Systematic review of Internet of medical things for cardiovascular disease prevention among Australian first nations.

Chronic diseases within Indigenous communities constitute the most compelling ill-health burdens and treatment inequalities, particularly in rural and remote Australia. In response to these vital issues, a systematic literature review of the adoption of wearable, Artificial Intelligence-driven, electrocardiogram sensors, in a telehealth Internet of Medical Things (IoMT) context was conducted to scale up rural Indigenous health. To this end, four preselected scientific databases were chosen for data extraction to align with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique. From the initially collected (n=4436) articles, a total of 32 articles were analysed, being synthesised from the review inclusion criteria, maintaining strict eligibility and eliminating duplicates. None of the various studies found on this innovative healthcare intervention has given a comprehensive picture of how this could be an effective method of care dedicated to rural Indigenous communities with cardiovascular diseases (CVDs). Herein, we presented the unique concepts of IoMT-driven wearable biosensors tailored for rural indigenous cardiac patients, their clinical implications, and cardiovascular disease management within the telehealth domain. This work contributes to understanding the adoption of wearable IoMT sensor-driven telehealth model, highlighting the need for real-time data from First Nations patients in rural and remote areas for CVD prevention. Pertinent implications, research impacts, limitations and future research directions are endorsed, securing long-term Wearable IoMT sensor-driven telehealth sustainability.

A cross-sectional online survey on patients' satisfaction using store-and-forward voice and text messaging teleconsultation service during the COVID­19 pandemic.

AIM: This study aimed to evaluate the patients' satisfaction with using store-and-forward voice and text messaging teleconsultation service to provide primary health care to patients during the COVID-19 pandemic. METHOD: A cross-sectional survey was conducted between October 1 and December 1, 2020, in Iran. The study population consisted of patients who used the service. Three hundred-ninety-six patients were enrolled in the study by convenience sampling. Data were collected by a researcher-made questionnaire. The face, comprehensibility, and content validity of the questionnaire were tested and met. The reliability of this questionnaire was confirmed (r = 0.9). Descriptive statistics and multinomial logistic regression were conducted. Data were analyzed using STATA 14.0 software. RESULTS: In total, 396 patients responded to the online questionnaire. The mean age of patients was 37 ± 10.31 years. More than half of them had an academic degree (65.40 %). Teleconsultation was considered satisfactory by 172 patients (43.43 %), while more than half of the patients (56.57 %) were unsatisfied with teleconsultation. In terms of "quality of care provided" and "patient information privacy" components, around 41 % of patients were satisfied. However, the number of patients who feel satisfied with teleconsultation's similarity to a face-to-face encounter was lower (37.88 %). The results showed no significant relationship between age, gender, education, and overall satisfaction (p > 0.05). The association between overall satisfaction and health status was (AOR = 1.51, 95 % CI = 1.16-1.96). CONCLUSION: More than half of patients from our study did not have a good experience with teleconsultation. This is also partially due to the use of existing communication platform, instead of custom-made solution. It is necessary to improve the services' quality and meet patients' needs to optimize patients' experience, particularly during a health crisis, resulting in better health outcomes and end-user satisfaction.

myPainPal: Co-creation of a mHealth app for the management of chronic pain in young people.

Chronic pain is common in young people aged 10-14 years. Interdisciplinary, clinician-delivered treatments, while effective, are often criticized for failing to be readily accessible. Mobile health applications (mHealth apps) have been proposed as effective treatment adjuncts that address these challenges, while meeting the needs of tech-savvy young people. The objectives of this study were to co-create a mHealth app with consumers and health care professionals and evaluate the acceptability and feasibility of the resulting mHealth app (myPainPal). A phased, qualitative approach within a consumer engagement framework was employed. Interviews with young people (n = 14), parents (n = 12) and health care professionals (n = 8) identified key health needs that formed the underlying structure of the myPainPal app. Testing showed that the app is an acceptable and feasible platform to facilitate young people's self-management of chronic pain. The myPainPal app has the potential to positively influence young people's experiences of chronic pain. Further testing in controlled settings is required.

Women's Wellness with Type 2 Diabetes Program (WWDP): Qualitative findings from the UK and Australian feasibility study.

