ORCID Profile
0000-0001-6500-9629
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Materials engineering | Functional materials | Nanotechnology not elsewhere classified
Publisher: BMJ
Date: 10-2023
Publisher: JMIR Publications Inc.
Date: 18-12-2022
Abstract: s the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to an in idual’s risk of illness progression. The application of staging has been traditionally limited to trained clinicians, yet if digital technologies could be leveraged to apply clinical staging, then this could increase the scalability and utility of this model in services.` he aim of this study is to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders. he cohort comprised 131 young people, aged between 16 to 25 years, who presented to youth mental health services in Australia for the first time between November 2018 to March 2021. Expert psychiatrists independently assigned clinical stages (either stage 1a or stage 1b+), which were then compared to the digital algorithm’s allocation based on a multidimensional self-report questionnaire. f the 131 participants, the mean (SD) age was 20.3 (2.4) years and 94 (71.8%) were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the expert ratings with a substantial interrater agreement (κ=0.67, P .001). The algorithm demonstrated an accuracy of 90.8% (95% CI 85.6 – 95.2%, P=0.03), sensitivity of 80.0%, specificity of 92.8%, and F1-score of 72.7%. Of the concordant ratings, 16 young people were allocated to stage 1a, while 103 were assigned to stage 1b+. Among the 12 discordant cases, the digital algorithm allocated a lower stage (stage 1a) to eight participants compared to the experts. These in iduals had significantly milder symptoms of depressive mood (P .001) and anxiety symptoms (P .001) compared to those with concordant stage 1b+ ratings. his novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in early stages of common anxiety and depressive disorders. Between 11% and 27% of young people seeking care may be benefit from low-intensity, online or brief interventions. Finding of this study suggests the possibility of redirecting clinical capacity to focus on in iduals in stage 1b+ for further assessment and intervention. >
Publisher: JMIR Publications Inc.
Date: 12-2022
Abstract: s the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to an in idual’s risk of illness progression. The application of staging has been traditionally limited to trained clinicians, yet if digital technologies could be leveraged to apply clinical staging, then this could increase the scalability and utility of this model in services. he aim of this proof-of-concept study is to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders. he cohort comprised 131 young people, aged between 16 to 25 years, who presented to youth mental health services in Australia for the first time between November 2018 to March 2021. Clinical stages (either stage 1a or stage 1b+) were allocated independently by expert psychiatrists and compared to the digital algorithm based on a multidimensional self-report questionnaire. f the 131 participants, the mean (SD) age was 20.3 (2.4) years and 94 (71.8%) were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the expert ratings with a substantial interrater agreement (κ=0.67, P .001). The algorithm demonstrated 90.8% (95% CI 85.6 – 95.2%, P=0.03) accuracy, 80.0% sensitivity, 92.8% specificity, and F1-score of 72.7%. Of the agreement, 16 young people were allocated to stage 1a, while 103 were assigned to stage 1b+. Among the 12 discordant cases, eight participants with lower levels of depressive mood (P .001) and anxiety (P .001) were rated lower (stage 1a) by the algorithm compared to the experts. his novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in early stages of common anxiety and depressive disorders. Between 11% and 27% of young people presenting for care may be suitable for low intensity online or brief interventions, creating additional clinical capacity to be directed towards those who are stage 1b+ for further assessment and intervention.
Publisher: Frontiers Media SA
Date: 18-11-2021
Abstract: Enhanced care coordination is essential to improving access to and navigation between youth mental health services. By facilitating better communication and coordination within and between youth mental health services, the goal is to guide young people quickly to the level of care they need and reduce instances of those receiving inappropriate care (too much or too little), or no care at all. Yet, it is often unclear how this goal can be achieved in a scalable way in local regions. We recommend using technology-enabled care coordination to facilitate streamlined transitions for young people across primary, secondary, more specialised or hospital-based care. First, we describe how technology-enabled care coordination could be achieved through two fundamental shifts in current service provisions a model of care which puts the person at the centre of their care and a technology infrastructure that facilitates this model. Second, we detail how dynamic simulation modelling can be used to rapidly test the operational features of implementation and the likely impacts of technology-enabled care coordination in a local service environment. Combined with traditional implementation research, dynamic simulation modelling can facilitate the transformation of real-world services. This work demonstrates the benefits of creating a smart health service infrastructure with embedded dynamic simulation modelling to improve operational efficiency and clinical outcomes through participatory and data driven health service planning.
Publisher: Elsevier BV
Date: 10-2023
Publisher: JMIR Publications Inc.
