There are FIVE SEPARATE PARTS. Follow instructions and rubric. Complete all sections thoroughly. NOTE: Student Name Team Date Part 1. GENERAL...

QuestionThere are FIVE SEPARATE PARTS. Follow instructions and rubric. Complete all sections thoroughly. NOTE: This assignment is worth 20% of the final grade. Put significant work into it – seek help if needed. Student NameTeam Date Part 1. GENERAL INFORMATIONEvidence level I II III (bold when complete level assessment)Quality Rating A B C (bold when complete quality appraisal)Article Citation (APA format)Study Setting (city/state/country, hospital/units/clinic, etc.)Study Sample (demographics & size)Team EBP Question : Within the elderly population, what is the effect of daily exercise on the prevention of falls.Is this article/report Quantitative Research (collection, analysis, and reporting of numerical data)? Measurable data (how many; how much; or how often) used to formulate facts, uncover patterns, and generalize results from a larger sample population; provides observed effects of a program, problem, or condition, measured precisely, rather than through researcher interpretation of data. Common methods of data collection are surveys, face-to-face structured interviews, observations, and reviews of records or documents. Statistical tests are used in data analysis. This is NOT just a literature review, NOT a systematic review, and NOT a qualitative research article. -Bold answer – Yes?INSTRUCTIONS TO COMPLETE THIS ASSIGNMENT: PARTS 2 & 4: ANSWER YES, NO, OR N/A TO EVERY QUESTION AND IN THE BOX EXPLAIN/DESCRIBE YOUR ANSWER TO EVERY QUESTION. PARTS 3 & 5: FOLLOW INSTRUCTIONS IN BOX. DO NOT COPY AND PASTE. PARAPHRASE/SUMMARIZE IN YOUR OWN WORDS. Quantitative ResearchBOLD YES or NO ANSWERS AND DESCRIBE WHY YOU GAVE THAT ANSWER (Whether YES or NO) Part 2. LEVEL ASSESSMENTDetermine the LEVEL of this Research (Study Design)Bold answers1. Was there manipulation of an independent variable (Intervention)? Describe:Yes No2. Was there a control group (A structured comparison group)? Describe:Yes No3. Were participants randomly assigned to intervention or control groups? Describe:Yes NoBased on the answers choose the LEVEL of this research study:Bold answersIf Yes to questions 1, 2, & 3, this is an experimental study or randomized controlled trial (RCT).LEVEL IIf Yes to questions 1 & 2 and No to question 3 or Yes to question 1 and No to questions 2 & 3, this is quasi-experimental (manipulation of an independent variable, lacks random assignment to groups, and may lack a control group).LEVEL IIIf No to questions 1, 2, & 3, this is non-experimental (No manipulation of independent variable -no experiment; can be observational or descriptive; may use secondary data from records or reports).LEVEL IIIENTER LEVEL AT THE TOP OF TOOLPart 3. HOW DOES RESEARCH HELP ANSWER THE EBP QUESTION?DO NOT COPY AND PASTE – USE YOUR OWN WORDS – PARAPHRASE AND SUMMARIZE – You will need to explain this information to your EBP teammates.Include all relevant details – use bullets. Do not just copy and paste – use your own words: Highlight what you learned from this research article that your team can use to change nursing practice related to the EBP question. THIS IS NOT A SUMMARY. PUT SIGNIFICANT THOUGHT INTO THIS SECTION. Erase these instructions after completing this section. SEE EXAMPLE.Part 4. APPRAISE THE QUALITY OF THE RESEARCH ARTICLETHIS SECTION FOLLOWS THE ARTICLE – START AT THE BEGINNING AND MOVE THROUGH IT. BOLD YES, NO, or N/A ANSWERS AND DESCRIBE/EXPLAIN THE DETAILS FOR EVERY QUESTION WHETHER ANSWER IS YES, NO, N/A. DO NOT COPY AND PASTE – USE YOUR OWN WORDS – PARAPHRASE AND SUMMARIZE. You will need to explain this information to your EBP teammates. N/A DOES NOT COUNT AS A “NO” ANSWER. Was the purpose of the study clearly presented? Describe the purpose:YesNoIn the Introduction/Background/Literature Review section is the problem described clearly? Does it explain what is known and not known about the problem? Does it explain how the research will help solve the problem? Explain details of the literature review and answer question thoroughly:YesNoWas the literature review (sources/references used for the article) current – published within the past five years OF PUBLICATION)? (Hint – information is in Introduction/Literature Review section AND references)Explain:YesNoWas the sample size sufficient based on the study design? How do you know? (The article may explain sample size, if not check text/notes).Explain: YesNoIF there was an intervention, was it explained clearly? If there is no intervention, bold N/A.Describe it:YesNoN/AIF there was a control group ANSWER these 3 questions. IF NO