Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-18T04:28:45.109Z Has data issue: false hasContentIssue false

Smart health and innovation: facilitating health-related behaviour change

Published online by Cambridge University Press:  31 August 2017

J. Redfern*
Affiliation:
The George Institute for Global Health, UNSW Medicine, Australia Sydney Medical School, University of Sydney, Sydney, Australia
*
Corresponding author: J. Redfern, email jredfern@georgeinstitute.org.au
Rights & Permissions [Opens in a new window]

Abstract

Non-communicable diseases (NCD) are the leading cause of death globally. Smart health technology and innovation is a potential strategy for increasing reach and for facilitating health behaviour change. Despite rapid growth in the availability and affordability of technology there remains a paucity of published and robust research in the area as it relates to health. The objective of the present paper is to review and provide a snapshot of a variety of contemporary examples of smart health strategies with a focus on evidence and research as it relates to prevention with a CVD management lens. In the present analysis, five examples will be discussed and they include a physician-directed strategy, consumer directed strategies, a public health approach and a screening strategy that utilises external hardware that connects to a smartphone. In conclusion, NCD have common risk factors and all have an association with nutrition and health. Smart health and innovation is evolving rapidly and may help with diagnosis, treatment and management. While on-going research, development and knowledge is needed, the growth of technology development and utilisation offers opportunities to reach more people and achieve better health outcomes at local, national and international levels.

Type
Conference on ‘New technology in nutrition research and practice’
Copyright
Copyright © The Author 2017 

Chronic and non-communicable diseases burden

Non-communicable diseases (NCD), also known as chronic diseases, are the leading cause of death globally, accounting for 68 % of the total deaths globally in 2012( 1 ). NCD tend to be of long duration and are the result of a combination of genetic, physiological, environmental and behavioural factors( 2 ). The main types of NCD are CVD (including heart attack and stroke), cancers, chronic respiratory diseases and diabetes( 2 ). NCD are responsible for the deaths of more than 40 million people each year, equivalent to 70 % of all deaths globally( 2 ). In 2013, the WHO member countries adopted a comprehensive global monitoring framework at the World Health Assembly and set a global target of 25 % relative reduction in overall mortality from NCD, including a 25 % relative reduction in the prevalence of raised blood pressure and halting the rise in diabetes and obesity( 1 ). However, these targets cannot be achieved unless concerted actions are taken by all stakeholders immediately. The associated challenge is that more people are living with chronic diseases, hospital stays are becoming much shorter and there are escalating numbers of people requiring ongoing management and support. Chronic diseases generally cannot be cured, do not resolve spontaneously and management is ongoing and long-term.

Potential opportunities resulting from technology

Mobile phone technology offers opportunities to maximise reach and effectiveness of health-related behaviour change. Worldwide, more than seven billion people own mobile phones and two billion own and use a smartphone( 3 ). The rapid development in affordable technology has led to predictions that >50 % of people globally will own a smartphone by 2018( 3 ). Mobile health (mHealth) and smart health are terms used to describe the use of mobile and wireless technology for delivering and improving healthcare. However, despite rapid growth in the availability and affordability of technology and mobile devices, to date there remains a paucity of published robust research in the area as it relates to health. This is potentially due to the time constraints associated with securing research funding, conducting, analysing and reporting robust clinical trials( Reference Neubeck, Lowres and Benjamin 4 ). Therefore, research in this area is vital and expanding with increasing opportunities to develop and evaluate innovative strategies. A recent review by Neubeck et al.( Reference Neubeck, Lowres and Benjamin 4 ) explored features and evidence associated with mobile phone applications (commonly known as apps) for CVD management. The authors offer some excellent examples but conclude that the complexity of selecting and evaluating an app remains challenging( Reference Neubeck, Lowres and Benjamin 4 ). Overall mobile devices and smart health technology have great potential in terms of flexibility particularly in relation to modes of delivery and time. Indeed mobile technology can reach people in remote and resource-poor environments in efficient ways that were previously unachievable( Reference Santo, Chalmers and Chow 5 ).

