Publications by authors named "J Knights"
Article Synopsis
- - The study focused on the mental health and wellbeing of dental practice managers and receptionists in the UK, aiming to highlight their challenges and inform future research.
- - An online questionnaire collected data on burnout, depression, trauma, and preparedness for care, revealing high levels of negative psychosocial effects among participants.
- - Key stressors included workplace pressure, difficult relationships, and lack of recognition, which negatively impact health and could compromise the quality of care provided.
Introduction: This study investigated the acceptability and feasibility of digital phenotyping in a military sample with a history of traumatic brain injury and co-occurring psychological and cognitive symptoms. The first aim was to evaluate the acceptability of digital phenotyping by (1a) quantifying the proportion of participants willing to download the app and rates of dropout and app discontinuation and (1b) reviewing the stated reasons for both refusing and discontinuing use of the app. The second aim was to investigate technical feasibility by (2a) characterizing the amount and frequency of transferred data and (2b) documenting technical challenges.
View Article and Find Full Text PDFStudy Objectives: We sought to develop behavioral sleep measures from passively sensed human-smartphone interactions and retrospectively evaluate their associations with sleep disturbance, anxiety, and depressive symptoms in a large cohort of real-world patients receiving virtual behavioral medicine care.
Methods: Behavioral sleep measures from smartphone data were developed: daily longest period of smartphone inactivity (inferred sleep period [ISP]); 30-day expected period of inactivity (expected sleep period [ESP]); regularity of the daily ISP compared to the ESP (overlap percentage); and smartphone usage during inferred sleep (disruptions, wakefulness during sleep period). These measures were compared to symptoms of sleep disturbance, anxiety, and depression using linear mixed-effects modeling.
Background: One in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients.
Methods: Historical observational clinical data was leveraged from a virtual healthcare system.
Background: Various behavioral sensing research studies have found that depressive symptoms are associated with human-smartphone interaction behaviors, including lack of diversity in unique physical locations, entropy of time spent in each location, sleep disruption, session duration, and typing speed. These behavioral measures are often tested against the total score of depressive symptoms, and the recommended practice to disaggregate within- and between-person effects in longitudinal data is often neglected.
Objective: We aimed to understand depression as a multidimensional process and explore the association between specific dimensions and behavioral measures computed from passively sensed human-smartphone interactions.