control group, bold N/A Were the characteristics and/or demographics similar in the control and intervention groups? Describe:YesNoN/AIf multiple settings were used, were the settings similar? (N/A if only one setting)Explain:YesNoN/AWere the control and intervention groups treated equally except for the intervention? Explain:YesNoN/AAre data collection methods described clearly (how they gathered the data from the participants or existing records)? Describe with detail how data was collected:YesNoIF instruments/tools/surveys/questionnaires were used to collect data, were they stated as reliable (Cronbach’s [alpha] > 0.70)? (This is in the METHODS section, not RESULTS. If only biophysical data or secondary data/records used, bold N/A)Explain:YesNoN/AIF instruments/tools/surveys/questionnaires were used to collect data, were they stated as valid? (This is in the METHODS section, not RESULTS. If only biophysical data or secondary data/records used, bold N/A)Explain:YesNoN/AIF surveys or questionnaires were emailed or mailed to participants (not completed in person), how many were sent out and how many were returned? Was the response rate > 25%? Describe:YesNoN/AWere the results presented clearly? What were they?Explain thoroughly:YesNoIF tables were used, did they help you understand the results? What information was in them?Explain: YesNoN/AWere study ‘limitations’ identified? (towards end of article)Explain:YesNoWere researchers’ conclusions and recommendations based on the results? What were they? Explain:YesNoAdd up YES or NO answers ONLYBased on the number of “YES” and “NO” answers and referring to the table below, rate the QUALITY of this research evidence as A, B, or C. (Hint the more “YES” answers, the higher the quality). (BOLD the rating below AND ENTER AT TOP OF TOOL). A- High quality: Consistent, generalizable results; sufficient sample size for the study design; adequate control; definitive conclusions; consistent recommendations based on comprehensive literature review that includes thorough reference to scientific evidence. (All “yes” or almost all “yes”)B- Good quality: Reasonably consistent results; sufficient sample size for the study design; some control, and fairly definitive conclusions; reasonably consistent recommendations based on fairly comprehensive literature review that includes some reference to scientific evidence. (Majority “yes”)C- Low quality or major flaws: Little evidence with inconsistent results; insufficient sample size for the study design; conclusions cannot be drawn. (Many “no” answers)Part 5. EXPLAIN YOUR QUALITY APPRAISAL RATING (PART 4 ONLY) IN DETAIL. Create a narrative summary explaining why you made the QUALITY rating you did. This is NOT about the LEVEL or a summary of the article. Describe your yes and no Quality answers. Yes answers give it a higher quality rating and no answers a lower rating. Why? Use your own words, do not copy and paste. 275-300 words. Below is the article that needs to be appraised:Citation: Delbaere, K., Valenzuela, T., Lord, S. R., Clemson, L., Zijlstra, G., Close, J., Lung, T., Woodbury, A., Chow, J., McInerney, G., Miles, L., Toson, B., Briggs, N., & van Schooten, K. S. (2021). E-health StandingTall balance exercise for fall prevention in older people: results of a two year randomised controlled trial. BMJ (Clinical research ed.), 373, n740. https://doi.org/10.1136/bmj.n740E-health StandingTall balance exercise for fall prevention in older people: results of a two year randomised controlled trialKim Delbaere, professor,1 ,2 Trinidad Valenzuela, postdoctoral research associate,3 ,4 Stephen R Lord, professor,1 ,2 Lindy Clemson, professor,3 G A Rixt Zijlstra, professor,5 Jacqueline C T Close, professor,1 ,6 Thomas Lung, senior research fellow,3 ,7 Ashley Woodbury, research assistant,1 Jessica Chow, research assistant,1 Garth McInerney, research assistant,1 Lillian Miles, research assistant,1 Barbara Toson, senior research fellow,1 ,8 Nancy Briggs, senior statistical consultant,9 and Kimberley S van Schooten, senior postdoctoral fellow1 ,2Author information Article notes Copyright and License information DisclaimerThis article has been cited by other articles in PMC.Associated DataSupplementary MaterialsGo to:AbstractObjectiveTo test whether StandingTall, a home based, e-health balance exercise programme delivered through an app, could provide an effective, self-managed fall prevention programme for community dwelling older people.DesignAssessor blinded, randomised controlled trial.SettingOlder people living independently in the community in Sydney, Australia.Participants503 people aged 70 years and older who were independent in activities of