Can smartphone technology be used to support an effective home exercise intervention to prevent falls amongst community dwelling older people?

The feasibility and acceptability of the design and procedures including:
1. Willingness of participants to be randomised (selected to go into either the intervention or control group)
2. Willingness of clinicians to recruit participants
3. Number of eligible patients
4. Characteristics of the proposed outcome measures e.g. reliability of falls detector, Instrumented Timed up and Go
5. Follow-up rates, adherence/compliance rates
6. Time needed to collect and analyse data
7. Determine effect sizes for use in sample-size calculations at 3 months and 6 months., enabling power calculations for the reduction in falls for a definitive large scale RCT (allowing the trialists to estimate how many patients need to be recruited to show a difference between the standard service and the intervention)
This will be assessed through data collected on the CRF and through interviews (at 6 months).

Measured at at baseline, 3, and 6 months:
1. Falls: The primary outcome for any future definitive trial would be falls, expressed as the fall rate per person per months of follow-up. For this study falls will be detected via the falls alarm. All participants will wear the smartphone in their pocket or on a waistband and this will act as a falls detector. This will be used as an outcome measure for the study. If the patient chooses then they can also use the app on the phone as a falls alarm. The fall detection system application allows the user to identify a list of formal/informal caregivers who will receive an SMS if a fall is detected. Patients are given an opportunity to de-active the falls alarm through an application on the smartphone if there is a false alarm, enabling the user to maintain control and prevent unwanted intrusion. Data collected through the smartphone will be downloaded from the phone at the end of the intervention period. Participants will be asked if we can use their anonymised falls data for further development of the app and in the Farseeing real-world falls database (http://farseeingresearch.eu/the-farseeing-real-world-fall-repository-a-large-scale-collaborative-database-to-collect-and-share-sensor-signals-from-real-world-falls/). However, to validate this as an outcome measure the trialists will also use the internationally agreed ProFaNE falls definition (Lamb et al 2005). Patients are asked to record falls as they happen on a falls calendar completed daily and returned by mail monthly. If a person does not return their fall calendar, they are telephoned to determine whether they have fallen and if they report a fall they are contacted to record the circumstances of fall, whether any injuries occurred and whether any help was sought as a result of the fall.
2. Fear of falling, measured using the Short Falls Efficacy Scale-International (Short FES-1) (Kempen et al., 2008). This is a measure often used by UK falls services as part of standard outcome measures.
3. Function, assessed using the Timed Get Up and Go test (TGUG). The TGUG will be applied as described by Podsiadlo and Richardson (1991). Participants will be asked to perform the TGUG at their self-selected walking speed. An application developed by the University of Bologna implements an instrumented (on the smartphone) version of the TGUG called the mTUG. The application is able to automatically provide guidance to the user for administering the test, capture and process the data, and generates summary reports of function for the health professional. The mTUG is registered as a type 1 medical device and is CE marked. mTUG includes a certified external sensor for accuracy. The health professional will carry out a standard mTUG at baseline and follow-up with a sub-sample of 10 patients at each site to assess their experiences of its use. The blinded assessor will complete the normal TUG and the mTUG as outcome measures (the standard TUG as a validation measure).
4. Balance, assessed using the Berg balance scale. This has good validity and sensitivity in this population (Berg et al, 1991) and is an assessment tool that has been used before by falls services. This will be used to estimate the number of patients needed for a full trial.
5. Strength, assessed using the 30 seconds chair stand test (Jones et al, 1999).
6. Adherence, measured in a number of ways:
6.1. Self-report app will be used for both control and intervention group (which exercises from their programme they have done and intensity). The health professional will be asked to provide a copy of the participants prescribed exercise plan, any changes to it and the dates any changes were made (both sites record this as part of standard intervention). For any group based exercise or face to face home based exercise offered, the health professional will be asked to report what is delivered (this will be used to validate the self-report from participants). The participants in the intervention arm will have their programme set on the ‘motivate me’ and ‘my activity programme’ applications as part of behavioural goals. Adherence will be classed as the participant carrying out 80% of their prescribed programme. This percentage is based on the evidence base for effective strength and balance (Nyman and Victor, 2011; Osho et al, 2017). During follow-up if participants move on to other strength and balance provision (e.g. community based strength and balance exercises) they will be encouraged to report this through the self-report app. The trialists will record any transitions between group exercise and only home exercise within the CRF. They will also ask the follow-up groups (delivered by leisure services, AgeUK and NHS exercise referral service) for a copy of what was delivered the day any of our participants attended.
6.2. Exercise Adherence Rating Scale (EARS, Newman-Benairt et al, 2017). This is a validated 16 question tool with a 6 question subscale specifically measuring adherence (the remaining questions measure reasons for adherence/non-adherence).
7. Health economics: The health economics analysis is focussed on informing relevant measures and means of collection of health related quality of life and resource use for the future definitive study. No formal tests of cost-effectiveness will be conducted, for all measures we will report mean values and sample variability alongside information on missing values. The health-related quality of life measures will include the European Quality of Life 5 Dimensions (EQ-5D-5L, 2009) and an additional measure used in previous trials related to falls prevention (the ICEpop CAPability measure for Older people (ICE-CAP-O) Davis et al, 2012; Grewal et al, 2006). Costs of delivering the intervention will be observed based on staff training, delivery costs and equipment costs. Additional resource use measures will be captured via a Resource Use Questionnaire which will seek to measure costs related to an NHS and social care perspective (secondary, primary, and community care service use), and a patient perspective (costs related to informal care, see Resource Use Questionnaire).