Published on in Vol 3, No 1 (2016): Jan-Jun

Mental Health Technologies: Designing With Consumers

Mental Health Technologies: Designing With Consumers

Mental Health Technologies: Designing With Consumers

Journals

  1. Shuggi I, Shewokis P, Herrmann J, Gentili R. Changes in motor performance and mental workload during practice of reaching movements: a team dynamics perspective. Experimental Brain Research 2018;236(2):433 View
  2. Leiker A, Pathania A, Miller M, Lohse K. Exploring the Neurophysiological Effects of Self-Controlled Practice in Motor Skill Learning. Journal of Motor Learning and Development 2019;7(1):13 View
  3. Jaquess K, Lo L, Oh H, Lu C, Ginsberg A, Tan Y, Lohse K, Miller M, Hatfield B, Gentili R. Changes in Mental Workload and Motor Performance Throughout Multiple Practice Sessions Under Various Levels of Task Difficulty. Neuroscience 2018;393:305 View
  4. Jaquess K, Gentili R, Lo L, Oh H, Zhang J, Rietschel J, Miller M, Tan Y, Hatfield B. Empirical evidence for the relationship between cognitive workload and attentional reserve. International Journal of Psychophysiology 2017;121:46 View
  5. Alvarez-Lopez F, Maina M, Saigí-Rubió F. Use of Commercial Off-The-Shelf Devices for the Detection of Manual Gestures in Surgery: Systematic Literature Review. Journal of Medical Internet Research 2019;21(5):e11925 View
  6. Sawangjai P, Hompoonsup S, Leelaarporn P, Kongwudhikunakorn S, Wilaiprasitporn T. Consumer Grade EEG Measuring Sensors as Research Tools: A Review. IEEE Sensors Journal 2020;20(8):3996 View
  7. Rhoads J, Daou M, Lohse K, Miller M. The Effects of Expecting to Teach and Actually Teaching on Motor Learning. Journal of Motor Learning and Development 2019;7(1):84 View
  8. Short C, DeSmet A, Woods C, Williams S, Maher C, Middelweerd A, Müller A, Wark P, Vandelanotte C, Poppe L, Hingle M, Crutzen R. Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies. Journal of Medical Internet Research 2018;20(11):e292 View
  9. Shuggi I, Oh H, Shewokis P, Gentili R. Mental workload and motor performance dynamics during practice of reaching movements under various levels of task difficulty. Neuroscience 2017;360:166 View
  10. Rohrbach N, Chicklis E, Levac D. What is the impact of user affect on motor learning in virtual environments after stroke? A scoping review. Journal of NeuroEngineering and Rehabilitation 2019;16(1) View
  11. Leiker A, Bruzi A, Miller M, Nelson M, Wegman R, Lohse K. The effects of autonomous difficulty selection on engagement, motivation, and learning in a motion-controlled video game task. Human Movement Science 2016;49:326 View
  12. Levac D, Lu A. Does Narrative Feedback Enhance Children's Motor Learning in a Virtual Environment?. Journal of Motor Behavior 2019;51(2):199 View
  13. Rogers J, Jensen J, Valderrama J, Johnstone S, Wilson P. Single-channel EEG measurement of engagement in virtual rehabilitation: a validation study. Virtual Reality 2020 View
  14. Karpin H, Misha T, Herling N, Bartur G, Shahaf G. Bedside patient engagement monitor for rehabilitation in disorders of consciousness – demonstrative case-reports. Disability and Rehabilitation: Assistive Technology 2020:1 View
  15. Gvion A, Stark R, Bartur G, Shahaf G. Behavioural and electrophysiological evaluation of the impact of different cue types upon individuals with acquired anomia. Aphasiology 2020:1 View

Books/Policy Documents

  1. Bakkali Yedri O, El Aachak L, Belahbib A, Zili H, Bouhorma M. Innovations in Smart Cities and Applications. View
  2. Kokoç M, Ilgaz H, Altun A. Handbook of Research on Fostering Student Engagement With Instructional Technology in Higher Education. View
  3. O’Brien H. New Directions in Third Wave Human-Computer Interaction: Volume 2 - Methodologies. View
  4. Kokoç M, Ilgaz H, Altun A. Research Anthology on Developing Effective Online Learning Courses. View