Upper limb intention tremor assessment: opportunities and challenges in wearable technology.

BACKGROUND: Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature. METHODOLOGY & RESULTS: This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table. CONCLUSIONS: Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.

Less Pain, Better Sleep? The Effect of a Multidisciplinary Back Pain App on Sleep Quality in Individuals Suffering from Back Pain - a Secondary Analysis of App User Data.

PURPOSE: Mobile health solutions are finding their way into health systems. The Kaia app has been shown to be able to reduce back pain in two studies. Since pain often comes along with disturbed sleep and both symptoms are strongly related we investigated whether the Kaia app training is associated with improved sleep quality. METHODS: User data of individuals with back pain were collected in two app versions (cohort 1: N = 180; cohort 2: N = 159). We analyzed the ratings of sleep quality and pain intensity on a 11-point numeric ratings scale (NRS; 0-10) both at the beginning of usage (baseline: BL) and on the individual last day of usage (follow-up: LU) within a 3-month training program. RESULTS: In both cohorts, we found a significant reduction in pain intensity from BL to LU (cohort 1: MBL = 4.80; SD = 1.59 to MLU = 3.75; SD = 1.76, Δpain = -1.04; SD = 2.12; t(158) = 6.207; p<.001/cohort 2: MBL = 4.20; SD = 1.98 to MLU = 3.65; SD = 1.78; Δpain = -0.50; SD = 2.04; t(147) = 3.001; p = 0.003) and a significant improvement of sleep quality (cohort 1: MBL = 5.76; SD = 2.12 to MLU = 6.56; SD = 1.72; Δsleep = t(158) = 4.310; p < 0.001/cohort 2: MBL = 6.08; SD = 2.08 to MLU = 6.76; SD = 1.55; Δsleep = 0.67; SD = 2.13; sleep: t(147) = 3.825; p < 0.001). Interestingly, improvement of sleep quality was not fully mediated by pain reduction. CONCLUSION: Our analysis underlines the relationship between pain and sleep in the clinical context. Improvement of sleep quality came along with pain reduction and vice versa. Further study should explain the exact mechanisms of action which are associated with the improvement of both symptom parameters.

Digital Treatment of Back Pain versus Standard of Care: The Cluster-Randomized Controlled Trial, Rise-uP.

PURPOSE: Non-specific low back pain (NLBP) causes an enormous burden to patients and tremendous costs for health care systems worldwide. Frequently, treatments are not oriented to existing guidelines. In the future, digital elements may be promising tools to support guideline-oriented treatment in a broader range of patients. The cluster-randomized controlled "Rise-uP" trial aims to support a General Practitioner (GP)-centered back pain treatment (Registration No: DRKS00015048) and includes the following digital elements: 1) electronic case report form (eCRF), 2) a treatment algorithm for guideline-based clinical decision making of GPs, 3) teleconsultation between GPs and pain specialists for patients at risk for development of chronic back pain, and 4) a multidisciplinary mobile back pain app for all patients (Kaia App). METHODS: In the Rise-uP trial, 111 GPs throughout Bavaria (southern Germany) were randomized either to the Rise-uP intervention group (IG) or the control group (CG). Rise-uP patients were treated according to the guideline-oriented Rise-uP treatment algorithm. Standard of care was applied to the CG patients with consideration given to the "National guideline for the treatment of non-specific back pain". Pain rating on the numeric rating scale was the primary outcome measure. Psychological measures (anxiety, depression, stress), functional ability, as well as physical and mental wellbeing, served as secondary outcomes. All values were assessed at the beginning of the treatment and at 3-month follow-ups. RESULTS: In total, 1245 patients (IG: 933; CG: 312) with NLBP were included in the study. The Rise-uP group showed a significantly stronger pain reduction compared to the control group after 3 months (IG: M=-33.3% vs CG: M=-14.3%). The Rise-uP group was also superior in secondary outcomes. Furthermore, high-risk patients who received a teleconsultation showed a larger decrease in pain intensity (-43.5%) than CG patients (-14.3%). ANCOVA analysis showed that the impact of teleconsultation was mediated by an increased training activity in the Kaia App. CONCLUSION: Our results show the superiority of the innovative digital treatment algorithm realized in Rise-uP, even though the CG also received relevant active treatment by their GPs. This provides clear evidence that digital treatment may be a promising tool to improve the quality of treatment of non-specific back pain. In 2021, analyses of routine data from statutory health insurances will enable us to investigate the cost-effectiveness of digital treatment.

App-based multidisciplinary back pain treatment versus combined physiotherapy plus online education: a randomized controlled trial.

Non-specific low back pain (LBP) is one of the leading causes of global disability. Multidisciplinary pain treatment (MPT) programs comprising educational, physical, and psychological interventions have shown positive treatment effects on LBP. Nonetheless, such programs are costly and treatment opportunities are often limited to specialized medical centers. mHealth and other digital interventions may be a promising method to successfully support patient self-management in LBP. To address these issues, we investigated the clinical effects of a multidisciplinary mHealth back pain App (Kaia App) in a randomized controlled trial (registered at German Clinical Trials Register under DRKS00016329). One-hundred one adult patients with non-specific LBP from 6 weeks to 1 year were randomly assigned to an intervention group or a control group. In the intervention group, the Kaia App was provided for 3 months. Control treatment consisted of six individual physiotherapy sessions over 6 weeks and high-quality online education. The primary outcome, pain intensity, was assessed at 12-week follow-up on an 11-point numeric rating scale (NRS). Our per-protocol analysis showed no significant differences between the groups at baseline (Kaia App group: M = 5.10 (SD = 1.07) vs. control group: M = 5.41 (SD = 1.15). At 12-week follow-up the Kaia App group reported significantly lower pain intensity (M = 2.70 (SD = 1.51)) compared to the control group (M = 3.40 (SD = 1.63)). Our results indicate that the Kaia App as a multidisciplinary back pain app is an effective treatment in LBP patients and is superior to physiotherapy in combination with online education.

Brain dysfunction in chronic pain patients assessed by resting-state electroencephalography.

Chronic pain is a common and severely disabling disease whose treatment is often unsatisfactory. Insights into the brain mechanisms of chronic pain promise to advance the understanding of the underlying pathophysiology and might help to develop disease markers and novel treatments. Here, we systematically exploited the potential of electroencephalography to determine abnormalities of brain function during the resting state in chronic pain. To this end, we performed state-of-the-art analyses of oscillatory brain activity, brain connectivity, and brain networks in 101 patients of either sex suffering from chronic pain. The results show that global and local measures of brain activity did not differ between chronic pain patients and a healthy control group. However, we observed significantly increased connectivity at theta (4-8 Hz) and gamma (>60 Hz) frequencies in frontal brain areas as well as global network reorganization at gamma frequencies in chronic pain patients. Furthermore, a machine learning algorithm could differentiate between patients and healthy controls with an above-chance accuracy of 57%, mostly based on frontal connectivity. These results suggest that increased theta and gamma synchrony in frontal brain areas are involved in the pathophysiology of chronic pain. Although substantial challenges concerning the reproducibility of the findings and the accuracy, specificity, and validity of potential electroencephalography-based disease markers remain to be overcome, our study indicates that abnormal frontal synchrony at theta and gamma frequencies might be promising targets for noninvasive brain stimulation and/or neurofeedback approaches.