@selfhealthtech: Using self-administered health monitoring technologies to support the self-management of long-term conditions: what about behaviour change?
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1
University of Aberdeen, Health Services Research Unit, United Kingdom
Background Proliferation of digital technologies (e.g. self-administered mobile applications and wearable devices used to record and monitor biomedical and behavioural measures) is changing the ways services, professionals and individuals approach, manage and experience health and wellbeing.1 Supporting the self-management (SSM) of long-term conditions is a priority area for population health as more people have chronic conditions and people are living longer. Policies and practices must respond and, where pressure on services and resources is increasing, the need to find effective and cost efficient ways to deliver care is imperative. The potential of digital technologies for SSM is especially relevant, particularly where positive health behaviour changes are implicated. Internationally, strategic health agendas promote a shift towards increased reliance on digital technologies in health and social care, including in relation to SSM (e.g. USA2, Australia3, Europe4, UK5, Scotland6), but the evidence around self-administered health monitoring technologies, their theoretical underpinnings, integration into SSM and relationship with and influence on behaviour (and behaviour change) is lacking. It is therefore necessary and timely to review the data available and synthesise the evidence around uses of these technologies for SSM to: improve the delivery and quality of care; improve condition management; better conceptualise behaviour change models; inform developments in policy and practice; guide future research.
Aim(s) The aim of this research is to address the question: How do health services and professionals support the self-management of long-term conditions through patients’ uses of self-administered health monitoring technologies? The objectives are: to identify relevant papers; to describe and characterise technologies and synthesise evidence around their use; to identify key themes emerging from their use; and to highlight implications for policy, practice and research.
Method(s) A subset of literature identified in a recent review7 was revisited to focus on the use of digital technologies for SSM in chronic health conditions. 5246 titles and abstracts were reviewed to produce an evidence synthesis focused on SSM and the integration of these technologies into care. A systematic two-stage process of inclusion/exclusion of titles/abstracts and full-text papers was undertaken to identify relevant literature. A critical interpretive synthesis8 approach, which draws on conventional systematic review methodology as well as traditional qualitative inquiry, was applied.
Results (or combined Methods/Results) Papers not referring to digital technologies, SSM and chronic conditions were excluded at title/abstract screening stage. 186 full-text papers were considered relevant and read closely. Further papers were then excluded because the technologies they referred to were not self-administered or were not used for SSM or were set aside for reference. Finally, 82 papers were considered directly relevant. Within these, 50 related to monitoring a single biological variable or clinical marker related to a clinical condition, e.g. self-monitoring of blood glucose in diabetes, electronic peak flow log in asthma. These papers were grouped together as they provide insights into condition specific technologies incorporated into well-documented medical regimes typical for diabetes, hypertension, asthma and thromboembolic disorders. The remaining 32 papers offered new and interesting insights into a range of complex interventions targeting multiple biomarkers and/or behaviours for a broader range of long-term conditions. These studies involved the use of digital technologies (e.g. telephone-linked communications system; electronic portal accessed through smart devices; various peripherals to measure vital signs) for SSM of long-term conditions including diabetes, asthma, chronic heart failure, migraine, schizophrenia, cancers, COPD and chronic conditions more generally. Whilst the purpose of these apps, devices, systems in terms of condition management and support was usually clear – they promote health and healthy lifestyles in general, as well as in ways that are related to the condition – behaviours targeted for change and their link with the condition were typically implicit and were not critically reflected upon. There is no framework that distinguishes which technologies can support generic lifestyle interventions from those customised to specific clinical conditions. Many studies focused on the evaluation of outcomes and/or professional and patient use and acceptability, but few explored integration into care packages or sustainability. Although descriptions of technologies tended to characterise them as simple technical interventions, they involved delivery of complex interventions through integrated interfaces (e.g. a system addressing multiple behaviours using multiple techniques provided through one platform). In addition, while this review did not extract behaviour change techniques data per se, these technologies ostensibly (rather than explicitly) involved a number of behaviour change techniques that are identified in BCT taxonomy v.19 and possibly new ones. A range of combinations of techniques were used, often similar combinations for different conditions. Description of these and their purpose was limited, as was discussion of user and health professional implications apart from the technical, for example little attention was paid to relational understandings of using technologies within care. The theoretical underpinnings tended to be technological or clinical, rather than behavioural or sociological. In summary, a focus on or framework for comparing, contrasting and characterising these interventions, and how precise technologies are implemented, is currently missing. An additional finding was that personal, social and ethical consequences of using technologies were not explored in depth and are not well understood.
