Wearable devices for anxiety & depression: A scoping review.

Background: The rates of mental health disorders such as anxiety and depression are at an all-time high especially since the onset of COVID-19, and the need for readily available digital health care solutions has never been greater. Wearable devices have increasingly incorporated sensors that were previously reserved for hospital settings. The availability of wearable device features that address anxiety and depression is still in its infancy, but consumers will soon have the potential to self-monitor moods and behaviors using everyday commercially-available devices. Objective: This study aims to explore the features of wearable devices that can be used for monitoring anxiety and depression. Methods: Six bibliographic databases, including MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar were used as search engines for this review. Two independent reviewers performed study selection and data extraction, while two other reviewers justified the cross-checking of extracted data. A narrative approach for synthesizing the data was utilized. Results: From 2408 initial results, 58 studies were assessed and highlighted according to our inclusion criteria. Wrist-worn devices were identified in the bulk of our studies (n = 42 or 71%). For the identification of anxiety and depression, we reported 26 methods for assessing mood, with the State-Trait Anxiety Inventory being the joint most common along with the Diagnostic and Statistical Manual of Mental Disorders (n = 8 or 14%). Finally, n = 26 or 46% of studies highlighted the smartphone as a wearable device host device. Conclusion: The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies for illnesses such as anxiety and depression. We believe that purposefully-designed wearable devices that combine the expertise of technologists and clinical experts can play a key role in self-care monitoring and diagnosis.

Ocular Vascular Events following COVID-19 Vaccines: A Systematic Review.

The main aim of this study is to investigate the current evidence regarding the association between COVID-19 vaccination and ocular vascular events. The protocol is registered on PROSPERO (CRD42022358133). On 18 August 2022, an electronic search was conducted through five databases. All original articles reporting individuals who were vaccinated with COVID-19 vaccines and developed ophthalmic vascular events were included. The methodological quality of the included studies was assessed using the NIH tool. A total of 49 studies with 130 ocular vascular cases were included. Venous occlusive events were the most common events (54.3%), which mostly occurred following the first dose (46.2%) and within the first five days following vaccination (46.2%). Vascular events occurred more with the Pfizer and AstraZeneca vaccines (81.6%), and mostly presented unilaterally (73.8%). The most frequently reported treatment was intravitreal anti-VEGF (n = 39, 30.4%). The majority of patients (90.1%) demonstrated either improvement (p = 0.321) or persistence (p = 0.414) in the final BCVA. Ophthalmic vascular events are serious vision-threatening side effects that have been associated with COVID-19 vaccination. Clinicians should be aware of the possible association between COVID-19 vaccines and ocular vascular events to provide early diagnosis and treatment.

Machine learning models to detect anxiety and depression through social media: A scoping review.

Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on social media forums such Facebook. We explored machine learning models used to detect anxiety and depression through social media. Six bibliographic databases were searched for conducting the review following PRISMA-ScR protocol. We included 54 of 2219 retrieved studies. Users suffering from anxiety or depression were identified in the reviewed studies by screening their online presence and their sharing of diagnosis by patterns in their language and online activity. Majority of the studies (70%, 38/54) were conducted at the peak of the COVID-19 pandemic (2019-2020). The studies made use of social media data from a variety of different platforms to develop predictive models for the detection of depression or anxiety. These included Twitter, Facebook, Instagram, Reddit, Sina Weibo, and a combination of different social sites posts. We report the most common Machine Learning models identified. Identification of those suffering from anxiety and depression disorders may be achieved using prediction models to detect user's language on social media and has the potential to complimenting traditional screening. Such analysis could also provide insights into the mental health of the public especially so when access to health professionals can be restricted due to lockdowns and temporary closure of services such as we saw during the peak of the COVID-19 pandemic.