Mobile technology to deliver healthcare

Health-related services via mobile and wireless communication technologies can be delivered via mobile phones, smartphones, tablets and personal digital assistants to support the achievement of health objectives( 6 ). Strategies for delivery include (but are not limited to) text messaging, smartphone apps and utilisation of hardware that connects to mobile devices. Each strategy requires different resources, technology and connectivity. For example, text messaging requires a connection to a telecommunications service, while apps require connection to the internet( Reference Santo, Chalmers and Chow 5 ). Text messages have the advantages of an instant transmission and low cost, being less intrusive compared with phone calls( Reference Vodopivec-Jamsek, de Jongh and Gurol-Urganci 7 ), as well as being a push technology where the text message will be received independent of the mobile phone being in use at the time. In addition, automatic computer systems can be used to deliver text messages on a large scale, being easy and saving the costs of hiring dedicated personnel for this task( Reference Santo, Chalmers and Chow 5 ). Hence, text messages are being used in the healthcare setting as a tool to support healthy behaviours and deliver preventive care. In contrast, smartphones require an internet connection and combine features of a traditional mobile phone with computer capabilities( Reference Pandher and Bhullar 8 ). This enables the download of an enormous number of software apps that can greatly impact in the day-to-day management of chronic disease( Reference Pandey, Hasan and Dubey 9 ).

The aim of the present review is to provide insight and highlight of a variety of strategies that utilise smart health and innovation to facilitate health-related behaviour change. Five examples focused on management of CVD risk are included. They are the (i) TORPEDO (The Treatment of cardiovascular Risk in Primary care using Electronic Decision supOrt) study( Reference Peiris, Usherwood and Panaretto 10 , Reference Peiris, Usherwood and Panaretto 11 ) that tested a web-based physician-directed electronic decision system; (ii) CONNECT (The COnsumer NavigatioN of Electronic Cardiovascular Tools) study( Reference Redfern, Usherwood and Harris 12 , Reference Neubeck, Coorey and Peiris 13 ) that is testing a consumer-directed responsive web-app; (iii) TEXTME (The Tobacco, EXercise and diet Messages) study( Reference Chow, Redfern and Thiagalingam 14 Reference Redfern, Santo and Coorey 16 ) that is a stand-alone text message intervention; (iv) FOODSWITCH( Reference Dunford, Trevena and Goodsell 17 ) texting a public health approach to improving food choices at the point of purchase and (v) SEARCH AF (The screening for atrial fibrillation (AF) using iPhone electrocardiogram (ECG) in pharmacies)( Reference Lowres, Neubeck and Salkeld 18 ) that utilised smartphone hardware to provide widespread screening for AF.

The Treatment of cardiovascular Risk in Primary care using Electronic Decision supOrtstudy

The TORPEDO study provides one example of robust research conducted to determine effectiveness of a physician-directed technology-based strategy. The trial itself was a parallel arm cluster-randomised controlled trial involving forty general practices and twenty Aboriginal Medical Services in Australia( Reference Peiris, Usherwood and Panaretto 10 ). Practices were eligible to participate if there was exclusive use of one of the two compliant software systems to record risk factor information, pathology test results and prescribe medications and a willingness from all staff to use the intervention( Reference Peiris, Usherwood and Panaretto 10 ).

The TORPEDO intervention was multifaceted and comprised point-of-care electronic decision support, audit and feedback tools, and clinical workforce training improved CVD risk management( Reference Peiris, Usherwood and Panaretto 10 ). The final TORPEDO cohort comprised 38 725 patients( Reference Peiris, Usherwood and Panaretto 11 ). At follow-up, the proportion of patients with recommended CVD risk factor measurements was higher in the intervention than control arm (63 v. 53 %, P = 0·02)( Reference Peiris, Usherwood and Panaretto 11 ). There were also significant treatment escalations (new prescriptions or increased numbers of medicines) for antiplatelet (17·9 v. 2·7 %; P < 0·001), lipid-lowering (19·2 v. 4·8 %; P < 0·001) and blood pressure-lowering medications (23·3 v. 12·1 %; P = 0·02)( Reference Peiris, Usherwood and Panaretto 11 ). Overall, the TORPEDO study demonstrated that a computer-guided intervention comprising point-of-care decision support, audit and feedback tools, training and support improved cardiovascular risk factor screening for CVD in primary care( Reference Peiris, Usherwood and Panaretto 11 ). It provides a contemporary example of how technology can be used to facilitate better delivery of healthcare via physician-directed strategy.

