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Citing this Article

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Published on 09.02.18 in Vol 6, No 2 (2018): February

This paper is in the following e-collection/theme issue:

Works citing "Exploring the Specific Needs of Persons with Multiple Sclerosis for mHealth Solutions for Physical Activity: Mixed-Methods Study"

Guido Giunti, Jan Kool, Octavio Rivera Romero, Enrique Dorronzoro Zubiete

JMIR Mhealth Uhealth 2018 (Feb 09); 6(2):e37 

According to Crossref, the following articles are citing this article (DOI 10.2196/mhealth.8996):

(note that this is only a small subset of citations)

  1. Giunti G, Guisado Fernández E, Dorronzoro Zubiete E, Rivera Romero O. Supply and Demand in mHealth Apps for Persons With Multiple Sclerosis: Systematic Search in App Stores and Scoping Literature Review. JMIR mHealth and uHealth 2018;6(5):e10512
    CrossRef
  2. Fernandez-Luque L, Labarta JI, Palmer E, Koledova E. Content Analysis of Apps for Growth Monitoring and Growth Hormone Treatment: Systematic Search in the Android App Store. JMIR mHealth and uHealth 2020;8(2):e16208
    CrossRef
  3. Mirkovic J, Jessen S, Kristjansdottir OB, Krogseth T, Koricho AT, Ruland CM. Developing Technology to Mobilize Personal Strengths in People with Chronic Illness: Positive Codesign Approach. JMIR Formative Research 2018;2(1):e10774
    CrossRef
  4. Giunti G, Mylonopoulou V, Rivera Romero O. More Stamina, a Gamified mHealth Solution for Persons with Multiple Sclerosis: Research Through Design. JMIR mHealth and uHealth 2018;6(3):e51
    CrossRef
  5. Rossmann C, Riesmeyer C, Brew-Sam N, Karnowski V, Joeckel S, Chib A, Ling R. Appropriation of Mobile Health for Diabetes Self-Management: Lessons From Two Qualitative Studies. JMIR Diabetes 2019;4(1):e10271
    CrossRef
  6. Schleimer E, Pearce J, Barnecut A, Rowles W, Lizee A, Klein A, Block VJ, Santaniello A, Renschen A, Gomez R, Keshavan A, Gelfand JM, Henry RG, Hauser SL, Bove R. A Precision Medicine Tool for Patients With Multiple Sclerosis (the Open MS BioScreen): Human-Centered Design and Development. Journal of Medical Internet Research 2020;22(7):e15605
    CrossRef
  7. Pardhan S, Nakafero G, Raman R, Sapkota R. Barriers to diabetes awareness and self-help are influenced by people's demographics: perspectives of South Asians with type 2 diabetes. Ethnicity & Health 2020;25(6):843
    CrossRef
  8. Apolinário-Hagen J, Menzel M, Hennemann S, Salewski C. Acceptance of Mobile Health Apps for Disease Management Among People With Multiple Sclerosis: Web-Based Survey Study. JMIR Formative Research 2018;2(2):e11977
    CrossRef
  9. Van Geel F, Geurts E, Abasıyanık Z, Coninx K, Feys P. Feasibility study of a 10-week community-based program using the WalkWithMe application on physical activity, walking, fatigue and cognition in persons with Multiple Sclerosis. Multiple Sclerosis and Related Disorders 2020;42:102067
    CrossRef
  10. Molina-Recio G, Molina-Luque R, Jiménez-García AM, Ventura-Puertos PE, Hernández-Reyes A, Romero-Saldaña M. Proposal for the User-Centered Design Approach for Health Apps Based on Successful Experiences: Integrative Review. JMIR mHealth and uHealth 2020;8(4):e14376
    CrossRef
  11. Minen MT, Schaubhut KB, Morio K. Smartphone based behavioral therapy for pain in multiple sclerosis (MS) patients: A feasibility acceptability randomized controlled study for the treatment of comorbid migraine and ms pain. Multiple Sclerosis and Related Disorders 2020;46:102489
    CrossRef
