Published on in Vol 18, No 9 (2016): September

Electronic Quality of Life Assessment Using Computer-Adaptive Testing

Electronic Quality of Life Assessment Using Computer-Adaptive Testing

Electronic Quality of Life Assessment Using Computer-Adaptive Testing

Journals

  1. Gibbons C, Richards S, Valderas J, Campbell J. Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy. Journal of Medical Internet Research 2017;19(3):e65 View
  2. Geerards D, Pusic A, Hoogbergen M, van der Hulst R, Sidey-Gibbons C. Computerized Quality of Life Assessment: A Randomized Experiment to Determine the Impact of Individualized Feedback on Assessment Experience. Journal of Medical Internet Research 2019;21(7):e12212 View
  3. Murphy P. Measuring Quality of Life Loss in Litigation. Physical Medicine and Rehabilitation Clinics of North America 2019;30(3):589 View
  4. Morris S, Bass M, Howard E, Neapolitan R. Stopping Rules for Computer Adaptive Testing When Item Banks Have Nonuniform Information. International Journal of Testing 2020;20(2):146 View
  5. Gibbons C, Small N, Rick J, Burt J, Hann M, Bower P. The Patient Assessment of Chronic Illness Care produces measurements along a single dimension: results from a Mokken analysis. Health and Quality of Life Outcomes 2017;15(1) View
  6. Gibbons C, Skevington S. Adjusting for cross-cultural differences in computer-adaptive tests of quality of life. Quality of Life Research 2018;27(4):1027 View
  7. Aiyegbusi O, Kyte D, Cockwell P, Anderson N, Calvert M. A patient-centred approach to measuring quality in kidney care. Current Opinion in Nephrology and Hypertension 2017;26(6):442 View
  8. Gibbons C. Turning the Page on Pen-and-Paper Questionnaires: Combining Ecological Momentary Assessment and Computer Adaptive Testing to Transform Psychological Assessment in the 21st Century. Frontiers in Psychology 2017;7 View
  9. Panda N, Haynes A. Prioritizing the Patient Perspective in Oncologic Surgery. Annals of Surgical Oncology 2020;27(1):43 View
  10. Morris S, Bass M, Lee M, Neapolitan R. Advancing the efficiency and efficacy of patient reported outcomes with multivariate computer adaptive testing. Journal of the American Medical Informatics Association 2017;24(5):897 View
  11. Harrison C, Geerards D, Ottenhof M, Klassen A, Riff K, Swan M, Pusic A, Sidey-Gibbons C. Computerised adaptive testing accurately predicts CLEFT-Q scores by selecting fewer, more patient-focused questions. Journal of Plastic, Reconstructive & Aesthetic Surgery 2019;72(11):1819 View
  12. Loe B, Stillwell D, Gibbons C. Computerized Adaptive Testing Provides Reliable and Efficient Depression Measurement Using the CES-D Scale. Journal of Medical Internet Research 2017;19(9):e302 View
  13. Henneghan A, Gibbons C, Harrison R, Edwards M, Rao V, Blayney D, Palesh O, Kesler S. Predicting Patient Reported Outcomes of Cognitive Function Using Connectome-Based Predictive Modeling in Breast Cancer. Brain Topography 2020;33(1):135 View
  14. Liu J, Pusic A, Matroniano A, Aryal R, Willarson P, Hall B, Temple L, Ko C. First Report of a Multiphase Pilot to Measure Patient-Reported Outcomes in the American College of Surgeons National Surgical Quality Improvement Program. The Joint Commission Journal on Quality and Patient Safety 2019;45(5):319 View
  15. Young-Afat D, Gibbons C, Klassen A, Vickers A, Cano S, Pusic A. Introducing BREAST-Q Computerized Adaptive Testing: Short and Individualized Patient-Reported Outcome Assessment following Reconstructive Breast Surgery. Plastic & Reconstructive Surgery 2019;143(3):679 View
  16. Geerards D, Klassen A, Hoogbergen M, van der Hulst R, van den Berg L, Pusic A, Gibbons C. Streamlining the Assessment of Patient-Reported Outcomes in Weight Loss and Body Contouring Patients: Applying Computerized Adaptive Testing to the BODY-Q. Plastic & Reconstructive Surgery 2019;143(5):946e View
  17. Hall E, Tam E, Liang M, Zhang Q, Liu L, Wong L, Sarabia S, Yeung S, Gill G, Eng L, Perez-Cosio A, Brown M, Xu W, Li M, Mittmann N, Jones J, Howell D, Liu G. Development and prospective evaluation of CAPLET, a cancer ambulatory patient physical function longitudinal evaluation tool for routine clinical practice. Supportive Care in Cancer 2019;27(2):521 View
  18. Liu J, Pusic A, Gibbons C, Opelka F, Sage J, Thompson V, Ko C, Hall B, Temple L. Association of Patient-reported Experiences and Surgical Outcomes Among Group Practices. Annals of Surgery 2020;271(3):475 View
  19. Skevington S, Böhnke J. How is subjective well-being related to quality of life? Do we need two concepts and both measures?. Social Science & Medicine 2018;206:22 View
  20. Sidey-Gibbons C, Brooks H, Gellatly J, Small N, Lovell K, Bee P, Solari A. Assessing mental health service user and carer involvement in physical health care planning: The development and validation of a new patient-reported experience measure. PLOS ONE 2019;14(2):e0206507 View
  21. Khajuria A. Patient-reported outcome measures: the need for new and reliable tools. The Lancet Neurology 2020;19(3):206 View
