Exploration of digital biomarkers in chronic low back pain and Parkinson’s disease

Chronic pain and Parkinson’s disease are illnesses with personal disease progression, symptoms, and the experience of these. The ability to measure and monitor the symptoms by digitally and remotely is still limited. The aim was to study the usability and feasibility of real-world data from wearables, mobile devices, and patients in exploring digital biomarkers in these diseases. The key hypothesis was that this allows us to measure, analyse and detect clinically valid digital signals in movement, heart rate and skin conductance data. The laboratory grade data in chronic pain were collected in an open feasibility study by using a program and built-in sensors in virtual reality devices. The real-world data were collected with a randomized clinical study by clinical assessments, built-in sensors, and two wearables. The laboratory grade dataset in Parkinson’s disease was obtained from Michael J. Fox Foundation. It contained sensor data from three wearables with clinical assessments. The real-world data were collected with a clinical study by clinical assessments, a wearable, and a mobile application. With both diseases the laboratory grade data were first explored, before the real-world data were analyzed. The classification of chronic pain patients with the laboratory grade movement data was possible with a high accuracy. A novel real-world digital signal that correlates with clinical outcomes was found in chronic low back pain patients. A model that was able to detect different movement states was developed with laboratory grade Parkinson’s disease data. A detection of these states followed by the quantification of symptoms was found to be a potential method for the future. The usability of data collection methods in both diseases were found promising. In the future the analyses of movement data in these diseases could be further researched and validated as a movement based digital biomarkers to be used as a surrogate or additional endpoint. Combining the data science with the optimal usability enables the exploitation of digital biomarkers in clinical trials and treatment.Digitaalisten biomarkkereiden tunnistaminen kroonisessä alaselkäkivussa ja Parkinsonin taudissa Krooninen kipu ja Parkinsonin tauti ovat oireiden, oirekokemuksen sekä taudin kehittymisen osalta yksilöllisiä sairauksia. Kyky mitata ja seurata oireita etänä on vielä alkeellista. Väitöskirjassa tutkittiin kaupallisten mobiili- ja älylaitteiden hyödyntämistä digitaalisten biomarkkereiden löytämisessä näissä taudeissa. Pääolettamus oli, että kaupallisten älylaitteiden avulla kyetään tunnistamaan kliinisesti hyödyllisiä digitaalisia signaaleja. Kroonisen kivun laboratorio-tasoinen data kerättiin tätä varten kehitettyä ohjelmistoa sekä kaupallisia antureita käyttäen. Reaaliaikainen kipudata kerättiin erillisen hoito-ohjelmiston tehoa ja turvallisuutta mitanneessa kliinisessä tutkimuksessa sekä kliinisiä arviointeja että anturidataa hyödyntäen. Laboratorio-tasoinena datana Parkinsonin taudissa käytettiin Michael J. Fox Foundationin kolmella eri älylaitteella ja kliinisin arvioinnein kerättyä dataa. Reaaliaikainen data kerättiin käyttäen kliinisia arviointeja, älyranneketta ja mobiilisovellusta. Molempien indikaatioiden kohdalla laboratoriodatalle tehtyä eksploratiivista analyysia hyödynnettiin itse reaaliaikaisen datan analysoinnissa. Kipupotilaiden tunnistaminen laboratorio-tasoisesta liikedatasta oli mahdollista korkealla tarkkuudella. Reaaliaikaisesta liikedatasta löytyi uusi kliinisten arviointien kanssa korreloiva digitaalinen signaali. Parkinsonin taudin datasta kehitettiin uusi liiketyyppien tunnistamiseen tarkoitettu koneoppimis-malli. Sen hyödyntäminen liikedatan liiketyyppien tunnistamisessa ennen varsinaista oireiden mittausta on lupaava menetelmä. Käytettävyys molempien tautien reaaliaikaisissa mittausmenetelmissä havaittiin toimivaksi. Reaaliaikaiseen, kaupallisin laittein kerättävään liikedataan pohjautuvat digitaaliset biomarkkerit ovat lupaava kohde jatkotutkimukselle. Uusien analyysimenetelmien yhdistäminen optimaaliseen käytettävyyteen mahdollistaa tulevaisuudessa digitaalisten biomarkkereiden hyödyntämisen sekä kroonisten tautien kliinisessä tutkimuksessa että itse hoidossa

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

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

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

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.Peer reviewe

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

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

Abstract 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 (&lt;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