A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity.

Drug development for mood disorders can greatly benefit from the development of robust, reliable, and objective biomarkers. The incorporation of smartphones and wearable devices in clinical trials provide a unique opportunity to monitor behavior in a non-invasive manner. The objective of this study is to identify the correlations between remotely monitored self-reported assessments and objectively measured activities with depression severity assessments often applied in clinical trials. 30 unipolar depressed patients and 29 age- and gender-matched healthy controls were enrolled in this study. Each participant's daily physiological, physical, and social activity were monitored using a smartphone-based application (CHDR MORE™) for 3 weeks continuously. Self-reported depression anxiety stress scale-21 (DASS-21) and positive and negative affect schedule (PANAS) were administered via smartphone weekly and daily respectively. The structured interview guide for the Hamilton depression scale and inventory of depressive symptomatology-clinical rated (SIGHD-IDSC) was administered in-clinic weekly. Nested cross-validated linear mixed-effects models were used to identify the correlation between the CHDR MORE™ features with the weekly in-clinic SIGHD-IDSC scores. The SIGHD-IDSC regression model demonstrated an explained variance (R2) of 0.80, and a Root Mean Square Error (RMSE) of ± 15 points. The SIGHD-IDSC total scores were positively correlated with the DASS and mean steps-per-minute, and negatively correlated with the travel duration. Unobtrusive, remotely monitored behavior and self-reported outcomes are correlated with depression severity. While these features cannot replace the SIGHD-IDSC for estimating depression severity, it can serve as a complementary approach for assessing depression and drug effects outside the clinic.

Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review.

BACKGROUND: Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. OBJECTIVE: This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. METHODS: This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. RESULTS: This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. CONCLUSION: mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.

Fit for purpose of on-the-road driving and simulated driving: A randomised crossover study using the effect of sleep deprivation.

INTRODUCTION: Drivers should be aware of possible impairing effects of alcohol, medicinal substance, or fatigue on driving performance. Such effects are assessed in clinical trials, including a driving task or related psychomotor tasks. However, a choice between predicting tasks must be made. Here, we compare driving performance with on-the-road driving, simulator driving, and psychomotor tasks using the effect of sleep deprivation. METHOD: This two-way cross over study included 24 healthy men with a minimum driving experience of 3000km per year. Psychomotor tasks, simulated driving, and on-the-road driving were assessed in the morning and the afternoon after a well-rested night and in the morning after a sleep-deprived night. Driving behaviour was examined by calculating the Standard Deviation of Lateral Position (SDLP). RESULTS: SDLP increased after sleep deprivation for simulated (10cm, 95%CI:6.7-13.3) and on-the-road driving (2.8cm, 95%CI:1.9-3.7). The psychomotor test battery detected effects of sleep deprivation in almost all tasks. Correlation between on-the-road tests and simulator SDLP after a well-rested night (0.63, p < .001) was not present after a night of sleep deprivation (0.31, p = .18). Regarding the effect of sleep deprivation on the psychomotor test battery, only adaptive tracking correlated with the SDLP of the driving simulator (-0.50, p = .02). Other significant correlations were related to subjective VAS scores. DISCUSSION: The lack of apparent correlations and difference in sensitivity of performance of the psychomotor tasks, simulated driving and, on-the-road driving indicates that the tasks may not be interchangeable and may assess different aspects of driving behaviour.

A crossover study evaluating the sex-dependent and sensitizing effects of sleep deprivation using a nociceptive test battery in healthy subjects.

