Subjective sleep alterations in healthy subjects worldwide during COVID-19 pandemic: A systematic review, meta-analysis and meta-regression.

OBJECTIVE: We conducted a systematic review and meta-analysis to provide an update on sleep quality in different world areas and better characterize subjective sleep alterations during the COVID-19 pandemic. Considering gender distribution and specific pandemic-related parameters, we also intend to identify significant predictors of sleep problems. METHODS: Six electronic databases were searched from December 2019 to November 2021 for studies investigating sleep during COVID-19 employing the Pittsburgh Sleep Quality Index, the Medical Outcomes Study Sleep, the Insomnia Severity Index or the Epworth Sleepiness Scale. Random-effects models were implemented to estimate the pooled raw means of subjective sleep alterations. Also, we considered the role of several pandemic-related parameters (i.e., days from the first COVID-19 case, government stringency index, new cases for a million people, new deaths for a million people) by means of meta-regression analyses. RESULTS: A total of 139 studies were selected. The pooled mean of the global Pittsburgh Sleep Quality Index score (PSQIgen) was 6.73 (95% CI, 6.61-6.85). The insomnia severity index score was reported from 50 studies with a pooled mean of 8.44 (95% CI, 7.53-9.26). Subgroup analyses confirmed that most subcategories had poor sleep quality and subclinical insomnia. Meta-regressions showed that PSQIgen was predicted by days from the first COVID-19 case and government restrictions with a negative slope and by female gender with a positive slope. The government stringency index was positively correlated with the direct subjective evaluation of sleep quality. CONCLUSIONS: We found an overall impaired sleep and widespread subthreshold insomnia during the COVID-19 pandemic. The female percentage seems to be the best predictor of impaired sleep quality, consistently to the available literature. Noteworthy, sleep alterations were inversely associated with governmental restrictions and decreased during the pandemic. Our results give a contribution to critically orienting further studies on sleep since COVID-19 pandemic.

The SleepFit Tablet Application for Home-Based Clinical Data Collection in Parkinson Disease: User-Centric Development and Usability Study.

BACKGROUND: Parkinson disease (PD) is a common, multifaceted neurodegenerative disorder profoundly impacting patients' autonomy and quality of life. Assessment in real-life conditions of subjective symptoms and objective metrics of mobility and nonmotor symptoms such as sleep disturbance is strongly advocated. This information would critically guide the adaptation of antiparkinsonian medications and nonpharmacological interventions. Moreover, since the spread of the COVID-19 pandemic, health care practices are being reshaped toward a more home-based care. New technologies could play a pivotal role in this new approach to clinical care. Nevertheless, devices and information technology tools might be unhandy for PD patients, thus dramatically limiting their widespread employment. OBJECTIVE: The goals of the research were development and usability evaluation of an application, SleepFit, for ecological momentary assessment of objective and subjective clinical metrics at PD patients' homes, and as a remote tool for researchers to monitor patients and integrate and manage data. METHODS: An iterative and user-centric strategy was employed for the development of SleepFit. The core structure of SleepFit consists of (1) an electronic finger-tapping test; (2) motor, sleepiness, and emotional subjective scales; and (3) a sleep diary. Applicable design, ergonomic, and navigation principles have been applied while tailoring the application to the specific patient population. Three progressively enhanced versions of the application (alpha, v1.0, v2.0) were tested by a total of 56 patients with PD who were asked to perform multiple home assessments 4 times per day for 2 weeks. Patient compliance was calculated as the proportion of completed tasks out of the total number of expected tasks. Satisfaction on the latest version (v2.0) was evaluated as potential willingness to use SleepFit again after the end of the study. RESULTS: From alpha to v1.0, SleepFit was improved in graphics, ergonomics, and navigation, with automated flows guiding the patients in performing tasks throughout the 24 hours, and real-time data collection and consultation were made possible thanks to a remote web portal. In v2.0, the kiosk-mode feature restricts the use of the tablet to the SleepFit application only, thus preventing users from accidentally exiting the application. A total of 52 (4 dropouts) patients were included in the analyses. Overall compliance (all versions) was 88.89% (5707/6420). SleepFit was progressively enhanced and compliance increased from 87.86% (2070/2356) to 89.92% (2899/3224; P=.04). Among the patients who used v2.0, 96% (25/26) declared they would use SleepFit again. CONCLUSIONS: SleepFit can be considered a state-of-the-art home-based system that increases compliance in PD patients, ensures high-quality data collection, and works as a handy tool for remote monitoring and data management in clinical research. Thanks to its user-friendliness and modular structure, it could be employed in other clinical studies with minimum adaptation efforts. TRIAL REGISTRATION: ClinicalTrials.gov NCT02723396; https://clinicaltrials.gov/ct2/show/NCT02723396.