University of Oulu

Korhonen O, Väyrynen K, Krautwald T, Bilby G, Broers HAT, Giunti G, Isomursu M., Data-Driven Personalization of a Physiotherapy Care Pathway: Case Study of Posture Scanning, JMIR Rehabil Assist Technol 2020;7(2):e18508, URL:, DOI: 10.2196/18508, PMID: 32930667

Data-driven personalization of a physiotherapy care pathway : case study of posture scanning

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Author: Korhonen, Olli1; Väyrynen, Karin1; Krautwald, Tino2,3;
Organizations: 1University of Oulu, Oulu, Finland
2Qinematic, Stockholm, Sweden
3Blekinge Institute of Technology, Karlshamn, Sweden
4Bright Cape, Eindhoven, Netherlands
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
Persistent link:
Language: English
Published: JMIR Publications, 2020
Publish Date: 2020-09-24


Background: Advanced sensor, measurement, and analytics technologies are enabling entirely new ways to deliver health care. The increased availability of digital data can be used for data-driven personalization of care. Data-driven personalization can complement expert-driven personalization by providing support for decision making or even by automating some parts of decision making in relation to the care process.

Objective: The aim of this study was to analyze how digital data acquired from posture scanning can enhance physiotherapy services and enable more personalized delivery of physiotherapy.

Methods: A case study was conducted with a company that designed a posture scan recording system (PSRS), which is an information system that can digitally record, measure, and report human movement for use in physiotherapy. Data were collected through interviews with different stakeholders, such as health care professionals, health care users, and the information system provider, and were analyzed thematically.

Results: Based on the results of our thematic analysis, we propose three different types of support that posture scanning data can provide to enhance and enable more personalized delivery of physiotherapy: 1) modeling the condition, in which the posture scanning data are used to detect and understand the health care user’s condition and the root cause of the possible pain; 2) visualization for shared understanding, in which the posture scanning data are used to provide information to the health care user and involve them in more collaborative decision-making regarding their care; and 3) evaluating the impact of the intervention, in which the posture scanning data are used to evaluate the care progress and impact of the intervention.

Conclusions: The adoption of digital tools in physiotherapy has remained low. Physiotherapy has also lacked digital tools and means to inform and involve the health care user in their care in a person-centered manner. In this study, we gathered insights from different stakeholders to provide understanding of how the availability of digital posture scanning data can enhance and enable personalized physiotherapy services.

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Series: JMIR rehabilitation and assistive technologies
ISSN: 2369-2529
ISSN-E: 2369-2529
ISSN-L: 2369-2529
Volume: 7
Issue: 2
Article number: e18508
DOI: 10.2196/18508
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
217 Medical engineering
3111 Biomedicine
3121 General medicine, internal medicine and other clinical medicine
315 Sport and fitness sciences
Copyright information: ©Olli Korhonen, Karin Väyrynen, Tino Krautwald, Glenn Bilby, Hedwig Anna Theresia Broers, Guido Giunti, Minna Isomursu. Originally published in JMIR Rehabilitation and Assistive Technology (, 15.09.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Rehabilitation and Assistive Technology, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.