Comparison of urodynamic parameters with respect to neurological levels in post-traumatic spinal cord injury patients

Background: Urodynamic evaluation is mandatory in order to correctly assess and classify bladder dysfunction in spinal cord injury (SCI) patients. Study investigated patterns of neurogenic bladder dysfunction in patients with post traumatic spinal cord injury and assessed the relationship of detrusor leak point pressure with compliance, post void residual urine volume and maximum cystometric capacity.Methods: Eighty six patients with neurogenic bladder secondary to traumatic spinal cord injury (SCI) underwent cystometry with electromyography (EMG). T-test was used to compare detrusor leak point pressure (LPP) between complete and incomplete injury groups. Pearson correlation test was used to seek correlation between detrusor LPP and compliance, post void residual volume (PRV) and maximum cystometric capacity (MCC).Results: Mean detrusor LPP in suprasacral complete injury group, suprasacral incomplete injury group and sacral complete injury was 52±21 cm of H2O, 53±18 cm of H2O and 16±9 cm of H2O respectively. No significant difference in detrusor LPP was found between suprasacral complete and incomplete group on t-Test (p= 0.571068). Significant difference in detrusor LPP was found between suprasacral and sacral group (p= 5.71891E-12). Mean compliance in sacral injury group was 24±16 and in suprasacral complete injury group was 5±6. Mean compliance in suprasacral incomplete injury group was 4±2. Pearson correlation showed negative correlation (r = -0.6918934) between detrusor leak point pressure and compliance (p= 1.2744E-13). Negative correlation (r = -0.311409922) was observed between detrusor leak point pressure and post leak/ void residual urine volume (p= 0.003335033) and between detrusor LPP and maximum cystometric capacity (r = -0.31354), (p= 0.003115).Conclusions: Significant difference in urodynamic parameters exists between sacral and suprasacral injury patients. However there is no significant difference in urodynamic parameters between complete and incomplete injury at suprasacral level

Barriers of Drug Adherence among Patients with Epilepsy: in Tertiary Care Hospital, South India

Introduction: Epilepsy is a treatable and curable brain disorder. However major proportion of individuals with this disease in developing countries receives no treatment because of misunderstandings of the public. Other than that, poor adherence to ordered medication is considered the primary cause of drug therapy failure in epilepsy. This study conducted to assess the adherence pattern to antiepileptic regimen, among patients with epilepsy and to identify the clinical and patient-related factors contributing as barriers. Methods: A cross sectional survey design was used in 100 epilepsy patients in an Outpatient unit of tertiary care center. A Convenient sampling technique was used to enroll the patients who meet inclusion criteria. Structured interview with pre-tested questionnaire and eight item Morisky Medication Adherence Scale was used to collect the data. Descriptive and inferential statistics were used for analysis of data. Descriptive statistics (mean, standard deviation, frequency and percentages) were used to describe the clinical and demographic variables of study participants. The determinants of medication adherence were analyzed using Chi-Square test and independent student t- test. The analysis was done with SPSS 20th version. Results: Majority (71%) of patients were not adherent to antiepileptic treatment. Severity of seizure (indicated by the presence of seizure last year), medication frequency and complexity of treatment were found to have significant association with the Anti-Epileptic Drugs (AED) adherence status. Status of adherence is significantly associated with frequency of seizure/year and positive life style. Conclusion: As Medication adherence was observed to be low, services for adherence counseling and health educational interventions in the epilepsy clinics is recommended

It’s a long shot, but it just might work! Perspectives on the future of medicine

Abstract What does the future of medicine hold? We asked six researchers to share their most ambitious and optimistic views of the future, grounded in the present but looking out a decade or more from now to consider what’s possible. They paint a picture of a connected and data-driven world in which patient value, patient feedback, and patient empowerment shape a continually learning system that ensures each patient’s experience contributes to the improved outcome of every patient like them, whether it be through clinical trials, data from consumer devices, hacking their medical devices, or defining value in thoughtful new ways

Classifying depression symptom severity: Assessment of speech representations in personalized and generalized machine learning models

There is an urgent need for new methods that improve the management and treatment of Major Depressive Disorder (MDD). Speech has long been regarded as a promising digital marker in this regard, with many works highlighting that speech changes associated with MDD can be captured through machine learning models. Typically, findings are based on cross-sectional data, with little work exploring the advantages of personalization in building more robust and reliable models. This work assesses the strengths of different combinations of speech representations and machine learning models, in personalized and generalized settings in a two-class depression severity classification paradigm. Key results on a longitudinal dataset highlight the benefits of personalization. Our strongest performing model set-up utilized self-supervised learning features and convolutional neural network (CNN) and long short-term memory (LSTM) back-end

Digital endpoints in clinical trials of Alzheimer's disease and other neurodegenerative diseases: challenges and opportunities.