daily living, without cognitive impairment, progressive neurological disease, or any other unstable or acute medical condition precluding exercise.InterventionsParticipants were block randomised to an intervention group (two hours of StandingTall per week and health education; n=254) or a control group (health education; n=249) for two years.Main outcome measuresThe primary outcomes were the rate of falls (number of falls per person year) and the proportion of people who had a fall over 12 months. Secondary outcomes were the number of people who had a fall and the number who had an injurious fall (resulting in any injury or requiring medical care), adherence, mood, health related quality of life, and activity levels over 24 months; and balance and mobility outcomes over 12 months.ResultsThe fall rates were not statistically different in the two groups after the first 12 months (0.60 falls per year (standard deviation 1.05) in the intervention group; 0.76 (1.25) in the control group; incidence rate ratio 0.84, 95% confidence interval 0.62 to 1.13, P=0.071). Additionally, the proportion of people who fell was not statistically different at 12 months (34.6% in intervention group, 40.2% in control group; relative risk 0.90, 95% confidence interval 0.67 to 1.20, P=0.461). However, the intervention group had a 16% lower rate of falls over 24 months compared with the control group (incidence rate ratio 0.84, 95% confidence interval 0.72 to 0.98, P=0.027). Both groups had a similar proportion of people who fell over 24 months (relative risk 0.87, 95% confidence interval 0.68 to 1.10, P=0.239), but the proportion of people who had an injurious fall over 24 months was 20% lower in the intervention group compared with the control group (relative risk 0.80, 95% confidence interval 0.66 to 0.98, P=0.031). In the intervention group, 68.1% and 52.0% of participants exercised for a median of 114.0 min/week (interquartile range 53.5) after 12 months and 120.4 min/week (38.6) after 24 months, respectively. Groups remained similar in mood and activity levels. The intervention group had a 0.03 (95% confidence interval 0.01 to 0.06) improvement on the EQ-5D-5L (EuroQol five dimension five level) utility score at six months, and an improvement in standing balance of 11 s (95% confidence interval 2 to 19 s) at six months and 10 s (1 to 19 s) at 12 months. No serious training related adverse events occurred.ConclusionsThe StandingTall balance exercise programme did not significantly affect the primary outcomes of this study. However, the programme significantly reduced the rate of falls and the number of injurious falls over two years, with similar but not statistically significant effects at 12 months. E-health exercise programmes could provide promising scalable fall prevention strategies.Trial registrationACTRN12615000138583Go to:IntroductionFalls and fall related injuries have persisted over the past three decades as a leading cause of morbidity and mortality in older people.1 With a rapidly ageing population globally, sustainable access to evidence based, cost effective fall prevention programmes is a priority. Evidence from high quality systematic reviews and meta-regressions has confirmed that well designed exercise programmes are among the most effective fall prevention strategies for community dwelling older people, with fall reduction rates averaging 23%.2 However, to achieve similar effectiveness at a population level, we need a programme that people can access easily and adhere to in the long term. Previous studies have found that older people prefer home based exercises and that the inclusion of balance exercises is associated with higher adherence.3 Nevertheless, sustained adherence to prescribed home exercise programmes is low, with pooled estimates of 21% (range 0-68%).4 Studies providing a physiotherapist led programme or a moderate level of home visits (that is, less than one home visit per month and more than two home visits in total) achieve higher levels of adherence4; however, such measures substantially increase the cost and reduce the feasibility as a population approach.Digital technology can provide engaging and widely accessible methods for delivery of exercise programmes to enhance long term motivation and adherence at relatively low cost.5 However, the provision of a well designed, unsupervised exercise programme that is tailored and progressive in nature, yet safe, could be a challenge. StandingTall is a home based, e-health balance exercise programme provided through an app that was developed by using principles of consumer design to ensure an appropriate and user friendly interface for older people. Behavioural