JMIR Mental Health

The use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated.

Fully-automated digital interventions delivered via smartphone apps have proven efficacious for a wide variety of mental health outcomes. An important value is that they are accessible at a low cost, thereby increasing their potential public impact and reducing disparities. However, a major challenge to their successful implementation is the phenomenon of users dropping out early.

Digital wearable devices, worn on or close to the body, have potential for passively detecting mental and physical health symptoms among people with severe mental illness (SMI); however, the roles of consumer-grade devices are not well understood.

Digital interventions typically involve using smartphones or PCs to access online or downloadable self-help and may offer a more accessible and convenient option than face-to-face interventions for some people with mild to moderate eating disorders. They have been shown to substantially reduce eating disorder symptoms, but treatment dropout rates are higher than for face-to-face interventions. We need to understand user experiences and preferences for digital interventions to support the design and development of user-centered digital interventions that are engaging and meet users’ needs.

Digital health technologies are increasingly being integrated into mental health care. However, the adoption of these technologies can be influenced by patients’ digital literacy and attitudes, which may vary based on sociodemographic factors. This variability necessitates a better understanding of patient digital literacy and attitudes to prevent a digital divide, which can worsen existing health care disparities.

The rise of wearable sensors marks a significant development in the era of affective computing. Their popularity is continuously increasing, and they have the potential to improve our understanding of human stress. A fundamental aspect within this domain is the ability to recognize perceived stress through these unobtrusive devices.

Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as Major Depressive Disorder. The aim of this study was to learn if machine learning (ML) models based on longitudinal speech data are helpful in predicting momentary depression severity. Data analyses were based on a dataset including 30 inpatients during an acute depressive episode receiving Sleep Deprivation Therapy in stationary care, an intervention inducing a rapid change in depressive symptomatology in a relatively short period of time. Using an ambulatory assessment approach, we captured speech samples and assessed concomitant depression severity via self-report questionnaire over the course of three weeks (before, during, and after therapy). We extracted 89 speech features from the speech samples using the eGeMAPS parameter set from openSMILE and the additional parameter speech rate.

Sleep-wake patterns are important behavioral biomarkers for patients with serious mental illness (SMI), providing insight into their well-being. The gold standard for monitoring sleep is polysomnography (PSG), which requires a sleep lab facility; however, advances in wearable sensor technology allow for real-world sleep-wake monitoring.

Digital mental health is a promising paradigm for individualized, patient-driven health care. For example, cognitive bias modification programs that target interpretation biases (cognitive bias modification for interpretation [CBM-I]) can provide practice thinking about ambiguous situations in less threatening ways on the web without requiring a therapist. However, digital mental health interventions, including CBM-I, are often plagued with lack of sustained engagement and high attrition rates. New attrition detection and mitigation strategies are needed to improve these interventions.

The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are a guideline to improve the reusability of data. However, properly implementing these principles is challenging due to a wide range of barriers.

Therapists and their patients increasingly discuss digital data from social media, smartphone sensors, and other online engagement within the context of psychotherapy.

The integrated motivational-volitional model (IMV) is one of the leading theoretical models of suicidal thoughts and behavior. There has been a recent proliferation in the assessment of suicidal and nonsuicidal self-harm thoughts and behaviors (SHTBs) in daily life.