A Speech-Based Mobile Screening Tool for Mild Cognitive Impairment: Technical Performance and User Engagement Evaluation

Traditional screening methods for Mild Cognitive Impairment (MCI) face limitations in accessibility and scalability. To address this, we developed and validated a speech-based automatic screening app implementing three speech–language tasks with user-centered design and server–client architecture. T...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Bioengineering (Basel) Jg. 12; H. 2; S. 108
Hauptverfasser: Ruzi, Rukiye, Pan, Yue, Ng, Menwa Lawrence, Su, Rongfeng, Wang, Lan, Dang, Jianwu, Liu, Liwei, Yan, Nan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 01.02.2025
MDPI
Schlagworte:
Accessibility
Alzheimer's disease
Applications programs
automatic screening
Cognitive ability
Cognitive tasks
Disease
Geriatrics
Hypotheses
Impairment
Information processing
Language
MCI detection
Medical tests
mild cognitive impairment
Mobile computing
mobile health applications
Older people
Perception
Performance evaluation
Speech
Speech perception
Speech processing
Support vector machines
Technology adoption
Telemedicine
user engagement
User interface
User-centered design
China
mobile health applications
automatic screening
mild cognitive impairment
MCI detection
user engagement
ISSN:2306-5354, 2306-5354
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Traditional screening methods for Mild Cognitive Impairment (MCI) face limitations in accessibility and scalability. To address this, we developed and validated a speech-based automatic screening app implementing three speech–language tasks with user-centered design and server–client architecture. The app integrates automated speech processing and SVM classifiers for MCI detection. Functionality validation included comparison with manual assessment and testing in real-world settings (n = 12), with user engagement evaluated separately (n = 22). The app showed comparable performance with manual assessment (F1 = 0.93 vs. 0.95) and maintained reliability in real-world settings (F1 = 0.86). Task engagement significantly influenced speech patterns: users rating tasks as “most interesting” produced more speech content (p < 0.05), though behavioral observations showed consistent cognitive processing across perception groups. User engagement analysis revealed high technology acceptance (86%) across educational backgrounds, with daily cognitive exercise habits significantly predicting task benefit perception (H = 9.385, p < 0.01). Notably, perceived task difficulty showed no significant correlation with cognitive performance (p = 0.119), suggesting the system’s accessibility to users of varying abilities. While preliminary, the mobile app demonstrated both robust assessment capabilities and sustained user engagement, suggesting the potential viability of widespread cognitive screening in the geriatric population.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
These authors contributed equally to this work.
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering12020108