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Background Healthcare worker (HCW) anxiety and depression worsened during the pandemic, prompting the expansion of digital mental health platforms as potential solutions offering online assessments, access to resources and counselling. The use of these digital engagement tools may reflect tendencies and trends for the mental health needs of HCWs.Objectives This retrospective, cross-sectional study investigated the association between the use of an online mental health platform within a large academic health system and measures of that system’s COVID-19 burden during the first 3 years of the pandemic.Methods The study investigated the use of Cobalt, an online mental health platform, comprising deidentified mental health assessments and utilisation metrics. Cobalt, serves as an online mental health resource broadly available to health system employees, offering online evidence-based tools, coaching, therapy options and asynchronous content (podcasts, articles, videos and more). The analyses use validated mental health assessments (Generalised Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9) and post-traumatic stress disorder (PTSD)) alongside publicly available COVID-19 data. Statistical analyses employed univariate linear regression with Stata SE Statistical Software.Results Between March 2020 and March 2023, 43 308 independent user sessions were created on Cobalt, a majority being anonymous sessions (72%, n=31 151). Mental health assessments, including PHQ-4, PHQ-9, GAD-7 and primary care-PTSD, totalled 9462 over the time period. Risk for self-harm was noted in 17.1% of PHQ-9 assessments. Additionally, 4418 appointments were scheduled with mental health counsellors and clinicians. No significant associations were identified between COVID-19 case burden and Cobalt utilisation or assessment scores.Conclusion Cobalt emerged as an important access point for assessing the collective mental health of the workforce, witnessing increased engagement over time. Notably, the study indicates the nuanced nature of HCW assessments of anxiety, depression and PTSD, with mental health scores reflecting moderate decreases in depression and anxiety but signalling potential increases in PTSD. Tailored resources are imperative, acknowledging varied mental health needs within the healthcare workforce. Ultimately, this investigation lays the groundwork for continued exploration of the impact and effectiveness of digital platforms in supporting HCW mental health.

BackgroundThe COVID-19 pandemic has placed strains on communities. During this public health crisis, health systems have created remote methods of monitoring symptom progression and delivering care virtually. ObjectiveUsing an SMS text message-based system, we sought to build and test a remote model to explore community needs, connect individuals to curated resources, and facilitate community health worker intervention when needed during the pandemic. The primary aims of this pilot study were to establish the feasibility (ie, engagement with the text line) and acceptability (ie, participant ratings of resources and service) of delivering automated well-being resources via smartphone technology. MethodsEligible patients (aged 18 years or older, having a cell phone with SMS text messaging capability, and recently visited the emergency department) were identified using the electronic health record. The patients were consented to enroll and begin receiving COVID-19–related information and links to community resources. We collected open-ended and close-ended resource and mood ratings. We calculated the frequencies and conducted a thematic review of the open-ended responses. ResultsIn 7 weeks, 356 participants were enrolled; 13,917 messages were exchanged including 333 resource ratings (mean 4) and 673 well-being scores (mean 6.8). We received and coded 386 open-ended responses, most of which elaborated upon their self-reported mood score (29%). Overall, 77% (n=274) of our participants rated the platform as a service they would highly recommend to a family member or friend. ConclusionsThis approach is designed to broaden the reach of health systems, tailor to community needs in real time, and connect at-risk individuals with robust community health support.

Objectives Loneliness is a major public health problem and an estimated 17% of adults aged 18–70 in the USA reported being lonely. We sought to characterise the (online) lives of people who mention the words ‘lonely’ or ‘alone’ in their Twitter timeline and correlate their posts with predictors of mental health.Setting and design From approximately 400 million tweets collected from Twitter in Pennsylvania, USA, between 2012 and 2016, we identified users whose Twitter posts contained the words ‘lonely’ or ‘alone’ and compared them to a control group matched by age, gender and period of posting. Using natural-language processing, we characterised the topics and diurnal patterns of users’ posts, their association with linguistic markers of mental health and if language can predict manifestations of loneliness. The statistical analysis, data synthesis and model creation were conducted in 2018–2019.Primary outcome measures We evaluated counts of language features in the users with posts including the words lonely or alone compared with the control group. These language features were measured by (a) open-vocabulary topics, (b) Linguistic Inquiry Word Count (LIWC) lexicon, (c) linguistic markers of anger, depression and anxiety, and (d) temporal patterns and number of drug words. Using machine learning, we also evaluated if expressions of loneliness can be predicted in users’ timelines, measured by area under curve (AUC).Results Twitter timelines of users (n=6202) with posts including the words lonely or alone were found to include themes about difficult interpersonal relationships, psychosomatic symptoms, substance use, wanting change, unhealthy eating and having troubles with sleep. Their posts were also associated with linguistic markers of anger, depression and anxiety. A random forest model predicted expressions of loneliness online with an AUC of 0.86.Conclusions Users’ Twitter timelines with the words lonely or alone often include psychosocial features and can potentially have associations with how individuals express and experience loneliness. This can inform low-resource online assessment for high-risk individuals experiencing loneliness and interventions focused on addressing morbidities in this condition.