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(MRC DTP) Evaluating new technologies for promotion of healthy active ageing: using smartphone apps and sensors to promote activity- acceptability and adherence measurement?

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  • Full or part time
    Prof C Todd
    Dr E Boulton
    Dr H Hague
  • Application Deadline
    Applications accepted all year round

Project Description

Health apps, and wearable technologies such as smartwatches and other body worn sensors connected to smartphones or other recording and transmitting systems, are becoming increasingly common. Indeed over recent years smartwatches and activity monitors have become life-style fashion items for many younger people, who use them to monitor activity and exercise levels as part of fitness regimens. But it is clear that these are not so attractive to older people, who are the largest users of health care resources and whom could potentially greatly benefit from use of devices aimed at prevention of functional decline (Helbostad et al 2016). It is also clear that although data from devices may be reliable, activity data derived from such devices is not well validated (van Remoortelet al 2012).

This PhD will investigate the acceptability of smartphones and sensors to young older people (61-70) and how these can be designed to be attractive to this age group. The literature and our own experience (Waterman et al 2016) reveal that there can be mismatch between activity data from sensors and report data. Whilst at first sight one is tempted to argue the sensor data must be correct and self-report in some way biased, sensors can misclassify or miss activity because of e.g. gait characteristics.

During year 1 the student will be given access to large pre-existing data-sets collected as part of Horizon 2020 funded projects in which older persons wear sensors and or use smartphones to monitor activity levels. A specific issue to be addressed during year 1 is the measurement of adherence to activity. Conceptually, adherence to an exercise/activity regimen is problematic and requires considerable unpacking (Hawley-Hague et al 2017). During the PreventIT project RCT participants have worn smartwatches and carried smartphones, which have been used to collect activity data as well as prompt daily reports of activity and adherence to an activity regimen. In addition participants have completed standardised adherence instruments at 3 time points. The student will develop statistical models of adherence and acceptability based on integration and analysis of these different sorts of data. These models will be tested in subsequent years in studies to be designed by the student (e.g. follow-up of cohort).


This is a potential studentship to be funded via the MRC Doctoral Training Programme. Projects under this scheme are competitively funded; i.e. there are more projects advertised than available.

Funding Notes

Please make direct contact with the Principal Supervisor to arrange to discuss the project and submit an online application form as soon as possible. There is no set closing date; projects will be removed as soon as they are filled.

Applications are invited from UK/EU nationals. Candidates from outside of the UK must have resided in the UK for 3 years prior to commencing the PhD in order to be eligible to apply. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.


Hawley-Hague H et al. Review of how we should define (and measure) adherence in studies examining older adults’ participation in exercise classes? BMJ Open. 2016; 6:e011560 doi: 10.1136/bmjopen-2016-011560
Helbostad J et al. Mobile health applications to promote active and healthy ageing. Sensors 2017, 17, 622; doi:10.3390/s17030622
van Remoortel H et al. Validity of activity monitors in health and chronic disease: a systematic review. International Journal of Behavioral Nutrition and Physical Activity 2012 9:84 doi:10.1186/1479-5868-9-84
Waterman H et al. A feasibility study to prevent falls in older people who are sight impaired: the VIP2UK randomised controlled trial. BMC Trials 2016, 17(464) doi:10.1186/s13063-016-1565-0

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