Date: 25-07-2023
DOI: 10.2196/42993
Abstract: Highly personalized care is substantially improved by technology platforms that assess and track patient outcomes. However, evidence regarding how to successfully implement technology in real-world mental health settings is limited. This study aimed to naturalistically monitor how a health information technology (HIT) platform was used within 2 real-world mental health service settings to gain practical insights into how HIT can be implemented and sustained to improve mental health service delivery. An HIT (The Innowell Platform) was naturally implemented in 2 youth mental health services in Sydney, Australia. Web-based surveys (n=19) and implementation logs were used to investigate staff attitudes toward technology before and after implementation. Descriptive statistics were used to track staff attitudes over time, whereas qualitative thematic analysis was used to explore implementation log data to gain practical insights into useful implementation strategies in real-world settings. After the implementation, the staff were nearly 3 times more likely to agree that the HIT would improve care for their clients (3/12, 25% agreed before the implementation compared with 7/10, 70% after the implementation). Despite this, there was also an increase in the number of staff who disagreed that the HIT would improve care (from 1/12, 8% to 2/10, 20%). There was also decreased uncertainty (from 6/12, 50% to 3/10, 30%) about the willingness of the service to implement the technology for its intended purpose, with similar increases in the number of staff who agreed and disagreed with this statement. Staff were more likely to be uncertain about whether colleagues in my service are receptive to changes in clinical processes (not sure rose from 5/12, 42% to 7/10, 70%). They were also more likely to report that their service already provides the best mental health care (agreement rose from 7/12, 58% to 8/10, 80%). After the implementation, a greater proportion of participants reported that the HIT enabled shared or collaborative decision-making with young people (2/10, 20%, compared with 1/12, 8%), enabled clients to proactively work on their mental health care through digital technologies (3/10, 30%, compared with 2/12, 16%), and improved their response to suicidal risk (4/10, 40% compared with 3/12, 25%). This study raises important questions about why clinicians, who have the same training and support in using technology, develop more polarized opinions on its usefulness after implementation. It seems that the uptake of HIT is heavily influenced by a clinician’s underlying beliefs and attitudes toward clinical practice in general as well as the role of technology, rather than their knowledge or the ease of use of the HIT in question.
Publisher: JMIR Publications Inc.
Date: 27-09-2022
Abstract: ighly personalized care is substantially improved by technology platforms that assess and track patient outcomes. However, evidence regarding how to successfully implement technology in real-world mental health settings is limited. his study aimed to naturalistically monitor how a health information technology (HIT) platform was used within 2 real-world mental health service settings to gain practical insights into how HIT can be implemented and sustained to improve mental health service delivery. n HIT (The Innowell Platform) was naturally implemented in 2 youth mental health services in Sydney, Australia. Web-based surveys (n=19) and implementation logs were used to investigate staff attitudes toward technology before and after implementation. Descriptive statistics were used to track staff attitudes over time, whereas qualitative thematic analysis was used to explore implementation log data to gain practical insights into useful implementation strategies in real-world settings. fter the implementation, the staff were nearly 3 times more likely to agree that the HIT would i improve care for their clients /i (3/12, 25% agreed before the implementation compared with 7/10, 70% after the implementation). Despite this, there was also an increase in the number of staff who disagreed that the HIT would improve care (from 1/12, 8% to 2/10, 20%). There was also decreased uncertainty (from 6/12, 50% to 3/10, 30%) about the willingness of the service to i implement the technology for its intended purpose /i , with similar increases in the number of staff who agreed and disagreed with this statement. Staff were more likely to be uncertain about whether i colleagues in my service are receptive to changes in clinical processes /i ( i not sure /i rose from 5/12, 42% to 7/10, 70%). They were also more likely to report that their service i already provides the best mental health care /i (agreement rose from 7/12, 58% to 8/10, 80%). After the implementation, a greater proportion of participants reported that the HIT enabled shared or collaborative decision-making with young people (2/10, 20%, compared with 1/12, 8%), enabled clients to proactively work on their mental health care through digital technologies (3/10, 30%, compared with 2/12, 16%), and improved their response to suicidal risk (4/10, 40% compared with 3/12, 25%). his study raises important questions about why clinicians, who have the same training and support in using technology, develop more polarized opinions on its usefulness after implementation. It seems that the uptake of HIT is heavily influenced by a clinician’s underlying beliefs and attitudes toward clinical practice in general as well as the role of technology, rather than their knowledge or the ease of use of the HIT in question.
