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BackgroundAn ongoing challenge for smart homes research for aging-in-place is how to make sense of the large amounts of data from in-home sensors to facilitate real-time monitoring and develop reliable alerts. ObjectiveThe objective of our study was to explore the usefulness of a routine-based approach for making sense of smart home data for the elderly. MethodsMaximum variation sampling was used to select three cases for an in-depth mixed methods exploration of the daily routines of three elderly participants in a smart home trial using 180 days of power use and motion sensor data and longitudinal interview data. ResultsSensor data accurately matched self-reported routines. By comparing daily movement data with personal routines, it was possible to identify changes in routine that signaled illness, recovery from bereavement, and gradual deterioration of sleep quality and daily movement. Interview and sensor data also identified changes in routine with variations in temperature and daylight hours. ConclusionsThe findings demonstrated that a routine-based approach makes interpreting sensor data easy, intuitive, and transparent. They highlighted the importance of understanding and accounting for individual differences in preferences for routinization and the influence of the cyclical nature of daily routines, social or cultural rhythms, and seasonal changes in temperature and daylight hours when interpreting information based on sensor data. This research has demonstrated the usefulness of a routine-based approach for making sense of smart home data, which has furthered the understanding of the challenges that need to be addressed in order to make real-time monitoring and effective alerts a reality.

Advancements in digital monitoring solutions collaborate closely with electronic medical records. These fine-grained monitoring capacities can generate and process extensive electronic record data. Such capacities promise to enhance mental health care but also risk contributing to further stigmatization, prejudicial decision-making, and fears of disempowerment. This article discusses the problems and solutions identified by nine people with lived experience of being mental health care consumers or informal carers. Over the course of ten facilitated focus group format sessions (two hours) between October 2019 and April 2021, the participants shared their lived experience of mental health challenges, care, and recovery within the Australian context. To support the development, design, and implementation of monitoring technologies, problems, and solutions were outlined in the following areas—access, agency, interactions with medical practitioners, medication management, and self-monitoring. Emergent design insights include recommendations for strengthened consent procedures, flexible service access options, and humanized consumer interactions. While consumers and carers saw value in digital monitoring technologies that could enable them to take on a more proactive involvement in their personal wellness, they had questions about their level of access to such services and expressed concerns about the changes to interactions with health professionals that might emerge from these digitally enabled processes. [ABSTRACT FROM AUTHOR]

Background: A high proportion of patients with severe mental illness relapse due to nonadherence to psychotropic medication. In this paper, we use the normalization process theory (NPT) to describe the implementation of a web-based clinical decision support system (CDSS) for Community Mental Health Services (CMHS) called Actionable Intime Insights or AI2 . AI2 has two distinct functions: (1) it provides an overview of medication and treatment history to assist in reviewing patient adherence and (2) gives alerts indicating nonadherence to support early intervention. Objective: Our objective is to evaluate the pilot implementation of the AI2 application to better understand the challenges of implementing a web-based CDSS to support medication adherence and early intervention in CMHS. Methods: The NPT and participatory action framework were used to both explore and support implementation. Qualitative data were collected over the course of the 14-month implementation, in which researchers were active participants. Data were analyzed and coded using the NPT framework. Qualitative data included discussions, meetings, and work products, including emails and documents. Results: This study explores the barriers and enablers of implementing a CDSS to support early intervention within CMHS using Medicare data from Australia’s national electronic record system, My Health Record (MyHR). The implementation was a series of ongoing negotiations, which resulted in a staged implementation with compromises on both sides. Clinicians were initially hesitant about using a CDSS based on MyHR data and expressed concerns about the changes to their work practice required to support early intervention. Substantial workarounds were required to move the implementation forward. This pilot implementation allowed us to better understand the challenges of implementation and the resources and support required to implement and sustain a model of care based on automated alerts to support early intervention. Conclusions: The use of decision support based on electronic health records is growing, and while implementation is challenging, the potential benefits of early intervention to prevent relapse and hospitalization and ensure increased efficiency of the health care system are worth pursuing. [ABSTRACT FROM AUTHOR]

