Published on in Vol 21, No 8 (2019): August

Adherence and Satisfaction of Smartphone- and Smartwatch-Based Remote Active Testing and Passive Monitoring in People With Multiple Sclerosis: Nonrandomized Interventional Feasibility Study

Adherence and Satisfaction of Smartphone- and Smartwatch-Based Remote Active Testing and Passive Monitoring in People With Multiple Sclerosis: Nonrandomized Interventional Feasibility Study

Adherence and Satisfaction of Smartphone- and Smartwatch-Based Remote Active Testing and Passive Monitoring in People With Multiple Sclerosis: Nonrandomized Interventional Feasibility Study

Journals

  1. Ziemssen T, Kern R, Voigt I, Haase R. Data Collection in Multiple Sclerosis: The MSDS Approach. Frontiers in Neurology 2020;11 View
  2. Umbricht D, Cheng W, Lipsmeier F, Bamdadian A, Lindemann M. Deep Learning-Based Human Activity Recognition for Continuous Activity and Gesture Monitoring for Schizophrenia Patients With Negative Symptoms. Frontiers in Psychiatry 2020;11 View
  3. van Beek J, van Wegen E, Rietberg M, Nyffeler T, Bohlhalter S, Kamm C, Nef T, Vanbellingen T. Feasibility of a Home-Based Tablet App for Dexterity Training in Multiple Sclerosis: Usability Study. JMIR mHealth and uHealth 2020;8(6):e18204 View
  4. Block V, Bove R. We should monitor our patients with wearable technology instead of neurological examination – Yes. Multiple Sclerosis Journal 2020;26(9):1024 View
  5. Middleton R, Pearson O, Ingram G, Craig E, Rodgers W, Downing-Wood H, Hill J, Tuite-Dalton K, Roberts C, Watson L, Ford D, Nicholas R. A Rapid Electronic Cognitive Assessment Measure for Multiple Sclerosis: Validation of Cognitive Reaction, an Electronic Version of the Symbol Digit Modalities Test. Journal of Medical Internet Research 2020;22(9):e18234 View
  6. Matthews P, Block V, Leocani L. E-health and multiple sclerosis. Current Opinion in Neurology 2020;33(3):271 View
  7. Allen-Philbey K, Middleton R, Tuite-Dalton K, Baker E, Stennett A, Albor C, Schmierer K. Can We Improve the Monitoring of People With Multiple Sclerosis Using Simple Tools, Data Sharing, and Patient Engagement?. Frontiers in Neurology 2020;11 View
  8. Creagh A, Simillion C, Scotland A, Lipsmeier F, Bernasconi C, Belachew S, van Beek J, Baker M, Gossens C, Lindemann M, De Vos M. Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the Draw a Shape Test. Physiological Measurement 2020;41(5):054002 View
  9. Gromisch E, Turner A, Haselkorn J, Lo A, Agresta T. Mobile health (mHealth) usage, barriers, and technological considerations in persons with multiple sclerosis: a literature review. JAMIA Open 2020 View
  10. Bourke A, Scotland A, Lipsmeier F, Gossens C, Lindemann M. Gait Characteristics Harvested during a Smartphone-Based Self-Administered 2-Minute Walk Test in People with Multiple Sclerosis: Test-Retest Reliability and Minimum Detectable Change. Sensors 2020;20(20):5906 View
  11. Torkildsen Ø, Linker R, Sesmero J, Fantaccini S, la Rosa R, Seze J, Duddy M, Chan A. Living with secondary progressive multiple sclerosis in Europe: perspectives of multiple stakeholders. Neurodegenerative Disease Management 2021;11(1):9 View
  12. Schmalz O, Jacob C, Ammann J, Liss B, Iivanainen S, Kammermann M, Koivunen J, Klein A, Popescu R. Digital Monitoring and Management of Patients With Advanced or Metastatic Non-Small Cell Lung Cancer Treated With Cancer Immunotherapy and Its Impact on Quality of Clinical Care: Interview and Survey Study Among Health Care Professionals and Patients. Journal of Medical Internet Research 2020;22(12):e18655 View
  13. D’Souza M, Papadopoulou A, Girardey C, Kappos L. Standardization and digitization of clinical data in multiple sclerosis. Nature Reviews Neurology 2021;17(2):119 View