Publisher: Springer Science and Business Media LLC
Date: 14-12-2022
DOI: 10.1186/S12916-022-02666-W
Abstract: Clinical staging proposes that youth-onset mental disorders develop progressively, and that active treatment of earlier stages should prevent progression to more severe disorders. This retrospective cohort study examined the longitudinal relationships between clinical stages and multiple clinical and functional outcomes within the first 12 months of care. Demographic and clinical information of 2901 young people who accessed mental health care at age 12–25 years was collected at predetermined timepoints (baseline, 3 months, 6 months, 12 months). Initial clinical stage was used to define three fixed groups for analyses (stage 1a: ‘non-specific anxious or depressive symptoms’, 1b: ‘attenuated mood or psychotic syndromes’, 2+: ‘full-threshold mood or psychotic syndromes’). Logistic regression models, which controlled for age and follow-up time, were used to compare clinical and functional outcomes (role and social function, suicidal ideation, alcohol and substance misuse, physical health comorbidity, circadian disturbances) between staging groups within the initial 12 months of care. Of the entire cohort, 2093 young people aged 12–25 years were followed up at least once over the first 12 months of care, with 60.4% female and a baseline mean age of 18.16 years. Longitudinally, young people at stage 2+ were more likely to develop circadian disturbances (odds ratio [OR]=2.58 CI 1.60–4.17), compared with in iduals at stage 1b. Additionally, stage 1b in iduals were more likely to become disengaged from education/employment (OR=2.11, CI 1.36–3.28), develop suicidal ideations (OR=1.92 CI 1.30–2.84) and circadian disturbances (OR=1.94, CI 1.31–2.86), compared to stage 1a. By contrast, we found no relationship between clinical stage and the emergence of alcohol or substance misuse and physical comorbidity. The differential rates of emergence of poor clinical and functional outcomes between early versus late clinical stages support the clinical staging model's assumptions about illness trajectories for mood and psychotic syndromes. The greater risk of progression to poor outcomes in those who present with more severe syndromes may be used to guide specific intervention packages.
Publisher: SAGE Publications
Date: 10-04-2023
DOI: 10.1177/10398562231167681
Abstract: This study utilised digital technology to assess the clinical needs of young people presenting for care at headspace centres across Australia. 1490 young people (12–25 years) who presented to one of 11 headspace services from four geographical locations (urban New South Wales, urban South Australia, regional New South Wales, and regional Queensland) completed a digital multidimensional assessment at initial presentation. Characteristics were compared between services and geographical locations. We identified major variation in the demographics, and the type and severity of needs across different services. In iduals from regional services were more likely to be younger, of Aboriginal and Torres Strait Islander origin, and present with psychotic-like symptoms and suicidality, while those in urban areas were more likely to have previously sought help and have problematic alcohol use. Further differences in age, distress, depressive symptoms, psychotic-like experiences, trauma, family history, alcohol use, education/employment engagement, and days out of role were identified between different urban sites. The variability between services provides insight into the heterogeneity of youth mental health populations which has implications for appropriate early intervention and prevention service provisions. We propose that integrating digital technologies has the potential to provide insights for smarter service planning and evaluation.
Publisher: MDPI AG
Date: 17-01-2023
DOI: 10.3390/BIOMEDICINES11020237
Abstract: Despite a significant focus on the photochemical and photoelectrical mechanisms underlying photobiomodulation (PBM), its complex functions are yet to be fully elucidated. To date, there has been limited attention to the photophysical aspects of PBM. One effect of photobiomodulation relates to the non-visual phototransduction pathway, which involves mechanotransduction and modulation to cytoskeletal structures, biophotonic signaling, and micro-oscillatory cellular interactions. Herein, we propose a number of mechanisms of PBM that do not depend on cytochrome c oxidase. These include the photophysical aspects of PBM and the interactions with biophotons and mechanotransductive processes. These hypotheses are contingent on the effect of light on ion channels and the cytoskeleton, the production of biophotons, and the properties of light and biological molecules. Specifically, the processes we review are supported by the resonant recognition model (RRM). This previous research demonstrated that protein micro-oscillations act as a signature of their function that can be activated by resonant wavelengths of light. We extend this work by exploring the local oscillatory interactions of proteins and light because they may affect global body circuits and could explain the observed effect of PBM on neuro-cortical electroencephalogram (EEG) oscillations. In particular, since dysrhythmic gamma oscillations are associated with neurodegenerative diseases and pain syndromes, including migraine with aura and fibromyalgia, we suggest that transcranial PBM should target diseases where patients are affected by impaired neural oscillations and aberrant brain wave patterns. This review also highlights ex les of disorders potentially treatable with precise wavelengths of light by mimicking protein activity in other tissues, such as the liver, with, for ex le, Crigler-Najjar syndrome and conditions involving the dysregulation of the cytoskeleton. PBM as a novel therapeutic modality may thus behave as “precision medicine” for the treatment of various neurological diseases and other morbidities. The perspectives presented herein offer a new understanding of the photophysical effects of PBM, which is important when considering the relevance of PBM therapy (PBMt) in clinical applications, including the treatment of diseases and the optimization of health outcomes and performance.
Publisher: JMIR Publications Inc.
Date: 08-09-2023
DOI: 10.2196/45161
No related organisations have been discovered for William Capon.
Start Date: 2023
End Date: 12-2027
Amount: $5,000,000.00
Funder: Australian Research Council
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