mobile app for 3 months consecutively. The Mobile Application Rating Scale (MARS) was used to evaluate usability. Furthermore, pain self-efficacy and participation in treatment decisions were evaluated. The study received ethical approval (R1597/21-IEO 1701) from the Ethical Committee of the European Institute of Oncology. RESULTS: The MARS subscale scores were medium to high (range: 3.31-4.18), and the total app quality score was 3.90. Patients with breast cancer reported reduced pain intensity at 3 months, from a mean of 5 at T0 to a mean of 3.72 at T2 (P=.04). The total number of times the app was accessed was positively correlated with pain intensity at 3 months (P=.03). The engagement (P=.03), information (P=.04), and subjective quality (P=.007) subscales were positively correlated with shared decision-making. Furthermore, participants with a lower pain self-efficacy at T2 (mean 40.83) used the mobile app more than participants with a higher pain self-efficacy (mean 48.46; P=.057). CONCLUSIONS: The data collected in this study highlight that digital health technologies, when developed using a patient-driven approach, might be valuable tools for increasing participation in clinical care by patients with breast cancer, permitting them to achieve a series of key clinical outcomes and improving quality of life. Digital integrated health ecosystems might be important tools for improving ongoing monitoring of physical status, psychological burden, and socioeconomic issues during the cancer survivorship trajectory. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/41216.

PainRE-Life: A FHIR Based Telemonitoring Ecosystem for the Management of Patients with Chronic Pain.

Chronic pain is a condition in which the use of digital health technologies, ecological momentary assessments, and digital communication tools may boost patient's engagement and coping. Here we present the results of the PainRE-Life a project, financed by the Lombardy Region (Italy), aimed to develop a dynamic and integrated technology ecosystem based on big data management and analysis to allow care continuity in patients with pain, and able to act as a decision aid for patients and caregivers.

Usability Testing of a New Digital Integrated Health Ecosystem (PainRELife) for the Clinical Management of Chronic Pain in Patients With Early Breast Cancer: Protocol for a Pilot Study.

BACKGROUND: Chronic pain (CP) and its management are critical issues in the care pathway of patients with breast cancer. Considering the complexity of CP experience in cancer, the international scientific community has advocated identifying cutting-edge approaches for CP management. Recent advances in the field of health technology enable the adoption of a novel approach to care management by developing integrated ecosystems and mobile health apps. OBJECTIVE: The primary end point of this pilot study is to evaluate patients' usability experience at 3 months of a new digital and integrated technological ecosystem, PainRELife, for CP in a sample of patients with breast cancer. The PainRELife ecosystem is composed of 3 main technological assets integrated into a single digital ecosystem: Fast Healthcare Interoperability Resources-based cloud platform (Nu platform) that enables care pathway definition and data collection; a big data infrastructure connected to the Fast Healthcare Interoperability Resources server that analyzes data and implements dynamic dashboards for aggregate data visualization; and an ecosystem of personalized applications for patient-reported outcomes collection, digital delivery of interventions and tailored information, and decision support of patients and caregivers (PainRELife app). METHODS: This is an observational, prospective pilot study. Twenty patients with early breast cancer and chronic pain will be enrolled at the European Institute of Oncology at the Division of Medical Senology and the Division of Pain Therapy and Palliative Care. Each patient will use the PainRELife mobile app for 3 months, during which data extracted from the questionnaires will be sent to the Nu Platform that health care professionals will manage. This pilot study is nested in a large-scale project named "PainRELife," which aims to develop a cloud technology platform to interoperate with institutional systems and patients' devices to collect integrated health care data. The study received approval from the Ethical Committee of the European Cancer Institute in December 2021 (number R1597/21-IEO 1701). RESULTS: The recruitment process started in May 2022 and ended in October 2022. CONCLUSIONS: The new integrated technological ecosystems might be considered an encouraging affordance to enhance a patient-centered approach to managing patients with cancer. This pilot study will inform about which features the health technological ecosystems should have to be used by cancer patients to manage CP. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41216.

Dominant and Nondominant Leg Kinematics During Kicking in Young Soccer Players: A Cross-Sectional Study.

The goal of the study is to analyze the kinematics and provide an EMG analysis of the support limb during an instep kick in adolescent players. We set a video camera, two torque transducers on the knee, and EMG sensors. A sample of 16 adolescent soccer players between 10 and 12 years old performed kicks. The kinematics shows a p = .039 on frontal plane (dominant 15.4 ± 1.8, nondominant 18.8 ± 1.7); the EMG analysis shows a p = .04 on muscular activation timing for the vastus medialis. A difference between the legs on the frontal plane emerges. Moreover, a huge difference on sagittal plane between the adolescent pattern and adult pattern exists (15° in adolescent population, 40° in adult population). The result shows a greater activation of the vastus medialis in the nondominant leg; probably, in this immature pattern, the adolescents use this muscle more than necessary.