Conclusions This evidence synthesis adds to emerging research concerning digital technologies, contributing to the literature where there is a knowledge gap around SSM and self-administered health monitoring technologies. It highlights a need to better understand the delivery and quality of care when technologies are used for SSM. It would be beneficial to re-characterise or reconceptualise these technologies and their implementation. More rigorous description of interventions, e.g. using the TIDIER template for intervention description and replication checklist10, or linking systems with BCT taxonomy v.19 through the smartphone app11, as well as a requirement to attend to behaviour change theory and techniques in the design, use and description is also required. Future research should address these concerns to inform developments in SSM for chronic conditions involving technologies, as well as in policy and practices more generally where digital technologies are implicated. In addition, the results of this review suggest that detailed primary research should be undertaken to explore the personal, social and ethical considerations of users in everyday life.
Acknowledgements
Thanks to Professors Lorna McKee and Marion Campbell for helpful discussions regarding analysis and for commenting on this abstract.
References
References
1 Husain. BMJ 2015;350:h1887 2 Digital Health Strategy. 2015. Washington, D.C.: US Department of Health and Human Sciences. Online: http://www.hhs.gov/web/governance/digital-strategy/index.html 3 National e-Health Strategy. 2012. Canberra: Australian Government Department of Health. Online:
http://www.health.gov.au/internet/main/publishing.nsf/content/national+ehealth+strategy 4 Digital agenda for Europe: A Europe 2020 Initiative. 2015. European Commission. Online: https://ec.europa.eu/digital-agenda/en/news/digital-single-market-strategy-could-help-healthcare-transcend-borders 5 Digital health: working in partnership. 2014. London: Crown Copyright. Online: http://bit.ly/1ci9sbY 6 eHealth Strategy 2011-2017. Edinburgh: The Scottish Government. Online: http://bit.ly/1HKxWsB 7 Morgan et al. Support for self-management of long term conditions: a critical interpretive synthesis of health and social care professionals’
perspectives, practices and experiences In preparation (Concept:SSM, http://www.abdn.ac.uk/hsru/research/delivery/users/conceptssm--conceptualising-su/ the Health Foundation) 8 Dixon Woods et al. BMC Medical Research Methodology 2006,6:35. doi:10.1186/1471-2288-6-35 9 Michie et al. Annals of Behavioral Medicine 2013,46;1:81-95 10 Hoffman et al. BMJ 2014,348;g1687 11 BCTT v1 smartphone app. Online: https://www.ucl.ac.uk/health-psychology/bcttaxonomy/BCT_app1
Keywords:
Chronic conditions,
digital technologies,
self-management support,
Behaviour change techniques,
complex intervention
Conference:
2nd Behaviour Change Conference: Digital Health and Wellbeing, London, United Kingdom, 24 Feb - 25 Feb, 2016.
Presentation Type:
Oral presentation
Topic:
Academic
Citation:
Morgan
HM
(2016). @selfhealthtech: Using self-administered health monitoring technologies to support the self-management of long-term conditions: what about behaviour change?.
Front. Public Health.
Conference Abstract:
2nd Behaviour Change Conference: Digital Health and Wellbeing.
doi: 10.3389/conf.FPUBH.2016.01.00045
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Received:
29 Oct 2015;
Published Online:
09 Jan 2016.
*
Correspondence:
Dr. Heather M Morgan, University of Aberdeen, Health Services Research Unit, Aberdeen, AB25 2ZD, United Kingdom, h.morgan@abdn.ac.uk