change strategies are incorporated to enhance exercise uptake and long term adherence.6This randomised controlled trial aimed to determine the effect of StandingTall on the recommended set of core outcomes for fall prevention trials in older people (fall rate, number of people who fall and those who have an injurious fall; and known fall risk factors, including balance, gait, concern about falling, health related quality of life, and physical activity levels).7 The trial had a 24 month follow-up period and compared the outcomes of the intervention with a health promotion education control programme. We hypothesised that StandingTall would lead to a reduced fall rate compared with a control group with minimal intervention.Go to:MethodsStudy designWe conducted a prospective, assessor blinded, two arm, parallel randomised controlled trial with two year follow-up in Sydney, Australia. The trial was approved by the University of New South Wales ethics committee in December 2014 (HC#14/266) and was registered prospectively in the Australian and New Zealand Clinical Trials Registry (ACTRN12615000138583) on 13 February 2015. The study protocol was published in 2015.8 The statistical analysis plan was preregistered in October 2018 through the OpenScience framework (https://osf.io/42gje/) before completion of data collection in November 2019. We used the CONSORT (consolidated standards of reporting trials) statement, ICMJE recommendations, and TiDieR (template for intervention description and replication) checklist when preparing this article.ParticipantsWe recruited community living older people in the Sydney metropolitan area by using flyers, printed advertisements in local newspapers, presentations at residential and community senior centres, and word of mouth. Study participants lived on average 12 km (range 1.2-46.9 km) from Neuroscience Research Australia (Randwick, NSW). After initial screening by telephone, eligible people were invited to participate if they were aged 70 years or older, living in the community, independent in activities of daily living, able to walk household distances without the use of a walking aid, and willing and able to give informed consent and comply with the study protocol. People were excluded if they had an unstable or acute medical condition that precluded exercise participation, suffered from a progressive neurological condition (such as Parkinson’s disease or multiple sclerosis), were cognitively impaired as defined by a Pfeiffer short portable mental status questionnaire score less than 8,9 or were currently participating in a fall prevention programme. Eligibility was determined after informed verbal consent. People who were eligible and agreed to participate in the study were asked to provide informed written consent.Randomisation and maskingParticipants were randomised after completion of the baseline assessment. Permuted block randomisation with mixed block lengths of four and six was applied to form two groups of similar size (allocation ratio 1:1). People living in the same household were treated as one unit to avoid contamination. Allocation was performed centrally using a custom randomisation programme by an investigator not involved in participant assessments or delivery of the intervention. Allocation concealment was ensured because the randomisation code was only released after the baseline assessment was completed. Only the first 226 participants were invited for repeated physical tests to reduce costs and participants’ time. Outcome assessors were blinded to study group assignment throughout the trial. Statistical analyses were performed blinded for intervention or control group allocation.ProceduresAll participants received a tablet computer with a health promotion education programme that focused on health related information relevant to older people, in addition to usual care, for two years. This health promotion education programme comprised weekly fact sheets (104 in total) with information on healthy diet, drugs, fall risk factors, and exercise. Tablet based health education alone was chosen as the active control intervention to regulate the use of technology and allow data collection (such as number of falls during the trial period) through a tablet computer for both groups. Participants received a manual on how to use the tablet computer. After their baseline assessment, participants received guidance on the basic features of the tablet computer and health promotion education programme.The intervention group received the StandingTall programme, with exercise equipment (foam cushion, stepping box, exercise mat), in addition to the health promotion education