The relationship between syncope, depression and anti-depressant use in older adults.

BACKGROUND: syncope is a common problem which increases in older age groups. In syncope clinics, patients who are depressed have higher rates of unexplained syncope and higher rates of recurrent syncope. OBJECTIVES: : we aim to examine the rates of depression in older patients reporting syncope and the effect of anti-depressants (ADs) on the rates of syncope. DESIGN: : epidemiological, point-prevalence study. SETTING AND PARTICIPANTS: : data came from the Irish Longitudinal Study on Ageing, which includes 8,175 adults aged 50 and older, living in the community in Ireland. MEASUREMENTS: : the Centre for Epidemiological Studies Depression scale was used to assess levels of depression. Multinomial regression was used to analyse the data with a P-value of <0.05 determining significance. RESULTS: : 7,993 participants aged 50 and older were included, and of these 349 reported at least one syncopal episode in the last year. Prevalence of syncope was 4.4%. After controlling for participant characteristics and general health, those with severe depression had a greater risk of single and multiple syncopal events (relative risk ratios [RRR]: 2.78 and 2.84, respectively, P < 0.050) and participants treated with tricyclic anti-depressants (TCAs) were also at greater risk for single and multiple syncopal episode in the last year (RRR: 2.31, P = 0.062; RRR: 2.95, P < 0.05). CONCLUSIONS: : this study demonstrates an increased risk of syncope in patients with depression, with higher rates of syncope reported with increasing severity of depression. Treatment with TCAs increases both the risk and frequency of syncope in the community. Depression is a potentially modifiable risk factor for syncope but treatment options need to be tailored in the older patient population.

Corrigendum: Evaluation of Wearable Technology in Dementia: A Systematic Review and Meta-Analysis.

[This corrects the article DOI: 10.3389/fmed.2020.501104.].

Evaluation of Wearable Technology in Dementia: A Systematic Review and Meta-Analysis.

Background: The objective of this analysis was to systematically review studies employing wearable technology in patients with dementia by quantifying differences in digitally captured physiological endpoints. Methods: This systematic review and meta-analysis was based on web searches of Cochrane Database, PsycInfo, Pubmed, Embase, and IEEE between October 25-31st, 2017. Observational studies providing physiological data measured by wearable technology on participants with dementia with a mean age ≥50. Data were extracted according to PRISMA guidelines and methodological quality assessed independently using Downs and Black criteria. Standardized mean differences between cases and controls were estimated using random-effects models. Results: Forty-eight studies from 18,456 screened abstracts (Dementia: n = 2,516, Control: n = 1,224) met inclusion criteria for the systematic review. Nineteen of these studies were included in one or multiple meta-analyses (Dementia: n = 617, Control: n = 406). Participants with dementia demonstrated lower levels of daily activity (standardized mean difference (SMD), -1.60; 95% CI, -2.66 to -0.55), decreased sleep efficiency (SMD, -0.52; 95% CI, -0.89 to -0.16), and greater intradaily circadian variability (SMD, 0.46; 95% CI, 0.27 to 0.65) than controls, among other measures. Statistical between-study heterogeneity was observed, possibly due to variation in testing duration, device type or patient setting. Conclusions and Relevance: Digitally captured data using wearable devices revealed that adults with dementia were less active, demonstrated increased fragmentation of their sleep-wake cycle and a loss of typical diurnal variation in circadian rhythm as compared to controls.

mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson's disease.

Accurately monitoring motor and non-motor symptoms as well as complications in people with Parkinson's disease (PD) is a major challenge, both during clinical management and when conducting clinical trials investigating new treatments. A variety of strategies have been relied upon including questionnaires, motor diaries, and the serial administration of structured clinical exams like part III of the MDS-UPDRS. To evaluate the potential use of mobile and wearable technologies in clinical trials of new pharmacotherapies targeting PD symptoms, we carried out a project (project BlueSky) encompassing four clinical studies, in which 60 healthy volunteers (aged 23-69; 33 females) and 95 people with PD (aged 42-80; 37 females; years since diagnosis 1-24 years; Hoehn and Yahr 1-3) participated and were monitored in either a laboratory environment, a simulated apartment, or at home and in the community. In this paper, we investigated (i) the utility and reliability of self-reports for describing motor fluctuations; (ii) the agreement between participants and clinical raters on the presence of motor complications; (iii) the ability of video raters to accurately assess motor symptoms, and (iv) the dynamics of tremor, dyskinesia, and bradykinesia as they evolve over the medication cycle. Future papers will explore methods for estimating symptom severity based on sensor data. We found that 38% of participants who were asked to complete an electronic motor diary at home missed ~25% of total possible entries and otherwise made entries with an average delay of >4 h. During clinical evaluations by PD specialists, self-reports of dyskinesia were marked by ~35% false negatives and 15% false positives. Compared with live evaluation, the video evaluation of part III of the MDS-UPDRS significantly underestimated the subtle features of tremor and extremity bradykinesia, suggesting that these aspects of the disease may be underappreciated during remote assessments. On the other hand, live and video raters agreed on aspects of postural instability and gait. Our results highlight the significant opportunity for objective, high-resolution, continuous monitoring afforded by wearable technology to improve upon the monitoring of PD symptoms.