Akiko Kishi Svensson
1 – 10 of 22
- show: 10
- |
- sort: year (new to old)
Close
Embed this list
<iframe src=" "
width=" "
height=" "
allowtransparency="true"
frameborder="0">
</iframe>
- 2024
-
Mark
Very short sleep duration reveals a proteomic fingerprint that is selectively associated with incident diabetes mellitus but not with incident coronary heart disease : a cohort study
(
- Contribution to journal › Article
-
Mark
Response to comment on “Validity and reliability of the Oura Ring Generation 3 (Gen3) with Oura Sleep Staging Algorithm 2.0 (OSSA 2.0) when compared to multi-night ambulatory polysomnography : A validation study of 96 participants and 421,045 epochs”
(
- Contribution to journal › Letter
-
Mark
Validity and reliability of the Oura Ring Generation 3 (Gen3) with Oura sleep staging algorithm 2.0 (OSSA 2.0) when compared to multi-night ambulatory polysomnography : A validation study of 96 participants and 421,045 epochs
(
- Contribution to journal › Article
-
Mark
Associations of Subjective Sleep Quality with Wearable Device-Derived Resting Heart Rate During REM Sleep and Non-REM Sleep in a Cohort of Japanese Office Workers
(
- Contribution to journal › Article
- 2023
-
Mark
A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection
(
- Contribution to journal › Article
-
Mark
Association between Metabolic Syndrome Status and Daily Physical Activity Measured by a Wearable Device in Japanese Office Workers
(
- Contribution to journal › Article
- 2022
-
Mark
Sleep Satisfaction May Modify the Association between Metabolic Syndrome and BMI, Respectively, and Occupational Stress in Japanese Office Workers
(
- Contribution to journal › Article
- 2021
-
Mark
Association of managerial position with cardiovascular risk factors : A fixed-effects analysis for japanese employees
(
- Contribution to journal › Article
-
Mark
Association of Sleep Duration with All- And Major-Cause Mortality among Adults in Japan, China, Singapore, and Korea
(
- Contribution to journal › Article
-
Mark
Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods
(
- Contribution to journal › Article