AIMS: To undertake a qualitative study of a multimodal behavioural intervention and research protocol developed to improve wellness in women with type 2 diabetes mellitus (T2DM), the Women's Wellness with Type 2 Diabetes program (WWDP). METHODS: Semi-structured interviews were conducted with 15 participants who completed the WWDP. The interviews were transcribed verbatim and analysed thematically in an iterative process. RESULTS: Themes developing from interviews were broadly grouped into three domains, 1) Hope for a better everyday life; 2) Reflection of the program and its contents; and 3) Impacts on health and wellbeing. Participants viewed the WWDP as a necessary and valuable approach that was crucial in helping them adopt strategies to improve their wellbeing and prevent complications associated with T2DM. Some participants expressed ambivalence towards their adherence to the program due to day-to-day life commitments. The most appreciated feature of the program were the individualised approach adopted by the consultation nurse via skype, convenient appointments, the provision of credible and factual information and the accessible website. CONCLUSIONS: This study critically evaluated perceptions of participants towards the WWDP and provided important recommendations for improving the delivery and sustainability of the program in future. Participants perceived the program as an effective means of supporting their T2DM self-management and improving wellbeing.

International feasibility study for the Women's Wellness with Type 2 Diabetes Programme (WWDP): An eHealth enabled 12-week intervention programme for midlife women with type 2 diabetes.

AIMS: The current study aimed to examine feasibility of participant recruitment and retention rates for the Women's Wellness with Type 2 Diabetes program (WWDP), and to assess initial efficacy of the program in improving wellbeing outcomes. METHODS: 70 midlife women with type 2 diabetes mellitus (T2DM) participated in a 12-week wellness-focused intervention, the WWDP. The WWDP involved a structured book (with participatory activities), an interactive website and nurse consultations. This study had an Australian and a UK arm. Analyses were conducted using chi-square, McNemar, paired t-test, and Wilcoxon signed-ranks tests. RESULTS: The attrition rate for the sample was 22.2%. Overall, significant improvement was observed in diabetes distress (DD), diabetes self-efficacy, weight, BMI, menopausal symptoms and sleep symptoms from baseline to program completion at 12 weeks. Australian participants were also more likely to meet fruit recommendation guidelines and had significant waist- and hip-circumference reductions. CONCLUSIONS: Good retention rates and initial efficacy findings indicated feasibility of the WWDP as a promising 12-week health and wellness program for women with T2DM. They also suggest incorporating a focus on self-efficacy and gendered information may be important in improving wellness and health outcomes related to distress and menopause.

Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological Data.

This study examined the feasibility of a non-laboratory approach that uses machine learning on multimodal sensor data to predict relative physical activity (PA) intensity. A total of 22 participants completed up to 7 PA sessions, where each session comprised 5 trials (sitting and standing, comfortable walk, brisk walk, jogging, running). Participants wore a wrist-strapped sensor that recorded heart-rate (HR), electrodermal activity (Eda) and skin temperature (Temp). After each trial, participants provided ratings of perceived exertion (RPE). Three classifiers, including random forest (RF), neural network (NN) and support vector machine (SVM), were applied independently on each feature set to predict relative PA intensity as low (RPE ≤ 11), moderate (RPE 12-14), or high (RPE ≥ 15). Then, both feature fusion and decision fusion of all combinations of sensor modalities were carried out to investigate the best combination. Among the single modality feature sets, HR provided the best performance. The combination of modalities using feature fusion provided a small improvement in performance. Decision fusion did not improve performance over HR features alone. A machine learning approach using features from HR provided acceptable predictions of relative PA intensity. Adding features from other sensing modalities did not significantly improve performance.

Efficacy and Outcomes of a Music-Based Emotion Regulation Mobile App in Distressed Young People: Randomized Controlled Trial.

BACKGROUND: Emotion dysregulation increases the risk of depression, anxiety, and substance use disorders. Music can help regulate emotions, and mobile phones provide constant access to it. The Music eScape mobile app teaches young people how to identify and manage emotions using music. OBJECTIVE: This study aimed to examine the effects of using Music eScape on emotion regulation, distress, and well-being at 1, 2, 3, and 6 months. Moderators of outcomes and user ratings of app quality were also examined. METHODS: A randomized controlled trial compared immediate versus 1-month delayed access to Music eScape in 169 young people (aged 16 to 25 years) with at least mild levels of mental distress (Kessler 10 score>17). RESULTS: No significant differences between immediate and delayed groups on emotion regulation, distress, or well-being were found at 1 month. Both groups achieved significant improvements in 5 of the 6 emotion regulation skills, mental distress, and well-being at 2, 3, and 6 months. Unhealthy music use moderated improvements on 3 emotion regulation skills. Users gave the app a high mean quality rating (mean 3.8 [SD 0.6]) out of 5. CONCLUSIONS: Music eScape has the potential to provide a highly accessible way of improving young people's emotion regulation skills, but further testing is required to determine its efficacy. Targeting unhealthy music use in distressed young people may improve their emotion regulation skills. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12615000051549; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=365974.

Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An additional paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with reference monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resolution than the monitoring network can resolve.

Physical Activity Recognition Using Posterior-Adapted Class-Based Fusion of Multiaccelerometer Data.

This paper proposes the use of posterior-adapted class-based weighted decision fusion to effectively combine multiple accelerometer data for improving physical activity recognition. The cutting-edge performance of this method is benchmarked against model-based weighted fusion and class-based weighted fusion without posterior adaptation, based on two publicly available datasets, namely PAMAP2 and MHEALTH. Experimental results show that: 1) posterior-adapted class-based weighted fusion outperformed model-based and class-based weighted fusion; 2) decision fusion with two accelerometers showed statistically significant improvement in average performance compared to the use of a single accelerometer; 3) generally, decision fusion from three accelerometers did not show further improvement from the best combination of two accelerometers; and 4) a combination of ankle and wrist located accelerometers showed the best overall performance compared to any combination of two or three accelerometers.

Efficacy and outcomes of a mobile app targeting alcohol use in young people.

Mobile apps provide a highly accessible way of reducing alcohol use in young people. This paper determines the 1-month efficacy and 2, 3 and 6month outcomes of the Ray's Night Out app, which aims to increase alcohol knowledge and reduce alcohol use in young people. User-experience design and agile development processes, informed by the Information-Motivation-Behavioral skills model and evidence-based motivational interviewing treatment approaches guided app development. A randomized controlled trial comparing immediate versus 1-month delayed access to the app was conducted in 197 young people (16 to 25years) who drank alcohol in the previous month. Participants were assessed at baseline, 1, 2, 3 and 6months. Alcohol knowledge, alcohol use and related harms and the severity of problematic drinking were assessed. App quality was evaluated after 1-month of app use. Participants in the immediate access group achieved a significantly greater increase in alcohol knowledge than the delayed access group at 1-month, but no differences in alcohol use or related problems were found. Both groups achieved significant reductions in the typical number of drinks on a drinking occasion over time. A reduction in maximum drinks consumed was also found at 1month. These reductions were most likely to occur in males and problem drinkers. Reductions in alcohol-related harm were also found. The app received a high mean quality (M=3.82/5, SD=0.51). The Ray app provides a youth-friendly and easily-accessible way of increasing young people's alcohol knowledge but further testing is required to determine its impact on alcohol use and related problems.

Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.

PURPOSE: To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). METHODS: The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. RESULTS: In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. CONCLUSIONS: Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

The Women's wellness after cancer program: a multisite, single-blinded, randomised controlled trial protocol.

BACKGROUND: Despite advances in cancer diagnosis and treatment have significantly improved survival rates, patients post-treatment-related health needs are often not adequately addressed by current health services. The aim of the Women's Wellness after Cancer Program (WWACP), which is a digitised multimodal lifestyle intervention, is to enhance health-related quality of life in women previously treated for blood, breast and gynaecological cancers. METHODS: A single-blinded, multi-centre randomized controlled trial recruited a total of 351 women within 24 months of completion of chemotherapy (primary or adjuvant) and/or radiotherapy. Women were randomly assigned to either usual care or intervention using computer-generated permuted-block randomisation. The intervention comprises an evidence-based interactive iBook and journal, web interface, and virtual health consultations by an experienced cancer nurse trained in the delivery of the WWACP. The 12 week intervention focuses on evidence-based health education and health promotion after a cancer diagnosis. Components are drawn from the American Cancer Research Institute and the World Cancer Research Fund Guidelines (2010), incorporating promotion of physical activity, good diet, smoking cessation, reduction of alcohol intake, plus strategies for sleep and stress management. The program is based on Bandura's social cognitive theoretical framework. The primary outcome is health-related quality of life, as measured by the Functional Assessment of Cancer Therapy-General (FACT-G). Secondary outcomes are menopausal symptoms as assessed by Greene Climacteric Scale; physical activity elicited with the Physical Activity Questionnaire Short Form (IPAQ-SF); sleep measured by the Pittsburgh Sleep Quality Index; habitual dietary intake monitored with the Food Frequency Questionnaire (FFQ); alcohol intake and tobacco use measured by the Australian Health Survey and anthropometric measures including height, weight and waist-to-hip ratio. All participants were assessed with these measures at baseline (at the start of the intervention), 12 weeks (at completion of the intervention), and 24 weeks (to determine the level of sustained behaviour change). Further, a simultaneous cost-effectiveness evaluation will consider if the WWACP provides value for money and will be reported separately. DISCUSSION: Women treated for blood, breast and gynaecological cancers demonstrate increasingly good survival rates. However, they experience residual health problems that are potentially modifiable through behavioural lifestyle interventions such as the WWACP. TRIAL REGISTRATION: The protocol for this study was registered with the Australian and New Zealand Clinical Trials Registry, Trial ID: ACTRN12614000800628 , July 28, 2014.

