Published on in Vol 7, No 12 (2019): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13671, first published .
Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study

Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study

Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study

Journals

  1. Shouval R, Fein J, Savani B, Mohty M, Nagler A. Machine learning and artificial intelligence in haematology. British Journal of Haematology 2021;192(2):239 View
  2. Naranjo-Hernández D, Reina-Tosina J, Roa L. Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review. Sensors 2020;20(2):365 View
  3. Vatansever S, Schlessinger A, Wacker D, Kaniskan H, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions. Medicinal Research Reviews 2021;41(3):1427 View
  4. Shah N. ELIPSIS: developing tools to better understand VOC in SCD. Blood 2021;137(15):1987 View
  5. Badawy S, Cronin R, Liem R, Palermo T. Digital behavioural interventions for people with sickle cell disease. Cochrane Database of Systematic Reviews 2021 View
  6. Baek Y, Jeong K, Lee S, Kim H, Seo B, Jin H. Feasibility and Effectiveness of Assessing Subhealth Using a Mobile Health Management App (MibyeongBogam) in Early Middle-Aged Koreans: Randomized Controlled Trial. JMIR mHealth and uHealth 2021;9(8):e27455 View
  7. Shah A, O’Dwyer L, Badawy S. Telemedicine in Malignant and Nonmalignant Hematology: Systematic Review of Pediatric and Adult Studies. JMIR mHealth and uHealth 2021;9(7):e29619 View
  8. Ng Z, Ling L, Chew H, Lau Y. The role of artificial intelligence in enhancing clinical nursing care: A scoping review. Journal of Nursing Management 2022;30(8):3654 View
  9. Leroux A, Rzasa-Lynn R, Crainiceanu C, Sharma T. Wearable Devices: Current Status and Opportunities in Pain Assessment and Management. Digital Biomarkers 2021;5(1):89 View
  10. Padhee S, Nave Jr G, Banerjee T, Abrams D, Shah N. Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study. JMIR Formative Research 2022;6(6):e36998 View
  11. Stojancic R, Subramaniam A, Vuong C, Utkarsh K, Golbasi N, Fernandez O, Shah N. Predicting Pain in People With Sickle Cell Disease in the Day Hospital Using the Commercial Wearable Apple Watch: Feasibility Study. JMIR Formative Research 2023;7:e45355 View
  12. Wickersham K, Dawson R, Becker K, Everhart K, Miles H, Schultz B, Tucker C, Wright P, Jenerette C. Experiences of African Americans Living With Sickle Cell Disease. Journal of Transcultural Nursing 2022;33(3):334 View
  13. Vega J, Li M, Aguillera K, Goel N, Joshi E, Khandekar K, Durica K, Kunta A, Low C. Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices. Frontiers in Digital Health 2021;3 View
  14. Somani S, Yu K, Chiu A, Sykes K, Villwock J. Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review. Otolaryngology–Head and Neck Surgery 2022;167(4):620 View
  15. Connelly M, Lee R. Technology to Assess and Treat Pain in Pediatric Rheumatology. Rheumatic Disease Clinics of North America 2022;48(1):31 View
  16. Tsai P, Wang C, Zhou Y, Ren J, Jones A, Watts S, Chou C, Ku W. A classification algorithm to predict chronic pain using both regression and machine learning – A stepwise approach. Applied Nursing Research 2021;62:151504 View
  17. Gao X, Xin X, Li Z, Zhang W. Predicting postoperative pain following root canal treatment by using artificial neural network evaluation. Scientific Reports 2021;11(1) View
  18. Fernandez Rojas R, Brown N, Waddington G, Goecke R. A systematic review of neurophysiological sensing for the assessment of acute pain. npj Digital Medicine 2023;6(1) View
  19. Kyytsönen M, Vehko T, Anttila H, Ikonen J, Lai Y. Factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the adult population and among older adults. PLOS Digital Health 2023;2(5):e0000245 View
  20. Elsabagh A, Elhadary M, Elsayed B, Elshoeibi A, Ferih K, Kaddoura R, Alkindi S, Alshurafa A, Alrasheed M, Alzayed A, Al-Abdulmalek A, Altooq J, Yassin M. Artificial intelligence in sickle disease. Blood Reviews 2023;61:101102 View
  21. Vuong C, Utkarsh K, Stojancic R, Subramaniam A, Fernandez O, Banerjee T, Abrams D, Fijnvandraat K, Shah N. Use of consumer wearables to monitor and predict pain in patients with sickle cell disease. Frontiers in Digital Health 2023;5 View
  22. Moscato S, Orlandi S, Di Gregorio F, Lullini G, Pozzi S, Sabattini L, Chiari L, La Porta F. Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol. BMJ Open 2023;13(11):e073534 View
  23. Jiang Z, Van Zoest V, Deng W, Ngai E, Liu J. Leveraging Machine Learning for Disease Diagnoses Based on Wearable Devices: A Survey. IEEE Internet of Things Journal 2023;10(24):21959 View
  24. O’Toole E, Kelsell D, Caterina M, de Brito M, Hansen D, Hickerson R, Hovnanian A, Kaspar R, Lane E, Paller A, Schwartz J, Shroot B, Teng J, Titeux M, Coulombe P, Sprecher E. Pachyonychia Congenita: A Research Agenda Leading to New Therapeutic Approaches. Journal of Investigative Dermatology 2024;144(4):748 View
  25. Bibbo D, De Marchis C, Schmid M, Ranaldi S. Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review. Physiological Measurement 2023;44(12):12TR01 View
  26. Ahmed Z, Almuharib A, Abdulkarim A, Alhassoon A, Alanazi A, Alhaqbani M, Alshalawi M, Almuqayrin A, Almahmoud M. Artificial Intelligence and Its Application in Endodontics: A Review. The Journal of Contemporary Dental Practice 2024;24(11):912 View
  27. Cloß K, Verket M, Müller-Wieland D, Marx N, Schuett K, Jost E, Crysandt M, Beier F, Brümmendorf T, Kobbe G, Brandts J, Jacobsen M. Application of wearables for remote monitoring of oncology patients: A scoping review. DIGITAL HEALTH 2024;10 View
  28. Conceição M, Borges C, Do Vale P, Gomes A, Carvalho E. APLICATIVOS MÓVEIS PARA AUTOGESTÃO DA DOR NA DOENÇA FALCIFORME: UMA REVISÃO INTEGRATIVA. Revista de Enfermagem UFPE on line 2023;17(1) View
  29. Doxzen K, Adair J, Fonseca Bazzo Y, Bukini D, Cornetta K, Dalal V, Guerino-Cunha R, Hongeng S, Jotwani G, Kityo-Mutuluuza C, Lakshmanan K, Mahlangu J, Makani J, Mathews V, Ozelo M, Rangarajan S, Scholefield J, Batista Silva Júnior J, McCune J. The translational gap for gene therapies in low- and middle-income countries. Science Translational Medicine 2024;16(746) View

Books/Policy Documents

  1. Issom D. Sickle Cell Disease. View