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

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
  12. Shahabi F, Battalio S, Pfammatter A, Hedeker D, Spring B, Alshurafa N. A machine-learned model for predicting weight loss success using weight change features early in treatment. npj Digital Medicine 2024;7(1) 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