programme and usual care that was received by the control group. The StandingTall intervention consisted of balance exercises delivered through a tablet computer in the participants’ homes with embedded behavioural change techniques, including a weekly calendar for scheduling exercises, goal setting, and educational fact sheets. The exercises focus on standing balance, targeted stepping, and step-up (box) exercises. More information about the programme can be found in the study protocol8 and online (https://www.standingtall.org.au/). Participants were asked to exercise for at least two hours per week for the duration of the trial, in line with the international recommendations for fall prevention at the time of the study.2 The intervention was introduced gradually; participants started with 40 min/week of exercise, which was increased by 20 min fortnightly until participants reached the required amount of two hours per week in week 9.StandingTall delivers individually tailored balance exercises that increase in difficulty over time; the programme also allows people to choose the time and duration of their exercise sessions. The intensity of the balance exercises is monitored by using a self-report modified rate of perceived exertion scale and is adjusted as performance changes throughout the trial without the need for supervision. Exercise adherence (volume and frequency) was monitored for two years after automatic data transfer to a server. During the first six months, participants were encouraged to inform the research team when they were going away or would not be able to exercise for a few weeks. Participants who did not inform the team and did not reach 100 min/week for two consecutive weeks were contacted by telephone so that the reason for non-adherence could be recorded, any issues related to the programme could be discussed, and the team could encourage adherence. These calls stopped after six months to gain a better understanding of behavioural change and long term exercise adherence.Intervention group participants received two home visits. During the first visit, a qualified exercise physiologist instructed the participant on how to use the StandingTall programme; this visit occurred between one and three weeks after the baseline assessment and lasted approximately one hour. The second home visit after one month lasted approximately 30 min and ensured safe use of the programme and progression of training. Control group participants received two phone calls by qualified exercise physiologists at the same time points to discuss any issues with accessing the health education programme and using additional features of the tablet computer.OutcomesThe primary outcome measures were the rate of falls and the proportion of people who fell over the first 12 months of the trial. A fall was defined as ‘an unexpected event in which the participant comes to rest on the ground, floor or lower level’.7 Falls were monitored by using prospective weekly fall diaries through the tablet computer (completed from baseline assessment for 24 months). Fall information was automatically uploaded to a database. Research staff contacted participants by telephone at the end of each month when their fall diaries were incomplete to record missing data. The falls database was checked, reviewed, and locked before group allocation was unmasked. Falls that occurred up to one year after randomisation were included in the primary analysis. Falls that occurred up to two years after randomisation were included as secondary fall outcomes. Injurious falls were defined as falls that resulted in any injury (eg, bruises, cuts or grazes, joint dislocations, sprains or strains, fractures, pain), or falls that required medical care (eg, visit to physician or emergency department).Secondary outcome measures were assessed at baseline, at six months to examine acute effects, and at 12, 18, and 24 months to examine retention effects. These measures included common fall risk factors: laboratory based balance and neuropsychological assessments (at baseline and at six and 12 months after baseline assessment in the first 226 participants), and remote measures (taken at home) of wellbeing, quality of life, and activity levels (at baseline and at six, 12, 18, and 24 months after baseline assessment in all participants). Physiological fall risk was assessed using the physiological profile assessment.10 Balance, functional mobility, and gait were evaluated by using tests of standing balance (standing with feet in different positions for a maximum of 30 s per condition: feet together, near tandem, and tandem on floor and foam cushion, and left and right foot