Published on 01.02.18 in Vol 4, No 1 (2018): Jan-Mar
Works citing "Objectively Measured Baseline Physical Activity Patterns in Women in the mPED Trial: Cluster Analysis"
According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.9138):
(note that this is only a small subset of citations)
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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
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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
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Gasparetti F, Aiello LM, Quercia D. Personalized weight loss strategies by mining activity tracker data. User Modeling and User-Adapted Interaction 2020;30(3):447
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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)
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Grafe CJ, Horth RZ, 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
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Aqeel M, Guo J, Lin L, Gelfand S, Delp E, Bhadra A, Richards EA, Hennessy E, Eicher-Miller HA. Temporal physical activity patterns are associated with obesity in U.S. adults. Preventive Medicine 2021;148:106538
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Figueroa CA, 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
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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
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Oh YJ, Hoffmann TJ, 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
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Diaz C, Caillaud C, Yacef K. Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data. Sensors 2022;22(21):8255
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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
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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
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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)
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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
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Zhou Z, Athey S, Wager S. Offline Multi-Action Policy Learning: Generalization and Optimization. Operations Research 2023;71(1):148
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Zhan R, Ren Z, Athey S, Zhou Z. Policy Learning with Adaptively Collected Data. Management Science 2023;
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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
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