Published on in Vol 4, No 1 (2018): Jan-Mar

Preprints (earlier versions) of this paper are available at, first published .
Objectively Measured Baseline Physical Activity Patterns in Women in the mPED Trial: Cluster Analysis

Objectively Measured Baseline Physical Activity Patterns in Women in the mPED Trial: Cluster Analysis

Objectively Measured Baseline Physical Activity Patterns in Women in the mPED Trial: Cluster Analysis


  1. Niemelä M, Kangas M, Farrahi V, Kiviniemi A, Leinonen A, Ahola R, Puukka K, Auvinen J, Korpelainen R, Jämsä T. Intensity and temporal patterns of physical activity and cardiovascular disease risk in midlife. Preventive Medicine 2019;124:33 View
  2. Fukuoka Y, Haskell W, Lin F, Vittinghoff E. Short- and Long-term Effects of a Mobile Phone App in Conjunction With Brief In-Person Counseling on Physical Activity Among Physically Inactive Women. JAMA Network Open 2019;2(5):e194281 View
  3. Gasparetti F, Aiello L, Quercia D. Personalized weight loss strategies by mining activity tracker data. User Modeling and User-Adapted Interaction 2020;30(3):447 View
  4. Zhou M, Fukuoka Y, Goldberg K, Vittinghoff E, Aswani A. Applying machine learning to predict future adherence to physical activity programs. BMC Medical Informatics and Decision Making 2019;19(1) View
  5. Grafe C, Horth R, Clayton N, Dunn A, Forsythe N. How to Classify Super-Utilizers: A Methodological Review of Super-Utilizer Criteria Applied to the Utah Medicaid Population, 2016–2017. Population Health Management 2020;23(2):165 View
  6. Aqeel M, Guo J, Lin L, Gelfand S, Delp E, Bhadra A, Richards E, Hennessy E, Eicher-Miller H. Temporal physical activity patterns are associated with obesity in U.S. adults. Preventive Medicine 2021;148:106538 View
  7. Figueroa C, Vittinghoff E, Aguilera A, Fukuoka Y. Differences in objectively measured daily physical activity patterns related to depressive symptoms in community dwelling women – mPED trial. Preventive Medicine Reports 2021;22:101325 View
  8. Ma T, Song F. A Trajectory Privacy Protection Method Based on Random Sampling Differential Privacy. ISPRS International Journal of Geo-Information 2021;10(7):454 View
  9. Oh Y, Hoffmann T, Fukuoka Y. A Novel Approach to Assess Weekly Self-efficacy for Meeting Personalized Physical Activity Goals Via a Cellphone: 12-Week Longitudinal Study. JMIR Formative Research 2023;7:e38877 View
  10. Diaz C, Caillaud C, Yacef K. Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data. Sensors 2022;22(21):8255 View
  11. Fukuoka Y, Haskell W, Vittinghoff E. Mechanisms of an App-Based Physical Activity Intervention and Maintenance in Community-Dwelling Women. Journal of Cardiovascular Nursing 2023;38(2):E61 View
  12. Diaz C, Caillaud C, Yacef K. Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review. JMIR Medical Informatics 2023;11:e41153 View
  13. Ji H, Li J, Zhang Q, Yang J, Duan J, Wang X, Ma B, Zhang Z, Pan W, Zhang H. Clinical feature-related single-base substitution sequence signatures identified with an unsupervised machine learning approach. BMC Medical Genomics 2021;14(1) View
  14. Wang X, Wang Y, Xu Z, Guo X, Mao H, Liu T, Gong W, Gong Z, Zhuo Q. Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia. Nutrients 2023;15(2):328 View
  15. Zhou Z, Athey S, Wager S. Offline Multi-Action Policy Learning: Generalization and Optimization. Operations Research 2023;71(1):148 View
  16. Zhan R, Ren Z, Athey S, Zhou Z. Policy Learning with Adaptively Collected Data. Management Science 2023 View
  17. Ren X, Li L. Economic, energy and environmental analysis and evaluation of hybrid CCHP system considering different buildings: A two-level optimization model. Applied Thermal Engineering 2024;241:122293 View

Books/Policy Documents

  1. Cinaglia P, Milano M. Computational Science – ICCS 2024. View