Evidence and theory-based interventions to support healthy ageing

Dr. Lis Boulton, research associate. PreventIT project, school of health sciences, faculty of biology, medicine and health, University of Manchester
Dr. Lis Boulton, research associate. PreventIT project, school of health sciences, faculty of biology, medicine and health, University of Manchester

Dr Lis Boulton moved into behaviour change research following a successful career in management in the    health and social care sector, working for local government and various charities as both a service provider and commissioner. Her PhD considered the involvement of older adults in designing and implementing interventions to promote physical activity. Lis currently works on the EC PreventIT project, designing and evaluating an App- based behavioural intervention. Lis has a particular interest in the translation of evidence into practice. She is Chair of the Board of a large local charity, Age UK Calderdale & Kirklees, and is a member of Age UK’s national Policy Panel.


Despite the health benefits of physical activity being well known, few people achieve the 150 minutes of moderate intensity physical activity per week recommended by the World Health Organization (2010). In addition to walking more and sitting less, older adults should also be working on their strength and balance in order to maintain good health and function (NHS, 2015). It is known that tailored interventions can be successful, so researchers in the PreventIT project have been working on developing and trialling two behaviour change interventions, targeting risk factors for functional decline, that are tailored to the needs and preferences of individual participants. The interventions have been designed to change behaviour, supporting older adults in the early stages of retirement to form long term physical activity habits at this point of major lifestyle change.

The European Horizon 2020 Project ‘PreventIT’ has adapted the Lifestyle-integrated Functional Exercise (LiFE) programme, which reduced falls in people 75 years and over (Clemson et al., 2012), for a younger age group (aLiFE). The aim has shifted from reducing and preventing falls to preventing age-related functional decline. Muscle mass and balance deteriorates as one ages, so our PreventIT interventions are aiming to prevent this decline. The aLiFE programme incorporates strength and balance/agility tasks, as well as specific recommendations for increasing physical activity and reducing sedentary time in 60-70 year-olds. Personalised advice is given on how to integrate strength, balance and physical activities into daily life, in a way which should not be time consuming. Participants plan and monitor their strength, balance, sitting time and physical activities, with initial support from a trainer making home visits, using a paper-based manual. The aLiFE intervention can then be operationalised to be delivered using smartphones and smartwatches (eLiFE), providing the opportunity to send timely encouraging messages and real-time feedback to the user. Guidance and instruction is provided through videos and text within the PreventIT app and participants plan and monitor their strength, balance and physical activities using the app. Goal setting, planning, prompts and real-time feedback are used to deliver a person-centred experience for participants in the intervention.

Smartphones and smartwatches are used by an increasing number of people, with thousands of smartphone applications available to promote healthy lifestyles. However, few of these applications are evidence based, meaning that their contribution to overcoming the challenges presented by an ageing population is limited. PreventIT has taken the original LiFE concept, which included Habit Formation Theory (Gardner et al., 2016) and further developed the behaviour change elements, explicitly relating and mapping them to Social Cognitive Theory (Schwarzer, 2008) and specific Behaviour Change Techniques (Michie et al., 2013).

The PreventIT mHealth intervention (eLiFE) focusses on behaviour change from initiation to long-term maintenance, addressing the different phases of adopting a healthier lifestyle. As such, it makes a strong contribution to the developing field of evidence-based mHealth. The interventions (aLiFE and eLiFE) have been trialled in a three-arm feasibility randomised controlled trial in Norway, the Netherlands and Germany and the results are now being analysed.

An overview of the project can be viewed on YouTube:


The PreventIT project is just one of several research studies being undertaken by the Healthy Ageing Research Group at the University of Manchester under the Novel Technologies theme. For more information about projects using Apps, Exergaming, sensors and social networking see:https://www.bmh.manchester.ac.uk/research/nursing -groups/healthy-ageing/

Learning points

• It is important for older adults to maintain a level of physical activity; the PreventIT project aims to provide tailored behavioural interventions to encourage physical activity and prevent functional decline.

• There has been a shift from reducing and preventing falls in old age, to tackling the root causes of falls; age-related decline in strength and balance.

• Personalised advice and intervention through technologies such as the PreventIT programme allow healthy, long term habits to be developed in older people.

Published in Innov-age Digital Health Issue, Autumn 2018


Clemson L, Fiatarone Singh, Bundy A, Cumming RG, Manollaras K, O’Loughlin P, Black D (2012). Integration of balance and strength training into daily life activity to reduce rate of falls in older people (the LiFE study): randomised parallel trial. British Medical Journal, 345: e4547. Gardner B, Phillips LA, Judah G (2016). Habitual instigation and habitual execution: Definition, measurement, and effects on behaviour frequency. British Journal of Health Psychology, 21(3) 613-630.
Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, Eccles MP, Cane J, Wood CE (2013). The Behavior Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques: Building an International Consensus for the Reporting of Behavior Change Interventions. Annals of Behavioral Medicine, 46: 81.
NHS (2015). Physical activity guidelines for older adults. London: National Health Service https://www.nhs.uk/Livewell/fitness/Pages/physical-activity-guidelines- for-older-adults.aspx
Schwarzer R (2008). Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology, 57: 1-29. World Health Organization Global (2010). Recommendations on Physical Activity for Health. Geneva: World Health Organization.