Digital Mental Health: A Practitioner's Guide

✍ Scribed by Ives Cavalcante Passos, Francisco Diego Rabelo-da-Ponte, Flavio Kapczinski

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 263
• Category: Library

✦ Synopsis

This innovative book focuses on potential, limitations, and recommendations for the digital mental health landscape. Authors synthesize existing literature on the validity of digital health technologies, including smartphones apps, sensors, chatbots and telepsychiatry for mental health disorders. They also note that collecting real-time biological information is usually better than just collect filled-in forms, and that will also mitigate problems related to recall bias in clinical appointments. Limitations such as confidentiality, engagement and retention rates are moreover discussed. Presented in fifteen chapters, the work addresses the following questions: may smartphones and sensors provide more accurate information about patients’ symptoms between clinical appointments, which in turn avoid recall bias? Is there evidence that digital phenotyping could help in clinical decisions in mental health? Is there scientific evidence to support the use of mobile interventions in mental health? 

Digital Mental Health will help clinicians and researchers, especially psychiatrists and psychologists, to define measures and to determine how to test apps or usefulness, feasibility and efficacy in order to develop a consensus about reliability. These professionals will be armed with the latest evidence as well as prepared to a new age of mental health.

✦ Table of Contents

Foreword
Preface
Contents
Chapter 1: The Dawn of Digital Psychiatry
Introduction
Digital Mental Health
Digital Clinic
Regulation of Mobile Apps
Artificial Intelligence
Future
References
Chapter 2: Digital Biomarkers and Passive Digital Indicators of Generalized Anxiety Disorder
Introduction
GAD as a Diagnostic Category
Current GAD Assessment
Reliance on Retrospective Self-Report
Limitation in Accounting for Contextual and Time-Dependent Factors
Heterogeneity in GAD
Potential for Improvements with the Use of Passively Collected Data
Machine Learning Models Applied to Passive Data
Passive Sensing of GAD with Data from the Electronic Medical Record
Passive Sensing of GAD with Data from Mobile and Wearable Devices
Overview and Basis
Passive Sensing of GAD with Social Media Data
Conclusions
Limitations
Ethical and Privacy Considerations
References
Chapter 3: Digital Phenotyping in Mood Disorders
Introduction
Digital Phenotyping Data
Data Sources
Data Processing
Digital Phenotyping as a Resource for Mood Disorders
As a Biomarker
As a Diagnosis Tool
Monitoring Treatment
Tailored-Treatment Delivery
Addressing Special Populations
Limitations
Ethical Issues
Conclusion
References
Chapter 4: Mental Health Assessment via Internet: The Psychometrics in the Digital Era
Introduction
Psychometrics: A Brief Overview
Content Validity
The Internal Structure of the Scale
Internal Consistency Reliability
Construct Validity (Convergent Validity and Discriminant Validity)
Convergent Validity
Discriminant Validity
Criterion Validity (Concurrent and Predictive Validity)
The Psychological Process Used in the Scale Responses
Consequences of Using Test
Psychometric of Mental Health Instruments: Paper-and-Pencil Versus Digital Formats
Online Web Page Self-Reported Questionnaires
Computer-Based Instruments
Mobile Application (App) Format
Conclusion
References
Chapter 5: Smartphone-Based Treatment in Psychiatry: A Systematic Review
Introduction
Methods
Study Selection and Search Strategy
Study Selection and Data Extraction
Risk of Bias in Individual Studies
Results
Search Strategy and Selection of Trials
Trials According to Psychiatric Disorder
Psychotic Disorders
Risk of Bias
Affective Disorders
Risk of Bias
Other Disorders
Risk of Bias
Discussion
Limitations
Limitations on a Study Level
Limitations on Chapter Level
Recommendations for Future Trials
References
Chapter 6: Digital Therapies for Insomnia
Insomnia Disorder and Current Treatments
Current Evidence for the Efficacy of dCBT-I and dMBT-I
Using dCBT-I in Real World Settings: Evidence from Effectiveness Trials
What Factors Influence the Effectiveness of dCBT-I and MBTI?
Integration of Digital Tools for Insomnia with Standard Care
