Detecting Depression-related Movement Changes in Older Adults using Smart Home Motion Sensors - A Feasibility Study.

Smart home sensor data is being increasingly used to identify health risks through passive tracking of specific behaviours and activity patterns. This study explored the feasibility of using motion sensor data to track changes in daytime movement patterns within the home, and their potential association with depression in older adults. This study analysed the motion sensor data collected during a one-year smart home trial, and explored their association with Geriatric Depression Scale (GDS) scores collected at three different time points during the trial (i.e., baseline, mid-trial, and end-trial). Our results showed that movement patterns are generally reduced when older adults are in a depressed state compared to when being in a not-depressed state. In particular, the reduced movement activity in depressed states was significant (p<.05) when the participant's GDS state changed between depressed and not-depressed for the first time during the three time points of the trial when GDS was collected.Clinical relevance- Our results establish the feasibility and potential use of motion sensor data from ambient sensors in a smart home for passive and remote assessment of older adults' depression status, that is comparable to their GDS scores, through changes in their in-home day-time movement patterns. Also since reduced movement activity may be a general indicator of potential health risks, this study provides preliminary evidence for using in-home movement activity monitoring as an general indicator of health risks.

The Significance and Limitations of Sensor-based Agitation Detection in People Living with Dementia.

Agitation, a commonly observed behaviour in people living with dementia (PLwD), is frequently interpreted as a response to physiological, environmental, or emotional stress. Agitation has the potential to pose health risks to both individuals and their caregivers, and can contribute to increased caregiver burden and stress. Early detection of agitation can facilitate with timely intervention, which has the potential to prevent escalation to other challenging behaviors. Wearable and ambient sensors are frequently used to monitor physiological and behavioral conditions and the collected signals can be engaged to detect the onset of an agitation episode. This paper delves into the current sensor-based methods for detecting agitation in PLwD, and reviews the strengths and limitations of existing works. Future directions to enable real-time agitation detection to empower caregivers are also deliberated, with a focus on their potential to reduce caregiver burden by facilitating early support, assistance and interventions to timely manage agitation episodes in PLwD.

A Scoping Review of mHealth Interventions for Secondary Prevention of Stroke: Implications for Policy and Practice.

Secondary prevention is a major priority for those living with stroke and may be improved through the use of mobile Health (mHealth) interventions. While evidence for the effectiveness of mHealth interventions for secondary prevention of stroke is growing, little attention has been given to the translation of these interventions into real-world use. In this review, we aimed to provide an update on the effectiveness of mHealth interventions for secondary prevention of stroke, and investigate their translation into real-world use. Four electronic databases and the gray literature were searched for randomized controlled trials of mHealth interventions for secondary prevention of stroke published between 2010 and 2023. Qualitative and mixed-methods evaluations of the trials were also included. Data were extracted regarding study design, population, mHealth technology involved, the intervention, and outcomes. Principal researchers from these trials were also contacted to obtain further translational information. From 1151 records, 13 randomized controlled trials and 4 evaluations were identified; sample sizes varied widely (median, 56; range, 24-4298). Short message service messages (9/13) and smartphone applications (6/13) were the main technologies used to deliver interventions. Primary outcomes of feasibility of the intervention were achieved in 4 trials, and primary outcomes of changes in risk factors, lifestyle behaviors, and adherence to medication improved in 6 trials. Only 1 trial had a hard end point (ie, stroke recurrence) as a primary outcome, and no significant differences were observed between groups. There was evidence for only 1 intervention being successfully translated into real-world use. Further evidence is required on the clinical effectiveness of mHealth interventions for preventing recurrent stroke, and the associated delivery costs and cost-effectiveness, before adoption into real-world settings.

Sensor-Based Assessment of Social Isolation and Loneliness in Older Adults: A Survey.