  12. Flors-Sidro JJ, Househ M, Abd-Alrazaq A, Vidal-Alaball J, Fernandez-Luque L, Sanchez-Bocanegra CL. Analysis of Diabetes Apps to Assess Privacy-Related Permissions: Systematic Search of Apps. JMIR Diabetes 2021;6(1):e16146
    CrossRef
  13. Simblett SK, Evans J, Greer B, Curtis H, Matcham F, Radaelli M, Mulero P, Arévalo MJ, Polhemus A, Ferrao J, Gamble P, Comi G, Wykes T. Engaging across dimensions of diversity: A cross-national perspective on mHealth tools for managing relapsing remitting and progressive multiple sclerosis. Multiple Sclerosis and Related Disorders 2019;32:123
    CrossRef
  14. Floch J, Vilarinho T, Zettl A, Ibanez-Sanchez G, Calvo-Lerma J, Stav E, Haro PH, Aalberg AL, Fides-Valero A, Bayo Montón JL. Users’ Experiences of a Mobile Health Self-Management Approach for the Treatment of Cystic Fibrosis: Mixed Methods Study. JMIR mHealth and uHealth 2020;8(7):e15896
    CrossRef
  15. . 3MD for Chronic Conditions, a Model for Motivational mHealth Design: Embedded Case Study. JMIR Serious Games 2018;6(3):e11631
    CrossRef
  16. Jongen PJ, ter Veen G, Lemmens W, Donders R, van Noort E, Zeinstra E. The Interactive Web-Based Program MSmonitor for Self-Management and Multidisciplinary Care in Persons With Multiple Sclerosis: Quasi-Experimental Study of Short-Term Effects on Patient Empowerment. Journal of Medical Internet Research 2020;22(3):e14297
    CrossRef
  17. Monteiro-Guerra F, Signorelli GR, Tadas S, Dorronzoro Zubiete E, Rivera Romero O, Fernandez-Luque L, Caulfield B. A Personalized Physical Activity Coaching App for Breast Cancer Survivors: Design Process and Early Prototype Testing. JMIR mHealth and uHealth 2020;8(7):e17552
    CrossRef
  18. Benjumea J, Ropero J, Rivera-Romero O, Dorronzoro-Zubiete E, Carrasco A. Assessment of the Fairness of Privacy Policies of Mobile Health Apps: Scale Development and Evaluation in Cancer Apps. JMIR mHealth and uHealth 2020;8(7):e17134
    CrossRef
  19. Vo V, Auroy L, Sarradon-Eck A. Patients’ Perceptions of mHealth Apps: Meta-Ethnographic Review of Qualitative Studies. JMIR mHealth and uHealth 2019;7(7):e13817
    CrossRef
  20. Benjumea J, Ropero J, Rivera-Romero O, Dorronzoro-Zubiete E, Carrasco A. Privacy Assessment in Mobile Health Apps: Scoping Review. JMIR mHealth and uHealth 2020;8(7):e18868
    CrossRef
  21. Giunti G, Rivera-Romero O, Kool J, Bansi J, Sevillano JL, Granja-Dominguez A, Izquierdo-Ayuso G, Giunta D. Evaluation of More Stamina, a Mobile App for Fatigue Management in Persons with Multiple Sclerosis: Protocol for a Feasibility, Acceptability, and Usability Study. JMIR Research Protocols 2020;9(8):e18196
    CrossRef
  22. Bhattacharyya O, Mossman K, Gustafsson L, Schneider EC. Using Human-Centered Design to Build a Digital Health Advisor for Patients With Complex Needs: Persona and Prototype Development. Journal of Medical Internet Research 2019;21(5):e10318
    CrossRef
  23. Wendrich K, van Oirschot P, Martens MB, Heerings M, Jongen PJ, Krabbenborg L. Toward Digital Self-monitoring of Multiple Sclerosis. International Journal of MS Care 2019;21(6):282
    CrossRef
  24. Jessen S, Mirkovic J, Ruland CM. Creating Gameful Design in mHealth: A Participatory Co-Design Approach. JMIR mHealth and uHealth 2018;6(12):e11579
    CrossRef
  25. Marrie RA, Leung S, Tyry T, Cutter GR, Fox R, Salter A. Use of eHealth and mHealth technology by persons with multiple sclerosis. Multiple Sclerosis and Related Disorders 2019;27:13
    CrossRef