Baby Steps - An Online Program Promoting the Well-Being of New Mothers and Fathers: A Study Protocol.

BACKGROUND: Parental well-being can be seriously impacted during the challenging perinatal period. Most research and support services focus on perinatal psychopathology, leaving a need for programs that recognize and enhance the strengths and well-being of parents. Furthermore, fathers have received minimal attention and support relative to mothers, despite experiencing perinatal distress. New parents have limited time and energy to invest in program attendance, and web-based programs provide an ideal platform for delivering perinatal well-being programs. Such programs are globally accessible, available at any time, and can be accessed anywhere with an Internet connection. OBJECTIVE: This paper describes the protocol of a randomized controlled trial investigating the effects on first-time parents' perinatal well-being, comparing two versions of the online program Baby Steps. METHODS: The clinical trial will randomize 240 primiparous mother-father couples to either (1) Babycare, an online information-only program providing tips on selected childcare issues, or (2) Well-being, an online interactive program including all content from the Babycare program, plus parental well-being-focused content with tools for goal-setting and problem solving. Both programs will be supported by short message service (SMS) texts at two, four, seven, and ten weeks to encourage continued use of the program. Primary outcomes will be measures of perinatal distress and quality of life. Secondary outcomes will be couple relationship satisfaction, parent self-efficacy, and social support. Cost-effectiveness will also be measured for each Baby Steps program. RESULTS: Participant recruitment commenced March, 2015 and continued until October, 2015. Follow-up data collection has commenced and will be completed May, 2016 with results expected in July, 2016. CONCLUSIONS: Perinatal distress has substantial impacts on parents and their infants, with potential to affect later childhood adjustment, relationships, and development. This study aims to test the impact of a highly accessible online program to support parental coping, and maximize the well-being of both parents. By including fathers in the program, Baby Steps has the potential to engage and support this often neglected group who can make a substantial contribution to familial well-being. CLINICALTRIAL: Australian & New Zealand Clinical Trials Registry: ANZCTR12614001256662; https://www.anzctr.org.au/ Trial/Registration/TrialReview.aspx?id=367277 (Archived by WebCite at http://www.webcitation.org/6ibUsjFIL).

Mobile app rating scale: a new tool for assessing the quality of health mobile apps.

BACKGROUND: The use of mobile apps for health and well being promotion has grown exponentially in recent years. Yet, there is currently no app-quality assessment tool beyond "star"-ratings. OBJECTIVE: The objective of this study was to develop a reliable, multidimensional measure for trialling, classifying, and rating the quality of mobile health apps. METHODS: A literature search was conducted to identify articles containing explicit Web or app quality rating criteria published between January 2000 and January 2013. Existing criteria for the assessment of app quality were categorized by an expert panel to develop the new Mobile App Rating Scale (MARS) subscales, items, descriptors, and anchors. There were sixty well being apps that were randomly selected using an iTunes search for MARS rating. There were ten that were used to pilot the rating procedure, and the remaining 50 provided data on interrater reliability. RESULTS: There were 372 explicit criteria for assessing Web or app quality that were extracted from 25 published papers, conference proceedings, and Internet resources. There were five broad categories of criteria that were identified including four objective quality scales: engagement, functionality, aesthetics, and information quality; and one subjective quality scale; which were refined into the 23-item MARS. The MARS demonstrated excellent internal consistency (alpha = .90) and interrater reliability intraclass correlation coefficient (ICC = .79). CONCLUSIONS: The MARS is a simple, objective, and reliable tool for classifying and assessing the quality of mobile health apps. It can also be used to provide a checklist for the design and development of new high quality health apps.

Alcohol consumption in young adults: the role of multisensory imagery.

Little is known about the subjective experience of alcohol desire and craving in young people. Descriptions of alcohol urges continue to be extensively used in the everyday lexicon of young, non-dependent drinkers. Elaborated Intrusion (EI) Theory contends that imagery is central to craving and desires, and predicts that alcohol-related imagery will be associated with greater frequency and amount of drinking. This study involved 1535 age stratified 18-25 year olds who completed an alcohol-related survey that included the Imagery scale of the Alcohol Craving Experience (ACE) questionnaire. Imagery items predicted 12-16% of the variance in concurrent alcohol consumption. Higher total Imagery subscale scores were linearly associated with greater drinking frequency and lower self-efficacy for moderate drinking. Interference with alcohol imagery may have promise as a preventive or early intervention target in young people.