Background: In Australia, telehealth services were used as an alternative method of health care delivery during the COVID-19 pandemic. Through a realist analysis of a survey of health professionals, we have sought to identify the underlying mechanisms that have assisted Australian health services adapt to the physical separation between clinicians and patients.Methods: Using a critical realist ontology and epistemology, we undertook an online survey of health professionals subscribing to the Australian Telehealth Society newsletter. The survey had close- and open-ended questions, constructed to identify contextual changes in the operating environment for telehealth services, and assess the mechanisms which had contributed to these changes. We applied descriptive and McNemar's Chi-square analysis for the close-ended component of the survey, and a reflexive thematic analysis approach for the open-ended questions which were framed within the activity based funding system which had previously limited telehealth services to regional Australia.Results: Of the 91 respondents most (73%) reported a higher volume of telephone-based care since COVID and an increase in use of video consultations (60% of respondents). Respondents felt that the move to provide care using telehealth services had been a “forced adoption” where clinicians began to use telehealth services (often for the first time) to maintain health care. Respondents noted significant changes in managerial and medical culture which supported the legitimisation of telehealth services as a mode of access to care. The support of leaders and the use personal and organisational networks to facilitate the operation of telehealth service were felt to be particularly valuable. Access to, and reliability of, the technology were considered extremely important for services. Respondents also welcomed the increased availability of more human and financial resources.Conclusions: During the pandemic, mechanisms that legitimise practise, build confidence, support relationships and supply resources have fostered the use of telehealth. This ongoing interaction between telehealth services, contexts and mechanisms is complex. The adoption of telehealth access to enable physically separated care, may mark a “new context;” or it could be that once the pandemic passes, previous policies and practises will re-assert themselves and curb support for telehealth-enabled care.

Issue addressed Digital health technologies can potentially reduce health disparities in cancer care. However, the benefits of digital health technology depend partly on users’ digital health literacy, that is, “capabilities and resources required for individuals to use and benefit from digital health resources,” which combines health and digital literacy. We examined issues for digital health technology implementation in cancer care regarding digital health literacy, via stakeholder consultation. Methods Consumers, health care professionals, researchers, developers, nongovernment and government/policy stakeholders (N = 51) participated in focus groups/interviews discussing barriers, enablers, needs and opportunities for digital health implementation in cancer care. Researchers applied framework analysis to identify themes of digital health literacy in the context of disparity and inclusion. Results Limited digital and traditional health literacy were identified as barriers to digital technology engagement, with a range of difficulties identified for older, younger and socio‐economically or geographically disadvantaged groups. Digital health technology was a potential enabler of health care access and literacy, affording opportunities to increase reach and engagement. Education combined with targeted design and implementation were identified means of addressing health and digital literacy to effectively implement digital health in cancer care. Conclusions Implementing digital health in cancer care must address the variability of digital health literacy in recipients, including groups living with disadvantage and older and younger people, in order to be effective. So what? If cancer outcome disparity is to be reduced via digital health technologies, they must be implemented strategically to address digital health literacy needs. Health policy should reflect this approach.

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Objectives: This paper provides a discussion about the potential scope of applicability of Artificial Intelligence methods within the telehealth domain. These methods are focussed on clinical needs and provide some insight to current directions, based on reports of recent advances. Methods: Examples of telehealth innovations involving Artificial Intelligence to support or supplement remote health care delivery were identified from recent literature by the authors, on the basis of expert knowledge. Observations from the examples were synthesized to yield an overview of contemporary directions for the perceived role of Artificial Intelligence in telehealth. Results: Two major focus areas for related contemporary directions were established. These were first, quality improvement for existing clinical practice and service delivery, and second, the development and support of new models of care. Case studies from each focus area have been chosen for illustration purposes. Conclusion: Examples of the role of Artificial Intelligence in delivery of health care remotely include use of tele-assessment, tele-diagnosis, tele-interactions, and tele-monitoring. Further developments of underlying algorithms and validation of methods will be required for wider adoption. Certain key social and ethical considerations also need consideration more generally in the health system, as Artificial-Intelligence-enabled-telehealth becomes more commonplace.