Artificial intelligence to support early diagnosis of temporomandibular disorders: A preliminary case study.

BACKGROUND: Temporomandibular disorders (TMDs) are disabling conditions with a negative impact on the quality of life. Their diagnosis is a complex and multi-factorial process that should be conducted by experienced professionals, and most TMDs remain often undetected. Increasing the awareness of un-experienced dentists and supporting the early TMD recognition may help reduce this gap. Artificial intelligence (AI) allowing both to process natural language and to manage large knowledge bases could support the diagnostic process. OBJECTIVE: In this work, we present the experience of an AI-based system for supporting non-expert dentists in early TMD recognition. METHODS: The system was based on commercially available AI services. The prototype development involved a preliminary domain analysis and relevant literature identification, the implementation of the core cognitive computing services, the web interface and preliminary testing. Performance evaluation included a retrospective review of seven available clinical cases, together with the involvement of expert professionals for usability testing. RESULTS: The system comprises one module providing possible diagnoses according to a list of symptoms, and a second one represented by a question and answer tool, based on natural language. We found that, even when using commercial services, the training guided by experts is a key factor and that, despite the generally positive feedback, the application's best target is untrained professionals. CONCLUSION: We provided a preliminary proof of concept of the feasibility of implementing an AI-based system aimed to support non-specialists in the early identification of TMDs, possibly allowing a faster and more frequent referral to second-level medical centres. Our results showed that AI is a useful tool to improve TMD detection by facilitating a primary diagnosis.

Energy Delivered by Subthalamic Deep Brain Stimulation for Parkinson Disease Correlates With Depressive Personality Trait Shift.

OBJECTIVES: Despite the large amount of literature examining the potential influence of subthalamic nucleus deep brain stimulation (STN-DBS) on psychiatric symptoms and cognitive disorders, only a few studies have focused on its effect on personality. We investigated the correlation between total electrical energy delivered (TEED) and the occurrence of depressive traits in patients with Parkinson disease (PD) after one year of DBS. MATERIALS AND METHODS: Our study involved 20 patients with PD (12 women, mean [±SD] age 57.60 ± 7.63 years) who underwent bilateral STN-DBS, whose personality characteristics were assessed using the Minnesota Multiphasic Personality Inventory-2 (MMPI-2), according to the core assessment program for surgical interventional therapies in Parkinson's disease (CAPSIT-PD) procedure. RESULTS: We found that despite a marked improvement in motor functions and quality of life after 12 months, patients showed a significant increase in MMPI-2 subscales for depression (D scale and Depression scale) and in other content component scales (low self-esteem, work interference, and negative treatment indicators). Interestingly, only the TEED on the right side was inversely correlated with the changes in scale D (rs = -0.681, p = 0.007), whereas depressive traits did not correlate with disease duration, levodopa equivalent daily dose (LEDD) reduction, patient's age, or severity of motor symptoms. CONCLUSIONS: Our preliminary observations indicate that despite the excellent motor outcome and general improvement in quality of life, DBS treatment can result in patients poorly adjusting to their personal, familiar, and socio-professional life. Different influences and multiple factors (such as TEED, intra/postsurgical procedure, coping mechanisms, and outcome expectations) may affect depressive traits. Further advances are expected to improve stimulation methods.

Peri-lead edema and local field potential correlation in post-surgery subthalamic nucleus deep brain stimulation patients.

Implanting deep brain stimulation (DBS) electrodes in patients with Parkinson's disease often results in the appearance of a non-infectious, delayed-onset edema that disappears over time. However, the time window between the DBS electrode and DBS stimulating device implant is often used to record local field potentials (LFPs) which are used both to better understand basal ganglia pathophysiology and to improve DBS therapy. In this work, we investigated whether the presence of post-surgery edema correlates with the quality of LFP recordings in eight patients with advanced Parkinson's disease implanted with subthalamic DBS electrodes. The magnetic resonance scans of the brain after 8.5 ± 1.5 days from the implantation surgery were segmented and the peri-electrode edema volume was calculated for both brain hemispheres. We found a correlation (ρ = -0.81, p < 0.0218, Spearman's correlation coefficient) between left side local field potentials of the low beta band (11-20 Hz) and the edema volume of the same side. No other significant differences between the hemispheres were found. Despite the limited sample size, our results suggest that the effect on LFPs may be related to the edema localization, thus indicating a mechanism involving brain networks instead of a simple change in the electrode-tissue interface.