Alzheimer's disease (AD) and other neurodegenerative diseases such as Parkinson's disease (PD) and Huntington's disease (HD) are associated with progressive cognitive, motor, affective and consequently functional decline considerably affecting Activities of Daily Living (ADL) and quality of life. Standard assessments, such as questionnaires and interviews, cognitive testing, and mobility assessments, lack sensitivity, especially in early stages of neurodegenerative diseases and in the disease progression, and have therefore a limited utility as outcome measurements in clinical trials. Major advances in the last decade in digital technologies have opened a window of opportunity to introduce digital endpoints into clinical trials that can reform the assessment and tracking of neurodegenerative symptoms. The Innovative Health Initiative (IMI)-funded projects RADAR-AD (Remote assessment of disease and relapse-Alzheimer's disease), IDEA-FAST (Identifying digital endpoints to assess fatigue, sleep and ADL in neurodegenerative disorders and immune-mediated inflammatory diseases) and Mobilise-D (Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement) aim to identify digital endpoints relevant for neurodegenerative diseases that provide reliable, objective, and sensitive evaluation of disability and health-related quality of life. In this article, we will draw from the findings and experiences of the different IMI projects in discussing (1) the value of remote technologies to assess neurodegenerative diseases; (2) feasibility, acceptability and usability of digital assessments; (3) challenges related to the use of digital tools; (4) public involvement and the implementation of patient advisory boards; (5) regulatory learnings; and (6) the significance of inter-project exchange and data- and algorithm-sharing

Automatic assessment of the 2-minute walk distance for remote monitoring of people with multiple sclerosis

The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes

Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis

The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes

The Relationship between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multi-centre Longitudinal Observational Study

Research in mental health has implicated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography, is not suitable for long-term, continuous, monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. The main aim of this study was to devise and extract sleep features, from data collected using a wearable device, and analyse their correlation with depressive symptom severity and sleep quality, as measured by the self-assessed Patient Health Questionnaire 8-item. Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every two weeks by the PHQ-8. The data used in this paper included 2,812 PHQ-8 records from 368 participants recruited from three study sites in the Netherlands, Spain, and the UK.We extracted 21 sleep features from Fitbit data which describe sleep in the following five aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z-test was used to evaluate the significance of the coefficient of each feature. We tested our models on the entire dataset and individually on the data of three different study sites. We identified 16 sleep features that were significantly correlated with the PHQ-8 score on the entire dataset. Associations between sleep features and the PHQ-8 score varied across different sites, possibly due to the difference in the populations

Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device:Multicenter Longitudinal Observational Study

BACKGROUND: Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. OBJECTIVE: The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). METHODS: Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. RESULTS: We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. CONCLUSIONS: We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant

The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones

BACKGROUND: Most smartphones and wearables are currently equipped with location sensing (using GPS and mobile network information), which enables continuous location tracking of their users. Several studies have reported that various mobility metrics, as well as home stay, that is, the amount of time an individual spends at home in a day, are associated with symptom severity in people with major depressive disorder (MDD). Owing to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with MDD symptoms. OBJECTIVE: The objective of this study is to examine the relationship between the overall severity of depressive symptoms, as assessed by the 8-item Patient Health Questionnaire, and median daily home stay over the 2 weeks preceding the completion of a questionnaire in individuals with MDD. METHODS: We used questionnaire and geolocation data of 164 participants with MDD collected in the observational Remote Assessment of Disease and Relapse-Major Depressive Disorder study. The participants were recruited from three study sites: King's College London in the United Kingdom (109/164, 66.5%); Vrije Universiteit Medisch Centrum in Amsterdam, the Netherlands (17/164, 10.4%); and Centro de Investigación Biomédica en Red in Barcelona, Spain (38/164, 23.2%). We used a linear regression model and a resampling technique (n=100 draws) to investigate the relationship between home stay and the overall severity of MDD symptoms. Participant age at enrollment, gender, occupational status, and geolocation data quality metrics were included in the model as additional explanatory variables. The 95% 2-sided CIs were used to evaluate the significance of model variables. RESULTS: Participant age and severity of MDD symptoms were found to be significantly related to home stay, with older (95% CI 0.161-0.325) and more severely affected individuals (95% CI 0.015-0.184) spending more time at home. The association between home stay and symptoms severity appeared to be stronger on weekdays (95% CI 0.023-0.178, median 0.098; home stay: 25th-75th percentiles 17.8-22.8, median 20.9 hours a day) than on weekends (95% CI -0.079 to 0.149, median 0.052; home stay: 25th-75th percentiles 19.7-23.5, median 22.3 hours a day). Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay (employed participants: 25th-75th percentiles 16.1-22.1, median 19.7 hours a day; unemployed participants: 25th-75th percentiles 20.4-23.5, median 22.6 hours a day). CONCLUSIONS: Our findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future studies. In addition, they illustrate that passive sensing of individuals with depression is feasible and could provide clinically relevant information to monitor the course of illness in patients with MDD