  26. Thomas S, Pulman A, Thomas P, Collard S, Jiang N, Dogan H, Davies Smith A, Hourihan S, Roberts F, Kersten P, Pretty K, Miller JK, Stanley K, Gay M. Digitizing a Face-to-Face Group Fatigue Management Program: Exploring the Views of People With Multiple Sclerosis and Health Care Professionals Via Consultation Groups and Interviews. JMIR Formative Research 2019;3(2):e10951
    CrossRef
  27. van Oirschot P, Heerings M, Wendrich K, den Teuling B, Martens MB, Jongen PJ. Symbol Digit Modalities Test Variant in a Smartphone App for Persons With Multiple Sclerosis: Validation Study. JMIR mHealth and uHealth 2020;8(10):e18160
    CrossRef
  28. 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
    CrossRef
  29. Scholz M, Haase R, Schriefer D, Voigt I, Ziemssen T. Electronic Health Interventions in the Case of Multiple Sclerosis: From Theory to Practice. Brain Sciences 2021;11(2):180
    CrossRef
  30. Meshgin D, Kersten-Oertel M. Multiple sclerosis image-guided subcutaneous injections using augmented reality guided imagery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2021;9(4):370
    CrossRef
  31. van Kessel K, Babbage DR, Kersten P, Drown J, Sezier A, Thomas PW, Thomas S. Design considerations for a multiple sclerosis fatigue mobile app MS Energize: A pragmatic iterative approach using usability testing and resonance checks. Internet Interventions 2021;24:100371
    CrossRef
  32. Gromisch ES, Turner AP, Haselkorn JK, Lo AC, Agresta T. Mobile health (mHealth) usage, barriers, and technological considerations in persons with multiple sclerosis: a literature review. JAMIA Open 2021;4(3)
    CrossRef
  33. Chassan C, Jost C, Sévène M, Cras O, De Broucker T, Archambault D. Encouraging physical activities at home for people with multiple sclerosis with mHealth tools: A literature review. Technology and Disability 2021;33(2):87
    CrossRef
  34. Palotai M, Wallack M, Kujbus G, Dalnoki A, Guttmann C. Usability of a Mobile App for Real-Time Assessment of Fatigue and Related Symptoms in Patients With Multiple Sclerosis: Observational Study. JMIR mHealth and uHealth 2021;9(4):e19564
    CrossRef
  35. Lam H, Ho G, Mo DY, Tang V. Enhancing data-driven elderly appointment services in domestic care communities under COVID-19. Industrial Management & Data Systems 2021;121(7):1552
    CrossRef
  36. REMY C, VALET M, STOQUART G, EL SANKARI S, VAN PESCH V, DE HAAN A, LEJEUNE T. Telecommunication and rehabilitation for patients with multiple sclerosis: access and willingness to use. A cross-sectional study. European Journal of Physical and Rehabilitation Medicine 2020;56(4)
    CrossRef
  37. Velez M, Lugo-Agudelo LH, Patiño Lugo DF, Glenton C, Posada AM, Mesa Franco LF, Negrini S, Kiekens C, Spir Brunal MA, Roberg AB, Cruz Sarmiento KM. Factors that influence the provision of home-based rehabilitation services for people needing rehabilitation: a qualitative evidence synthesis. Cochrane Database of Systematic Reviews 2023;2023(2)
    CrossRef
  38. Petracca F, Tempre R, Cucciniello M, Ciani O, Pompeo E, Sannino L, Lovato V, Castaman G, Ghirardini A, Tarricone R. An Electronic Patient-Reported Outcome Mobile App for Data Collection in Type A Hemophilia: Design and Usability Study. JMIR Formative Research 2021;5(12):e25071
    CrossRef
  39. van Beek JJW, Lehnick D, Pastore-Wapp M, Wapp S, Kamm CP, Nef T, Vanbellingen T. Tablet app-based dexterity training in multiple sclerosis (TAD-MS): a randomized controlled trial. Disability and Rehabilitation: Assistive Technology 2024;19(3):889
    CrossRef
  40. Galliford N, Yin K, Blandford A, Jung J, Lau AYS. Patient Work Personas of Type 2 Diabetes—A Data-Driven Approach to Persona Development and Validation. Frontiers in Digital Health 2022;4
    CrossRef
