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Title: Enabling older adults to carry out paperless falls-risk self-assessments using guidetomeasure-3D: A mixed methods study Corresponding Author: Dr. Arthur Money
Authors: Hamm, J
Money, A
Atwal, A
Keywords: Health informatics;Falls;Occupational therapy;Assistive equipment;Self-assessment;Measurement guidance
Issue Date: 2019
Publisher: Elsevier
Citation: Journal of Biomedical Informatics
Abstract: Background The home environment falls-risk assessment process (HEFAP) is a widely used falls prevention intervention strategy which involves a clinician using paper-based measurement guidance to ensure that appropriate information and measurements are taken and recorded accurately. Despite the current use of paper-based guidance, over 30% of all assistive devices installed within the home are abandoned by patients. This is in part due to poor fit between the device, the patient, and the environment in which it is installed. Currently HEFAP is a clinician-led process, however, older adult patients are increasingly being expected to collect HEFAP measurements themselves as part of the personalisation agenda. Without appropriate patient-centred guidance, levels of device abandonment to are likely to rise to unprecedented levels. This study presents guidetomeasure-3D, a mobile 3D measurement guidance application designed to support patients in carrying out HEFAP self-assessments. Aim The aim of this study is to present guidetomeasure-3D, a web-enabled 3D mobile application that enables older-adult patients to carry out self-assessment measurement tasks, and to carry out a mixed-methods evaluation of its performance, and associated user perceptions of the application, compared with a 2D paper-based equivalent. Methods Thirty-four older adult participants took part in a mixed-methods within-subjects repeated measures study set within a living lab. A series of HEFAP self-assessment tasks were carried out according to two treatment conditions: 1) using the 3D guidetomeasure-3D application; 2) using a 2D paper-based guide. SUS questionnaires and semi-structured interviews were completed at the end of the task. A comparative statistical analysis explored performance with regards to measurement accuracy, accuracy consistency, task efficiency, and system usability. Interview transcripts were analysed using inductive and deductive thematic analysis (informed by UTAUT). Results The guidetomeasure-3D application outperformed the 2D paper-based guidance in terms of accuracy (smaller mean error difference in 11 out of 12 items), accuracy consistency (p < 0.05, for 6 out of 12 items), task efficiency (p = 0.003), system usability (p < 0.00625, for two out of 10 SUS items), and clarity of guidance (p < 0.0125, for three out of four items). Three high-level themes emerged from interviews: Performance Expectancy, Effort Expectancy, and Social Influence. Participants reported that guidetomeasure-3D provided improved visual quality, clarity, and more precise guidance overall. Real-time audio instruction was reported as being particularly useful, as was the use of the object rotation and zoom functions which were associated with improving user confidence particularly when carrying out more challenging tasks. Conclusions This study reveals that older adults using guidetomeasure-3D achieved improved levels of accuracy and efficiency along with improved satisfaction and increased levels of confidence compared with the 2D paper-based equivalent. These results are significant and promising for overcoming HEFAP equipment abandonment issue. Furthermore they constitute an important step towards overcoming challenges associated with older adult patients, the digitisation of healthcare, and realising the enablement of patient self-care and management via the innovative use of mobile technologies. Numerous opportunities for the generalisability and transferability of the findings of this research are also proposed. Future research will explore the extent to which mobile 3D visualisation technologies may be utilised to optimise the clinical utility of HEFAP when deployed by clinicians.
ISSN: 1532-0464
Appears in Collections:Dept of Computer Science Research Papers

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