Background: Maintain Your Brain (MYB) is a randomized controlled trial of an online multi-modal lifestyle intervention targeting modifiable dementia risk factors with its primary aim being to reduce cognitive decline in an older age cohort. Methods: MYB aims to recruit 8,500 non-demented community dwelling 55 to 77 year olds from the Sax Institute's 45 and Up Study in New South Wales, Australia. Participants will be screened for risk factors related to four modules that comprise the MYB intervention: physical activity, nutrition, mental health, and cognitive training. Targeting risk factors will enable interventions to be personalized so that participants receive the most appropriate modules. MYB will run for three years and up to four modules will be delivered sequentially each quarter during year one. Upon completing a module, participants will continue to receive less frequent booster activities for their eligible modules (except for the mental health module) until the end of the trial. Discussion: MYB will be the largest internet-based trial to attempt to prevent cognitive decline and potentially dementia. If successful, MYB will provide a model for not just effective intervention among older adults, but an intervention that is scalable for broad use.

ACTRN12614000184673.

Background: There are growing concerns over the health impacts of occupational sedentary behaviour on officebased workers and increasing workplace recognition of the need to increase physical activity at work. Social ecological models provide a holistic framework for increasing opportunities for physical activity at work. In this paper we propose a social ecological model of office-based physical activity and map it against the Capability Motivation Opportunity (COM-B) framework to highlight the mechanisms of behaviour change that can increase levels of physical activity of office-based workers. Discussion: The paper proposes a social ecological model of physical activity associated with office-based settings. The model considers opportunities for both incidental and discretionary activities, as well as macro and micro factors on both socio-cultural and physical dimensions. The COMB framework for characterising behaviour change interventions is used to highlight the underlying mechanisms of behaviour change inherent in the model. Summary: The broad framework provided by social ecological models is important for understanding physical activity in office-based settings because of the non-discretionary nature of sedentary behaviour of office-based work. It is important for interventions not to rely on individual motivation for behaviour change alone but to incorporate changes to the broader social ecological and physical context to build capability and create opportunities for more sustainable change.

Background: There is little known about pre-frailty attributes or when changes which contribute to frailty might be detectable and amenable to change. This study explores pre-frailty and frailty in independent community-dwelling adults aged 40-75 years. Methods: Participants were recruited through local council networks, a national bank and one university in Adelaide, Australia. Fried frailty phenotype scores were calculated from measures of unintentional weight loss, exhaustion, low physical activity levels, poor hand grip strength and slow walking speed. Participants were identified as not frail (no phenotypes), prefrail (one or two phenotypes) or frail (three or more phenotypes). Factor analysis was applied to binary forms of 25 published frailty measures Differences were tested in mean factor scores between the three Fried frailty phenotypes and ROC curves estimated predictive capacity of factors. Results: Of 656 participants (67% female; mean age 59.9 years, SD 10.6) 59.2% were classified as not frail, 39.0% pre-frail and 1.8% frail. There were no gender or age differences. Seven frailty factors were identified, incorporating all 25 frailty measures. Factors 1 and 7 significantly predicted progression from not-frail to pre-frail (Factor 1 AUC 0.64 (95%CI 0.60-0.68, combined dynamic trunk stability and lower limb functional strength, balance, foot sensation, hearing, lean muscle mass and low BMI; Factor 7 AUC 0.55 (95%CI 0.52-0.59) comprising continence and nutrition. Factors 3 and 4 significantly predicted progression from pre-frail to frail (Factor 3 AUC 0.65 (95% CI 0.59-0.70)), combining living alone, sleep quality, depression and anxiety, and lung function; Factor 4 AUC 0.60 (95%CI 0.54-0.66) comprising perceived exertion on exercise, and falls history. Conclusions: This research identified pre-frailty and frailty states in people aged in their 40s and 50s. Pre-frailty in body systems performance can be detected by a range of mutable measures, and interventions to prevent progression to frailty could be commenced from the fourth decade of life.