Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson's disease.

In the quantification of symptoms of Parkinson's disease (PD), healthcare professional assessments, patient reported outcomes (PRO), and medical device grade wearables are currently used. Recently, also commercially available smartphones and wearable devices have been actively researched in the detection of PD symptoms. The continuous, longitudinal, and automated detection of motor and especially non-motor symptoms with these devices is still a challenge that requires more research. The data collected from everyday life can be noisy and frequently contains artefacts, and novel detection methods and algorithms are therefore needed. 42 PD patients and 23 control subjects were monitored with Garmin Vivosmart 4 wearable device and asked to fill a symptom and medication diary with a mobile application, at home, for about four weeks. Subsequent analyses are based on continuous accelerometer data from the device. Accelerometer data from the Levodopa Response Study (MJFFd) were reanalyzed, with symptoms quantified with linear spectral models trained on expert evaluations present in the data. Variational autoencoders (VAE) were trained on both our study accelerometer data and on MJFFd to detect movement states (e.g., walking, standing). A total of 7590 self-reported symptoms were recorded during the study. 88.9% (32/36) of PD patients, 80.0% (4/5) of DBS PD patients and 95.5% (21/22) of control subjects reported that using the wearable device was very easy or easy. Recording a symptom at the time of the event was assessed as very easy or easy by 70.1% (29/41) of subjects with PD. Aggregated spectrograms of the collected accelerometer data show relative attenuation of low (<5Hz) frequencies in patients. Similar spectral patterns also separate symptom periods from immediately adjacent non-symptomatic periods. Discriminative power of linear models to separate symptoms from adjacent periods is weak, but aggregates show partial separability of patients vs. controls. The analysis reveals differential symptom detectability across movement tasks, motivating the third part of the study. VAEs trained on either dataset produced embedding from which movement states in MJFFd could be predicted. A VAE model was able to detect the movement states. Thus, a pre-detection of these states with a VAE from accelerometer data with good S/N ratio, and subsequent quantification of PD symptoms is a feasible strategy. The usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients. Finally, the usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients.

A prospective, double-blind, pilot, randomized, controlled trial of an "embodied" virtual reality intervention for adults with low back pain.

ABSTRACT: Adults with chronic low back pain, disability, moderate-to-severe pain, and high fear of movement and reinjury were recruited into a trial of a novel, automated, digital therapeutics, virtual reality, psychological intervention for pain (DTxP). We conducted a 3-arm, prospective, double-blind, pilot, randomized, controlled trial comparing DTxP with a sham placebo comparator and an open-label standard care. Participants were enrolled for 6 to 8 weeks, after which, the standard care control arm were rerandomized to receive either the DTxP or sham placebo. Forty-two participants completed assessments at baseline, immediately posttreatment (6-8 weeks), 9-week, and 5-month follow-up. We found that participants in the DTxP group reported greater reductions in fear of movement and better global impression of change when compared with sham placebo and standard care post treatment. No other group differences were noted at posttreatment or follow-up. When compared with baseline, participants in the DTxP group reported lower disability at 5-month follow-up, lower pain interference and fear of movement post treatment and follow-up, and lower pain intensity at posttreatment. The sham placebo group also reported lower disability and fear of movement at 5-month follow-up compared with baseline. Standard care did not report any significant changes. There were a number of adverse events, with one participant reporting a serious adverse event in the sham placebo, which was not related to treatment. No substantial changes in medications were noted, and participants in the DTxP group reported positive gaming experiences.

Body movement as a biomarker for use in chronic pain rehabilitation: An embedded analysis of an RCT of a virtual reality solution for adults with chronic pain.

Introduction: Chronic low back pain (CLBP) is a major public health problem. Reliably measuring the effects of chronic pain on movement and activity, and any changes due to treatment, is a healthcare challenge. A recently published paper demonstrated that a novel digital therapeutic (DTxP) was efficacious in reducing fear of movement and increasing the quality of life of adult patients with moderate to severe CLBP. In this paper, we report a study of how data from wearable devices collected in this study could be used as a digital measure for use in studies of chronic low back pain. Methods: Movement, electrodermal recording, general activity and clinical assessment data were collected in a clinical trial of a novel digital therapeutic intervention (DTxP) by using the sensors in commercial Garmin Vivosmart 4, Empatica Embrace2 and Oculus Quest wearables. Wearable data were collected during and between the study interventions (frequent treatment sessions of DTxP). Data were analyzed using exploratory statistical analysis. Results: A pattern of increased longitudinal velocity in the movement data collected with right-hand, left-hand, and head sensors was observed in the study population. Correlations were observed with the changes in clinical scales (Tampa Scale of Kinesiophobia, EQ5D Overall health VAS, and EQ5D QoL score). The strongest correlation was observed with the increased velocity of head and right-hand sensors (Spearman correlation with increasing head sensor velocity and Tampa Scale of Kinesiophobia -0.45, Overall health VAS +0.67 and EQ5D QoL score -0.66). The sample size limited interpretation of electrodermal and general activity data. Discussion/Conclusion: We found a novel digital signal for use in monitoring the efficacy of a digital therapeutics (DTxP) in adults with CLBP. We discuss the potential use of such movement based digital markers as surrogate or additional endpoints in studies of chronic musculoskeletal pain. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT04225884?cond=NCT04225884&draw=2&rank=1, identifier: NCT04225884.

Medication reminder service for mobile phones: an open feasibility study in patients with Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder in which drug dosing regimens become increasingly complicated with the progression of the disease. This poses a significant risk of nonadherence to drug dosing and a failure in treatment response. We hypothesized that a medication reminder delivered by short message service (SMS) could be one way to ameliorate the problem of medication errors. We conducted an open feasibility study in 50 patients with advanced PD. The subjects set up the process to receive reminders by a Web tool, after which they started to receive automatically transmitted text messages as a medication reminder for 4 weeks. In total, 35 of 50 subjects (70.0%) were able to set up the reminder system without any help. The majority (69%) of the subjects rated the set-up process as "very easy" or "easy." Almost all (41 subjects, 91%) felt that SMS reminders worked well for them, and only 4 subjects (9%) felt that SMS texts were totally valueless. Almost half of the subjects (22 of 45, 49%) considered that there were clear benefits, and an additional 17 subjects (38%) enjoyed some benefits in using the medication reminder system. Our results indicate that an SMS medication reminder system is a feasible method, even in subjects with advanced PD.