AIM: We assessed whether total sleep deprivation (TSD) in combination with pain tests yields a reliable method to assess altered pain thresholds, which subsequently may be used to investigate (novel) analgesics in healthy subjects. METHODS: This was a two-part randomized crossover study in 24 healthy men and 24 women. Subjects were randomized 1:1 to first complete a day of nonsleep-deprived nociceptive threshold testing, followed directly by a TSD night and morning of sleep-deprived testing, or first complete the TSD night and morning sleep-deprived testing, returning 7 days later for a day of nonsleep-deprived testing. A validated pain test battery (heat, pressure, electrical burst and stair, cold pressor pain test and conditioned pain modulation [CPM] paradigm) and sleep questionnaires were performed. RESULTS: Subjects were significantly sleepier after TSD as measured using sleepiness questionnaires. Cold pressor pain tolerance (PTT, estimate of difference [ED] -10.8%, 95% CI -17.5 to -3.6%), CPM PTT (ED -0.69 mA, 95% CI -1.36 to -0.03 mA), pressure PTT (ED -11.2%, 95% CI -17.5% to -4.3%) and heat pain detection thresholds (ED -0.74 °C, 95% CI -1.34 to -0.14 °C) were significantly decreased after TSD compared to the baseline morning assessment in the combined analysis (men + women). Heat hyperalgesia was primarily driven by an effect of TSD in men, whereas cold and pressure hyperalgesia was primarily driven by the effects of TSD observed in women. CONCLUSIONS: TSD induced sex-dependent hyperalgesia on cold, heat and pressure pain, and CPM response. These results suggest that the TSD model may be suitable to evaluate (novel) analgesics in early-phase drug studies.

A Placebo-Controlled Study to Assess the Sensitivity of Finger Tapping to Medication Effects in Parkinson's Disease.

Background: Movement Disorder Society-Unified Parkinson's Rating Scale Part III (MDS-UPDRS III) is the gold standard for assessing medication effects in patients with Parkinson's disease (PD). However, short and rater-independent measurements would be ideal for future trials. Objectives: To assess the ability of 3 different finger tapping tasks to detect levodopa/carbidopa-induced changes over time and to determine their correlation and compare their discriminatory power with MDS-UPDRS III. Methods: This was a randomized, double-blind, crossover study in 20 patients with PD receiving levodopa/carbidopa and placebo capsules after overnight medication withdrawal. Pre- and up to 3.5 hours postdose, MDS-UPDRS III and tapping tasks were performed. Tasks included 2 touchscreen-based alternate finger tapping tasks (index finger versus index-middle finger tapping) and a thumb-index finger task using a goniometer. Results: In the alternate index finger tapping task, levodopa/carbidopa compared with placebo resulted in significantly faster (total taps: 12.5 [95% confidence interval, CI, 6.7-18.2]) and less accurate tapping (total spatial error: 240 mm [95% CI, 123-357 mm]) with improved rhythm (intertap interval standard deviation [SD], -16.3% [95% CI, -29.9% to 0.0%]). In the thumb-index finger task, tapping was significantly faster (mean opening velocity, 151 degree/s [64-237 degree/s]), with a higher mean amplitude (8.4 degrees [3.7-13.0 degrees]) and improved rhythm (intertap interval SD, -46.4% [95% CI, -63.7% to -20.9%]). The speed-related endpoints showed a moderate-to-strong correlation with the MDS-UPDRS III (r = 0.45-0.70). The effect sizes of total taps and spatial error in the alternate index finger tapping task and opening velocity in the thumb-index finger task were comparable with the MDS-UPDRS III. In contrast, the MDS-UPDRS III performed better than the alternate index-middle finger task. Conclusion: The alternate index finger and the thumb-index finger tapping tasks provide short, rater-independent measurements that are sensitive to levodopa/carbidopa effects with a similar effect size as the MDS-UPDRS III.

Cardiac age detected by machine learning applied to the surface ECG of healthy subjects: Creation of a benchmark.

