Harnessing digital health to objectively assess cognitive impairment in people undergoing hemodialysis process: The Impact of cognitive impairment on mobility performance measured by wearables

Zhou, He; Al-Ali, Fadwa; Wang, Changhong; Hamad, Abdullah; Ibrahim, Rania; Talal, Talal; Najafi, Bijan

doi:10.1371/journal.pone.0225358

Cited by 9 publications

(15 citation statements)

References 62 publications

(87 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assessment of functionality in everyday activities and mobility performance metrics such as the gait speed assessment could also offer hints of cognitive decline. For instance, mobility performance metrics that involved multifaceted coordination between different parts of neuropsychology, such as cumulated posture duration and postural transition, could assist in discriminating those with and without CI [ 14 ]. CI suspicion may lead to a global and comprehensive patient assessment, especially screening for factors affecting cognitive performance.…”

Section: Introductionmentioning

confidence: 99%

Cognitive disorders in patients with chronic kidney disease: specificities of clinical assessment

Arıcı

Bachman

Barbieri

et al. 2021

Nephrology Dialysis Transplantation

View full text Add to dashboard Cite

Neurocognitive disorders are frequent among chronic kidney disease (CKD) patients. Identifying and characterizing cognitive impairment (CI) can help to assess the ability of adherence to CKD risk reduction strategy, identify potentially reversible causes of cognitive decline, modify pharmacotherapy, educate the patient and caregiver and provide appropriate patient and caregiver support. Numerous factors are associated with the development and progression of CI in CKD patients and various conditions can influence the results of cognitive assessment in these patients. Here we review clinical warning signs that should lead to cognitive screening; conditions frequent in CKD at risk to interfere with cognitive testing or performance, including specificities of cognitive assessment in dialysis patients or after kidney transplantation; and available tests for screening and observed cognitive patterns in CKD patients.

show abstract

Section: Introductionmentioning

confidence: 99%

Cognitive disorders in patients with chronic kidney disease: specificities of clinical assessment

Arıcı

Bachman

Barbieri

et al. 2021

Nephrology Dialysis Transplantation

View full text Add to dashboard Cite

show abstract

“…Even though it has high computational requirements, it delivered consistent accuracy (above 89%) across multiple studies. 33 Moreover, it incorporates characteristics of SVM which is the most commonly adopted AI algorithm, as evaluated in this study.…”

Section: Discussionmentioning

confidence: 99%

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

et al. 2022

View full text Add to dashboard Cite

Background The purpose of this scoping review was to explore the current applications of objective gait analysis using inertial measurement units, custom algorithms and artificial intelligence algorithms in detecting neurological and musculoskeletal gait altering pathologies from healthy gait patterns. Methods Literature searches were conducted of four electronic databases (Medline, PubMed, Embase and Web of Science) to identify studies that assessed the accuracy of these custom gait analysis models with inputs derived from wearable devices. Data was collected according to the preferred reporting items for systematic reviews and meta-analysis statement guidelines. Results A total of 23 eligible studies were identified for inclusion in the present review, including 10 custom algorithms articles and 13 artificial intelligence algorithms articles. Nine studies evaluated patients with Parkinson’s disease of varying severity and subtypes. Support vector machine was the commonest adopted artificial intelligence algorithm model, followed by random forest and neural networks. Overall classification accuracy was promising for articles that use artificial intelligence algorithms, with nine articles achieving more than 90% accuracy. Conclusions Current applications of artificial intelligence algorithms are reasonably effective discrimination between pathological and non-pathological gait. Of these, machine learning algorithms demonstrate the additional capacity to handle complicated data input, when compared to other custom algorithms. Notably, there has been increasing application of machine learning algorithms for conducting gait analysis. More studies are needed with unsupervised methods and in non-clinical settings to better reflect the community and home-based usage.

show abstract

“…Six studies (13%) originated from countries in the Western Pacific region, including Australia [ 54 , 55 ], Japan [ 56 , 57 ], and South Korea [ 58 , 59 ]. Only 1 study (2.2%) came from the Southeast Asian (China) [ 60 ] and Eastern Mediterranean (Qatar) [ 61 ] regions. Table 2 summarizes the studies’ regions of origin.…”

Section: Resultsmentioning

confidence: 99%

Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review

Dlima¹,

Shevade²,

Menezes³

et al. 2022

JMIR Bioinform Biotech

View full text Add to dashboard Cite

Background Digital phenotyping is the real-time collection of individual-level active and passive data from users in naturalistic and free-living settings via personal digital devices, such as mobile phones and wearable devices. Given the novelty of research in this field, there is heterogeneity in the clinical use cases, types of data collected, modes of data collection, data analysis methods, and outcomes measured. Objective The primary aim of this scoping review was to map the published research on digital phenotyping and to outline study characteristics, data collection and analysis methods, machine learning approaches, and future implications. Methods We utilized an a priori approach for the literature search and data extraction and charting process, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews). We identified relevant studies published in 2020, 2021, and 2022 on PubMed and Google Scholar using search terms related to digital phenotyping. The titles, abstracts, and keywords were screened during the first stage of the screening process, and the second stage involved screening the full texts of the shortlisted articles. We extracted and charted the descriptive characteristics of the final studies, which were countries of origin, study design, clinical areas, active and/or passive data collected, modes of data collection, data analysis approaches, and limitations. Results A total of 454 articles on PubMed and Google Scholar were identified through search terms associated with digital phenotyping, and 46 articles were deemed eligible for inclusion in this scoping review. Most studies evaluated wearable data and originated from North America. The most dominant study design was observational, followed by randomized trials, and most studies focused on psychiatric disorders, mental health disorders, and neurological diseases. A total of 7 studies used machine learning approaches for data analysis, with random forest, logistic regression, and support vector machines being the most common. Conclusions Our review provides foundational as well as application-oriented approaches toward digital phenotyping in health. Future work should focus on more prospective, longitudinal studies that include larger data sets from diverse populations, address privacy and ethical concerns around data collection from consumer technologies, and build “digital phenotypes” to personalize digital health interventions and treatment plans.

show abstract

Harnessing digital health to objectively assess cognitive impairment in people undergoing hemodialysis process: The Impact of cognitive impairment on mobility performance measured by wearables

Cited by 9 publications

References 62 publications

Cognitive disorders in patients with chronic kidney disease: specificities of clinical assessment

Cognitive disorders in patients with chronic kidney disease: specificities of clinical assessment

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review

Contact Info

Product

Resources

About