  22. Harrison C, Loe B, Lis P, Sidey-Gibbons C. Maximizing the Potential of Patient-Reported Assessments by Using the Open-Source Concerto Platform With Computerized Adaptive Testing and Machine Learning. Journal of Medical Internet Research 2020;22(10):e20950 View
  23. Litchfield I, Greenfield S, Turner G, Finnikin S, Calvert M. Implementing PROMs in routine clinical care: a qualitative exploration of GP perspectives. BJGP Open 2021;5(1):bjgpopen20X101135 View
  24. Skevington S, Rowland C, Panagioti M, Bower P, Krägeloh C. Enhancing the multi-dimensional assessment of quality of life: introducing the WHOQOL-Combi. Quality of Life Research 2021;30(3):891 View
  25. Henneghan A, Van Dyk K, Kaufmann T, Harrison R, Gibbons C, Heijnen C, Kesler S. Measuring Self-Reported Cancer-Related Cognitive Impairment: Recommendations From the Cancer Neuroscience Initiative Working Group. JNCI: Journal of the National Cancer Institute 2021;113(12):1625 View
  26. Xu C, Schaverien M, Christensen J, Sidey-Gibbons C. Efficient and precise Ultra-QuickDASH scale measuring lymphedema impact developed using computerized adaptive testing. Quality of Life Research 2022;31(3):917 View
  27. Ottenhof M, Geerards D, Harrison C, Klassen A, Hoogbergen M, van der Hulst R, Lee E, Pusic A, Sidey-Gibbons C. Applying Computerized Adaptive Testing to the FACE-Q Skin Cancer Module: Individualizing Patient-Reported Outcome Measures in Facial Surgery. Plastic & Reconstructive Surgery 2021;148(4):863 View
  28. Kesler S, Henneghan A, Thurman W, Rao V. Identifying Themes for Assessing Cancer-Related Cognitive Impairment: Topic Modeling and Qualitative Content Analysis of Public Online Comments. JMIR Cancer 2022;8(2):e34828 View
  29. Xu C, Smith G, Chen Y, Checka C, Giordano S, Kaiser K, Lowenstein L, Ma H, Mendoza T, Peterson S, Shih Y, Shete S, Tang C, Volk R, Sidey-Gibbons C, Jayadevappa R. Short-form adaptive measure of financial toxicity from the Economic Strain and Resilience in Cancer (ENRICh) study: Derivation using modern psychometric techniques. PLOS ONE 2022;17(8):e0272804 View
  30. Xu C, Christensen J, Haykal T, Asaad M, Sidey-Gibbons C, Schaverien M. Measurement Properties of the Lymphedema Life Impact Scale. Lymphatic Research and Biology 2022;20(4):425 View
  31. Kadakia K, Halperin D, Offodile A. Operationalizing Virtual Trials in Oncology—From Aspiration to Action. JCO Clinical Cancer Informatics 2021;(5):953 View
  32. Harrison C, Plessen C, Liegl G, Rodrigues J, Sabah S, Beard D, Fischer F. Item response theory assumptions were adequately met by the Oxford hip and knee scores. Journal of Clinical Epidemiology 2023;158:166 View
  33. T. Kárász J, Széll K, Takács S. Closed formula of test length required for adaptive testing with medium probability of solution. Quality Assurance in Education 2023;31(4):637 View
  34. Giordano A, Testa S, Bassi M, Cilia S, Bertolotto A, Quartuccio M, Pietrolongo E, Falautano M, Grobberio M, Niccolai C, Allegri B, Viterbo R, Confalonieri P, Giovannetti A, Cocco E, Grasso M, Lugaresi A, Ferriani E, Nocentini U, Zaffaroni M, De Livera A, Jelinek G, Solari A, Rosato R. Applying multidimensional computerized adaptive testing to the MSQOL-54: a simulation study. Health and Quality of Life Outcomes 2023;21(1) View
  35. Kraska J, Bell K, Costello S. Graded Response Model Analysis and Computer Adaptive Test Simulation of the Depression Anxiety Stress Scale 21: Evaluation and Validation Study. Journal of Medical Internet Research 2023;25:e45334 View
  36. Harrison C, Plessen C, Liegl G, Rodrigues J, Sabah S, Beard D, Fischer F. Overcoming floor and ceiling effects in knee arthroplasty outcome measurement. Bone & Joint Research 2023;12(10):624 View
  37. Harrison C, Trickett R, Wormald J, Dobbs T, Lis P, Popov V, Beard D, Rodrigues J. Remote Symptom Monitoring With Ecological Momentary Computerized Adaptive Testing: Pilot Cohort Study of a Platform for Frequent, Low-Burden, and Personalized Patient-Reported Outcome Measures. Journal of Medical Internet Research 2023;25:e47179 View
  38. DEMİR S, ÇOBANOĞLU AKTAN D, GÜLER N. Application of the professional maturity scale as a computerized adaptive testing. International Journal of Assessment Tools in Education 2023;10(3):580 View
  39. Ramgovind P, Pramjeeth S. Students’ perceptions of Computerised Adaptive Testing in higher education. The Independent Journal of Teaching and Learning 2023;18(2):109 View
  40. Cui Y, Ge L, Ding Y, Yang F, Harrison L, Kay M. Adaptive Assessment of Visualization Literacy. IEEE Transactions on Visualization and Computer Graphics 2024;30(1):628 View
  41. Liu J, Kaplan R, Bates D, Edelen M, Sisodia R, Pusic A. Mass General Brigham’s Patient-Reported Outcomes Measurement System: A Decade of Learnings. NEJM Catalyst 2024;5(7) View
  42. Xu C, Sidey-Gibbons C, Lacourt T. Development of a PROMIS multidimensional cancer-related fatigue (mCRF) form using modern psychometric techniques. Quality of Life Research 2024 View

Books/Policy Documents

  1. Harrison C, Sidey-Gibbons C. Handbook of Quality of Life in Cancer. View