OBJECTIVE: The aim of the present study was to develop a neural network to characterize the effect of aging on the ECG in healthy volunteers. Moreover, the impact of the various ECG features on aging was evaluated. METHODS & RESULTS: A total of 6228 healthy subjects without structural heart disease were included in this study. A neural network regression model was created to predict age of the subjects based on their ECG; 577 parameters derived from a 12­lead ECG of each subject were used to develop and validate the neural network; A tenfold cross-validation was performed, using 118 subjects for validation each fold. Using SHapley Additive exPlanations values the impact of the individual features on the prediction of age was determined. Of 6228 subjects tested, 1808 (29%) were females and mean age was 34 years, range 18-75 years. Physiologic age was estimated as a continuous variable with an average error of 6.9 ± 5.6 years (R2 = 0.72 ± 0.04). The correlation was slightly stronger for men (R2 = 0.74) than for women (R2 = 0.66). The most important features on the prediction of physiologic age were T wave morphology indices in leads V4 and V5, and P wave amplitude in leads AVR and II. CONCLUSION: The application of machine learning to the ECG using a neural network regression model, allows accurate estimation of physiologic cardiac age. This technique could be used to pick up subtle age-related cardiac changes, but also estimate the reversing of these age-associated effects by administered treatments.

Observing Altered Nociceptive Detection Thresholds in Patients With Persistent Spinal Pain Syndrome Type 2 With a Dorsal Root Ganglion Stimulator.

OBJECTIVES: There is a lack of clinically relevant measures for quantification of maladaptive mechanisms of the nociceptive system leading to chronic pain. Recently, we developed a method that tracks nociceptive detection thresholds (NDTs) using intraepidermal electrical stimulation. In this study, we explored the feasibility of using this NDT method in patients with persistent spinal pain syndrome type 2 (PSPS-T2) and its potential to enable observation of altered nociceptive processing induced by dorsal root ganglion (DRG) stimulation. In addition, we compared NDTs with quantitative sensory testing (QST) measurements and numeric rating scale (NRS). MATERIALS AND METHODS: A total of 12 patients with PSPS-T2 (seven men; 60.4 ± 12.3 years) experiencing chronic unilateral lower limb pain treated with DRG stimulation were included in the study. Both the NDT method and electrical and pressure QST methods were performed twice in the L5 dermatome on both the affected and the unaffected foot, once with the DRG stimulator turned off and, subsequently, once with the DRG stimulator turned on. RESULTS: The NDT method can be applied to patients with PSPS-T2. With the DRG stimulator turned off, NDTs on the affected side were significantly higher than on the unaffected side. This difference was no longer present once the DRG stimulator was turned on. Furthermore, DRG stimulation affected QST (electrical and pressure) values and NRS scores. Finally, NDTs showed larger contrasts between the sides than QST measures. CONCLUSIONS: The NDT method permitted observation of altered nociceptive function. The effect of DRG stimulation also was reflected in QST outcomes and NRS scores. The larger contrast between the sides for NDTs suggests that the NDT method might be valuable for future quantification of nociceptive dysfunction in chronic pain.

Development and technical validation of a smartphone-based pediatric cough detection algorithm.

INTRODUCTION: Coughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone-based algorithm that objectively and automatically counts cough sounds of children. METHODS: The training set was composed of 3228 pediatric cough sounds and 480,780 noncough sounds from various publicly available sources and continuous sound recordings of 7 patients admitted due to respiratory disease. A Gradient Boost Classifier was fitted on the training data, which was subsequently validated on recordings from 14 additional patients aged 0-14 admitted to the pediatric ward due to respiratory disease. The robustness of the algorithm was investigated by repeatedly classifying a recording with the smartphone-based algorithm during various conditions. RESULTS: The final algorithm obtained an accuracy of 99.7%, sensitivity of 47.6%, specificity of 99.96%, positive predictive value of 82.2% and negative predictive value 99.8% in the validation dataset. The correlation coefficient between manual- and automated cough counts in the validation dataset was 0.97 (p < .001). The intra- and interdevice reliability of the algorithm was adequate, and the algorithm performed best at an unobstructed distance of 0.5-1 m from the audio source. CONCLUSION: This novel smartphone-based pediatric cough detection application can be used for longitudinal follow-up in clinical care or as digital endpoint in clinical trials.

