Perceptions of digital technology use for monitoring health in people living with neurological disorders.

BACKGROUND: Digital technology offers individuals the opportunity to monitor their symptoms. Information gathered from apps, devices, and web platforms may be used to direct clinical care and to support research. AIM: Using this survey, we aim to explore the views of people attending the Anne Rowling Regenerative Neurology Clinic (ARRNC) and their relatives/caregivers regarding the use of digital health technologies to monitor health. METHOD: People attending the ARRNC were invited to complete a structured 18-item questionnaire evaluating their experience and attitudes to using technology for monitoring health. People with neurodegenerative disease (pwND) and their caregivers completed a mix of closed and open-ended questions. RESULTS: 249 people responded, 51 relatives/caregivers and 198 pwND. 67.1% (n= 167) of respondents do not use technology for monitoring their health, but 98.2% (n = 164) of these are interested in their future use. 29.7% (n = 74) respondents currently use a smartphone for health monitoring, 20.9% (n = 52) use a wearable device, and 13.3% (n = 33) use a tablet. 79.3% (n = 65) of users use their technology for monitoring physical activity, 37.8% (n = 31) use it for assisting with self-management, and 41.5% (n = 34) use it for tracking sleep. Factors which would encourage use of technology are ease of access to devices and ability to monitor health. Respondents reported data security concerns and difficulty using technology as potential barriers. CONCLUSION: People attending a neurology clinic, and their relatives/caregivers, support the use of digital technologies as an adjunct to routine care. There is a need for coordinated digital strategies for development and delivery of validated measures.

A systematic review and narrative analysis of digital speech biomarkers in Motor Neuron Disease.

Motor Neuron Disease (MND) is a progressive and largely fatal neurodegeneritve disorder with a lifetime risk of approximately 1 in 300. At diagnosis, up to 25% of people with MND (pwMND) exhibit bulbar dysfunction. Currently, pwMND are assessed using clinical examination and diagnostic tools including the ALS Functional Rating Scale Revised (ALS-FRS(R)), a clinician-administered questionnaire with a single item on speech intelligibility. Here we report on the use of digital technologies to assess speech features as a marker of disease diagnosis and progression in pwMND. Google Scholar, PubMed, Medline and EMBASE were systematically searched. 40 studies were evaluated including 3670 participants; 1878 with a diagnosis of MND. 24 studies used microphones, 5 used smartphones, 6 used apps, 2 used tape recorders and 1 used the Multi-Dimensional Voice Programme (MDVP) to record speech samples. Data extraction and analysis methods varied but included traditional statistical analysis, CSpeech, MATLAB and machine learning (ML) algorithms. Speech features assessed also varied and included jitter, shimmer, fundamental frequency, intelligible speaking rate, pause duration and syllable repetition. Findings from this systematic review indicate that digital speech biomarkers can distinguish pwMND from healthy controls and can help identify bulbar involvement in pwMND. Preliminary evidence suggests digitally assessed acoustic features can identify more nuanced changes in those affected by voice dysfunction. No one digital speech biomarker alone is consistently able to diagnose or prognosticate MND. Further longitudinal studies involving larger samples are required to validate the use of these technologies as diagnostic tools or prognostic biomarkers.

The brief Dimensional Apathy Scale: A short clinical assessment of apathy.

Objective: Apathy is a prominent syndrome across neurodegenerative diseases. The Dimensional Apathy Scale (DAS) assesses three apathy subtypes-executive, emotional, and initiation-and is sensitive and valid in amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), and Parkinson's disease. This study describes the development of the brief DAS (b-DAS), which will enable apathy to be swiftly detected in the clinic.Method: 102 ALS and 102 AD patients' previously collected data were used. Mokken analyses were performed on item-level data of each informant/carer-rated DAS subscale (executive, emotional, and initiation) for the initial scale reduction. Item-total correlational analyses against standard apathy (convergent validity criteria) and depression (divergent validity criteria) measures and qualitative examination of items aided final item selection. Receiver operating curve analysis determined optimal cutoffs for the reduced subscales.Results: Mokken analyses suggested unidimensionality of each DAS subscale. Three items were removed that failed to satisfy monotone homogeneity model requirements, three items were removed due to validity criteria not being met, and six items were removed due to a combination of lower item scalability and item-total correlations. Item-theme examination further reduced the b-DAS to nine items, three per subscale, with a supplemental awareness deficit assessment being added. Sensitivity- and specificity-based optimal cutoffs were calculated for each b-DAS subscale.Conclusions: This study presents the b-DAS, an informant/carer-based robust yet short multidimensional apathy instrument with good convergent and divergent validity, with recommended clinical cutoffs. The b-DAS is appropriate for use in the clinic and for research to quickly and comprehensively screen for apathy subtype impairments.