Eight-hours conventional versus adaptive deep brain stimulation of the subthalamic nucleus in Parkinson's disease.

This study compares the effects on motor symptoms between conventional deep brain stimulation (cDBS) and closed-loop adaptive deep brain stimulation (aDBS) in patients with Parkinson's Disease. The aDBS stimulation is controlled by the power in the beta band (12-35 Hz) of local field potentials recorded directly by subthalamic nucleus electrodes. Eight subjects were assessed in two 8-h stimulation sessions (first day, cDBS; second day, aDBS) with regular levodopa intake and during normal daily activities. The Unified Parkinson's Disease Rating Scale (UPDRS) part III scores, the Rush scale for dyskinesias, and the total electrical energy delivered to the tissues per second (TEEDs) were significantly lower in the aDBS session (relative UPDRS mean, cDBS: 0.46 ± 0.05, aDBS: 0.33 ± 0.04, p = 0.015; UPDRS part III rigidity subset mean, cDBS: 2.9143 ± 0.6551 and aDBS: 2.1429 ± 0.5010, p = 0.034; UPDRS part III standard deviation cDBS: 2.95, aDBS: 2.68; p = 0.047; Rush scale, cDBS 2.79 ± 0.39 versus aDBS 1.57 ± 0.23, p = 0.037; cDBS TEEDs mean: 28.75 ± 3.36 µj s-1, aDBS TEEDs mean: 16.47 ± 3.33, p = 0.032 Wilcoxon's sign rank test). This work further supports the safety and effectiveness of aDBS stimulation compared to cDBS in a daily session, both in terms of motor performance and TEED to the patient.

The Relationship Between Electrical Energy Delivered by Deep Brain Stimulation and Levodopa-Induced Dyskinesias in Parkinson's Disease: A Retrospective Preliminary Analysis.

Background: Adaptive Deep Brain Stimulation (aDBS) is now considered as a new feasible and effective paradigm to deliver DBS to patients with Parkinson's disease (PD) in such a way that not only stimulation is personalized and finely tuned to the instantaneous patient's state, but also motor improvement is obtained with a lower amount of energy transferred to the tissue. Amplitude-controlled aDBS was shown to significantly decrease the amplitude-driven total electrical energy delivered to the tissue (aTEED), an objective measure of the amount of energy transferred by DBS amplitude to the patient's brain. However, there is no direct evidence of a relationship between aTEED and the occurrence of DBS-related adverse events in humans. Objective: In this work, we investigated the correlation of aTEED with the occurrence of levodopa-induced dyskinesias pooling all the data available from our previous experiments using aDBS and cDBS. Methods: We retrospectively analyzed data coming from 19 patients with PD undergoing surgery for STN-DBS electrode positioning and participating to experiments involving cDBS and aDBS delivery. Patients were all studied some days after the surgery (acute setting). The aTEED and dyskinesia assessments (Rush Dyskinesia Rating Scale, RDRS) considered in the Med ON-Stim ON condition. Results: We confirmed both that aTEED values and RDRS were significantly lower in the aDBS than in cDBS sessions (aTEED mean value, cDBS: 0.0278 ± 0.0011 j, vs. aDBS: 0.0071 ± 0.0003 j, p < 0.0001 Wilcoxon's rank sum; normalized RDRS mean score, cDBS: 0.66 ± 0.017 vs. aDBS: 0.45 ± 0.01, p = 0.025, Wilcoxon's rank sum test). In addition, we found a direct significant correlation between aTEED and RDRS (ρ = 0.44, p = 0.0032, Spearman's correlation). Conclusions: Our results provide a first piece of evidence that aTEED is correlated to the amount of levodopa-induced dyskinesias in patients with PD undergoing STN-DBS, thus supporting the role of aDBS as feasible and safe alternative to cDBS.

Thalamic Local Field Potentials Are Related to Long-Term DBS Effects in Tourette Syndrome.

