Abstract Generalized anxiety disorder (GAD) is a disabling and often chronic condition. Internet-based treatments for GAD have been shown to be effective, but many studies include weekly contact with a therapist with the aim to increase adherence and clinical outcomes. The current study evaluated a less therapist-intensive alternative: support on demand and automatic messages. Thirty-three participants with GAD went through a self-help program targeting excessive worry. Treatment lasted 9 weeks and consisted of seven modules. Participants received short messages with reminders and encouragement. Therapist support was given when asked for. The intervention led to significant and large within-group effects on the primary outcome, Penn State Worry Questionnaire (PSWQ; Cohen’s d = 1.17), as well as on secondary outcomes such as Generalized Anxiety Disorder Questionnaire-IV (GAD-Q-IV; Cohen’s d = 2.71) and Patient Health Questionaire-9 (PHQ-9; Cohen’s d = 1.05). The exception was a small effect on quality of life (d = −0.34). Twenty-four (74.9%) were satisfied with the treatment and one dropped out. Therapist support was used by 65.6%. Limitations include lack of control condition and a small sample. While preliminary, the findings suggest that self-guided internet interventions can work and be acceptable when automated messages and support on demand is provided.
Background Effectiveness of heart rate variability (HRV) biofeedback is dependent on the relationship between the measured respiratory-heart rate coherence (CHR) and the resonant frequency (RFB) breathing. CHR is driven by the central autonomic network which consists of the interplay between the components of the autonomic nervous system, brainstem modulatory nuclei and cortical function. The extent to which heart rate is in coherence with respiratory frequency and the function of the central autonomic network in major depressive disorder (MDD) and suicidal ideation (SI) has not been investigated. Method: Sixty-one patients provided informed consent to participate and were divided into no MDD (CONT), MDD and an MDD plus SI (MDDSI) group. HRV activity was determined by multi lag tone-entropy (T-E) and respiratory-CHR at rest was characterized by the relationship between respiratory rate and heart rate. Results: CONT had the highest entropy compared to the MDDSI group, which had the lowest entropy and highest tone (p < 0.05). Autonomic parasympathetic function was also significantly lower in the MDDSI compared to the MDD group (p < 0.05). CHR indicated a significant phase desynchronization (decrease in coherence) between MDDSI, MDD and CONT (p < 0.05), with MDDSI having the lowest coherence and CONT the highest. Conclusion: T-E analysis indicated that HRV was significantly different in patients with SI. CHR as measured by our proposed synchronization index provides a novel feature to adapt HRV biofeedback to individual psychiatric profiles and may provide better clinical outcomes for this patient group.
Advances in communication technologies have led to a new era of ubiquitous accessibility to Internet-based platforms (IBPs) and devices with an unprecedented potential for mental health. However, i...
A Learning Health System (LHS) promotes the patient being at the very center of his or her care. Patient coproduction of care in an LHS is enabled by a focus on improving outcomes through the use o...
Current mental health services across the world remain expert-centric and are based on traditional workflows, mostly using impractical and ineffective electronic record systems or even paper-based ...
The context of current education is quickly evolving. The traditional design and delivery of interventions for learning are being challenged and outdated to meet the needs of today’s learners. Formal education should provide more versatile learning systems to accommodate the varying needs and demands of students. A global change is urgently required to implement a fundamental shift in the learning paradigm for the technologically driven millennial learners. This is a well-known fact, but the challenge is to explore innovative methods to use technology to attain the actual needs and expectations of these learners. With the increasing problem of substance use disorders as a public health problem and scarcity in the availability of resources for providing adequate treatment, we propose to explore the role of digital technology as a training platform in addiction psychiatry. We have reviewed the currently available platforms which are using digital tools for training in addiction psychiatry. We have also shared our experience at NIMHANS in the use of various digital platforms for training medical officers, psychiatry residents and various health professionals in the area of addiction and we feel that it is having promising scope for expansion and upscaling to generate adequate facilities to provide best practices in addiction management to the rural, remote and underserved areas of the country. However, these digital tools should augment traditional teaching methods and cannot replace them.
The mass media is largely regarded as an integral cogwheel in health service delivery, with a decisive influence on public attitudes. Numerous studies identify connections between media use and negative outcomes such as increased depression, suicide, anxiety, substance use, aggressive behaviour, obesity and eating disorders. Digital tools in psychiatry may promote change and improve health service delivery, augment clinical relationships and influence the dynamic relationship between mass media depictions of mental illness and the public´s understanding. Unlike most journalists, who must rely on second-hand accounts, mental health professionals can weigh in directly with their advice, opinions, and expertise on social media, based on direct accounts from patients.
A warm welcome to the inaugural issue of the Journal of Computational and Cognitive Engineering (JCCE), a new exciting title to Bon View Press portfolio journals for 2022. The launch of this pioneering journal by Bon View Press marks a new era for the multidisciplinary field. With the increasingly complex situations being modeled to find reasonable answers, the role of computation and cognitive learning has become essential. Therefore, the journal’s focus is to provide a new platform for disseminating the latest research and current practices of the emerging fields from cognitive, computational engineering, and artificial intelligence to brain science, cognition, and machine intelligence. As reflected in the journal name, JCCE identifies distinct, effective, and timely approaches to solve the problems related to the fields in question. Computational Engineering is an emerging and rapidly growing multidisciplinary field that applies advanced computational methods and analysis to engineering practice. However, Cognitive Engineering emphasizes the application of knowledge and techniques from cognitive psychology to the design of human–machine systems. Models and simulations of cognition have gained popularity as interactive systems have been developed to shed light on human behaviour prediction while developing new approaches and testing cognitive theories themselves. Thus, Computational and Cognitive Engineering offers a broader systems perspective to analyzing and designing human–machine systems.
