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mHealth for Data Collection and Research (955) New Methods (944) Statistical Methods (23) Statistical Methods for Surveillance and Population Health (156) mHealth for Wellness, Behavior Change and Prevention (2871)

Published on in Vol 8, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17977, first published .
Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study

Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study

Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study

Authors of this article:

Jake Turicchi1 Author Orcid Image ;   Ruairi O'Driscoll1 Author Orcid Image ;   Graham Finlayson1 Author Orcid Image ;   Cristiana Duarte1 Author Orcid Image ;   A L Palmeira2 Author Orcid Image ;   Sofus C Larsen3 Author Orcid Image ;   Berit L Heitmann3, 4, 5 Author Orcid Image ;   R James Stubbs1 Author Orcid Image
Article Authors Cited by (14) Tweetations (1) Metrics

Journals

  1. Turicchi J, O’Driscoll R, Lowe M, Finlayson G, Palmeira A, Larsen S, Heitmann B, Stubbs J. The impact of early body-weight variability on long-term weight maintenance: exploratory results from the NoHoW weight-loss maintenance intervention. International Journal of Obesity 2021;45(3):525 View
  2. Frija-Masson J, Mullaert J, Vidal-Petiot E, Pons-Kerjean N, Flamant M, d'Ortho M. Accuracy of Smart Scales on Weight and Body Composition: Observational Study. JMIR mHealth and uHealth 2021;9(4):e22487 View
  3. Graham S, Pitter V, Hori J, Stein N, Branch O. Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence. DIGITAL HEALTH 2022;8:205520762211306 View
  4. Johannessen E, Johansson J, Hartvigsen G, Horsch A, Årsand E, Henriksen A. Collecting health-related research data using consumer-based wireless smart scales. International Journal of Medical Informatics 2023;173:105043 View
  5. Rahimi Rise Z, Ershadi M. An integrated HFMEA simulation-based multi-objective optimisation model to improve the performances of hospitals: A case study. Journal of Simulation 2023;17(4):422 View
  6. Lowe M, Benson L, Zhang F. Greater within‐person weight variability during infancy predicts future increases in z‐BMI. Obesity 2021;29(10):1684 View
  7. Kim H, Kim Y, Michaelides A, Park Y. Weight Loss Trajectories and Related Factors in a 16-Week Mobile Obesity Intervention Program: Retrospective Observational Study. Journal of Medical Internet Research 2022;24(4):e29380 View
  8. Lockwood K, Kulkarni P, Paruthi J, Buch L, Chaffard M, Schitter E, Branch O, Graham S. Evaluating a New Digital App–Based Program for Heart Health: Feasibility and Acceptability Pilot Study. JMIR Formative Research 2024;8:e50446 View
  9. Pham H, Do T, Baek J, Nguyen C, Pham Q, Nguyen H, Goldberg R, Pham Q, Giang L. Handling Missing Data in COVID-19 Incidence Estimation: Secondary Data Analysis. JMIR Public Health and Surveillance 2024;10:e53719 View
  10. Morgenshtern G, Rutishauser Y, Haag C, von Wyl V, Bernard J. MS Pattern Explorer: interactive visual exploration of temporal activity patterns for multiple sclerosis. Journal of the American Medical Informatics Association 2024;31(11):2496 View
  11. Pereira A, Costa C, Firmino P, Studart T, Oliveira C. Preenchimento de falhas em séries de dados meteorológicos de estações automáticas. Revista Brasileira de Climatologia 2024;35:22 View

Books/Policy Documents

  1. J. Ost K, W. Anderson D, W. Cadotte D. Machine Learning - Algorithms, Models and Applications. View
  2. Darji J, Biswas N, D. Jones L, Ashili S. Time Series Analysis - Recent Advances, New Perspectives and Applications. View