Background: Local field potential (LFP) recordings helped to clarify the pathophysiology of Tourette syndrome (TS) and to define new strategies for deep brain stimulation (DBS) treatment for refractory TS, based on the delivery of stimulation in accordance with changes in the electrical activity of the DBS target area. However, there is little evidence on the relationship between LFP pattern and DBS outcomes in TS. Objective: To investigate the relationship between LFP oscillations and DBS effects on tics and on obsessive compulsive behavior (OCB) comorbidities. Methods: We retrospectively analyzed clinical data and LFP recordings from 17 patients treated with DBS of the centromedian-parafascicular/ventralis oralis (CM-Pf/VO) complex, and followed for more several years after DBS in the treating center. In these patients, LFPs were recorded either in the acute setting (3-5 days after DBS electrode implant) or in the chronic setting (during impulse generator replacement surgery). LFP oscillations were correlated with the Yale Global Tic Severity Scale (YGTSS) and the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) collected at baseline (before DBS surgery), 1 year after DBS, and at the last follow-up available. Results: We found that, at baseline, in the acute setting, the power of the oscillations included in the 5-15-Hz band, previously identified as TS biomarker, is correlated with the pathophysiology of tics, being significantly correlated with total YGTSS before DBS (Spearman's ρ = 0.701, p = 0.011). The power in the 5-15-Hz band was also correlated with the improvement in Y-BOCS after 1 year of DBS (Spearman's ρ = -0.587, p = 0.045), thus suggesting a relationship with the DBS effects on OCB comorbidities. Conclusions: Our observations confirm that the low-frequency (5-15-Hz) band is a significant biomarker of TS, being related to the severity of tics and, also to the long-term response on OCBs. This represents a step toward both the understanding of the mechanisms underlying DBS effects in TS and the development of adaptive DBS strategies.

Application of IBM Watson to Support Literature Reviews: A Preliminary Experience in Restorative Dentistry.

Literature reviews are crucial in the choice of the best personalized material type and restauration type in restorative dentistry. We developed an IBM-Watson based system to support literature search for restorative dentistry, and compared its results to a literature search performed by a trained professional. We found that our system could assist the researcher in performing a literature review, but the grounding semantic model needs to be refined in order to provide more extensive results.

Personally Collected Health Data for Precision Medicine and Longitudinal Research.

Health data autonomously collected by users are presently considered as largely beneficial for wellness, prevention, disease management, as well as clinical research, especially when longitudinal, chronic, home-based monitoring is needed. However, data quality and reliability are the main barriers to overcome, in order to exploit such potential. To this end, we designed, implemented, and tested a system to integrate patient-generated personally collected health data into the clinical research data workflow, using a standards-based architecture that ensures the fulfillment of the major requirements for digital data in clinical studies. The system was tested in a clinical investigation for the optimization of deep brain stimulation (DBS) therapy in patients with Parkinson's disease that required both the collection of patient-generated data and of clinical and neurophysiological data. The validation showed that the implemented system was able to provide a reliable solution for including the patient as direct digital data source, ensuring reliability, integrity, security, attributability, and auditability of data. These results suggest that personally collected health data can be used as a reliable data source in longitudinal clinical research, thus improving holistic patient's personal assessment and monitoring.

Eight-hours adaptive deep brain stimulation in patients with Parkinson disease.

OBJECTIVES: To assess the feasibility and clinical efficacy of local field potentials (LFPs)-based adaptive deep brain stimulation (aDBS) in patients with advanced Parkinson disease (PD) during daily activities in an open-label, nonblinded study. METHODS: We monitored neurophysiologic and clinical fluctuations during 2 perioperative experimental sessions lasting for up to 8 hours. On the first day, the patient took his/her daily medication, while on the second, he/she additionally underwent subthalamic nucleus aDBS driven by LFPs beta band power. RESULTS: The beta band power correlated in both experimental sessions with the patient's clinical state (Pearson correlation coefficient r = 0.506, p < 0.001, and r = 0.477, p < 0.001). aDBS after LFP changes was effective (30% improvement without medication [3-way analysis of variance, interaction day × medication p = 0.036; 30.5 ± 3.4 vs 22.2 ± 3.3, p = 0.003]), safe, and well tolerated in patients performing regular daily activities and taking additional dopaminergic medication. aDBS was able to decrease DBS amplitude during motor "on" states compared to "off" states (paired t test p = 0.046), and this automatic adjustment of STN-DBS prevented dyskinesias. CONCLUSIONS: The main findings of our study are that aDBS is technically feasible in everyday life and provides a safe, well-tolerated, and effective treatment method for the management of clinical fluctuations. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that for patients with advanced PD, aDBS is safe, well tolerated, and effective in controlling PD motor symptoms.

Point of Care Research: Integrating patient-generated data into electronic health records for clinical trials.

Integrating patient-generated data into clinical research would improve the reliability of results, especially when longitudinal, chronic, home-based monitoring is needed. To this end, we designed, implemented, and tested a system that allows integrating patient-generated data into electronic case report form (eCRF), using a standards based architecture that ensured the fulfillment of the major requirements for digital data in clinical studies. The system was tested in a clinical investigation for the optimization of deep brain stimulation therapy in patients with Parkinson's disease that required both the collection of patient-generated data and of clinical and neurophysiological data. The validation showed that the implemented system was able to provide a reliable solution for including the patient as direct digital data source, ensuring reliability, integrity, security, attributability, and auditability of data.