  14. Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, Mangravite L, Omberg L. Evaluating the Utility of Smartphone-Based Sensor Assessments in Persons With Multiple Sclerosis in the Real-World Using an App (elevateMS): Observational, Prospective Pilot Digital Health Study. JMIR mHealth and uHealth 2020;8(10):e22108 View
  15. Cheng W, Bourke A, Lipsmeier F, Bernasconi C, Belachew S, Gossens C, Graves J, Montalban X, Lindemann M. U-turn speed is a valid and reliable smartphone-based measure of multiple sclerosis-related gait and balance impairment. Gait & Posture 2021;84:120 View
  16. Altmann P, Hinterberger W, Leutmezer F, Ponleitner M, Monschein T, Zrzavy T, Zulehner G, Kornek B, Lanzenberger R, Berek K, Rommer P, Berger T, Bsteh G. The Smartphone App haMSter for Tracking Patient-Reported Outcomes in People With Multiple Sclerosis: Protocol for a Pilot Study. JMIR Research Protocols 2021;10(5):e25011 View
  17. Creagh A, Simillion C, Bourke A, Scotland A, Lipsmeier F, Bernasconi C, van Beek J, Baker M, Gossens C, Lindemann M, De Vos M. Smartphone- and Smartwatch-Based Remote Characterisation of Ambulation in Multiple Sclerosis During the Two-Minute Walk Test. IEEE Journal of Biomedical and Health Informatics 2021;25(3):838 View
  18. BUOITE STELLA A, AJČEVIĆ M, FURLANIS G, CILLOTTO T, MENICHELLI A, ACCARDO A, MANGANOTTI P. Smart technology for physical activity and health assessment during COVID-19 lockdown. The Journal of Sports Medicine and Physical Fitness 2021;61(3) View
  19. Inojosa H, Akgün K, Haacke K, Ziemssen T. MSProDiscuss – Entwicklung eines digitalen Tools zur standardisierten Patientenanamnese, um Progredienz bei Multipler Sklerose zu identifizieren. Fortschritte der Neurologie · Psychiatrie 2021;89(07/08):374 View
  20. Abou L, Wong E, Peters J, Dossou M, Sosnoff J, Rice L. Smartphone applications to assess gait and postural control in people with multiple sclerosis: A systematic review. Multiple Sclerosis and Related Disorders 2021;51:102943 View
  21. Joshi M, Archer S, Morbi A, Arora S, Kwasnicki R, Ashrafian H, Khan S, Cooke G, Darzi A. Short-Term Wearable Sensors for In-Hospital Medical and Surgical Patients: Mixed Methods Analysis of Patient Perspectives. JMIR Perioperative Medicine 2021;4(1):e18836 View
  22. De Angelis M, Lavorgna L, Carotenuto A, Petruzzo M, Lanzillo R, Brescia Morra V, Moccia M. Digital Technology in Clinical Trials for Multiple Sclerosis: Systematic Review. Journal of Clinical Medicine 2021;10(11):2328 View
  23. Scholz M, Haase R, Trentzsch K, Stölzer-Hutsch H, Ziemssen T. Improving Digital Patient Care: Lessons Learned from Patient-Reported and Expert-Reported Experience Measures for the Clinical Practice of Multidimensional Walking Assessment. Brain Sciences 2021;11(6):786 View
  24. Vandendriessche B, Godfrey A, Izmailova E. Multimodal biometric monitoring technologies drive the development of clinical assessments in the home environment. Maturitas 2021;151:41 View
  25. Creagh A, Lipsmeier F, Lindemann M, Vos M. Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones. Scientific Reports 2021;11(1) View
  26. Montalban X, Graves J, Midaglia L, Mulero P, Julian L, Baker M, Schadrack J, Gossens C, Ganzetti M, Scotland A, Lipsmeier F, van Beek J, Bernasconi C, Belachew S, Lindemann M, Hauser S. A smartphone sensor-based digital outcome assessment of multiple sclerosis. Multiple Sclerosis Journal 2021:135245852110285 View
  27. Bove R, Bruce C, Lunders C, Pearce J, Liu J, Schleimer E, Hauser S, Stewart W, Jones J. Electronic Health Record Technology Designed for the Clinical Encounter. Neurology: Clinical Practice 2021;11(4):318 View

Books/Policy Documents

  1. Cancela J, Charlafti I, Colloud S, Wu C. Digital Health. View