Conclusions: What Are the Next Steps for Digital Therapeutics for Insomnia?
References
Chapter 7: The Efficacy of Smartphone-Based Interventions in Bipolar Disorder
Bipolar Disorders
The Digital Revolution and the Growing Interest and Use of Mental Health Tools
Digital Phenotyping in Bipolar Disorder
Smartphone-Related Research in Bipolar Disorder: State of the Art
The Dissociation Between Private Corporations and the Academic Field
The Efficacy of Smartphone-Based Interventions in Mental Health Disorders
Smartphone-Based Interventions in Bipolar Disorder
The Efficacy of Smartphone-Based Interventions in Bipolar Disorder (RCTs)
Smartphone-App Characteristics and Type of Interventions
The Potential Influence of Baseline Affective Symptoms
Active or Inactive Control Groups
User-Engagement Indicators of Smartphone Interventions
The Effectiveness of Smartphone-Based Interventions in Bipolar Disorder (Observational Studies)
Limitations of the Studies Assessing the Efficacy of Smartphone-Based Interventions and Potential Solutions
Evidence-Based Smartphone Interventions Still Trapped in Lab Cages
Future Directions
References
Chapter 8: Chatbots in the Field of Mental Health: Challenges and Opportunities
Human–Computer Interaction and Social Rules
What Is a Chatbot?
Mental Health Applications of Chatbots
Benefits of Using Chatbots
Increasing Access to Mental Health Care
Data Collection and Management in Research and Clinical Settings
Promoting Disclosure
Ethical and Safety Concerns
Privacy Breaches and Confidentiality of Data
Serious Health Concerns and Adverse Incidents
Attachment to Bot and Developmental Concerns
Future Directions
References
Chapter 9: How to Evaluate a Mobile App and Advise Your Patient About It?
References
Chapter 10: Telepsychiatry
Telepsychiatry and the COVID-19 Pandemic
Applying Telepsychiatry in Real Life
Special Populations
Barriers of Telepsychiatry and Strategies to Overcome it
Patients Experiences and Future Implications
Conclusion
References
Chapter 11: Prediction of Suicide Risk Using Machine Learning and Big Data
Introduction
Prediction of Suicide Risk
Machine Learning Models for Prediction of Suicide Risk
Populations at Higher Risk for Suicide
Electronic Health Records as Source of Data
Social Media and Other Sources of Data
Challenges and Limitations
Ethical Implications of Machine Learning and Big Data Applications in Suicide Risk Assessment
Conclusion
References
Chapter 12: Electronic Health Records to Detect Psychosis Risk
Primary Indicated Prevention and the Clinical High-Risk State
Challenges of Detecting Individuals at Risk
Detection Strategies in Primary Care
Development and Validation of a Transdiagnostic Risk Calculator for Psychosis in Secondary Mental Health Care
Replication of the Transdiagnostic Risk Calculator in Other Settings
UK Replications
International Replication
Implementation of the Transdiagnostic Risk Calculator
Updating and Refining the Transdiagnostic Risk Calculator
Dynamic Risk Prognostication
Conclusion
References
Chapter 13: The Use of Artificial Intelligence to Identify Trajectories of Severe Mental Disorders
Introduction
Bipolar Disorder
Major Depressive Disorder
Schizophrenia
Conclusion
References
Chapter 14: The Use of Machine Learning Techniques to Solve Problems in Forensic Psychiatry
Prevalence of Criminality Among Those with Mental Illness
Reoffending: Prevalence and Assessment Tools
Differences Between Actuarial and Machine Learning Approaches
Predictive Models of Criminal and Violence-Related Outcomes in Psychiatry
Predictive Models of Criminal and Violence-Related Outcomes in Non-psychiatric Individuals
Predictive Models of Malingering
Methodological Recommendations
Integrating Evidence-Based and Novel Biological and Physiological Features
Predicting Treatment Response to Routine Clinical Care
Predicting the Timing of Short-Term Inpatient Outcomes
Data-Driven Phenotyping of Forensic Patients
Methodological Pipeline for Prospective Machine Learning Cohorts in Forensic Psychiatry
Conclusion
References
Chapter 15: Gaming Disorder and Problematic Use of Social Media
Gaming Disorder
Introduction
Etiology and Explanatory Models
Assessment of Gaming Disorder
Treatment Strategies
Hikikomori
Problematic Use of Social Media
Fear of Missing Out
Dating Apps
Healthy Use of Social Media
Fake News and Hate Speech
Online Subcultures
Conclusion
References
Index