  41. Salimzadeh Z, Damanabi S, Ferdousi R, Shaafi S, Kalankesh LR. A mobile app (IDoThis) for multiple sclerosis self-management: development and initial evaluation. BMC Medical Informatics and Decision Making 2022;22(1)
    CrossRef
  42. Tedesco Triccas L, Maris A, Lamers I, Calcius J, Coninx K, Spooren A, Feys P. Do people with multiple sclerosis perceive upper limb improvements from robotic-mediated therapy? A mixed methods study. Multiple Sclerosis and Related Disorders 2022;68:104159
    CrossRef
  43. Wendrich K, Krabbenborg L. Digital Self-monitoring of Multiple Sclerosis: Interview Study With Dutch Health Care Providers on the Expected New Configuration of Roles and Responsibilities. JMIR mHealth and uHealth 2022;10(4):e30224
    CrossRef
  44. Polhemus A, Sieber C, Haag C, Sylvester R, Kool J, Gonzenbach R, von Wyl V, Li-Jessen NY. Non-equivalent, but still valid: Establishing the construct validity of a consumer fitness tracker in persons with multiple sclerosis. PLOS Digital Health 2023;2(1):e0000171
    CrossRef
  45. Neal WN, Richardson EV, Motl RW. Informing the development of a mobile application for the physical activity guidelines in multiple sclerosis: a qualitative, pluralistic approach. Disability and Rehabilitation: Assistive Technology 2022;:1
    CrossRef
  46. Thompson CM, Pulido MD, Babu S, Zenzola N, Chiu C. Communication between persons with multiple sclerosis and their health care providers: A scoping review. Patient Education and Counseling 2022;105(12):3341
    CrossRef
  47. Ouwerkerk M, Eijssen IC, van der Linden MM, Wijnands IM, Dorssers FJ, Rietberg MB, Beckerman H, de Groot V. A Smartphone Application to Assess Real-Time and Individual-Specific Societal Participation: A Development and Usability Study. Archives of Physical Medicine and Rehabilitation 2022;103(10):1958
    CrossRef
  48. Thomas S, Pulman A, Dogan H, Jiang N, Passmore D, Pretty K, Fairbanks B, Davies Smith A, Thomas PW. Creating a Digital Toolkit to Reduce Fatigue and Promote Quality of Life in Multiple Sclerosis: Participatory Design and Usability Study. JMIR Formative Research 2021;5(12):e19230
    CrossRef
  49. Torres-Castaño A, Abt-Sacks A, Toledo-Chávarri A, Suarez-Herrera JC, Delgado-Rodríguez J, León-Salas B, González-Hernández Y, Carmona-Rodríguez M, Serrano-Aguilar P. Ethical, Legal, Organisational and Social Issues of Teleneurology: A Scoping Review. International Journal of Environmental Research and Public Health 2023;20(4):3694
    CrossRef
  50. Bevens W, Reece J, Jelinek P, Weiland T, Nag N, Simpson-Yap S, Gray K, Jelinek G, Neate S. The feasibility of an online educational lifestyle program for people with multiple sclerosis: A qualitative analysis of participant semi-structured interviews. DIGITAL HEALTH 2022;8:205520762211237
    CrossRef
  51. Li X, You K. Real-time tracking and detection of patient conditions in the intelligent m-Health monitoring system. Frontiers in Public Health 2022;10
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  52. Lee J, Lee J. The Relationship Between the Characteristics of Self-Tracking Data and User Experience Factors for Data-Driven Product Service System Design. Archives of Design Research 2021;34(3):193
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  53. Barrios L, Amon R, Oldrati P, Hilty M, Holz C, Lutterotti A. Cognitive fatigability assessment test (cFAST): Development of a new instrument to assess cognitive fatigability and pilot study on its association to perceived fatigue in multiple sclerosis. DIGITAL HEALTH 2022;8:205520762211177
