Evaluating the Effectiveness of the SleepTracker App for Detecting Anxiety- and Depression-Related Sleep Disturbances.

This study emphasises the critical role of quality sleep in physical and mental well-being, exploring its impact on bodily recovery and cognitive function. Investigating poor sleep quality in approximately 40% of individuals with insomnia symptoms, the research delves into its potential diagnostic relevance for depression and anxiety, with a focus on intervention in mental health by understanding sleep patterns, especially in young individuals. This study includes an exploration of phone usage habits among young adults during PPI sessions, providing insights for developing the SleepTracker app. This pivotal tool utilises phone usage and movement data from mobile device sensors to identify indicators of anxiety or depression, with participant information organised comprehensively in a table categorising condition related to phone usage and movement data. The analysis compares this data with survey results, incorporating scores from the Sleep Condition Indicator (SCI), Patient Health Questionnaire-9 (PHQ-9), and Generalised Anxiety Disorder-7 (GAD-7). Generated confusion matrices offer a detailed overview of the relationship between sleep metrics, phone usage, and movement data. In summary, this study reveals the accurate detection of negative sleep disruption instances by the classifier. However, improvements are needed in identifying positive instances, reflected in the F1-score of 0.5 and a precision result of 0.33. While early intervention potential is significant, this study emphasises the need for a larger participant pool to enhance the model's performance.

Recurrent COVID-19 Infection in a Refractory/Classical Hodgkin's Lymphoma Patient Undergoing Autologous Stem Cell Transplantation: A Case Report.

Patients with challenging hematological malignancies like classic Hodgkin lymphoma (cHL) can be further complicated when affected by a concurrent coronavirus disease-2019 (COVID-19) infection and often face unique and complex management and outcomes. In this case report, we describe a refractory or relapsed classic Hodgkin lymphoma patient with a recurrent infection of COVID-19 three times preceding chemotherapy. A 52-year-old female presented to our hospital with a second incidence of COVID-19 and a complaint of fever, anorexia, night sweats, and abdominal lymphadenopathy, for which she was diagnosed with mixed cellularity classic Hodgkin lymphoma. Three weeks later, in consideration of her manifestation of lung disease, which was due to her past medical history of airway hypersensitivity and abnormal pulmonary function test along with testing positive for COVID-19, she was started with the first-line chemotherapy of the brentuximab vedotin, doxorubicin, vinblastine, and dacarbazine chemotherapy regimen, commonly referred to as Bv-AVD, without bleomycin. After six cycles of chemotherapy, at the end of treatment, positron emission tomography/computed tomography (PET/CT) revealed the progression of nodes in the abdomen and the development of new lymphadenopathy in the chest and right supraclavicular region. Hence, it was considered refractory Hodgkin's lymphoma, and the patient was referred for salvage therapy. She was started on salvage chemotherapy with brentuximab/bendamustine (BvB). Follow-up evaluations after two cycles of BvB continued to show newer lesions in the right sub-diaphragmatic area, internal mammary, and supraclavicular lymph nodes. Therefore, the patient was switched to pembrolizumab immunotherapy, a PD-1 inhibitor. After four cycles of pembrolizumab monotherapy, PET/CT showed significant improvement with a complete molecular response (CMR). Then, she was admitted for high-dose therapy/autologous stem cell transplantation (HDT/ASCT) after collecting stem cells. PET/CT: three months post-ASCT, she continued to be in a CMR with a Deauville score of 1. The patient was continued on pembrolizumab maintenance for six months afterward. Currently, the patient is healthy and doing well. COVID-19 patients with hematological malignancies may experience compromised viral elimination and a prolonged period of viral infection, which may also worsen the symptoms and outcomes and entitle them to comprehensive and extended care.

The Feasibility of Using Smartphone Sensors to Track Insomnia, Depression, and Anxiety in Adults and Young Adults: Narrative Review.

BACKGROUND: Since the era of smartphones started in early 2007, they have steadily turned into an accepted part of our lives. Poor sleep is a health problem that needs to be closely monitored before it causes severe mental health problems, such as anxiety or depression. Sleep disorders (eg, acute insomnia) can also develop to chronic insomnia if not treated early. More specifically, mental health problems have been recognized to have casual links to anxiety, depression, heart disease, obesity, dementia, diabetes, and cancer. Several researchers have used mobile sensors to monitor sleep and to study changes in individual mood that may cause depression and anxiety. OBJECTIVE: Extreme sleepiness and insomnia not only influence physical health, they also have a significant impact on mental health, such as by causing depression, which has a prevalence of 18% to 21% among young adults aged 16 to 24 in the United Kingdom. The main body of this narrative review explores how passive data collection through smartphone sensors can be used in predicting anxiety and depression. METHODS: A narrative review of the English language literature was performed. We investigated the use of smartphone sensors as a method of collecting data from individuals, regardless of whether the data source was active or passive. Articles were found from a search of Google Scholar records (from 2013 to 2020) with keywords including "mobile phone," "mobile applications," "health apps," "insomnia," "mental health," "sleep monitoring," "depression," "anxiety," "sleep disorder," "lack of sleep," "digital phenotyping," "mobile sensing," "smartphone sensors," and "sleep detector." RESULTS: The 12 articles presented in this paper explain the current practices of using smartphone sensors for tracking sleep patterns and detecting changes in mental health, especially depression and anxiety over a period of time. Several researchers have been exploring technological methods to detect sleep using smartphone sensors. Researchers have also investigated changes in smartphone sensors and linked them with mental health and well-being. CONCLUSIONS: The conducted review provides an overview of the possibilities of using smartphone sensors unobtrusively to collect data related to sleeping pattern, depression, and anxiety. This provides a unique research opportunity to use smartphone sensors to detect insomnia and provide early detection or intervention for mental health problems such as depression and anxiety if insomnia is detected.