Workplace health is of concern to management and employees alike. Managers are concerned about the impact of health and wellbeing on productivity, while employees are concerned about impacts on quality of life. In the past two decades, there has been growing concern about the impact of sedentary behaviour at work. Before any interventions can be made to encourage physical activity at work proactively, it is desirable to understand the baseline characteristics of office based physical activity at work. Consumer wearable technology has provided a new and convenient mechanism for using personal monitoring to achieve remote observation of lifestyle related health behaviours. This paper describes how this technology can potentially be used to characterize different levels of workplace physical activity. [ABSTRACT FROM AUTHOR]

For office workers as much as much as 77% of time at work is spent in sedentary behaviour, increasing the risk of cardio vascular disease, cancer, weight gain, obesity and musculoskeletal pain. Given the amount of time spent at work, increasing opportunities for moderate to vigorous physical activity for office workers can not only reduce risk of disease but also actively contribute to better brain function and reduced anxiety at work. In this research we adopt an ecological approach to understanding opportunities for physical activity at work. Using 1373 Fitbit data points from two collocated office workers with identical job descriptions, we demonstrate that episodes of movement in the office are of very short or short duration. Results were less clear for sedentary behaviour because of the potential for spurious step counts and the fact that accelerometers cannot distinguish between sitting and standing. The paper shows the limitations and potential of data from Fitbit devices as a means of understanding the opportunities and constraints of the organisational and physical environment for physical activity at work for office workers. [ABSTRACT FROM AUTHOR]

The vision for Australia's national electronic health record system included empowering consumers to become active participants in their own health care. This paper aims to critically review the literature on consumer perspectives of Australia's My Health Record (formerly PCEHR). The review is based on a subset of articles (n=12) identified in the Australian EHR Repository (N=143), a repository of metadata of Australian Research on EHR located at Flinders University. Results show low levels of awareness and concerns about sharing records and equity of access for all Australians, which in view of the change from opt in to opt out raises concerns about explicit consent. Improved promotion and support, along with different models of access might lead to higher consumer engagement with, and use, of My Health Record, especially for populations at risk of digital exclusion. [ABSTRACT FROM AUTHOR]

Medical informatics is a young and rapidly evolving field, influenced by and impacting on many different knowledge domains. Recent contributions on scoping the associated body of knowledge are confounded both by variations in popular use of terminology for established areas, and by the advent of new areas without yet established terminology. Determining the scope of a topic through online bibliographic search filters is a well-established approach in scientific research and has been developed as a human-directed task. Establishing the best approach and automating the process has proved a difficult problem. This paper explores the use of text analysis of bibliographic information using available search engines and NVIVO text analysis tools to test the potential for dynamic word based filters based on data mining. Results show that word searches of abstracts are more effective than topic searches for identifying health informatics papers, however more work is required to refine search terms to improve generalisability. Using data mining to track changes in word use in medical informatics journals, may make it possible to establish a more dynamic search filter to match the evolving nature of the field of health informatics. [ABSTRACT FROM AUTHOR]

Activity of Daily Living has become a clinical de facto instrument to assess daily functional status of older people living independently at home. Almost all ADLs scales are based on subjective assessment of clinical staff and self-reported responses of the elderly person. A great deal of variability in ADL assessment is likely due to the different cultural beliefs, language and education, and over-assessment of personal capability to potentially avoid negative consequences. This paper proposes automatic and objective ADLs assessment as key component of a technology platform that supports older people to live independently in their home, called Smarter Safer Homes. The objective ADL assessment is achieved through communicating data from simple non-intrusive, wireless sensors placed in a home environment. Pilot sensor data sets were collected over six months from nine independent living homes of participants aged 70+ year. The application of a clustering based, unsupervised learning method on these data sets demonstrates the potential to automatically detect five domains of activity contributing to functional independence. Furthermore, the method provides features that support elderlys self-monitoring of daily activities more regularly, that could provide the potential for timely and early intervention from family and carers.