The Huntington's Disease Health Index: Initial Evaluation of a Disease-Specific Patient Reported Outcome Measure.

BACKGROUND: When developed properly, disease-specific patient reported outcome measures have the potential to measure relevant changes in how a patient feels and functions in the context of a therapeutic trial. The Huntington's Disease Health Index (HD-HI) is a multifaceted disease-specific patient reported outcome measure (PROM) designed specifically to satisfy previously published FDA guidance for developing PROMs for product development and labeling claims. OBJECTIVE: In preparation for clinical trials, we examine the validity, reliability, clinical relevance, and patient understanding of the Huntington's Disease Health Index (HD-HI). METHODS: We partnered with 389 people with Huntington's disease (HD) and caregivers to identify the most relevant questions for the HD-HI. We subsequently utilized two rounds of factor analysis, cognitive interviews with fifteen individuals with HD, and test-retest reliability assessments with 25 individuals with HD to refine, evaluate, and optimize the HD-HI. Lastly, we determined the capability of the HD-HI to differentiate between groups of HD participants with high versus low total functional capacity score, prodromal versus manifest HD, and normal ambulation versus mobility impairment. RESULTS: HD participants identified 13 relevant and unique symptomatic domains to be included as subscales in the HD-HI. All HD-HI subscales had a high level of internal consistency and reliability and were found by participants to have acceptable content, relevance, and usability. The total HD-HI score and each subscale score statistically differentiated between groups of HD participants with high versus low disease burden. CONCLUSION: Initial evaluation of the HD-HI supports its validity and reliability as a PROM for assessing how individuals with HD feel and function.

Deep Phenotyping of Parkinson's Disease.

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.

Patient-reported impact of symptoms in Huntington disease: PRISM-HD.

OBJECTIVE: To determine the frequency and relative importance of symptoms experienced by adults with Huntington disease (HD) and to identify factors associated with a higher disease burden. METHODS: We performed 40 qualitative interviews (n = 20 with HD, n = 20 caregivers) and analyzed 2,082 quotes regarding the symptomatic burden of HD. We subsequently performed a cross-sectional study with 389 participants (n = 156 with HD [60 of whom were prodromal], n = 233 caregivers) to assess the prevalence and relative importance (scale 0-4) of 216 symptoms and 15 symptomatic themes in HD. Cross-correlation analysis was performed based on sex, disease duration, age, number of CAG repeats, disease burden, Total Functional Capacity score, employment status, disease status, and ambulatory status. RESULTS: The symptomatic themes with the highest prevalence in HD were emotional issues (83.0%), fatigue (82.5%), and difficulty thinking (77.0%). The symptomatic themes with the highest relative importance to participants were difficulty thinking (1.91), impaired sleep or daytime sleepiness (1.90), and emotional issues (1.81). High Total Functional Capacity scores, being employed, and having prodromal HD were associated with a lower prevalence of symptomatic themes. Despite reporting no clinical features of the disease, prodromal individuals demonstrated high rates of emotional issues (71.2%) and fatigue (69.5%). There was concordance between the prevalence of symptoms reported by manifest individuals and caregivers. CONCLUSIONS: Many symptomatic themes affect the lives of those with HD. These themes have a variable level of importance to the HD population and are identified both by those with HD and by their caregivers.

Teleneurology and mobile technologies: the future of neurological care.

Neurological disorders are the leading cause of global disability. However, for most people around the world, current neurological care is poor. In low-income countries, most individuals lack access to proper neurological care, and in high-income countries, distance and disability limit access. With the global proliferation of smartphones, teleneurology - the use of technology to provide neurological care and education remotely - has the potential to improve and increase access to care for billions of people. Telestroke has already fulfilled this promise, but teleneurology applications for chronic conditions are still in their infancy. Similarly, few studies have explored the capabilities of mobile technologies such as smartphones and wearable sensors, which can guide care by providing objective, frequent, real-world assessments of patients. In low-income settings, teleneurology can increase the capacity of local care systems through professional development, diagnostic support and consultative services. In high-income settings, teleneurology is likely to promote the expansion and migration of neurological care away from institutions, incorporate systems of asynchronous communication (such as e-mail), integrate clinicians with diverse skill sets and reach new populations. Inertia, outdated policies and social barriers - especially the digital divide - will slow this progress at considerable cost. However, a future increasingly will be possible in which neurological care can be accessed by anyone, anywhere. Here, we examine the emerging evidence regarding the benefits of teleneurology for chronic conditions, its role and risks in low-income countries and the promise of mobile technologies to measure disease status and deliver care. We conclude by discussing the future trends, barriers and timing for the adoption of teleneurology.

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score.

Importance: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings. Objectives: To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy. Design, Setting, and Participants: This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months. Main Outcomes and Measures: Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication. Results: The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy. Conclusions and Relevance: Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.