Social isolation (SI) and loneliness are 'invisible enemies'. They affect older people's health and quality of life and have significant impact on aged care resources. While in-person screening tools for SI and loneliness exist, staff shortages and psycho-social challenges fed by stereotypes are significant barriers to their implementation in routine care. Autonomous sensor-based approaches can be used to overcome these challenges by enabling unobtrusive and privacy-preserving assessments of SI and loneliness. This paper presents a comprehensive overview of sensor-based tools to assess social isolation and loneliness through a structured critical review of the relevant literature. The aim of this survey is to identify, categorise, and synthesise studies in which sensing technologies have been used to measure activity and behavioural markers of SI and loneliness in older adults. This survey identified a number of feasibility studies using ambient sensors for measuring SI and loneliness activity markers. Time spent out of home and time spent in different parts of the home were found to show strong associations with SI and loneliness scores derived from standard instruments. This survey found a lack of long-term, in-depth studies in this area with older populations. Specifically, research gaps on the use of wearable and smart phone sensors in this population were identified, including the need for co-design that is important for effective adoption and practical implementation of sensor-based SI and loneliness assessment in older adults.

Socially-Assistive Robots to Support Learning in Students on the Autism Spectrum: Investigating Educator Perspectives and a Pilot Trial of a Mobile Platform to Remove Barriers to Implementation.

Technology offers educators tools that can tailor learning to students' learning styles and interests. Research into the use of socially-assistive robots as a learning support for children on the autism spectrum are showing promising results. However, to date, few schools have introduced these robots to support learning in students on the autism spectrum. This paper reports on a research project that investigated the barriers to implementing socially-assistive robot supported learning, and the expectations, perceived benefits and concerns of school teachers and therapists of students on the autism spectrum and adults on the autism spectrum. First, three focus groups were conducted with six adults on the autism spectrum, and 13 teachers and therapists of students from two autism-specific schools. During the focus groups, there was cautious optimism from participants about the value of socially-assistive robots for teaching support. While the data showed that participants were in favour of trialling socially-assistive robots in the classroom, they also raised several concerns and potential barriers to implementation, including the need for teacher training. In response to their concerns, the second part of the project focussed on developing a software platform and mobile application (app) to support the introduction of robots into autism-specific classrooms. The software platform and app were then trialled in two schools (n = 7 teachers and therapists). Results from focus groups indicated that participants believe socially-assistive robots could be useful for learning support, as the mobile app provides an easy to use tool to support preparing and conducting lessons that would motivate them to trial robots in the classroom.

The Significance and Limitations of Monitoring Sleep during Pregnancy.

Sleep patterns often change during pregnancy and postpartum. However, if severe and persistent, these changes can depict a risk factor for significant health complications. It is thus essential to identify and understand changes in women's sleeping pattern over the course of pregnancy and postpartum, to offer an appropriate and timely intervention if necessary. In this paper, we discuss sleep disturbances during pregnancy and their association with pregnancy complications. We also review the state-of-the-art digital devices for real-time sleep assessment, and highlight their strengths and limitations.Clinical Relevance-This review highlights an importance of an individualized holistic pregnancy care program which engages both the healthcare professionals and the obstetric population, together with an educational module to increase the user awareness on the importance of sleep disturbances and their consequences during and after pregnancy.

Users' Perceptions Toward mHealth Technologies for Health and Well-being Monitoring in Pregnancy Care: Qualitative Interview Study.

BACKGROUND: Mobile health (mHealth) technologies, such as wearable sensors, smart health devices, and mobile apps, that are capable of supporting pregnancy care are emerging. Although mHealth could be used to facilitate the tracking of health changes during pregnancy, challenges remain in data collection compliance and technology engagement among pregnant women. Understanding the interests, preferences, and requirements of pregnant women and those of clinicians is needed when designing and introducing mHealth solutions for supporting pregnant women's monitoring of health and risk factors throughout their pregnancy journey. OBJECTIVE: This study aims to understand clinicians' and pregnant women's perceptions on the potential use of mHealth, including factors that may influence their engagement with mHealth technologies and the implications for technology design and implementation. METHODS: A qualitative study using semistructured interviews was conducted with 4 pregnant women, 4 postnatal women, and 13 clinicians working in perinatal care. RESULTS: Clinicians perceived the potential benefit of mHealth in supporting different levels of health and well-being monitoring, risk assessment, and care provision in pregnancy care. Most pregnant and postnatal female participants were open to the use of wearables and health monitoring devices and were more likely to use these technologies if they knew that clinicians were monitoring their data. Although it was acknowledged that some pregnancy-related medical conditions are suitable for an mHealth model of remote monitoring, the clinical and technical challenges in the introduction of mHealth for pregnancy care were also identified. Incorporating appropriate health and well-being measures, intelligently detecting any abnormalities, and providing tailored information for pregnant women were the critical aspects, whereas usability and data privacy were among the main concerns of the participants. Moreover, this study highlighted the challenges of engaging pregnant women in longitudinal mHealth monitoring, the additional work required for clinicians to monitor the data, and the need for an evidence-based technical solution. CONCLUSIONS: Clinical, technical, and practical factors associated with the use of mHealth to monitor health and well-being in pregnant women need to be considered during the design and feasibility evaluation stages. Technical solutions and appropriate strategies for motivating pregnant women are critical to supporting their long-term data collection compliance and engagement with mHealth technology during pregnancy.

