Enabling older adults to carry out paperless falls-risk self-assessments using guidetomeasure-3D: A mixed methods study.

3D mobile visualisation Assistive equipment Extrinsic risk factors Falls Health informatics Measurement guidance Occupational therapy Self-assessment Technology-based systems

Journal

Journal of biomedical informatics
ISSN: 1532-0480
Titre abrégé: J Biomed Inform
Pays: United States
ID NLM: 100970413

Informations de publication

Date de publication:
04 2019
Historique:
received: 12 04 2018
revised: 23 01 2019
accepted: 11 02 2019
pubmed: 4 3 2019
medline: 26 6 2020
entrez: 4 3 2019
Statut: ppublish

Résumé

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. 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. 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). 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. 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.

Sections du résumé

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.

Identifiants

pubmed: 30826542
pii: S1532-0464(19)30053-X
doi: 10.1016/j.jbi.2019.103135
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103135

Informations de copyright

Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.

Auteurs

Julian Hamm (J)

Department of Computer Science, Brunel University, Uxbridge UB8 3PH, UK. Electronic address: julian.hamm@brunel.ac.uk.

Arthur G Money (AG)

Department of Computer Science, Brunel University, Uxbridge UB8 3PH, UK. Electronic address: arthur.money@brunel.ac.uk.

Anita Atwal (A)

School of Health & Social Care, London South Bank University, 103 Borough Road, London SE1 0AA, UK. Electronic address: atwala@lsbu.ac.uk.

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