A cross-sectional study in healthy elderly subjects aimed at development of an algorithm to increase identification of Alzheimer pathology for the purpose of clinical trial participation.

BACKGROUND: In the current study, we aimed to develop an algorithm based on biomarkers obtained through non- or minimally invasive procedures to identify healthy elderly subjects who have an increased risk of abnormal cerebrospinal fluid (CSF) amyloid beta42 (Aß) levels consistent with the presence of Alzheimer's disease (AD) pathology. The use of the algorithm may help to identify subjects with preclinical AD who are eligible for potential participation in trials with disease modifying compounds being developed for AD. Due to this pre-selection, fewer lumbar punctures will be needed, decreasing overall burden for study subjects and costs. METHODS: Healthy elderly subjects (n = 200; age 65-70 (N = 100) and age > 70 (N = 100)) with an MMSE > 24 were recruited. An automated central nervous system test battery was used for cognitive profiling. CSF Aß1-42 concentrations, plasma Aß1-40, Aß1-42, neurofilament light, and total Tau concentrations were measured. Aß1-42/1-40 ratio was calculated for plasma. The neuroinflammation biomarker YKL-40 and APOE ε4 status were determined in plasma. Different mathematical models were evaluated on their sensitivity, specificity, and positive predictive value. A logistic regression algorithm described the data best. Data were analyzed using a 5-fold cross validation logistic regression classifier. RESULTS: Two hundred healthy elderly subjects were enrolled in this study. Data of 154 subjects were used for the per protocol analysis. The average age of the 154 subjects was 72.1 (65-86) years. Twenty-four (27.3%) were Aß positive for AD (age 65-83). The results of the logistic regression classifier showed that predictive features for Aß positivity/negativity in CSF consist of sex, 7 CNS tests, and 1 plasma-based assay. The model achieved a sensitivity of 70.82% (± 4.35) and a specificity of 89.25% (± 4.35) with respect to identifying abnormal CSF in healthy elderly subjects. The receiver operating characteristic curve showed an AUC of 65% (± 0.10). CONCLUSION: This algorithm would allow for a 70% reduction of lumbar punctures needed to identify subjects with abnormal CSF Aß levels consistent with AD. The use of this algorithm can be expected to lower overall subject burden and costs of identifying subjects with preclinical AD and therefore of total study costs. TRIAL REGISTRATION: ISRCTN.org identifier: ISRCTN79036545 (retrospectively registered).

Safety, pharmacokinetics and exploratory pro-cognitive effects of HTL0018318, a selective M1 receptor agonist, in healthy younger adult and elderly subjects: a multiple ascending dose study.

BACKGROUND: The cholinergic system and M1 receptor remain an important target for symptomatic treatment of cognitive dysfunction. The selective M1 receptor partial agonist HTL0018318 is under development for the symptomatic treatment of Dementia's including Alzheimer's disease (AD) and dementia with Lewy bodies (DLB). We investigated the safety, tolerability, pharmacokinetics and exploratory pharmacodynamics of multiple doses of HTL0018318 in healthy younger adults and elderly subjects. METHODS: This randomised, double blind, placebo-controlled study was performed, investigating oral doses of 15-35 mg/day HTL0018318 or placebo in 7 cohorts of healthy younger adult (n = 36; 3 cohorts) and elderly (n = 50; 4 cohorts) subjects. Safety, tolerability and pharmacokinetic measurements were performed. Pharmacodynamics were assessed using a battery of neurocognitive tasks and electrophysiological biomarkers of synaptic and cognitive functions. RESULTS: HTL0018318 was generally well-tolerated in multiple doses up to 35 mg/day and were associated with mild or moderate cholinergic adverse events. There were modest increases in blood pressure and pulse rate when compared to placebo-treated subjects, with tendency for the blood pressure increase to attenuate with repeated dosing. There were no clinically significant observations or changes in blood and urine laboratory measures of safety or abnormalities in the ECGs and 24-h Holter assessments. HTL0018318 plasma exposure was dose-proportional over the range 15-35 mg. Maximum plasma concentrations were achieved after 1-2 h. The apparent terminal half-life of HTL0018318 was 16.1 h (± 4.61) in younger adult subjects and 14.3 h (± 2.78) in elderly subjects at steady state. HTL0018318 over the 10 days of treatment had significant effects on tests of short-term (working) memory (n-back) and learning (Milner maze) with moderate to large effect sizes. CONCLUSION: Multiple doses of HTL0018138 showed well-characterised pharmacokinetics and were safe and generally well-tolerated in the dose range studied. Pro-cognitive effects on short-term memory and learning were demonstrated across the dose range. These data provide encouraging data in support of the development of HTL0018138 for cognitive dysfunction in AD and DLB. TRIAL REGISTRATION: Netherlands Trial Register identifier NTR5781 . Registered on 22 March 2016.

Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm.

Introduction: The duration and frequency of crying of an infant can be indicative of its health. Manual tracking and labeling of crying is laborious, subjective, and sometimes inaccurate. The aim of this study was to develop and technically validate a smartphone-based algorithm able to automatically detect crying. Methods: For the development of the algorithm a training dataset containing 897 5-s clips of crying infants and 1,263 clips of non-crying infants and common domestic sounds was assembled from various online sources. OpenSMILE software was used to extract 1,591 audio features per audio clip. A random forest classifying algorithm was fitted to identify crying from non-crying in each audio clip. For the validation of the algorithm, an independent dataset consisting of real-life recordings of 15 infants was used. A 29-min audio clip was analyzed repeatedly and under differing circumstances to determine the intra- and inter- device repeatability and robustness of the algorithm. Results: The algorithm obtained an accuracy of 94% in the training dataset and 99% in the validation dataset. The sensitivity in the validation dataset was 83%, with a specificity of 99% and a positive- and negative predictive value of 75 and 100%, respectively. Reliability of the algorithm appeared to be robust within- and across devices, and the performance was robust to distance from the sound source and barriers between the sound source and the microphone. Conclusion: The algorithm was accurate in detecting cry duration and was robust to various changes in ambient settings.

A Computerized Test Battery to Study Pharmacodynamic Effects on the Central Nervous System of Cholinergic Drugs in Early Phase Drug Development.

Investigating potential pharmacodynamic effects in an early phase of central nervous system (CNS) drug research can provide valuable information for further development of new compounds. A computerized and thoroughly validated battery of neuropsychological and neurophysiological tests has been shown to be sensitive to detect drug-induced effects of multiple new and existing compounds. The test battery covers the main CNS domains, which have been shown to respond to drug effects and can be repeatedly administered following drug administration to characterize the concentration-effect profile of a drug. The standard tests in the battery are saccadic eye movement, smooth pursuit eye movement, the Bowdle visual analog scale (VAS), the Bond and Lader VAS, body sway, adaptive tracking, visual verbal learning, and quantitative electroencephalography (qEEG). However, the test battery is adaptive in nature, meaning that it can be composed and adjusted with tests fit to investigate specific drug classes, or even specific receptors. Showing effects of new cholinergic drugs designed to have a pro-cognitive outcome has been difficult. The pharmacological challenge model is a tool for early proof-of-pharmacology. Here, a marketed drug is used to induce temporary and reversible disease-like symptoms in healthy subjects, via a pharmacological mechanism related to the disease that is targeted as indication for the new compound. The test battery was implemented to investigate the potential of the nicotinic receptor antagonist mecamylamine to be used as a challenge model for cholinergic dysfunction, as seen in neurodegenerative disorders. A worsening of scores in a dose dependent manner on the visual verbal learning test (VVLT; a test for learning and memory abilities) and the adaptive tracking test (a measure of visuomotor control and arousal), in particular, showed that the test battery is sensitive to showing acute pharmacodynamic effect after administration of anti-cholinergic drugs.