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  54. Polhemus A, Simblett S, Dawe Lane E, Elliott B, Jilka S, Negbenose E, Burke P, Weyer J, Novak J, Dockendorf MF, Temesi G, Wykes T. Experiences of health tracking in mobile apps for multiple sclerosis: A qualitative content analysis of user reviews. Multiple Sclerosis and Related Disorders 2023;69:104435
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  55. Reinhardt G, Schwarz PE, Harst L. Non-use of telemedicine: A scoping review. Health Informatics Journal 2021;27(4):146045822110431
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  56. Haase R, Voigt I, Scholz M, Schlieter H, Benedict M, Susky M, Dillenseger A, Ziemssen T. Profiles of eHealth Adoption in Persons with Multiple Sclerosis and Their Caregivers. Brain Sciences 2021;11(8):1087
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  57. Polhemus A, Novak J, Majid S, Simblett S, Morris D, Bruce S, Burke P, Dockendorf MF, Temesi G, Wykes T. Data Visualization for Chronic Neurological and Mental Health Condition Self-management: Systematic Review of User Perspectives. JMIR Mental Health 2022;9(4):e25249
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  58. Sieber C, Haag C, Polhemus A, Sylvester R, Kool J, Gonzenbach R, von Wyl V. Feasibility and scalability of a fitness tracker study: Results from a longitudinal analysis of persons with multiple sclerosis. Frontiers in Digital Health 2023;5
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  59. Alder G, Taylor D, Rashid U, Olsen S, Brooks T, Terry G, Niazi IK, Signal N. A Brain Computer Interface Neuromodulatory Device for Stroke Rehabilitation: Iterative User-Centered Design Approach. JMIR Rehabilitation and Assistive Technologies 2023;10:e49702
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  60. Lu Z, Signer T, Sylvester R, Gonzenbach R, von Wyl V, Haag C. Implementation of Remote Activity Sensing to Support a Rehabilitation Aftercare Program: Observational Mixed Methods Study With Patients and Health Care Professionals. JMIR mHealth and uHealth 2023;11:e50729
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  61. Guardado S, Mylonopoulou V, Rivera-Romero O, Patt N, Bansi J, Giunti G. An Exploratory Study on the Utility of Patient-Generated Health Data as a Tool for Health Care Professionals in Multiple Sclerosis Care. Methods of Information in Medicine 2023;62(05/06):165
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  62. Benjumea J, Ropero J, Dorronzoro-Zubiete E, Rivera-Romero O, Carrasco A. A Proposal for a Robust Validated Weighted General Data Protection Regulation-Based Scale to Assess the Quality of Privacy Policies of Mobile Health Applications: An eDelphi Study. Methods of Information in Medicine 2023;62(05/06):154
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  67. Simblett S, Dawe-Lane E, Gilpin G, Morris D, White K, Erturk S, Devonshire J, Lees S, Zorbas S, Polhemus A, Temesi G, Cummins N, Hotopf M, Wykes T. Data visualization preferences in remote measurement technology for individuals living with depression, epilepsy, and multiple sclerosis: a qualitative study (Preprint). Journal of Medical Internet Research 2022;
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According to Crossref, the following books are citing this article (DOI 10.2196/mhealth.8996):

  1. Chassan C, Jost C, Sévène M, Cras O, De Broucker T, Archambault D. Computers Helping People with Special Needs. 2020. Chapter 28:233
    CrossRef
  2. Dorronzoro-Zubiete E, Rivera-Romero O, Nuñez-Benjumea FJ, Cervera-Torres S. Personalized Health Systems for Cardiovascular Disease. 2022. :205
    CrossRef