At the BeRI Hub, we have three core objectives:
1. The development of innovative technologically-based biopsychosocial health care interventions through various platforms and applications that improve ill health and increase well-being
Innovative health technologies (e.g., web, mobile, virtual/gaming, and ubiquitous sensor-based health tools) can improve health and well-being. Big data mining, predictive analytics and machine learning applications are far more sophisticated and prevalent than ever before. Careful configuration of these technologies and methods, coupled with participatory co-design and regular optimisation cycles, will make healthcare more accessible, engaging, responsive, dynamic, precise and cost affordable.
We have developed multiple digital health innovations including:
Digital health platforms
- My Digital Health: An integrated ‘all in one’ digital health platform for consumers, healthcare professionals and researchers.
- iSeeBehaviour: A decision support and deep analytics platform for better dementia care.
- e-Connect: A digital health platform program for healthcare practitioners which allows them to digitally connect with their clients and ‘do’ dMH. Here practitioners can invite their clients into their account, schedule automated symptom monitoring flows, self-prescribe mental health and wellbeing content and symptom surveys, use private communication work spaces, monitor client activity and progress, receive alerts, etc.
- Workplace Wellbeing Assist: A digital monitoring and decision support platform that allows managers and staff to monitor the workplace climate and their personal wellbeing and respond in a timely manner to create a more positive culture by addressing workplace risk factors as well as personal wellbeing.
Digital health intervention programs
- COVID-19 ‘Community Resources’: During the COVID-19 pandemic we made some of our self-help strategy tools available via open access. These tools come from our My Digital Health mental health and wellbeing programs.
- Life Flex: A biopsychosocial digital health intervention for depression and anxiety.
- Life Flex 4 PTSD: A biopsychosocial digital health intervention for PTSD.
- Life Flex – LGBQ: A biopsychosocial digital health intervention for depression and anxiety tailored for lesbian, gay, bisexual, and queer adults
- iSleepWell: A biopsychosocial digital health intervention for insomnia.
- BDZ digital health: A digital health intervention for benzodiazepine reduction.
- iConsiderLife: A brief, self-help decision support digital health intervention for distressed adults.
- MonitorMe: A biopsychosocial self-monitoring and decision support digital health intervention.
- iChooseWell: A strategies-based biopsychosocial digital health well-being program.
- iMindTime: A mindfulness-based digital health intervention program.
- CompassionateUs: A compassion-focused digital intervention program.
- Digital Mental Health Training for Healthcare Professionals: A brief training program for healthcare professionals wanting to learn more about digital health and how to integrate My Digital Health into their daily practice.
- Physical activity digital health: A brief digital health intervention to help improve physical activity levels.
Digital health technologies
- eMental health symptom screeners
- Digital intake assessment system
- Cognitive modification bias and brain training games
- 'Affective' mobile app
- 3D Oculus Rift – Stress reduction technology
2. Health care digital health intervention research (mental, physical and disabilities) spanning the health care activity spectrum (health promotion, education, prevention, early intervention, treatment and chronic disease, rehabilitation and crisis management) and practice models to improve ill health and increase well-being
Public health digital health models
In more recent years, digital health technologies have been transforming the landscape of health care. eHealth or digital mental health refers to the incorporation of electronic/digital communication and information technology systems to improve both health care delivery and human health and well-being. We evaluate our digital health innovations rigorously and disseminate them to the public, for public good. We are also very focused on developing and disseminating digital health technologies that support the most vulnerable populations (e.g., GLBTI, the elderly, rural).
We are trialling numerous IT-enabled (typically fully automated) interventions (web and mobile) designed to improve ill health and increase well-being. For example, specific digital health programs focus on teaching people how to reduce stress, increase physical activity levels, mindfulness or personal strengths, reduce panic, anxiety, insomnia, and depression, or the reduce the use of benzodiazepines.
Clinical and professional practice digital health models
Many mental health community / psychology clinics are not research active, despite many clinic-based staff wanting to embrace the 'Scientist Practitioner' model. Logistical and pragmatic constraints (e.g., time, varying skill sets, expertise and interests, competing demands between 'research' and 'professional practice' activities) makes it difficult to undertake clinic-based research. Consequently, individual clinics are not well positioned to drive major research activities or investigate key clinic-based research questions given their small size (e.g., limited or absent critical mass in terms of staff numbers and client throughput). As many rural and regional mental health / psychology clinics share common goals (e.g., the training of future healthcare professionals, the desire to work in an evidence-based practice environment to promote continuous improvement of their current practices, as well as to investigate key applied research questions that will improve client outcomes and advance the field of psychology and human health and its practice more broadly), we aim to create a critical mass that brings together multiple regional and rural mental health sites and incorporates digital health technologies (as a service delivery mechanism).
We are engaging and working with community organisations, helping to support digital health practice development. This collaborative work should increase, strengthen and consolidate collaborative research efforts, as well as facilitate data sharing across multiple sites. Conducting clinic-based digital health research in multiple, 'real world' settings will also enhance the generalisability of the outcomes in a timelier manner.
3. Investigating the causal and disease maintenance mechanisms (biological, physiological, neural, psychological, social and environmental factors) associated with and contributing to ill health and well-being
There are large gaps in our understanding of health and disease (aetiology and maintenance) and we typically have inefficient ways of managing it effectively. The biopsychosocial (BPS) model of health proposes that health is best understood as a combination of bio-physiological, psychological, and social determinants, and thus, advocates for a far more comprehensive investigation of the relationships between 'mind-body' health. In order for us to truly improve human health, there is a need to engage in new knowledge building discoveries, and then use these insights to develop innovative health promotion, prevention, early intervention and treatment solutions that are reliable, valid, scalable, and sustainable. Given the recent availability of affordable and powerful biological marker testing technologies, as well as the advance of biometric wearable sensor devices, we are now able to more affordably and ubiquitously monitor human BPS indices in the natural environment. Such information will revolutionise our understanding and knowledge regarding how these health determining components interact, resulting in better targeted prevention and treatment applications.
We are conducting studies that holistically evaluate the biological factors that are associated with and contribute to health and well-being. We are also collecting and mining other biometric data (i.e., physiological functioning indices) to derive algorithms that predict gradual declines in mood and also look at the genetic and epigenetic/environmental factors (changes in genes over time) that contribute to health and well-being. Identifying these biological markers and predictive algorithms should also enable us to apply them within digital health devices/applications for health promotion, prevention, early intervention, and treatment purposes.