Background To date, delirium prevalence and incidence in acute hospitals has been estimated from pooled findings of studies performed in distinct patient populations. Objective To determine delirium prevalence across an acute care facility. Design A point prevalence study. Setting A large tertiary care, teaching hospital. Patients 311 general hospital adult inpatients were assessed over a single day. Of those, 280 had full data collected within the study's time frame (90%). Measurements Initial screening for inattention was performed using the spatial span forwards and months backwards tests by junior medical staff, followed by two independent formal delirium assessments: first the Confusion Assessment Method (CAM) by trained geriatric medicine consultants and registrars, and, subsequently, the Delirium Rating Scale-Revised-98 (DRS-R98) by experienced psychiatrists. The diagnosis of delirium was ultimately made using DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition) criteria. Results Using DSM-IV criteria, 55 of 280 patients (19.6%) had delirium versus 17.6% using the CAM. Using the DRS-R98 total score for independent diagnosis, 20.7% had full delirium, and 8.6% had subsyndromal delirium. Prevalence was higher in older patients (4.7% if 80 years) and particularly in those with prior dementia (OR=15.33, p<0.001), even when adjusted for potential confounders. Although 50.9% of delirious patients had pre-existing dementia, it was poorly documented in the medical notes. Delirium symptoms detected by medical notes, nurse interview and patient reports did not overlap much, with inattention noted by professional staff, and acute change and sleep-wake disturbance noted by patients. Conclusions Our point prevalence study confirms that delirium occurs in about 1/5 of general hospital inpatients and particularly in those with prior cognitive impairment. Recognition strategies may need to be tailored to the symptoms most noticed by the detector (patient, nurse or primary physician) if formal assessments are not available.

Background: Older adults may use wearable devices for various reasons, ranging from monitoring clinically relevant health metrics or detecting falls to monitoring physical activity. Little is known about how this population engages with wearable devices, and no qualitative synthesis exists to describe their shared experiences with long-term use. Objective: This study aims to synthesize qualitative studies of user experience after a multi-day trial with a wearable device to understand user experience and the factors that contribute to the acceptance and use of wearable devices. Methods: We conducted a systematic search in CINAHL, APA PsycINFO, PubMed, and Embase (2015-2020; English) with fixed search terms relating to older adults and wearable devices. A meta-synthesis methodology was used. We extracted themes from primary studies, identified key concepts, and applied reciprocal and refutational translation techniques; findings were synthesized into third-order interpretations, and finally, a “line-of-argument” was developed. Our overall goal was theory development, higher-level abstraction, and generalizability for making this group of qualitative findings more accessible. Results: In total, we reviewed 20 papers; 2 evaluated fall detection devices, 1 tested an ankle-worn step counter, and the remaining 17 tested activity trackers. The duration of wearing ranged from 3 days to 24 months. The views of 349 participants (age: range 51-94 years) were synthesized. Four key concepts were identified and outlined: motivation for device use, user characteristics (openness to engage and functional ability), integration into daily life, and device features. Motivation for device use is intrinsic and extrinsic, encompassing many aspects of the user experience, and appears to be as, if not more, important than the actual device features. To overcome usability barriers, an older adult must be motivated by the useful purpose of the device. A device that serves its intended purpose adds value to the user’s life. The user’s needs and the support structure around the device—aspects that are often overlooked—seem to play a crucial role in long-term adoption. Our “line-of-argument” model describes how motivation, ease of use, and device purpose determine whether a device is perceived to add value to the user’s life, which subsequently predicts whether the device will be integrated into the user’s life. Conclusions: The added value of a wearable device is the resulting balance of motivators (or lack thereof), device features (and their accuracy), ease of use, device purpose, and user experience. The added value contributes to the successful integration of the device into the daily life of the user. Useful device features alone do not lead to continued use. A support structure should be placed around the user to foster motivation, encourage peer engagement, and adapt to the user’s preferences.

Background: previous studies have indicated a prevalence of dementia in older admissions of ∼42% in a single London teaching hospital, and 21% in four Queensland hospitals. However, there is a lack of published data from any European country on the prevalence of dementia across hospitals and between patient groups. Objective: to determine the prevalence and associations of dementia in older patients admitted to acute hospitals in Ireland. Methods: six hundred and six patients aged ≥70 years were recruited on admission to six hospitals in Cork County. Screening consisted of Standardised Mini-Mental State Examination (SMMSE); patients with scores <27/30 had further assessment with the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). Final expert diagnosis was based on SMMSE, IQCODE and relevant medical and demographic history. Patients were screened for delirium and depression, and assessed for co-morbidity, functional ability and nutritional status. Results: of 598 older patients admitted to acute hospitals, 25% overall had dementia; with 29% in public hospitals. Prevalence varied between hospitals (P < 0.001); most common in rural hospitals and acute medical admissions. Only 35.6% of patients with dementia had a previous diagnosis. Patients with dementia were older and frailer, with higher co-morbidity, malnutrition and lower functional status (P < 0.001). Delirium was commonly superimposed on dementia (57%) on admission. Conclusion: dementia is common in older people admitted to acute hospitals, particularly in acute medical admissions, and rural hospitals, where services may be less available. Most dementia is not previously diagnosed, emphasising the necessity for cognitive assessment in older people on presentation to hospital.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real-life, as opposed to laboratory settings. Objective: This study explored the performance of two wrist-worn trackers (Fitbit Charge2 and Garmin Vivo Smart HR+) estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (TST and WASO) against gold-standard technologies in a cohort of healthy older adults in free-living environment. Methods: Overall, 20 subjects (> 65 years) took part in the study. The devices were worn by the subjects for 24 hours, and the results were compared against validated technology (ActiGraph and New Lifestyles NL-2000i). Mean error, MAPE, ICC and Bland-Altman plots were computed for all the parameters considered. Results: Regarding step-counting, all the trackers are highly correlated with each other (ICCs > 0.89). While Fitbit tends to over-count steps (MPE: 12.36%), Garmin and ActiGraph under-count with a MPE of 9.36% and 11.53%. Garmin had poor ICC values when considering energy expenditure compared against the criterion. Fitbit has moderate-to-good ICCs when compared against the other activity trackers, and showed the best results (MAPE: 12.25%) even if underestimated the amount of calories burnt. For MVPA levels estimation, the wristband trackers were highly correlated (ICC = 0.96), however, were moderately correlated against the criterion and over-estimated the minutes of MVPA activity. When analyzing the sleep parameters, the ICCs were poor for all cases, except when comparing Fitbit with the criterion, which showed a moderate agreement. TST is slightly over-estimated with the Fitbit, although providing good results with an average MAPE equal to 10.13%. On the other hand, WASO estimation was poorer and was over-estimated by Fitbit but under-estimated by Garmin. Again, Fitbit was the most accurate, with an average MAPE of 49.7%. Conclusions: Results suggest that the tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, while clinicians should be cautious in considering other parameters for clinical and research purposes.