The COnsumer NavigatioN of Electronic Cardiovascular Tools study

The CONNECT study provides an example of a CVD risk management app and website for consumers. The trial itself is ongoing and is a multi-centre single blind randomised controlled trial involving 1000 participants with 18 months follow-up( Reference Redfern, Usherwood and Harris 12 ). The aim is to test the effectiveness, compared with usual care, of a mobile intervention with a website and responsive app that is integrated with each patient's primary care record on CVD risk management( Reference Redfern, Usherwood and Harris 12 ). The trial is augmented by formal economic and process evaluations to assess acceptability, equity and cost-effectiveness of the intervention( Reference Redfern, Usherwood and Harris 12 ).

The intervention is electronically integrated with the primary care provider's software and includes interactive smartphone and internet platforms. For development, a collaborative user-centred design process with four phases was documented and followed( Reference Neubeck, Coorey and Peiris 13 ). The phases involved defining the target audience and needs, testing and refinement, software development including validation and algorithm testing as well as user acceptance evaluation and β testing( Reference Neubeck, Coorey and Peiris 13 ). Through this process, the team were able to better understand end-user needs and preferences, thereby improving and enriching the detailed system designs and prototypes for the mobile responsive web app( Reference Neubeck, Coorey and Peiris 13 ). The final technology is interactive, has visual appeal, content is based on credible health information, includes virtual rewards and there is a focus on CVD risk factors, medications and psychosocial support. A key learning from this process is that considerable upfront investment in user-centred design with consumer engagement aided the integration of their needs into the concept, specifications, development and refinement of the intervention( Reference Neubeck, Coorey and Peiris 13 ).

The Tobacco, EXercise and diet Messages study

The TEXTME study provides a contemporary example of how text messaging can be successfully used in management of a chronic disease. The study was a single-blind randomised controlled trial that aimed to reduce cardiovascular risk in 710 patients with CHD( Reference Chow, Redfern and Thiagalingam 14 , Reference Chow, Redfern and Hillis 15 ). The intervention was delivered by purpose built text message software system( Reference Thakkar, Barry and Thiagalingam 19 ). Therefore, participants required a mobile phone to participate but not a smartphone or internet connection. A program of four semi-personalised text messages per week were sent to participants for 6 months and messages were sent on random days and at random times( Reference Chow, Redfern and Thiagalingam 14 ). The text messaging program development is detailed elsewhere( Reference Redfern, Thiagalingam and Jan 20 ) but was based on behaviour change techniques linked to the theoretical framework( Reference Abraham and Michie 21 ). In brief, the messages provided practical tips and advice, motivation and information that aimed to improve general heart health, diet, physical activity and encourage smoking cessation if relevant( Reference Redfern, Thiagalingam and Jan 20 ).

After 6 months, those allocated to the text message intervention group had a significantly lower cholesterol, blood pressure and BMI, rate of smoking and significant increases in physical activity, compared with the control group( Reference Chow, Redfern and Hillis 15 ). The authors concluded that a lifestyle-focused text messaging service compared with usual care resulted in improvement in other CVD risk factors but the long-term outcomes are yet to be determined and require further research( Reference Chow, Redfern and Hillis 15 ). The program was also found to be cost-effective( Reference Burn, Nghiem and Jan 22 ) and a parallel process evaluation found that almost all participants found the text message program to be useful and easy to understand( Reference Redfern, Santo and Coorey 16 ). Detailed qualitative research revealed that factors increasing engagement with the program included: (i) ability to save and share messages, (ii) support of providers and family, (iii) a feeling of support, (iv) program initiation close to the time of a CVD event, (v) personalisation of the messages, (vi) opportunity for initial face-to-face contact and (vii) program having content from a credible source( Reference Redfern, Santo and Coorey 16 ). The authors highlighted that exploring the theoretical frameworks for engagement and effect provides insight into the causal pathway between the text message intervention, behaviour change and impact on risk factor measures.

