Emerging technologies for precision health: An insight into sensing technologies for health and wellbeing

Multicomponent Support Program for Secondary Prevention of Stroke Using Digital Health Technology: Co-Design Study With People Living With Stroke or Transient Ischemic Attack

BACKGROUND

Few individuals (<2%) who experience a stroke or transient ischemic attack (TIA) participate in secondary prevention lifestyle programs. Novel approaches that leverage digital health technology may provide a viable alternative to traditional interventions that support secondary prevention in people living with stroke or TIA. To be successful, these strategies should focus on user needs and preferences and be acceptable to clinicians and people living with stroke or TIA.

OBJECTIVE

This study aims to co-design, with people with lived experience of stroke or TIA (referred to as consumers) and clinicians, a multicomponent digital technology support program for secondary prevention of stroke.

METHODS

A consumer user needs survey (108 items) was distributed through the Australian Stroke Clinical Registry and the Stroke Association of Victoria. An invitation to a user needs survey (135 items) for clinicians was circulated via web-based professional forums and national organizations (eg, the Stroke Telehealth Community of Practice Microsoft Teams Channel) and the authors' research networks using Twitter (subsequently rebranded X, X Corp) and LinkedIn (LinkedIn Corp). Following the surveys, 2 rounds of user experience workshops (design and usability testing workshops) were completed with representatives from each end user group (consumers and clinicians). Feedback gathered after each round informed the final design of the digital health program.

RESULTS

Overall, 112 consumers (male individuals: n=63, 56.3%) and 54 clinicians (female individuals: n=43, 80%) responded to the survey; all items were completed by 75.8% (n=85) of consumers and 78% (n=42) of clinicians. Most clinicians (46/49, 94%) indicated the importance of monitoring health and lifestyle measures more frequently than current practice, particularly physical activity, weight, and sleep. Most consumers (87/96, 90%) and clinicians (41/49, 84%) agreed that providing alerts about potential deterioration in an individual's condition were important functions for a digital program. Intention to use a digital program for stroke prevention and discussing the data collected during face-to-face consultations was high (consumers: 79/99, 80%; clinicians 36/42, 86%). In addition, 7 consumers (male individuals: n=5, 71%) and 9 clinicians (female individuals: n=6, 67%) took part in the user experience workshops. Participants endorsed using a digital health program to help consumers manage stroke or TIA and discussed preferred functions and health measures in a digital solution for secondary prevention of stroke. They also noted the need for a mobile app that is easy to use. Clinician feedback highlighted the need for a customizable clinician portal that captures individual consumer goals.

CONCLUSIONS

Following an iterative co-design process, supported by evidence from user needs surveys and user experience workshops, a consumer-facing app that integrates wearable activity trackers and a clinician web portal were designed and developed to support secondary prevention of stroke. Feasibility testing is currently in progress to assess acceptability and use.

Leveraging continuous glucose monitoring as a catalyst for behaviour change: a scoping review

BACKGROUND

Amidst the escalating prevalence of glucose-related chronic diseases, the advancements, potential uses, and growing accessibility of continuous glucose monitors (CGM) have piqued the interest of healthcare providers, consumers, and health behaviour researchers. Yet, there is a paucity of literature characterising the use of CGM in behavioural intervention research. This scoping review aims to describe targeted populations, health behaviours, health-related outcomes, and CGM protocols in randomised controlled trials (RCTs) that employed CGM to support health behaviour change.

METHODS

We searched Ovid MEDLINE, Elsevier Embase, Cochrane Central Register of Controlled Trials, EBSCOhost PsycINFO, and ProQuest Dissertations & Theses Global from inception to January 2024 for RCTs of behavioural interventions conducted in adults that incorporated CGM-based biological feedback. Citation searching was also performed. The review protocol was registered ( https://doi.org/10.17605/OSF.IO/SJREA ).

FINDINGS

Collectively, 5389 citations were obtained from databases and citation searching, 3995 articles were screened, and 31 were deemed eligible and included in the review. Most studies (n = 20/31, 65%) included adults with type 2 diabetes and reported HbA1c as an outcome (n = 29/31, 94%). CGM was most commonly used in interventions to target changes in diet (n = 27/31, 87%) and/or physical activity (n = 16/31, 52%). 42% (n = 13/31) of studies provided prospective CGM-based guidance on diet or activity, while 61% (n = 19/31) included retrospective CGM-based guidance. CGM data was typically unblinded (n = 24/31, 77%) and CGM-based biological feedback was most often provided through the CGM and two-way communication (n = 12/31, 39%). Communication typically occurred in-person (n = 13/31, 42%) once per CGM wear (n = 13/31; 42%).

CONCLUSIONS

This scoping review reveals a predominant focus on diabetes in CGM-based interventions, pointing out a research gap in its wider application for behaviour change. Future research should expand the evidence base to support the use of CGM as a behaviour change tool and establish best practices for its implementation.

TRIAL REGISTRATION

doi.org/10.17605/OSF.IO/SJREA.

Reimagine the ICU: Healthcare Professionals' Perspectives on How Environments (Can) Promote Patient Well-Being

OBJECTIVE

This study aims (1) to understand the needs and challenges of the current intensive care unit (ICU) environments in supporting patient well-being from the perspective of healthcare professionals (HCPs) and (2) to explore the new potential of ICU environments enabled by technology.

