ORCID Profile
0000-0003-3812-9785
Current Organisation
Deakin University
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Software Engineering | Computer Software | Health Information Systems (incl. Surveillance) | Medical Devices
Application Software Packages (excl. Computer Games) | Behaviour and Health | Integrated Circuits and Devices |
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: JMIR Publications Inc.
Date: 13-09-2021
DOI: 10.2196/26315
Abstract: Traditional psychological theories are inadequate to fully leverage the potential of smartphones and improve the effectiveness of physical activity (PA) and sedentary behavior (SB) change interventions. Future interventions need to consider dynamic models taken from other disciplines, such as engineering (eg, control systems). The extent to which such dynamic models have been incorporated in the development of interventions for PA and SB remains unclear. This review aims to quantify the number of studies that have used dynamic models to develop smartphone-based interventions to promote PA and reduce SB, describe their features, and evaluate their effectiveness where possible. Databases including PubMed, PsycINFO, IEEE Xplore, Cochrane, and Scopus were searched from inception to May 15, 2019, using terms related to mobile health, dynamic models, SB, and PA. The included studies involved the following: PA or SB interventions involving human adults either developed or evaluated integrated psychological theory with dynamic theories used smartphones for the intervention delivery the interventions were adaptive or just-in-time adaptive included randomized controlled trials (RCTs), pilot RCTs, quasi-experimental, and pre-post study designs and were published from 2000 onward. Outcomes included general characteristics, dynamic models, theory or construct integration, and measured SB and PA behaviors. Data were synthesized narratively. There was limited scope for meta-analysis because of the variability in the study results. A total of 1087 publications were screened, with 11 publications describing 8 studies included in the review. All studies targeted PA 4 also included SB. Social cognitive theory was the major psychological theory upon which the studies were based. Behavioral intervention technology, control systems, computational agent model, exploit-explore strategy, behavioral analytic algorithm, and dynamic decision network were the dynamic models used in the included studies. The effectiveness of quasi-experimental studies involved reduced SB (1 study P=.08), increased light PA (1 study P=.002), walking steps (2 studies P=.06 and P .001), walking time (1 study P=.02), moderate-to-vigorous PA (2 studies P=.08 and P=.81), and nonwalking exercise time (1 study P=.31). RCT studies showed increased walking steps (1 study P=.003) and walking time (1 study P=.06). To measure activity, 5 studies used built-in smartphone sensors (ie, accelerometers), 3 of which used the phone’s GPS, and 3 studies used wearable activity trackers. To our knowledge, this is the first systematic review to report on smartphone-based studies to reduce SB and promote PA with a focus on integrated dynamic models. These findings highlight the scarcity of dynamic model–based smartphone studies to reduce SB or promote PA. The limited number of studies that incorporate these models shows promising findings. Future research is required to assess the effectiveness of dynamic models in promoting PA and reducing SB. International Prospective Register of Systematic Reviews (PROSPERO) CRD42020139350 www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=139350.
Publisher: ACM
Date: 29-08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: JMIR Publications Inc.
Date: 04-2021
DOI: 10.2196/25000
Abstract: Cardiovascular disease (CVD) is the greatest health problem in Australia, which kills more people than any other disease and incurs enormous costs for the health care system. In this study, we present a benchmark comparison of various artificial intelligence (AI) architectures for predicting the mortality rate of patients with CVD using structured medical claims data. Compared with other research in the clinical literature, our models are more efficient because we use a smaller number of features, and this study could help health professionals accurately choose AI models to predict mortality among patients with CVD using only claims data before a clinic visit. This study aims to support health clinicians in accurately predicting mortality among patients with CVD using only claims data before a clinic visit. The data set was obtained from the Medicare Benefits Scheme and Pharmaceutical Benefits Scheme service information in the period between 2004 and 2014, released by the Department of Health Australia in 2016. It included 346,201 records, corresponding to 346,201 patients. A total of five AI algorithms, including four classical machine learning algorithms (logistic regression [LR], random forest [RF], extra trees [ET], and gradient boosting trees [GBT]) and a deep learning algorithm, which is a densely connected neural network (DNN), were developed and compared in this study. In addition, because of the minority of deceased patients in the data set, a separate experiment using the Synthetic Minority Overs ling Technique (SMOTE) was conducted to enrich the data. Regarding model performance, in terms of discrimination, GBT and RF were the models with the highest area under the receiver operating characteristic curve (97.8% and 97.7%, respectively), followed by ET (96.8%) and LR (96.4%), whereas DNN was the least discriminative (95.3%). In terms of reliability, LR predictions were the least calibrated compared with the other four algorithms. In this study, despite increasing the training time, SMOTE was proven to further improve the model performance of LR, whereas other algorithms, especially GBT and DNN, worked well with class imbalanced data. Compared with other research in the clinical literature involving AI models using claims data to predict patient health outcomes, our models are more efficient because we use a smaller number of features but still achieve high performance. This study could help health professionals accurately choose AI models to predict mortality among patients with CVD using only claims data before a clinic visit.
