JMIR Mental Health

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.

Suicide is the third-leading cause of death among US adolescents aged 10-19 years, and about 10% attempt suicide each year. School-based universal prevention may reduce youth suicidal behavior. Sources of Strength uses a peer leader network diffusion model to promote healthy norms across a school population. A key challenge within schoolwide programs is reaching a large and diverse array of students, especially those less engaged with their peers. Motivated by this challenge, we developed and field-tested Text4Strength—a program of automated text messages targeting help-seeking attitudes and norms, social coping resources, and emotion regulation skills.

Indigenous Australians in custody experience much greater rates of poor mental health and well-being than those of the general community, and these problems are not adequately addressed. Digital mental health strategies offer innovative opportunities to address the problems, but little is known about their feasibility in or impact on this population.

First-episode psychosis (FEP) imposes a substantial burden not only on the individual affected but also on their families. Given that FEP usually occurs during adolescence, families overtake a big part of informal care. Early family interventions, especially psychoeducation, are crucial for informal family caregivers to best support the recovery of their loved one with FEP and to reduce the risk of a psychotic relapse as much as possible, but also to avoid chronic stress within the family due to the burden of care. Digital health interventions offer the possibility to access help quicker, use less resources, and improve informal family caregiver outcomes, for example, by reducing stress and improving caregiver quality of life.

The dual diagnosis of cannabis use disorder (CUD) and severe mental disorder (SMD) results in clinically complex individuals. Cannabis use is known to have negative consequences on psychiatric symptoms, medication compliance, and disease prognosis. Moreover, the effectiveness of currently available psychotherapeutic treatments is limited in this population.

The need for scalable solutions facilitating access to eating disorder (ED) treatment services that are efficient, effective, and inclusive is a major public health priority. Remote access to synchronous and asynchronous support delivered via health apps has shown promise, but results are so far mixed, and there are limited data on whether apps can enhance health care utilization.