Centre for Gerontology and Rehabilitation - Journal Articles
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- ItemFactors Influencing Continued Wearable Device Use in Older Adult Populations: Quantitative Study.(JMIR Publications, 2023-01-19) Muñoz Esquivel, Karla; Gillespie, James; Kelly, Daniel; Condell, Joan,; Davies, Richard; McHugh, Catherine; Duffy, William; Nevala, Elina; Alamäki, Antti; Jalovaara, Juha; Tedesco, Salvatore; Barton, John; Timmons, Suzanne; Nordström, Anna; InterregThe increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services. This study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device. Older adults across 4 different regions (Northern Ireland, Ireland, Sweden, and Finland) wore an activity tracker for 7 days under a free-living environment protocol. In total, 4 surveys were administered, and biometrics were measured by the researchers before the trial began. At the end of the trial period, the researchers administered 2 further surveys to gain insights into the perceived usability of the wearable device. These were the standardized System Usability Scale (SUS) and a custom usability questionnaire designed by the research team. Statistical analyses were performed to identify the key factors that affect participants' intention to continue using the wearable device in the future. Machine learning classifiers were used to provide an early prediction of the intention to continue using the wearable device. The study was conducted with older adult volunteers (N=65; mean age 70.52, SD 5.65 years) wearing a Xiaomi Mi Band 3 activity tracker for 7 days in a free-living environment. The results from the SUS survey showed no notable difference in perceived system usability regardless of region, sex, or age, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participants' age. There was also no statistically significant difference in SUS score between participants who had previously owned a wearable device and those who wore 1 or 2 devices during the trial. The bespoke usability questionnaire determined that the 2 most important factors that influenced an intention to continue device use in an older adult cohort were device comfort (t=0.34) and whether the device was fit for purpose (t=0.34). A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%. This study concludes that comfort and accuracy are the 2 main influencing factors in sustaining wearable device use. This study suggests that the reported factors influencing usability are transferable to other wearable sensor systems. Future work will aim to test this hypothesis using the same methodology on a cohort using other wearable technologies.
- ItemEducation and training interventions for physiotherapists working in dementia care: a scoping review(Elsevier Inc., 2022-11-10) O'Sullivan, Trish; McVeigh, Joseph G.; Timmons, Suzanne; Foley, TonyObjectives: Physiotherapy plays a key role in many aspects of dementia care, most notably in maintaining mobility. However, there is a lack of dementia care training at undergraduate and postgraduate level, and more importantly, a paucity of evidence as to what constitutes effective dementia education and training for physiotherapists. The aim of this scoping review was to explore and map the evidence, both quantitative and qualitative, relating to education and training for physiotherapists. Design: This scoping review followed the Joanna Briggs Institute methodology for scoping reviews. A chronological narrative synthesis of the data outlined how the results relate to the objectives of this study. Setting: All studies, both quantitative and qualitative on dementia education and training conducted in any setting, including acute, community care, residential or any educational setting in any geographical area were included. Participants: Studies that included dementia education and training for both qualified and student physiotherapists were considered. Results: A total of 11 papers were included in this review. The principal learning outcomes evaluated were knowledge, confidence, and attitudes. Immediate post- intervention scores showed an improvement in all three outcomes. The Kirkpatrick four level model was used to evaluate the level of outcome achieved. Most educational interventions reached Kirkpatrick level 2, which evaluates learning. A multi-modal approach, with active participation and direct patient involvement seems to enhance learning. Conclusions: Allowing for the heterogeneity of intervention design and evaluation, some common components of educational interventions were identified that led to positive outcomes. This review highlights the need for more robust studies in this area. Further research is needed to develop bespoke dementia curricula specific to physiotherapy.