Enabling Nurse-Patient Communication With a Mobile App: Controlled Pretest-Posttest Study With Nurses and Non-English-Speaking Patients.

BACKGROUND: There is growing concern regarding the implications of miscommunication in health care settings, the results of which can have serious detrimental impacts on patient safety and health outcomes. Effective communication between nurses and patients is integral in the delivery of timely, competent, and safe care. In a hospital environment where care is delivered 24 hours a day, interpreters are not always available. In 2014, we developed a communication app to support patients' interactions with allied health clinicians when interpreters are not present. In 2017, we expanded this app to meet the needs of the nursing workforce. The app contains a fixed set of phrases translated into common languages, and communication is supported by text, images, audio content, and video content. OBJECTIVE: This study aims to evaluate the efficacy of the communication app to support nursing staff during the provision of standard care to patients from non-English-speaking backgrounds when an interpreter is not available. METHODS: This study used a one-group pretest-posttest sequential explanatory mixed methods research design, with quantitative data analyzed using inferential statistics and qualitative data analyzed via thematic content analysis. A total of 134 observation sessions (82 pretest and 52 posttest) of everyday nurse-patient interactions and 396 app use sessions were recorded. In addition, a total of 134 surveys (82 pretest and 52 posttest) with nursing staff, 7 interviews with patients, and 3 focus groups with a total of 9 nursing staff participants were held between January and November 2017. RESULTS: In the absence of the app, baseline interactions with patients from English-speaking backgrounds were rated as more successful (t80=5.69; P<.001) than interactions with patients from non-English-speaking backgrounds. When staff used the app during the live trial, interactions with patients from non-English-speaking backgrounds were rated as more successful than interactions without the app (F2,119=8.17; P<.001; η2=0.37). In addition, the level of staff frustration was rated lower when the app was used to communicate (t80=2.71; P=.008; r=0.29). Most participants indicated that the app assisted them in communicating. CONCLUSIONS: Through the use of the app, a number of patients from non-English-speaking backgrounds experienced better provision of standard care, similar to their English-speaking peers. Thus, the app can be seen as contributing to the delivery of equitable health care.

Technology assessment framework for precision health applications.

OBJECTIVE: Established and emerging technologies-such as wearable sensors, smartphones, mobile apps, and artificial intelligence-are shaping positive healthcare models and patient outcomes. These technologies have the potential to become precision health (PH) innovations. However, not all innovations meet regulatory standards or have the required scientific evidence to be used for health applications. In response, an assessment framework was developed to facilitate and standardize the assessment of innovations deemed suitable for PH. METHODS: A scoping literature review undertaken through PubMed and Google Scholar identified approximately 100 relevant articles. These were then shortlisted (n = 12) to those that included specific metrics, criteria, or frameworks for assessing technologies that could be applied to the PH context. RESULTS: The proposed framework identified nine core criteria with subcriteria and grouped them into four categories for assessment: technical, clinical, human factors, and implementation. Guiding statements with response options and recommendations were used as metrics against each criterion. CONCLUSION: The proposed framework supports health services, health technology innovators, and researchers in leveraging current and emerging technologies for PH innovations. It covers a comprehensive set of criteria as part of the assessment process of these technologies.

Physiological Signal Monitoring for Identification of Emotional Dysregulation in Children.

Children, particularly those with atypical or delayed development, have a reduced ability to self-regulate their emotions and behaviour. After a number of anxiety or stress provoking events, this reduced regulatory ability can result in a meltdown. Extrinsic signals of an impending meltdown are often recognised and acted on by clinicians or parents. These external indications are also accompanied by internal physiological changes, such as increase in heart rate, skin electrodermal activity, and skin temperature. These physiological signals may be used to predict impending meltdown events and facilitate earlier and effective carer intervention, especially in complex management cases. We present a preliminary study using a wearable sensor system for continuous monitoring of physiological signals to measure and predict emotional changes in school-aged children. Our models are able to correctly classify the behavioural state of a child with 68% mean global model accuracy and up to 85% for person-dependent models. Prediction of emotion and identification of impending meltdowns will potentially assist parents, carers, teachers and clinicians to manage stress and problem behaviours before they escalate, and support self-management strategies throughout the variety of normal daily life.

CALD Assist-Nursing: Improving communication in the absence of interpreters.

AIMS AND OBJECTIVES: To develop a communication app to support nursing staff during the provision of standard care of patients from non-English-speaking backgrounds (NESBs), when an interpreter is not available. This paper reports on the user needs analysis phase that informed the development, content and functionality of the app. BACKGROUND: In 2014 we developed CALD Assist, a communication app to support patient interactions with allied health clinicians when interpreters are not present. It includes 95 commonly used phrases professionally interpreted into 10 languages and grouped by discipline. This work expands upon our previous app to meet the needs of the nursing workforce. DESIGN: Qualitative through focus groups, observations and interviews, with a quantitative component from observational data and staff surveys. METHODS: Four focus groups with hospital staff, ten interviews with patients from NESBs and 85 observation sessions of everyday patient-staff interactions followed by staff surveys (n = 85) were held between January and June 2017. RESULTS: Baseline data prior to app development revealed that staff confidence of the patients' level of understanding and the success of the interaction were significantly greater for English-speaking (ES) patients, than for non-English-speaking patients. A total of 143 phrases were identified and subdivided into 16 categories for inclusion in the new app. CONCLUSION: Staff participants highlighted that patients from NESBs are a challenging patient group to interact with. Patient and staff participants identified a range of areas where the nursing app could benefit, including pain management, mobility, hygiene and nutrition. RELEVANCE TO CLINICAL PRACTICE: The proposed app can be used to reduce variances in practice and provide a timely and positive patient experience for patients from NESBs who are unable to communicate in English during hospital admissions.

Developing Digital Facilitation of Assessments in the Absence of an Interpreter: Participatory Design and Feasibility Evaluation With Allied Health Groups.

BACKGROUND: To ensure appropriate and timely care, interpreters are often required to aid communication between clinicians and patients from non-English speaking backgrounds. In a hospital environment, where care is delivered 24 hours a day, interpreters are not always available. Subsequently, culturally and linguistically diverse (CALD) patients are sometimes unable to access timely assessment because of clinicians' inability to communicate directly with them. OBJECTIVE: The aim of this study was to design and evaluate CALD Assist, a tablet app to assist communication between patients and allied health clinicians in the absence of an interpreter. CALD Assist uses key phrases translated into common languages and uses pictorial, written, and voice-over prompts to facilitate communication during basic patient assessment. METHODS: CALD Assist's design, functionality, and content were determined through focus groups with clinicians and informed by interpreting and cultural services. An evaluation was conducted in a live trial phase on eight wards across 2 campuses of a hospital in Victoria, Australia. RESULTS: A commercial grade CALD Assist mobile app for five disciplines within allied health was developed and evaluated. The app includes a total of 95 phrases in ten different languages to assist clinicians during their initial assessment. Evaluation results show that clinicians' confidence in their assessment increased with use of the CALD Assist app: clinicians' reports of "complete confidence" increased from 10% (3/30) to 42% (5/12), and assessment reports of "no confidence" decreased from 57% (17/30) to 17% (2/12). Average time required to complete an assessment with patients from non-English speaking backgrounds reduced from 42.0 to 15.6 min. CONCLUSIONS: Through the use of CALD Assist, clinician confidence in communicating with patients from non-English speaking backgrounds in the absence of an interpreter increased, providing patients from non-English speaking backgrounds with timely initial assessments and subsequent care in line with their English speaking peers. Additionally, the inclusion of images and video demonstrations in CALD Assist increased the ability to communicate with patients and overcome literacy-related barriers. Although a number of hurdles were faced, user uptake and satisfaction were positive, and the app is now available in the Apple App Store.