FoodSwitch

FoodSwitch provides an example of a public health initiative using technology. FoodSwitch is a smartphone app that allows consumers to scan the barcodes of packaged foods at the supermarket or at home, using the camera on their phone( Reference Dunford, Trevena and Goodsell 17 ). The app was first developed as a collaboration between researchers based at The George Institute for Global Health, developers from Xyris Software Pty Ltd and the health communications team at Bupa Australia( Reference Dunford, Trevena and Goodsell 17 ). The objective was to deploy a mobile phone app that would allow users to access quick and easy-to-understand information about the nutritional characteristics of packaged foods and, where possible, would suggest healthier alternative products( Reference Dunford, Trevena and Goodsell 17 ). Simplicity was a clear objective during the development of FoodSwitch and the app uses a colour-coded system (red, amber and green) that is specific to each nutrient. If a barcode is scanned but the corresponding product is not identified in the database, then the user is asked to photograph the front of the package and the nutritional information. The data are then forwarded to the coordinating centre and the information is added to the centralised database( Reference Dunford, Trevena and Goodsell 17 ). This crowdsourcing function was a highly innovative concept that enabled consumers to contribute information on missing products.

When launched in Australia in 2012, the FoodSwitch app was downloaded by 400 000 users in the first 18 months( Reference Dunford, Trevena and Goodsell 17 ). The original database of food products was based on 17 000 Australian packaged foods but through crowdsourcing more than 60 000 additional products have since been added( Reference Dunford, Trevena and Goodsell 17 ). Based on this success and the clear potential for scalability, a series of further upgrades have been released and the app has been launched in multiple countries around the world( Reference Dunford, Trevena and Goodsell 17 ). The implementation of FoodSwitch in Australia was initially done with the objective of providing consumers with a tool that would enable them to make better food choices, both by providing nutritional information in an at-a-glance, easy-to-understand format and by suggesting healthier alternative products within the same category( Reference Dunford, Trevena and Goodsell 17 ). However, consumer-level behaviour change is not the only means by which FoodSwitch has had a public health impact. The development team highlight that data collected through crowdsourcing presents a new capacity to define and track the nutritional composition of the food supply over time( Reference Dunford, Trevena and Goodsell 17 ). These data potentially make it possible to influence the food industry and policy-makers as well as provide opportunity to objectively evaluate the extent to which the food industry and government deliver on commitments to improve the quality of the food supply( Reference Dunford, Trevena and Goodsell 17 ).

The screening for atrial fibrillation using iPhone electrocardiogram in pharmacies study

The SEARCH AF study is an example of a tested approach for screening of health conditions using external hardware that is connected to a user's smartphone( Reference Lowres, Neubeck and Salkeld 18 ). The aim of the study was to investigate the effectiveness and cost-effectiveness of community screening (in pharmacies) for AF using an external hardware device that enables real-time single lead ECG assessment. The overarching objective was to identify unknown AF in the community and enable subsequent anti-thrombotic treatment and reduction in stroke risk( Reference Lowres, Neubeck and Salkeld 18 ). For the study, pharmacists performed the ECG and an automated AF algorithm was applied to determine if the patients was experiencing AF( Reference Lowres, Neubeck and Salkeld 18 ).

