S5.3 Health technology in early prevention of age-related functional decline: the PreventIT project
Chair: Kristin Taraldsen
Discussant: Beatrix Vereijken
Balance, strength, and physical activity are important for healthy ageing and preventing age-related functional decline, as is maintaining complexity in behaviour. In order to be effective, preventive interventions should target important risk factors for age-related functional decline, be individually tailored to needs and preferences of older adults, and be designed to change behaviour to promote a healthier lifestyle over time. Smartphones and smartwatches are used by an increasing number of people of all ages. Because smartphones can send and receive wireless information and communicate with external servers, they can be suitable platforms for delivery of individualised interventions with direct feedback to the user. A plethora of mobile health applications have been developed. However, most of these target younger adults and focus on cardiovascular health. Furthermore, few are evidence-based, which is mandatory if they are to solve health-related challenges. The PreventIT group is currently running a feasibility randomised controlled trial (RCT) of a smartphone-delivered activity promotion progamme (eLiFE) versus a paper and pencil delivered programme (aLiFE) versus controls in Stuttgart, Amsterdam, and Trondheim. The aim is to test a personalised behaviour change intervention aimed at young older adults (61-70 years) to prevent functional decline at older age. This symposium focuses on how mobile technology (smartphones and smartwatches) can be used to monitor behaviour and to personalise and deliver an intervention. Objectives: First we present the rationale behind the PreventIT feasibility RCT. Then three presentations will present the recruitment and mHealth technology methods for assessing functional status, how to implement mHealth technology in the delivery of an intervention, and how to modify health-related behaviours by use of mHealth technology.
S5.3.1 mHealth and active ageing
Norwegian University of Science and Technology (NTNU), Norway
Background: mHealth is emerging technology empowering patients and elder citizens to maximise their potential to live an independent life and personalise the possible preventive efforts. So far the digitalisation of the health care sector has been focusing on eHealth records, knowledge systems and telemedicine. The mHealth revolution now allows patients to collect meaningful data via sensors and other devices and enables access to knowledge systems that originally were restricted to health care professionals. This is expected to drastically modify the delivery of prevention and health care in the near future. Methods: The presentation will describe how the PreventIT model was developed based on networking, user need analysis, recent research findings and how research gaps were identified that allowed funding in an extremely competitive environment. Results: The talk will present data from the pilot phase and the six month data of the feasibility RCT from 3 different European countries. Conclusions: The interest, uptake and adherence underline the attraction of the PreventIT mHealth Apps in the group of ‘baby boomers’. Geriatric medicine and allied health professionals should be key stakeholders in the domain of mHealth development and evaluation of mHealth.
S5.3.2 Using mHealth technology in assessing functional status in young older adults
Jeanine van Ancum
VU University, Amsterdam, The Netherlands
Background: Early identification of people at risk of functional decline is essential for optimising preventive interventions. mHealth technology can provide features on physical performance and daily physical activity. We investigated the discriminative ability of instrumented physical performance and activity features compared to conventional measures regarding the functional status of young older adults. Methods: Functional status was measured using the Late-Life Function & Disability Instrument. Instrumented physical performance features were obtained from smartphone acceleration signals: Timed-Up-and-Go, Five Times Sit-to-Stand, 30-Second Chair Stand and Tandem Stance with eyes closed. Feature selection was performed to reduce the number and features and avoid overfitting. We fitted logistic regression models and compared Area Under ROC Curves to estimate performance of the parameters in discriminating between high and low functional status. Results: Feature selection of the baseline data of 189 participants (mean age 66.3 ± 2.5 years) led to a subset of the features that were mostly related to functional status. Preliminary results on the model performance of instrumented features and conventional measures will be presented. Conclusions: The discussion and conclusion focus on the potential of mHealth technology to provide added information on physical performance and activity in assessing functional status.
S5.3.3 Using mHealth technology to support intervention delivery
Robert Bosch Hospital, Stuttgart, Germany
Background: A rapidly ageing population places increased strain on healthcare infrastructure. Advances in healthcare technology allow large numbers of older adults, currently underserved by health promotion services, to receive and complete individualised exercise programmes, bringing with it psychological and physical benefits. Methods: eLiFE represents an mHealth solution of transferring an evidence-based exercise programme to an ICT platform. eLiFE allows older adults to test their fitness and receive personalised advice on how to improve their strength, balance, and physical activity. Sensors embedded into smartphones and smartwatches monitor participants’ activity throughout the day. Results: Following detailed baseline assessment to determine individual starting levels, 60 participants used the eLiFE programme to integrate specific exercises into daily life. Four home visits and three support phone calls were provided over a 6-month follow-up period. Participants were encouraged to increase the number, difficulty, and location of their exercises. A virtual trainer was always accessible to provide guidance on how to perform activities and included additional information regarding the benefits of the exercises. Based on sensor data and participants’ self-reporting, personalised feedback was provided related to previously selected goals and personal exercise preference in order to improve long-term adherence. Conclusions: The potential of mHealth intervention delivery to provide a tailored exercise programme in a fast and location independent manner will be discussed.
S5.3.4 How to modify health-related behaviour by use of mHealth technology
University of Manchester, UK
Background: Balance and strength exercises and general physical activity
are important for healthy ageing. Even if effective in the short-term, most
exercise interventions have low adherence over time. In order to promote active
and healthy ageing, interventions are needed that focus on behavioural change
from initiation to long-term maintenance. Methods: In developing the
motivational strategy for PreventIT, we undertook literature reviews; drew on
previous work carried out by project partners; took expert advice in consortium
meetings; and received feedback from the target group end-users in two pilot
studies through questionnaires, issue logs, focus groups and interviews. We
continue to receive feedback from participants in the RCT, for further
refinement of the strategy. Results: We have developed two behaviour change
interventions based on Social Cognitive Models, and Habit Formation Theory, and
mapped these to Behaviour Change Techniques. The motivational elements of the
study are assessed through mixed-methods approaches to help us gain a rich
understanding of participants’ experience of receiving the intervention.
Preliminary findings will be presented.
Conclusions: Motivation and habituation theories can be used to develop
interventions targeting behavioural change. It will be discussed how to use
feedback to enhance uptake of and adherence to interventions.