- ItemGait speed, cognition and falls in people living with mild-to-moderate Alzheimer disease: Data from NILVAD(BioMed Central Ltd, 2020) Dyer, A. H.; Murphy, C.; Lawlor, B.; Kennelly, S. P.; Segurado, R.; Olde Rikkert, M. G. M.; Howard, R.; Pasquier, F.; Börjesson-Hanson, A.; Tsolaki, M.; Lucca, U.; Molloy, D. William; Coen, R.; Riepe, M. W.; Kálmán, J.; Kenny, R. A.; Cregg, F.; O'Dwyer, S.; Walsh, C.; Adams, J.; Banzi, R.; Breuilh, L.; Daly, L.; Hendrix, S.; Aisen, P.; Gaynor, S.; Sheikhi, A.; Taekema, D. G.; Verhey, F. R.; Nemni, R.; Nobili, F.; Franceschi, M.; Frisoni, G.; Zanetti, O.; Konsta, A.; Anastasios, O.; Nenopoulou, S.; Tsolaki-Tagaraki, F.; Pakaski, M.; Dereeper, O.; Sayette, V. D. L.; Sénéchal, O.; Lavenu, I.; Devendeville, A.; Calais, G.; Crawford, F.; Mullan, M.; Aalten, P.; Berglund, M. A.; Claassen, J. A.; De Heus, R. A.; De Jong, D. L. K.; Godefroy, O.; Hutchinson, S.; Ioannou, A.; Jonsson, M.; Kent, A.; Kern, J.; Nemtsas, P.; Panidou, M.-K.; Abdullah, L.; Paris, D.; Santoso, A. M.; van Spijker, G. J.; Spiliotou, M.; Thomoglou, G.; Wallin, A.; NILVAD Study Group; Seventh Framework ProgrammeBackground: Previous evidence suggests that slower gait speed is longitudinally associated with cognitive impairment, dementia and falls in older adults. Despite this, the longitudinal relationship between gait speed, cognition and falls in those with a diagnosis of dementia remains poorly explored. We sought to assess this longitudinal relationship in a cohort of older adults with mild to-moderate Alzheimer Disease (AD). Methods: Analysis of data from NILVAD, an 18-month randomised-controlled trial of Nilvadipine in mild to moderate AD. We examined: (i) the cross-sectional (baseline) association between slow gait speed and cognitive function, (ii) the relationship between baseline slow gait speed and cognitive function at 18 months (Alzheimer Disease Assessment Scale, Cognitive Subsection: ADAS-Cog), (iii) the relationship between baseline cognitive function and incident slow gait speed at 18 months and finally (iv) the relationship of baseline slow gait speed and incident falls over the study period. Results: Overall, one-tenth (10.03%, N = 37/369) of participants with mild-to-moderate AD met criteria for slow gait speed at baseline and a further 14.09% (N = 52/369) developed incident slow gait speed at 18 months. At baseline, there was a significant association between poorer cognition and slow gait speed (OR 1.05, 95% CI 1.01-1.09, p = 0.025). Whilst there was no association between baseline slow gait speed and change in ADAS-Cog score at 18 months, a greater cognitive severity at baseline predicted incident slow gait speed over 18 months (OR 1.04, 1.01-1.08, p = 0.011). Further, slow gait speed at baseline was associated with a significant risk of incident falls over the study period, which persisted after covariate adjustment (IRR 3.48, 2.05-5.92, p < 0.001). Conclusions: Poorer baseline cognition was associated with both baseline and incident slow gait speed. Slow gait speed was associated with a significantly increased risk of falls over the study period. Our study adds further evidence to the complex relationship between gait and cognition in this vulnerable group and highlights increased falls risk in older adults with AD and slow gait speed. Trial registration: Secondary analysis of the NILVAD trial (Clincaltrials.gov NCT02017340; EudraCT number 2012-002764-27). First registered: 20/12/2013.
- ItemThe views and needs of people with Parkinson disease regarding wearable devices for disease monitoring: Mixed methods exploration(JMIR Publications, 2022-01-06) Kenny, Lorna; Moore, Kevin; O'Riordan, Clíona; Fox, Siobhan; Barton, John; Tedesco, Salvatore; Sica, Marco; Crowe, Colum; Alamäki, Antti; Condell, Joan; Nordström, Anna; Timmons, Suzanne; European Regional Development Fund; Interreg; Enterprise Ireland; AbbVieObjective: This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management. Methods: This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach. Results: Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption. Conclusions: Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease–related health barriers and the users’ confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management.
- ItemComparison of machine learning techniques for mortality prediction in a prospective cohort of older adults(MDPI, 2021-12-04) Tedesco, Salvatore; Andrulli, Martina; Larsson, Markus Ãkerlund; Kelly, Daniel; Timmons, Suzanne; Barton, John; Condell, Joan; O'Flynn, Brendan; Nordström, Anna; European Regional Development Fund; Interreg; Science Foundation Ireland; Enterprise Ireland; Department of Business, Enterprise and Innovation, IrelandAs global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all-cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all-cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free-living settings, obtained for the “Healthy Ageing Initiative” study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random UnderSampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data-driven and disease-agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.