The hardware device is now marketed as Kardia Mobile (previously AliveCor Heart Monitor) and is able to record, store and transfer single-channel ECG rhythms( 23 ). To use the device, the user simply rests two or more fingers on the sensors for 30 s( 23 ). The associated software then displays ECG rhythms on the smartphone screen and detects the presence of AF and normal sinus rhythm( Reference Lowres, Neubeck and Salkeld 18 ). In the SEARCH AF cohort study (n 1000) newly identified AF was found in 1·5 % (95 % CI 0·8, 2·5 %) and overall AF prevalence was 6·7 %( Reference Lowres, Neubeck and Salkeld 18 ). The smartphone ECG algorithm showed 98 % sensitivity for AF detection and 91 % specificity and the strategy was found to be cost effective( Reference Lowres, Neubeck and Salkeld 18 ). The authors concluded that the smartphone hardware offered a feasible and cost-effective strategy for widespread community screening for AF( Reference Lowres, Neubeck and Salkeld 18 ).

Conclusion

NCD have common risk factors and all have an association with nutrition and health. Therefore, the management of these conditions requires a focus on nutrient intake, physical activity and weight management among other considerations. All the examples presented in the present paper address health concerns and management strategies directly related to nutrition. For example, the CONNECT responsive app includes interactive goal setting and support for weight management and nutritional advice that supports CVD prevention. The TEXTME program has a specific focus on dietary advice and behaviour change. The FoodSwitch app is a public health approach to point of purchase and government decision-making in relation to nutrient content of foods. Overall, within the context of NCD and CVD, management nutrition and diet-related health needs to be considered within the broader context of each individual's condition, risk factors and preferences.

Smart health and innovation is evolving rapidly and may help with diagnosis, treatment and management of NCD including a focus on nutrition and its role in health. There are various platforms available that target physicians, consumers and public health strategies. The challenge for researchers and clinicians is that technology is developing faster than evidence. Ideally, patients/consumers, clinicians, researchers and developers should work together to produce optimum models and services and balance rapid evaluation with quality. While on-going research, development and knowledge is needed, the growth of technology development and utilisation offers opportunities to reach more people and achieve better health outcomes at local, national and international levels.

Acknowledgement

The author thanks all colleagues and researchers involved in all the exemplified studies.

Financial Support

J. R. is funded by a Career Development and Future Leader Fellowship co-funded by the National Health and Medical Research Council (NHMRC, APP1061793) and National Heart Foundation (ID100054) as well as a NHMRC program grant (APP1052555). Travel for J. R. to the Nutrition Society Summer Conference in Dublin 2016 was provided by The Nutrition Society.

Conflict of Interest

The author is an academic researcher and has contributed to the development, analysis and reporting of the earlier mentioned studies summarised in the present review.

