AUTHOR=Kimura Noriyuki , Aso Yasuhiro , Yabuuchi Kenichi , Ishibashi Masato , Hori Daiji , Sasaki Yuuki , Nakamichi Atsuhito , Uesugi Souhei , Fujioka Hideyasu , Iwao Shintaro , Jikumaru Mika , Katayama Tetsuji , Sumi Kaori , Eguchi Atsuko , Nonaka Satoshi , Kakumu Masakazu , Matsubara Etsuro 

TITLE=Modifiable Lifestyle Factors and Cognitive Function in Older People: A Cross-Sectional Observational Study

JOURNAL=Frontiers in Neurology

VOLUME=Volume 10 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2019.00401

DOI=10.3389/fneur.2019.00401

ISSN=1664-2295

ABSTRACT=Background: The development of evidence-based interventions for the delaying or preventing cognitive impairment is an important challenge. Most previous studies using self-report questionnaires may have problems with reliability and consistency due to recall bias or misclassification among older people. Therefore, objective measurement of lifestyle components is needed to confirm the relationships between lifestyle factors and cognitive function.
Aims: The current study examined the relationship between lifestyle factors collected with wearable sensors and cognitive function among community-dwelling older people using machine learning.
Methods: 855 Participants (mean age: 73.8 years) wore a wristband sensor for 3-7 days every 3 months. Various lifestyle parameters were measured, including walking steps, conversation time, total sleep time (TST), sleep efficacy, time awake after sleep onset, awakening count, napping time, and heart rate. Random forest (RF) regression analysis was used to examine the relationships between total daily sensing data and Mini-Mental State Examination (MMSE) scores. Confounding factor analysis was conducted with models that were adjusted and unadjusted for demographic and vascular risk factors, and selected variables were assessed as risk and protective factors using partial dependence plots (PDPs).
Results: Lifestyle data were collected for 31.3 ± 7.1 days per year using wristband sensors. RF regression analysis adjusted for age, gender, and education levels selected four variables, including walking steps, conversation time, TST, and heart rate. Moreover, walking steps, conversation time, and heart rate remained after RF regression analysis adjusted for demographic and vascular risk factors. The walking steps, conversation time, and heart rate were categorized as protective factors, whereas TST were categorized as risk factors for cognitive function. Although PDPs of walking steps and heart rate revealed continuously increased MMSE scores, those of conversation time and TST and revealed that the tendency in the graph was reversed at the boundary of a particular threshold (321.1 min for conversation time, 434.1 min for TST). 
Conclusions: Lifestyle factors, such as physical activity, sleep, and social activity appear to be associated with cognitive function among older people. Physical activity and appropriate durations of sleep and conversation are important for cognitive function.