BACKGROUND

Evidence-based design has yielded how the design of environments can advocate for patient well-being, and digital technology offers new possibilities for indoor environments. However, the role of technology in facilitating ICU patient well-being has been unexplored.

METHOD

This study was conducted in two phases. First, a mixed-method study was conducted with ICU HCPs from four Dutch hospitals. The study investigated the current environmental support for care activities, as well as the factors that positively and negatively contribute to patient experience. Next, a co-creation session was held involving HCPs and health technology experts to explore opportunities for technology to support ICU patient well-being.

RESULTS

The mixed-method study revealed nine negative and eight positive patient experience factors. HCPs perceived patient emotional care as most challenging due to the ICU workload and a lack of environmental support in fulfilling patient emotional needs. The co-creation session yielded nine technology-enabled solutions to address identified challenges. Finally, drawing from insights from both studies, four strategies were introduced that guide toward creating technology to provide holistic and personalized care for patients.

CONCLUSION

Patient experience factors are intertwined, necessitating a multifactorial approach to support patient well-being. Viewing the ICU environment as a holistic unit, our findings provide guidance on creating healing environments using technology.

Rational Design of Flexible Mechanical Force Sensors for Healthcare and Diagnosis

Over the past decade, there has been a significant surge in interest in flexible mechanical force sensing devices and systems. Tremendous efforts have been devoted to the development of flexible mechanical force sensors for daily healthcare and medical diagnosis, driven by the increasing demand for wearable/portable devices in long-term healthcare and precision medicine. In this review, we summarize recent advances in diverse categories of flexible mechanical force sensors, covering piezoresistive, capacitive, piezoelectric, triboelectric, magnetoelastic, and other force sensors. This review focuses on their working principles, design strategies and applications in healthcare and diagnosis, with an emphasis on the interplay among the sensor architecture, performance, and application scenario. Finally, we provide perspectives on the remaining challenges and opportunities in this field, with particular discussions on problem-driven force sensor designs, as well as developments of novel sensor architectures and intelligent mechanical force sensing systems.

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.

Precision health in behaviour change interventions: A scoping review

Precision health seeks to optimise behavioural interventions by delivering personalised support to those in need, when and where they need it. Conceptualised a decade ago, progress toward this vision of personally relevant and effective population-wide interventions continues to evolve. This scoping review aimed to map the state of precision health behaviour change intervention research. This review included studies from a broader precision health review. Six databases were searched for studies published between January 2010 and June 2020, using the terms 'precision health' or its synonyms, and including an intervention targeting modifiable health behaviour(s) that was evaluated experimentally. Thirty-one studies were included, 12 being RCTs (39%), and 17 with weak study design (55%). Most interventions targeted physical activity (27/31, 87%) and/or diet (24/31, 77%), with 74% (23/31) targeting two to four health behaviours. Interventions were personalised via human interaction in 55% (17/31) and digitally in 35% (11/31). Data used for personalising interventions was largely self-reported, by survey or diary (14/31, 45%), or digitally (14/31, 45%). Data was mostly behavioural or lifestyle (20/31, 65%), and physiologic, biochemical or clinical (15/31, 48%), with no studies utilising genetic/genomic data. This review demonstrated that precision health behaviour change interventions remain dependent on human-led, low-tech personalisation, and have not fully considered the interaction between behaviour and the social and environmental contexts of individuals. Further research is needed to understand the relationship between personalisation and intervention effectiveness, working toward the development of sophisticated and scalable behaviour change interventions that have tangible public health impact.

Predicting Cardiovascular Stent Complications Using Self-Reporting Biosensors for Noninvasive Detection of Disease

Self-reporting implantable medical devices are the future of cardiovascular healthcare. Cardiovascular complications such as blocked arteries that lead to the majority of heart attacks and strokes are frequently treated with inert metal stents that reopen affected vessels. Stents frequently re-block after deployment due to a wound response called in-stent restenosis (ISR). Herein, an implantable miniaturized sensor and telemetry system are developed that can detect this process, discern the different cell types associated with ISR, distinguish sub plaque components as demonstrated with ex vivo samples, and differentiate blood from blood clot, all on a silicon substrate making it suitable for integration onto a vascular stent. This work shows that microfabricated sensors can provide clinically relevant information in settings closer to physiological conditions than previous work with cultured cells.

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.

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.

Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care

This paper is a collection of telemedicine techniques used by wireless body area networks (WBANs) for emergency conditions. Furthermore, Bayes’ theorem is proposed for predicting emergency conditions. With prior knowledge, the posterior probability can be found along with the observed evidence. The probability of sending emergency messages can be determined using Bayes’ theorem with the likelihood evidence. It can be viewed as medical decision-making, since diagnosis conditions such as emergency monitoring, delay-sensitive monitoring, and general monitoring are analyzed with its network characteristics, including data rate, cost, packet loss rate, latency, and jitter. This paper explains the network model with 16 variables, with one describing immediate consultation, as well as another three describing emergency monitoring, delay-sensitive monitoring, and general monitoring. The remaining 12 variables are observations related to latency, cost, packet loss rate, data rate, and jitter.