Publisher: JMIR Publications Inc.
Date: 27-07-2022
Abstract: educing sedentary behaviour (SB) and increasing physical activity (PA) in people with type 2 diabetes (T2D) is associated with various positive health benefits. Just-in-time Adaptive Interventions (JITAIs) offer potential to target both these behaviours via more contextually aware, tailored, and personalised support. We have developed a JITAI intervention to promote sitting less and moving more in people with T2D. his paper presents the study protocol for a micro-randomised trial (MRT) to investigate whether motivational messages are effective in reducing time spent sitting in people with T2D, and to determine what behaviour change techniques are effective and in which context (e.g., location, etc.). six-week MRT design will be used. Twenty-two adults with T2D will be recruited. The intervention aims to reduce sitting time and increase time spent standing and walking, and comprises a mobile app (iMove), and a bespoke activity sensor (SORD), a messaging system and a secured database. Dependant on the randomisation sequence, participants will potentially receive motivational messages five times a day. ecruitment was initiated in October 2022. As of now, six participants (2 females and 4 males) have consented and enrolled in the study. Their baseline measurements have been completed and they have started using iMOVE. The mean age of six participants is 56.8 years and they were diagnosed with T2D for 9.4 years on average. he current study will inform the optimisation of digital behaviour change interventions to support people with T2D sit less and move more to increase daily PA. This study will generate new evidence about the immediate effectiveness of SB interventions, their active ingredients and associated factors. ustralian New Zealand Clinical Trial Registry (ACTRN12622000426785) anzctr.org.au/Trial/Registration/TrialReview.aspx?id=383664
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer International Publishing
Date: 2019
Publisher: JMIR Publications Inc.
Date: 09-03-2022
DOI: 10.2196/30468
Abstract: There has been a steady rise in the availability of health wearables and built-in smartphone sensors that can be used to collect health data reliably and conveniently from end users. Given the feature overlaps and user tendency to use several apps, these are important factors impacting user experience. However, there is limited work on analyzing the data collection aspect of mobile health (mHealth) apps. This study aims to analyze what data mHealth apps across different categories usually collect from end users and how these data are collected. This information is important to guide the development of a common data model from current widely adopted apps. This will also inform what built-in sensors and wearables, a comprehensive mHealth platform should support. In our empirical investigation of mHealth apps, we identified app categories listed in a curated mHealth app library, which was then used to explore the Google Play Store for health and medical apps that were then filtered using our selection criteria. We downloaded these apps from a mirror site hosting Android apps and analyzed them using a script that we developed around the popular AndroGuard tool. We analyzed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects health data. We retrieved 3251 apps meeting our criteria, and our analysis showed that 10.74% (349/3251) of these apps requested Bluetooth access. We found that 50.9% (259/509) of the Bluetooth service universally unique identifiers to be known in these apps, with the remainder being vendor specific. The most common health-related Bluetooth Low Energy services using known universally unique identifiers were Heart Rate, Glucose, and Body Composition. App permissions showed the most used device module or sensor to be the camera (669/3251, 20.57%), closely followed by location (598/3251, 18.39%), with the highest occurrence in the staying healthy app category. We found that not many health apps used built-in sensors or peripherals for collecting health data. The small number of the apps using Bluetooth, with an even smaller number of apps using standard Bluetooth Low Energy services, indicates a wider use of proprietary algorithms and custom services, which restrict the device use. The use of standard profiles could open this ecosystem further and could provide end users more options for apps. The relatively small proportion of apps using built-in sensors along with a high reliance on manual data entry suggests the need for more research into using sensors for data collection in health and fitness apps, which may be more desirable and improve end user experience.
Publisher: Hindawi Limited
Date: 31-08-2022
DOI: 10.1111/JONM.13439
Publisher: JMIR Publications Inc.