on floor; sum of durations for all eight conditions), maximum forward-backwards and controlled leaning balance,10 11 timed sit-to-stand12 and up-and-go tests,13 short physical performance battery,14 and self-selected walking speed over 10 m.15 Stepping performance was assessed with choice, Stroop and inhibitory stepping reaction time tests.16 17 Cognitive function was measured with the Montreal cognitive assessment18 for global cognition, trail making tests19 for set shifting, and the Victoria Stroop task20 for response inhibition. Psychological outcome measures were assessed by using the iconographical falls efficacy scale (concern about falling),21 the nine item patient health questionnaire (mood)22 and the COMPAS-W scale (wellbeing).23 Health related quality of life was measured with the 12 item WHO disability assessment schedule,24 the EuroQol five dimension five level (EQ-5D-5L) questionnaire,25 and the AQOL-6D (20 item assessment of quality of life six dimensions) questionnaire.26Detailed self-report information on frequency and duration of physical activity was evaluated with the incidental and planned exercise questionnaire.27 Daily life activity was assessed with the McRoberts MoveMonitor (McRoberts, Netherlands) as the average duration of daily walking and standing, and the number of walking and standing bouts per day28; a bout was defined as a period of consecutive activity. Because participants were instructed to remove the device before going to bed, we required a minimum wear duration of 12 hours per day on one or more days for daily life activity data to be included in the analysis. Daily life activity data were collected over a median of six days (interquartile range one day) for both groups.Process outcome measures included exercise duration and were captured through the tablet computer. Because participants were allowed exercise breaks when they were sick or went on holiday, we averaged weekly exercise duration as median values for participants as a robust measure of central tendency. We obtained subjective user experience by assessing usability, enjoyment, and exercise self-efficacy with the system usability scale,29 the physical activity enjoyment scale,30 the exercise self-efficacy scale,31 and the attitudes to falls related interventions scale.32All outcome measures were assessed by trained exercise physiologists or physiotherapists who were blinded to group allocation. We assessed safety in terms of adverse events, which were defined as any fall related to the prescribed exercise programme or involving the intervention equipment.Statistical analysisSample size calculationbased on previous evidence, we carried out an a priori sample size calculation8 in Stata using a custom code with 5000 simulations. The calculation showed that 500 participants were required to achieve 80% power to find a fall rate reduction of 33% (incidence rate ratio of 0.67) in the intervention versus the control group that is statistically significant at a P value less than 0.05 (considering an overdispersion of 1.2, 0.8 falls/person year in the control group, and a follow-up duration of 22 months to account for 20% dropout rate). We then ran power calculations in G*Power (version 3.1.7) for our secondary outcomes (considering an analysis of variance design with four measurements and 20% dropout rate). These calculations showed that we would have 90% power to detect a statistically significant (P<0.05) small reduction (effect size f=0.15) in concern about falling in the intervention group versus the control group, assuming a within subject correlation of 0.75.8 A subsample of 200 participants with repeat physical assessments would provide us with 95% power to detect a statistically significant (P<0.05) large reduction (effect size f=0.38) in postural sway in the intervention group versus the control group, assuming a within subject correlation of 0.76.8Analysis plananalyses were conducted according to the predefined statistical analysis plan, as registered on the OpenScience framework (https://osf.io/42gje/ ). We coded data to maintain group allocation blinding during analysis. Effectiveness analyses of the primary outcome were conducted on an intention-to-treat basis by a statistician (BT or NB) and independently replicated by one of the investigators (KSvS). The a level was set to 5%. Analyses were performed with Stata (version 16, Stata Corp) and SPSS (version 25, IBM Corp).Missing dataparticipants who were randomly assigned to a group were included in the analysis irrespective of their level of compliance with their group assignment, which was in line with intention-to-treat principles. The primary outcome measures (number of falls per person year and proportion of people who fell over 12 months) were analysed without imputation or adjustment for descriptive characteristics, and with correction for follow-up duration when appropriate. We assumed that the faller status of people with incomplete follow-up (n=66 at 12 months and n=188 at 24 months, distributed evenly over the two groups) was maintained during censoring. We used Little's missing completely at random test to determine the missing data patterns of secondary outcome measures. The secondary outcome measures were imputed using estimated means single imputation if they were missing completely at random; or under the assumption of missing at random using multiple imputation to create 20 imputation datasets under joint multivariate normal imputation33 if they were not missing completely at random. Psychological wellbeing, health related quality of life, and physical activity questionnaire data were missing for 58 out of a total of 503 people at six months, for 82 people at 12 months, for 98 people at 18 months, and for 99 people at 24 months. Daily life activity monitoring data were unavailable for 21 people at baseline, 101 people at six months, 138 people at 12 months, 148 people at 18 months, and 156 people at 24 months. Clinic based balance and neuropsychological assessment data were missing for 42 people at six months and for 47 people at 12 months. These data were missing because of dropout, scheduling issues, non-adherence, or technical problems. Little's missing completely at random test indicated that all data were missing at random with respect to participant baseline characteristics.Primary outcomesprimary outcomes were the number of falls per person year, and the proportion of people who fell over 12 months. The number of falls per person year was analysed using Poisson regression to estimate the difference in fall rates between the two groups. Incidence rate ratios and 95% confidence intervals are reported. Poisson regression was selected over negative binomial regression (as a priori registered in our statistical analysis plan, but not in our protocol paper) to allow for a direct comparison with our planned complier average causal effects analysis because this analysis was based on a Poisson model. Online appendix 1 presents the results of the negative binomial regression. Days of follow-up was included as an exposure term in these models; that is, the natural logarithm of the days of follow-up was added as an offset. We examined the proportion of people who fell in the two groups by using modified Poisson regression models for binary outcomes. Faller status was compared (no falls v at least one fall) and relative risks and 95% confidence intervals are reported.Secondary outcomessecondary fall outcomes were the number of falls, the complier averaged causal effect, the proportion of people who fell, and the proportion of people who had injurious falls at 24 months. We used instrumental variable regression to correct for imperfect participant adherence and to gain insight into efficacy by estimating the complier averaged causal effect. We used a 2000 times bootstrapped, two stage complier averaged causal effect estimator composed of a linear regression with adherence as the dependent variable and group as the independent variable to obtain an estimate for adherence. A robust Poisson regression was then performed, with falls as the dependent variable and the natural logarithm of follow-up in days as exposure to estimate the effect of the intervention among people with perfect adherence. The number of injurious falls per person year was analysed using Poisson regression to estimate the difference in injurious fall rates between the two groups. We analysed secondary non-fall outcome measures with robust generalised linear models using an exchangeable working correlation matrix and compared the change in scores over time at six, 12, 18, and 24 months between the two groups. When the residuals of the generalised linear models deviated from normality, we used a 1000 times bootstrap for each imputation dataset to obtain confidence intervals.Patient and public involvementStandingTall was developed using consumer design principles. A group of older people were involved during the development of the StandingTall application. They were asked to evaluate an early version on its usability and age appropriateness as a means to engage in fall prevention exercises using tablet based technology. A two week feasibility study was conducted in 10 community dwelling older people in November 2013. The average age of the participants was 77.5 years (range 67-82 years), and s