Exploring the potential of technology to promote exercise snacking for older adults who are prefrail in the home setting: user-centered design study. (2023)
Journal Article
STAWARZ, K., LIANG, I.J., ALEXANDER, L., CARLIN, A., WIJEKOON, A. and WESTERN, M. 2023. Exploring the potential of technology to promote exercise snacking for older adults who are prefrail in the home setting: user-centered design study. JMIR aging [online], 6, article e41810. Available from: https://doi.org/10.2196/41810

Older adults have an increased risk of falls, injury, and hospitalization. Maintaining/increasing participation in physical activity (PA) into older age can prevent some of the age-related declines in physical functioning that may contribute to loss... Read More about Exploring the potential of technology to promote exercise snacking for older adults who are prefrail in the home setting: user-centered design study..

Effectiveness of app-delivered, tailored self-management support for adults with lower back pain-related disability: a selfBACK randomized clinical trial. [Dataset] (2021)
Dataset
SANDAL, L.F., BACH, K., ØVERÅS, C.K., WIRATUNGA, N., COOPER, K, et al. 2021. Effectiveness of app-delivered, tailored self-management support for adults with lower back pain-related disability: a selfBACK randomized clinical trial. [Dataset]. JAMA internal medicine [online], 181(10), pages 1288-1296. Available from: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2782459#supplemental-tab

SELFBACK is an evidence-based decision support system that supports self-management of nonspecific low back pain. In specific, SELFBACK provides the user with evidence-based advice on physical activity level, strength/ flexibility exercises, and educ... Read More about Effectiveness of app-delivered, tailored self-management support for adults with lower back pain-related disability: a selfBACK randomized clinical trial. [Dataset].

Effectiveness of app-delivered, tailored self-management support for adults with lower back pain-related disability: a selfBACK randomized clinical trial. (2021)
Journal Article
SANDAL, L.F., BACH, K., ØVERÅS, C.K., WIRATUNGA, N., COOPER, K, et al. 2021. Effectiveness of app-delivered, tailored self-management support for adults with lower back pain-related disability: a selfBACK randomized clinical trial. JAMA internal medicine [online], 181(10), pages 1288-1296. Available from: https://doi.org/10.1001/jamainternmed.2021.4097

Importance: Lower back pain (LBP) is a prevalent and challenging condition in primary care. The effectiveness of an individually tailored self-management support tool delivered via a smartphone app has not been rigorously tested. Objective: To invest... Read More about Effectiveness of app-delivered, tailored self-management support for adults with lower back pain-related disability: a selfBACK randomized clinical trial..

Personalised exercise recognition towards improved self-management of musculoskeletal disorders. (2021)
Thesis
WIJEKOON, A. 2021. Personalised exercise recognition towards improved self-management of musculoskeletal disorders. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1358224

Musculoskeletal Disorders (MSD) have been the primary contributor to the global disease burden, with increased years lived with disability. Such chronic conditions require self-management, typically in the form of maintaining an active lifestyle whil... Read More about Personalised exercise recognition towards improved self-management of musculoskeletal disorders..

Learning to compare with few data for personalised human activity recognition. (2020)
Conference Proceeding
WIRATUNGA, N., WIJEKOON, A. and COOPER, K. 2020. Learning to compare with few data for personalised human activity recognition. In Watson, I and Weber, R. (eds.) Case-based reasoning research and development: proceedings of the 28th International conference on case-based reasoning research and development (ICCBR 2020), 8-12 June 2020, Salamanca, Spain [virtual conference]. Lecture notes in computer science, 12311. Cham: Springer [online], pages 3-14. Available from: https://doi.org/10.1007/978-3-030-58342-2_1

Recent advances in meta-learning provides interesting opportunities for CBR research, in similarity learning, case comparison and personalised recommendations. Rather than learning a single model for a specific task, meta-learners adopt a generalist... Read More about Learning to compare with few data for personalised human activity recognition..

Usability and acceptability of an app (SELFBACK) to support self-management of low back pain: mixed methods study. (2020)
Journal Article
NORDSTOGA, A.L., BACH, K., SANI, S., WIRATUNGA, N., MORK, P.J., VILLUMSEN, M. and COOPER, K. 2020. Usability and acceptability of an app (SELFBACK) to support self-management of low back pain: mixed methods study. JMIR rehabilitation and assistive technologies [online], 7(2), article number e18729. Available from: https://doi.org/10.2196/18729

Self-management is the key recommendation for managing non-specific low back pain (LBP). However, there are well-documented barriers to self-management, therefore methods of facilitating adherence are required. Smartphone apps are increasingly being... Read More about Usability and acceptability of an app (SELFBACK) to support self-management of low back pain: mixed methods study..

Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition. (2020)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N. and COOPER, K. 2020. Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9206941. Available from: https://doi.org/10.1109/IJCNN48605.2020.9206941

Exercise adherence is a key component of digital behaviour change interventions for the self-management of musculoskeletal pain. Automated monitoring of exercise adherence requires sensors that can capture patients performing exercises and Machine Le... Read More about Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition..

Learning to recognise exercises for the self-management of low back pain. (2020)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N., COOPER, K. and BACH, K. 2020. Learning to recognise exercises for the self-management of low back pain. In Barták, R. and Bell, E. (eds.). Proceedings of the 33rd International Florida Artificial Intelligence Research Society (FLAIRS) 2020 conference (FLAIRS-33), 17-20 May 2020, Miami Beach, USA. Palo Alto: AAAI Press [online], pages 347-352. Available from: https://aaai.org/ocs/index.php/FLAIRS/FLAIRS20/paper/view/18460

Globally, Low back pain (LBP) is one of the top three contributors to years lived with disability. Self-management with an active lifestyle is the cornerstone for preventing and managing LBP. Digital interventions are introduced in the recent past to... Read More about Learning to recognise exercises for the self-management of low back pain..

A knowledge-light approach to personalised and open-ended human activity recognition. (2020)
Journal Article
WIJEKOON, A., WIRATUNGA, N., SANI, S. and COOPER, K. 2020. A knowledge-light approach to personalised and open-ended human activity recognition. Knowledge-based systems [online], 192, article ID 105651. Available from: https://doi.org/10.1016/j.knosys.2020.105651

Human Activity Recognition (HAR) is a core component of clinical decision support systems that rely on activity monitoring for self-management of chronic conditions such as Musculoskeletal Disorders. Deployment success of such applications in part de... Read More about A knowledge-light approach to personalised and open-ended human activity recognition..