Authorship

J. R. conceived the manuscript and prepared the draft and final paper.

References

1. World Health Organization (2014) Global Status Report on Noncommunicable Diseases 2014. Geneva: WHO.Google Scholar
2. World Health Organization (2017) Noncommunicable diseases, Fact sheet. Updated April 2017; available at http://www.who.int/mediacentre/factsheets/fs355/en/.Google Scholar
3. Fierce Wireless (2014) Worldwide Smartphone Usage to Grow 25% in 2014 (online). http://www.fiercewireless.com/press-releases/emarketer-worldwide-smartphone-usage-grow-25-2014.Google Scholar
4. Neubeck, L, Lowres, N, Benjamin, EJ et al. (2015) The mobile revolution – can smartphone apps help prevent cardiovascular disease? Nat Rev Cardiol 12, 350360.CrossRefGoogle Scholar
5. Santo, K, Chalmers, J, Chow, C et al. (2015) m-health in coronary disease preventive care. J Cardiol Ther 2, 459464.CrossRefGoogle Scholar
6. WHO (2011) mHealth – New horizons for health through mobile technologies. Global Observatory for eHealth series.Google Scholar
7. Vodopivec-Jamsek, V, de Jongh, T, Gurol-Urganci, I et al. (2012) Mobile phone messaging for preventive health care. Cochrane Database Syst Rev 12, CD007457.Google ScholarPubMed
8. Pandher, PS & Bhullar, KK (2014) Smartphone applications for seizure management. Health Inf J 22, 209222.CrossRefGoogle ScholarPubMed
9. Pandey, A, Hasan, S, Dubey, D et al. (2013) Smartphone apps as a source of cancer information: changing trends in health information-seeking behavior. J Cancer Educ 28, 138142.CrossRefGoogle ScholarPubMed
10. Peiris, D, Usherwood, T, Panaretto, K et al. (2012) The treatment of cardiovascular risk in primary care using electronic decision supOrt (TORPEDO) study- protocol for a cluster randomised, controlled trial of an electronic decision support and quality improvement intervention in Australian primary health care. BMJ Open 2, e002177.CrossRefGoogle Scholar
11. Peiris, D, Usherwood, T, Panaretto, P et al. (2015) Effect of a computer-guided, quality improvement program for cardiovascular disease risk management in primary health care: the TORPEDO cluster-randomized trial. Circ Cardiovasc Qual Outcomes 8, 8795.CrossRefGoogle Scholar
12. Redfern, J, Usherwood, T, Harris, MF et al. (2014) A randomised controlled trial of a consumer focussed e-health strategy for cardiovascular risk management in primary care: the consumer navigation of electronic cardiovascular tools (CONNECT) study protocol. BMJ Open 4, e004523.CrossRefGoogle Scholar
13. Neubeck, L, Coorey, G, Peiris, D et al. (2016) Development of an integrated e-health tool for people with, or at high risk of, cardiovascular disease: the consumer navigation of electronic cardiovascular tools (CONNECT) web application. Int J Med Inf 96, 2437.CrossRefGoogle ScholarPubMed
14. Chow, CK, Redfern, J, Thiagalingam, A et al. (2013) Design and rationale of the tobacco, exercise and diet messages (TEXT ME) trial of a text message based intervention for ongoing prevention of cardiovascular disease in people with coronary disease: a randomised controlled trial protocol. BMJ Open 2, e000606.CrossRefGoogle Scholar
15. Chow, C, Redfern, J, Hillis, GS et al. (2015) Lifestyle -focused support program delivered via mobile phone text message for secondary prevention in patients with coronary heart disease – The TEXT ME Randomized Controlled Trial. J Am Med Assoc 2, 273278.Google Scholar
16. Redfern, J, Santo, K, Coorey, G et al. (2016) Factors influencing engagement, perceived usefulness and behavioral mechanisms associated with a text message support program. PLoS ONE 11, e0163929.CrossRefGoogle ScholarPubMed
17. Dunford, E, Trevena, H, Goodsell, C et al. (2014) FoodSwitch: a mobile phone app to enable consumers to make healthier food choices and crowdsourcing of national food composition data. JMIR Mhealth Uhealth 2, e37.CrossRefGoogle ScholarPubMed
18. Lowres, N, Neubeck, L, Salkeld, G et al. (2014) Community screening for AF using iPhone ECG in pharmacies is feasible and cost effective for stroke prevention: the SEARCH-AF study. Thromb Haemost 111, 11671176.CrossRefGoogle ScholarPubMed
19. Thakkar, J, Barry, T, Thiagalingam, A et al. (2016) Design considerations in development of a mobile health intervention program: the TEXT ME and TEXTMEDS experience. JMIR Mhealth Uhealth 4, e127.CrossRefGoogle Scholar
20. Redfern, J, Thiagalingam, A, Jan, S et al. (2014) Development of a set of mobile phone text messages designed for prevention of recurrent cardiovascular events. Eur J Prev Cardiol 21, 492499.CrossRefGoogle ScholarPubMed
21. Abraham, C & Michie, S (2008) A taxonomy of behavior change techniques used in interventions. Health Psychol 27, 379387.CrossRefGoogle ScholarPubMed
22. Burn, E, Nghiem, S, Jan, S et al. (2017) Cost-effectiveness of a text-message program for cardiovascular disease secondary prevention. Heart 103, 923930.CrossRefGoogle Scholar
23. AliveCor, Inc. (2016) Kardia Mobile by AliveCor® Instructions For Use. 02LB49 Revision 2: CA, USA; available at https://www.alivecor.com/user-manuals/kardia-mobile-instructions-for-use-en.pdf.Google Scholar