Mobile phones have a particular attractions as a platform to encourage behaviour change given their wide adoption, portability and technical capabilities. They can be used to deliver motivational messages, support and information to the recipient. They can also be used to record information related diet or physical activity in real time. Whilst such functionality can theoretically be delivered through SMS, apps running on smartphones devices have expanded the utility of mobile phones for public health health interventions. The computational power of smartphones and associated networks can now handle complex aggregate providing salient information and useful support to the end user.
Improvements in interface design, batteries, processors and wireless technologies is enhancing the power, personalisation and mobility of handsets. We are now seeing smartphones as the hub of a body sensor network that sees wearable sensors on the body measuring health related parameters (e.g. blood presure, blood glucose). It is only time before such integrated systems form the basis of very sophisticated behaviour change programmes where ‘just in time’ interventions are delivered when support is most needed to encourage healthy choices (Intille 2004). So smokers may recieve motivational messages at certain times of the week or in specific contexts when they are most likely to light a cigarette. Interventions delivered over mobile phones are easily scalable over large populations and can be individualised and tailored.
The use of mobile phones in promoting health behaviour change through SMS is promising even if there are limited randomised trials and those that are are limited by relatively small sample sizes (Shaw and Bosworth 2012). Because of the relative newness of smartphones and the apps that run on them, there is a paucity of data and long-term follow up but huge interest and enthusiasm about their use. It is clearly important that in this fast moving field that an evidence base to these interventions.
There are concerns about the quality of study designs in mHealth interventions with reported trials often failing to be adequately powered, having poor retention rated and not always using validated scales (Shaw and Bosworth 2012). A systematic review of 42 controlled trials investigating the role of mobile-technology based interventions designed to enhance health care delivery found that none of the trials were of high quality and many had significant methodological problems (Free, Phillips et al. 2013) There is also little attention paid to the cost effectiveness of mHealth interventions although an analysis by Joo did show that a community-based obesity control programme was more cost-effectively delivered remotely (by SMS and through the internet) rather than in person (Joo, Park et al. 2010). In terms of improving the evidence base in regard to mHealth interventions, Shaw made the following recommendations in regard to SMS interventions in obesity, but they apply more widely (Shaw and Bosworth 2012):
- large randomized controlled trials with a significant sample size that can be used to determine effect sizes and statistical significance;
- intervention trials that are longitudinal in nature and evaluate maintenance of weight loss behaviors (12 months or longer);
- specific evaluation of cost-effectiveness, frequency, timing and optimal use of SMS;
- more detailed reporting of intervention content and outcomes with respect to the magnitude of between-group differences at follow-up, and the direction and magnitude of change between end-of-intervention and follow-up
Further studies and interventions should certainly describe the theoretical constructs being targeted as very few currently specify an underpinning theoretical construct (Cole-Lewis and Kershaw 2010). Very few apps are based on empirical evidence. Only seven of the reported 26 studies in a review of mHealth interventions for behaviour change or healthcare delivery reported using ‘specific behavioural theories to underpin their intervention’(Free, Phillips et al. 2013). This is particularly important given that interventions based on behavioural theory are more likely to be successful (Fry and Neff 2009). Behaviour change theories that have been incorporate into reported studies have included social cognitive theory (the most common), implementation theory and the transtheoretical model (Free, Phillips et al. 2013). A good example of an robust approach to the development of an app is described by King who describes the design on app to promote daily physical activity based on different motivational frames. Targeted users took part in the iterative design process and acceptability was also evaluated (King, Hekler et al. 2013).
Mobile phones not without challenges – access to handsets, good reception, cost appropriate interfaces are all important considerations in implementing public health interventions through this medium. Technologies intended for health related functions should be able to be easily used by the target population, which may include the very elderly, illiterate and disabled. Most smartphone devices provide intuitive operating systems but handsets like the Jitterbug phone are produced that meet the needs of those with reduced vision or dexterity (Patrick, Griswold et al. 2008). There are also some concerns about the reliability of what are in effect medical devices but at present fall through the cracks of regulation. Incorrect use of measurement or sensing devices or incorrect collaboration of physiological data could lead to significant errors and adverse events (Klasnja and Pratt 2012).
Initial studies have demonstrated the potential for delivering health behaviour change over smartphones but there is much to be learned about how such interventions can best work. There is no doubt that mHealth is here to stay and that smartphones and similar devices offer tremendous opportunities to improve the health and well-being of populations. Well designed trials will support wider implementation and appropriate support from governments and health providers.