Date: 07-12-2020
Abstract: raditional psychological theories are inadequate to fully leverage the potential of smartphones and improve the effectiveness of physical activity (PA) and sedentary behavior (SB) change interventions. Future interventions need to consider dynamic models taken from other disciplines, such as engineering (eg, control systems). The extent to which such dynamic models have been incorporated in the development of interventions for PA and SB remains unclear. his review aims to quantify the number of studies that have used dynamic models to develop smartphone-based interventions to promote PA and reduce SB, describe their features, and evaluate their effectiveness where possible. atabases including PubMed, PsycINFO, IEEE Xplore, Cochrane, and Scopus were searched from inception to May 15, 2019, using terms related to mobile health, dynamic models, SB, and PA. The included studies involved the following: PA or SB interventions involving human adults either developed or evaluated integrated psychological theory with dynamic theories used smartphones for the intervention delivery the interventions were adaptive or just-in-time adaptive included randomized controlled trials (RCTs), pilot RCTs, quasi-experimental, and pre-post study designs and were published from 2000 onward. Outcomes included general characteristics, dynamic models, theory or construct integration, and measured SB and PA behaviors. Data were synthesized narratively. There was limited scope for meta-analysis because of the variability in the study results. total of 1087 publications were screened, with 11 publications describing 8 studies included in the review. All studies targeted PA 4 also included SB. Social cognitive theory was the major psychological theory upon which the studies were based. Behavioral intervention technology, control systems, computational agent model, exploit-explore strategy, behavioral analytic algorithm, and dynamic decision network were the dynamic models used in the included studies. The effectiveness of quasi-experimental studies involved reduced SB (1 study i P /i =.08), increased light PA (1 study i P /i =.002), walking steps (2 studies i P /i =.06 and i P /i & .001), walking time (1 study i P /i =.02), moderate-to-vigorous PA (2 studies i P /i =.08 and i P /i =.81), and nonwalking exercise time (1 study i P /i =.31). RCT studies showed increased walking steps (1 study i P /i =.003) and walking time (1 study i P /i =.06). To measure activity, 5 studies used built-in smartphone sensors (ie, accelerometers), 3 of which used the phone’s GPS, and 3 studies used wearable activity trackers. o our knowledge, this is the first systematic review to report on smartphone-based studies to reduce SB and promote PA with a focus on integrated dynamic models. These findings highlight the scarcity of dynamic model–based smartphone studies to reduce SB or promote PA. The limited number of studies that incorporate these models shows promising findings. Future research is required to assess the effectiveness of dynamic models in promoting PA and reducing SB. nternational Prospective Register of Systematic Reviews (PROSPERO) CRD42020139350 www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=139350.
Publisher: JMIR Publications Inc.
Date: 06-09-2023
DOI: 10.2196/41502
Publisher: Wiley
Date: 20-12-2021
DOI: 10.1002/NUR.22096
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: ACM
Date: 08-11-2020
Publisher: JMIR Publications Inc.
Date: 16-05-2021
Abstract: here has been a steady rise in the availability of health wearables and built-in smartphone sensors that can be used to collect health data reliably and conveniently from end users. Given the feature overlaps and user tendency to use several apps, these are important factors impacting user experience. However, there is limited work on analyzing the data collection aspect of mobile health (mHealth) apps. his study aims to analyze what data mHealth apps across different categories usually collect from end users and how these data are collected. This information is important to guide the development of a common data model from current widely adopted apps. This will also inform what built-in sensors and wearables, a comprehensive mHealth platform should support. n our empirical investigation of mHealth apps, we identified app categories listed in a curated mHealth app library, which was then used to explore the Google Play Store for health and medical apps that were then filtered using our selection criteria. We downloaded these apps from a mirror site hosting Android apps and analyzed them using a script that we developed around the popular AndroGuard tool. We analyzed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects health data. e retrieved 3251 apps meeting our criteria, and our analysis showed that 10.74% (349/3251) of these apps requested Bluetooth access. We found that 50.9% (259/509) of the Bluetooth service universally unique identifiers to be known in these apps, with the remainder being vendor specific. The most common health-related Bluetooth Low Energy services using known universally unique identifiers were Heart Rate, Glucose, and Body Composition. App permissions showed the most used device module or sensor to be the camera (669/3251, 20.57%), closely followed by location (598/3251, 18.39%), with the highest occurrence in the i staying healthy /i app category. e found that not many health apps used built-in sensors or peripherals for collecting health data. The small number of the apps using Bluetooth, with an even smaller number of apps using standard Bluetooth Low Energy services, indicates a wider use of proprietary algorithms and custom services, which restrict the device use. The use of standard profiles could open this ecosystem further and could provide end users more options for apps. The relatively small proportion of apps using built-in sensors along with a high reliance on manual data entry suggests the need for more research into using sensors for data collection in health and fitness apps, which may be more desirable and improve end user experience.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: Springer International Publishing
Date: 2021
Publisher: JMIR Publications Inc.
Date: 02-10-2022
Abstract: everal research studies have demonstrated the potential of mobile health applications (apps) in supporting health management. However, the design and development process of these apps are rarely presented. e present the design and development of a smartphone-based lifestyle app integrating a wearable device for hypertension management. e used an intervention mapping approach for the development of theory- and evidence-based interventions for problem identification, problem-solving and mitigation strategies. This consisted of six fundamental steps of the intervention mapping approach: needs assessment, matrices, theoretical methods and practical strategies, program design, adoption and implementation plan, and evaluation plan. To design the contents of the intervention, we performed a literature review to determine the opinions and preferences of people with hypertension and implemented theoretical and practical strategies to support these needs in consultation with stakeholders and researchers. hrough the needs analysis, we identified that people with hypertension preferred having education, medication or treatment adherence, lifestyle modification, alcohol and smoking cessation and blood pressure monitoring support to manage their condition. Out of which, the authors utilized MoSCoW analysis to focus on four key elements, i.e., education, medication or treatment adherence, lifestyle modification and blood pressure support due to past experiences in developing interventions for hypertension, and its potential benefits in hypertension management. Theoretical models such as (i) the information, motivation, and behaviour skills (IMB) model, and (ii) the patient health engagement (PHE) model was implemented in the intervention development to ensure positive engagement and health behaviour. The app developed provides education to people with hypertension related to their condition, while utilizing wearable devices to promote lifestyle modification and blood pressure support. The app also contains rules and medication lists titrated by the clinician to ensure treatment adherence, with regular push notifications to prompt behavioural change. In addition, the app data can be reviewed by patients and clinicians as needed. his is the first study describing the development of an app that integrates a wearable blood pressure device and provides lifestyle support and hypertension management. Our theory-driven intervention for self-management of hypertension is founded on the critical needs of people with hypertension to ensure treatment adherence and supports medication review and titration by clinicians. The intervention will be evaluated clinically in future studies to determine its effectiveness and usability.
Publisher: JMIR Publications Inc.
Date: 14-10-2020
Abstract: ardiovascular disease (CVD) is the greatest health problem in Australia, which kills more people than any other disease and incurs enormous costs for the health care system. In this study, we present a benchmark comparison of various artificial intelligence (AI) architectures for predicting the mortality rate of patients with CVD using structured medical claims data. Compared with other research in the clinical literature, our models are more efficient because we use a smaller number of features, and this study could help health professionals accurately choose AI models to predict mortality among patients with CVD using only claims data before a clinic visit. his study aims to support health clinicians in accurately predicting mortality among patients with CVD using only claims data before a clinic visit. he data set was obtained from the Medicare Benefits Scheme and Pharmaceutical Benefits Scheme service information in the period between 2004 and 2014, released by the Department of Health Australia in 2016. It included 346,201 records, corresponding to 346,201 patients. A total of five AI algorithms, including four classical machine learning algorithms (logistic regression [LR], random forest [RF], extra trees [ET], and gradient boosting trees [GBT]) and a deep learning algorithm, which is a densely connected neural network (DNN), were developed and compared in this study. In addition, because of the minority of i deceased /i patients in the data set, a separate experiment using the Synthetic Minority Overs ling Technique (SMOTE) was conducted to enrich the data. egarding model performance, in terms of discrimination, GBT and RF were the models with the highest area under the receiver operating characteristic curve (97.8% and 97.7%, respectively), followed by ET (96.8%) and LR (96.4%), whereas DNN was the least discriminative (95.3%). In terms of reliability, LR predictions were the least calibrated compared with the other four algorithms. In this study, despite increasing the training time, SMOTE was proven to further improve the model performance of LR, whereas other algorithms, especially GBT and DNN, worked well with class imbalanced data. ompared with other research in the clinical literature involving AI models using claims data to predict patient health outcomes, our models are more efficient because we use a smaller number of features but still achieve high performance. This study could help health professionals accurately choose AI models to predict mortality among patients with CVD using only claims data before a clinic visit.
Start Date: 05-2017
End Date: 03-2020
Amount: $340,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 03-2018
End Date: 10-2023
Amount: $2,962,655.00
Funder: Australian Research Council
View Funded Activity