Your browser doesn't support javascript.
loading
Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment.
Schmitter-Edgecombe, Maureen; Luna, Catherine; Dai, Shenghai; Cook, Diane J.
Afiliación
  • Schmitter-Edgecombe M; Department of Psychology, Washington State University, Pullman, WA, USA.
  • Luna C; Department of Psychology, Washington State University, Pullman, WA, USA.
  • Dai S; College of Education, Washington State University, Pullman, WA, USA.
  • Cook DJ; School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA.
Clin Neuropsychol ; : 1-25, 2024 Mar 19.
Article en En | MEDLINE | ID: mdl-38503715
ABSTRACT

OBJECTIVE:

Extraction of digital markers from passive sensors placed in homes is a promising method for understanding real-world behaviors. In this study, machine learning (ML) and multilevel modeling (MLM) are used to examine types of digital markers and whether smart home sensors can predict cognitive functioning, lifestyle behaviors, and contextual factors measured through ecological momentary assessment (EMA).

METHOD:

Smart home sensors were installed in the homes of 44 community-dwelling midlife and older adults for 3-4 months. Sensor data were categorized into eight digital markers. Participants responded to iPad-delivered EMA prompts 4×/day for 2 wk. Prompts included an n-back task and survey on recent (past 2 h) lifestyle and contextual factors.

RESULTS:

ML marker rankings revealed that sensor counts (indicating increased activity) and time outside the home were among the most influential markers for all survey questions. Additionally, MLM revealed for every 1000 sensor counts, mental sharpness, social, physical, and cognitive EMA responses increased by 0.134-0.155 points on a 5-point scale. For every additional 30-minutes spent outside home, social, physical, and cognitive EMA responses increased by 0.596, 0.472, and 0.157 points. Advanced ML joint classification/regression significantly predicted EMA responses from smart home digital markers with error of 0.370 on a 5-point scale, and n-back performance with a normalized error of 0.040.

CONCLUSION:

Results from ML and MLM were complimentary and comparable, suggesting that machine learning may be used to develop generalized models to predict everyday cognition and track lifestyle behaviors and contextual factors that impact health outcomes using smart home sensor data.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin Neuropsychol Asunto de la revista: NEUROLOGIA / PSICOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin Neuropsychol Asunto de la revista: NEUROLOGIA / PSICOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido