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CC BY-NC-ND 4.0 · Methods Inf Med 2023; 62(05/06): 165-173
DOI: 10.1055/s-0043-1775718
Original Article for a Focus Theme

An Exploratory Study on the Utility of Patient-Generated Health Data as a Tool for Health Care Professionals in Multiple Sclerosis Care

Sharon Guardado
1   Empirical Software Engineering (M3S) Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
,
Vasiliki Mylonopoulou
2   Division of Human-Computer Interaction, Department Of Applied Information Technology, University of Gothenburg, Gothenburg, Sweden
,
Octavio Rivera-Romero
3   Department of Electronic Technology, Universidad de Sevilla, Seville, Spain
4   Instituto de Investigación en Informática, Universidad de Sevilla, Seville, Spain
5   SABIEN Group, ITACA Institute, Universitat Politécnica de Valéncia, Valencia, Spain
,
Nadine Patt
6   Department of Neurology, Kliniken Valens, Rehabilitationszentrum Valens, Valens, Switzerland
,
Jens Bansi
6   Department of Neurology, Kliniken Valens, Rehabilitationszentrum Valens, Valens, Switzerland
,
Guido Giunti
1   Empirical Software Engineering (M3S) Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
7   Health Sciences and Technology Unit, Faculty of Medicine, University of Oulu, Finland
8   Applied Ergonomics and Design, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, The Netherlands
9   Clinical Medicine Neurology, School of Medicine, Trinity College Dublin, Dublin, Ireland
› Author Affiliations Funding The More Stamina project has received funding from Business Finland, and O.R.-R. has received funding from the Universidad de Sevilla and the Ministerio de Universidades of the Spanish Government under the call “Recualificación del Sistema Español de Universidades” funded by European Union—NextGenerationEU. The study was partly funded by the EU’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No. 101034252 and by a research grant from Science Foundation Ireland (SFI) under Grant Number 16/RC/3948.
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Abstract

Background Patient-generated health data (PGHD) are data collected through technologies such as mobile devices and health apps. The integration of PGHD into health care workflows can support the care of chronic conditions such as multiple sclerosis (MS). Patients are often willing to share data with health care professionals (HCPs) in their care team; however, the benefits of PGHD can be limited if HCPs do not find it useful, leading patients to discontinue data tracking and sharing eventually. Therefore, understanding the usefulness of mobile health (mHealth) solutions, which provide PGHD and serve as enablers of the HCPs' involvement in participatory care, could motivate them to continue using these technologies.

Objective The objective of this study is to explore the perceived utility of different types of PGHD from mHealth solutions which could serve as tools for HCPs to support participatory care in MS.

Method A mixed-methods approach was used, combining qualitative research and participatory design. This study includes three sequential phases: data collection, assessment of PGHD utility, and design of data visualizations. In the first phase, 16 HCPs were interviewed. The second and third phases were carried out through participatory workshops, where PGHD types were conceptualized in terms of utility.

Results The study found that HCPs are optimistic about PGHD in MS care. The most useful types of PGHD for HCPs in MS care are patients' habits, lifestyles, and fatigue-inducing activities. Although these subjective data seem more useful for HCPs, it is more challenging to visualize them in a useful and actionable way.

Conclusion HCPs are optimistic about mHealth and PGHD as tools to further understand their patients' needs and support care in MS. HCPs from different disciplines have different perceptions of what types of PGHD are useful; however, subjective types of PGHD seem potentially more useful for MS care.

Keywords

patient-generated health data - mHealth - participatory - informatics - multiple sclerosis


Publication History

Received: 10 October 2022

Accepted: 05 August 2023

Article published online:
25 September 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 Lavallee DC, Lee JR, Austin E. et al. mHealth and patient generated health data: stakeholder perspectives on opportunities and barriers for transforming healthcare. mHealth 2020; 6: 8
    CrossrefPubMedGoogle Scholar
  • 2 Hsueh PY, Cheung YK, Dey S. et al. Added value from secondary use of person generated health data in consumer health informatics. Yearb Med Inform 2017; 26 (01) 160-171
    Article in Thieme ConnectPubMedGoogle Scholar
  • 3 Sim I. Mobile devices and health. N Engl J Med 2019; 381 (10) 956-968
    CrossrefPubMedGoogle Scholar
  • 4 Dillenseger A, Weidemann ML, Trentzsch K. et al. Digital biomarkers in multiple sclerosis. Brain Sci 2021; 11 (11) 1-26
    CrossrefPubMedGoogle Scholar
  • 5 Jeevanandan N, Nøhr C. Patient-generated health data in the clinic. In: Studies in Health Technology and Informatics. Vol. 270. Amsterdam: IOS Press; 2020: 766-770
    Google Scholar
  • 6 Feng S, Mäntymäki M, Dhir A, Salmela H. How self-tracking and the quantified self promote health and well-being: systematic review. J Med Internet Res 2021; 23 (09) e25171
    CrossrefPubMedGoogle Scholar
  • 7 Oh CY, Luo Y, St JB, Choe EK. Patients waiting for cues: information asymmetries and challenges in sharing patient-generated data in the clinic. Proc ACM Hum Comput Interact 2022; 6 (CSCW1): 1-23
    CrossrefPubMedGoogle Scholar
  • 8 Gordon WJ, Landman A, Zhang H, Bates DW. Beyond validation: getting health apps into clinical practice. NPJ Digit Med 2020; 3 (01) 14
    CrossrefPubMedGoogle Scholar
  • 9 Vo V, Auroy L, Sarradon-Eck A. Patients' perceptions of mhealth apps: meta-ethnographic review of qualitative studies. JMIR Mhealth Uhealth 2019; 7 (07) e13817
    CrossrefPubMedGoogle Scholar
  • 10 Sarradon-Eck A, Bouchez T, Auroy L, Schuers M, Darmon D. Attitudes of general practitioners toward prescription of mobile health apps: qualitative study. JMIR Mhealth Uhealth 2021; 9 (03) e21795
    CrossrefPubMedGoogle Scholar
  • 11 Figueiredo MC, Chen Y. Patient-generated health data: dimensions, challenges, and open questions. Foundations and Trends in Human-Computer Interaction. 2020; 13 (03) 165-297
    CrossrefPubMedGoogle Scholar
  • 12 Denecke K, Gabarron E, Petersen C, Merolli M. Defining participatory health informatics - a scoping review. Inform Health Soc Care 2021; 46 (03) 234-243
    CrossrefPubMedGoogle Scholar
  • 13 Marrie RA, Leung S, Tyry T, Cutter GR, Fox R, Salter A. Use of eHealth and mHealth technology by persons with multiple sclerosis. Mult Scler Relat Disord 2019; 27 (27) 13-19
    CrossrefPubMedGoogle Scholar
  • 14 Guardado S, Isomursu M, Giunti G. Health care professionals' perspectives on the uses of patient-generated health data. Stud Health Technol Inform 2022; 294: 750-754
    PubMedGoogle Scholar
  • 15 Wendrich K, van Oirschot P, Martens MB, Heerings M, Jongen PJ, Krabbenborg L. Toward digital self-monitoring of multiple sclerosis: investigating first experiences, needs, and wishes of people with MS. Int J MS Care 2019; 21 (06) 282-291
    CrossrefPubMedGoogle Scholar
  • 16 Chung CF, Dew K, Cole A. et al. Boundary negotiating artifacts in personal informatics: patient-provider collaboration with patient-generated data. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW. Vol. 27; 2016: 770-786
    PubMed
  • 17 Wannheden C, Åberg-Wennerholm M, Dahlberg M. et al. Digital health technologies enabling partnerships in chronic care management: scoping review. J Med Internet Res 2022; 24 (08) e38980
    CrossrefPubMedGoogle Scholar
  • 18 Rouidi M, Elouadi AE, Hamdoune A, Choujtani K, Chati A. TAM-UTAUT and the acceptance of remote healthcare technologies by healthcare professionals: a systematic review. Inform Med Unlocked 2022; 32: 101008
    CrossrefPubMedGoogle Scholar
  • 19 Jacob C, Sanchez-Vazquez A, Ivory C. Social, organizational, and technological factors impacting clinicians' adoption of mobile health tools: systematic literature review. JMIR Mhealth Uhealth 2020; 8 (02) e15935
    CrossrefPubMedGoogle Scholar
  • 20 Gourraud PA, Henry RG, Cree BAC. et al. Precision medicine in chronic disease management: the multiple sclerosis BioScreen. Ann Neurol 2014; 76 (05) 633-642
    CrossrefPubMedGoogle Scholar
  • 21 Ward M, Goldman MD, Goldman MD. Epidemiology and pathophysiology of multiple sclerosis. Continuum (Minneap Minn) 2022; 28 (04) 988-1005
    PubMedGoogle Scholar
  • 22 Giunti G, Kool J, Rivera Romero O, Dorronzoro Zubiete E. Exploring the specific needs of persons with multiple sclerosis for mhealth solutions for physical activity: mixed-methods study. JMIR Mhealth Uhealth 2018; 6 (02) e37
    CrossrefPubMedGoogle Scholar
  • 23 Gil-González I, Martín-Rodríguez A, Conrad R, Pérez-San-Gregorio MÁ. Quality of life in adults with multiple sclerosis: a systematic review. BMJ Open 2020; 10 (11) e041249
    CrossrefPubMedGoogle Scholar
  • 24 Marziniak M, Brichetto G, Feys P, Meyding-Lamadé U, Vernon K, Meuth SG. The use of digital and remote communication technologies as a tool for multiple sclerosis management: narrative review. JMIR Rehabil Assist Technol 2018; 5 (01) e5
    CrossrefPubMedGoogle Scholar
  • 25 Khan F, Amatya B. Rehabilitation in multiple sclerosis: a systematic review of systematic reviews. Arch Phys Med Rehabil 2017; 98 (02) 353-367
    CrossrefPubMedGoogle Scholar
  • 26 van der Walt A, Butzkueven H, Shin RK. et al. Developing a digital solution for remote assessment in multiple sclerosis: from concept to software as a medical device. Brain Sci 2021; 11 (09) 1247
    CrossrefPubMedGoogle Scholar
  • 27 Barin L, Salmen A, Disanto G. et al; Swiss Multiple Sclerosis Registry (SMSR). The disease burden of Multiple Sclerosis from the individual and population perspective: which symptoms matter most?. Mult Scler Relat Disord 2018; 25: 112-121
    CrossrefPubMedGoogle Scholar
  • 28 Ayobi A, Marshall P, Cox AL, Chen Y. Quantifying the body and caring for the mind: self-tracking in multiple sclerosis. In: Conference on Human Factors in Computing Systems - Proceedings. Vol. 2017, May.; 2017: 6889-6901
    PubMed
  • 29 Van Kessel K, Babbage DR, Reay N, Miner-Williams WM, Kersten P. Mobile technology use by people experiencing multiple sclerosis fatigue: survey methodology. JMIR Mhealth Uhealth 2017; 5 (02) e6
    CrossrefPubMedGoogle Scholar
  • 30 Floch J, Vilarinho T, Zettl A. et al. Users' experiences of a mobile health self-management approach for the treatment of cystic fibrosis: mixed methods study. JMIR Mhealth Uhealth 2020; 8 (07) e15896
    CrossrefPubMedGoogle Scholar
  • 31 Greiner P, Sawka A, Imison E. Patient and physician perspectives on MSdialog, an electronic PRO diary in multiple sclerosis. Patient 2015; 8 (06) 541-550
    CrossrefPubMedGoogle Scholar
  • 32 De Angelis M, Lavorgna L, Carotenuto A. et al. Digital technology in clinical trials for multiple sclerosis: systematic review. J Clin Med 2021; 10 (11) 2328
    CrossrefPubMedGoogle Scholar
  • 33 Bradway M, Gabarron E, Johansen M. et al. Methods and measures used to evaluate patient-operated mobile health interventions: scoping literature review. JMIR Mhealth Uhealth 2020; 8 (04) e16814
    CrossrefPubMedGoogle Scholar
  • 34 Giunti G, Mylonopoulou V, Rivera Romero O. More stamina, a gamified mHealth solution for persons with multiple sclerosis: research through design. JMIR Mhealth Uhealth 2018; 6 (03) e51
    CrossrefPubMedGoogle Scholar
  • 35 Giunti G, Rivera-Romero O, Kool J. et al. Evaluation of more stamina, a mobile app for fatigue management in persons with multiple sclerosis: protocol for a feasibility, acceptability, and usability study. JMIR Res Protoc 2020; 9 (08) e18196
    CrossrefPubMedGoogle Scholar
  • 36 Yrttiaho T, Isomursu M, Giunti G. Experiences using patient and public involvement in digital health research for multiple sclerosis. In: Studies in Health Technology and Informatics. Vol. 294. Amsterdam: IOS Press; 2022: 735-739
    Google Scholar
  • 37 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 Uhealth 2018; 6 (05) e10512
    CrossrefPubMedGoogle Scholar
  • 38 Giunti G, Haverinen J, Reponen J. Informing the product development of an mhealth solution for people with multiple sclerosis through early health technology assessment. Stud Health Technol Inform 2022; 290: 1042-1043
    PubMedGoogle Scholar
  • 39 Namoun A, Daskalopoulou A, Mehandjiev N, Xun Z. Exploring mobile end user development: existing use and design factors. IEEE Trans Softw Eng 2016; 42 (10) 960-976
    CrossrefPubMedGoogle Scholar
  • 40 Scariot CA, Heemann A, Padovani S. Understanding the collaborative-participatory design. Work 2012; 41 (Suppl. 01) 2701-2705
    CrossrefPubMedGoogle Scholar
  • 41 National Advisory Board on Research Ethics. Ethical principles of research in the humanities and social and behavioural sciences and proposals for ethical review. Published online 2009. Accessed September 4, 2023 at: https://tenk.fi/sites/default/files/2023-05/RI_Guidelines_2023.pdf
    PubMed
  • 42 Ozkaynak M, Sircar CM, Frye O, Valdez RS. A systematic review of design workshops for health information technologies. Informatics (MDPI) 2021; 8 (02) 34
    CrossrefPubMedGoogle Scholar
  • 43 Block VJ, Bove R, Nourbakhsh B. The role of remote monitoring in evaluating fatigue in multiple sclerosis: a review. Front Neurol 2022; 13: 878313
    CrossrefPubMedGoogle Scholar
  • 44 Afzal M, Riazul Islam SM, Hussain M, Lee S. Precision medicine informatics: principles, prospects, and challenges. IEEE Access 2020; 8: 13593-13612
    CrossrefPubMedGoogle Scholar