Effects of treadmill slip and trip perturbation-based balance training on falls in community-dwelling older adults (STABILITY): study protocol for a randomised controlled trial

INTRODUCTION: Falls among older adults are most frequently caused by slips and trips and can have devastating consequences. Perturbation-based balance training (PBT) have recently shown promising fall preventive effects after even small training dosages. However, the fall preventive effects of PBT delivered on a treadmill are still unknown. Therefore, this parallel-group randomised controlled trial aims to quantify the effects of a four-session treadmill-PBT training intervention on falls compared with treadmill walking among community-dwelling older adults aged 65 years or more. METHODS AND ANALYSIS: 140 community-dwelling older adults will be recruited and randomised into either the treadmill-PBT or the treadmill walking group. Each group will undergo three initial training sessions within a week and an additional ‘booster’ session after 26 weeks. Participants in the treadmill-PBT group will receive 40 slip and/or trip perturbations induced by accurately timed treadmill belt accelerations at each training session. The primary outcome of interest is daily life fall rates collected using fall calendars for a follow-up period of 52 weeks. Secondary outcomes include physical, cognitive and social–psychological fall-related risk factors and will be collected at the pre-training and post-training test and the 26-week and 52-week follow-up tests. All outcomes will be analysed using the intention-to-treat approach by an external statistician. A Poisson’s regressions with bootstrapping, to account for overdispersion, will be used to compare group differences in fall rates. ETHICS AND DISSEMINATION: The study protocol has been approved by the North Denmark Region Committee on Health Research Ethics (N-20200089). The results will be disseminated in peer-reviewed journals and at international conferences. TRIAL REGISTRATION NUMBER: NCT04733222

Predicting falls in community-dwelling older adults: a systematic review of prognostic models

Objective To systematically review and critically appraise prognostic models for falls in community-dwelling older adults.Eligibility criteria Prospective cohort studies with any follow-up period. Studies had to develop or validate multifactorial prognostic models for falls in community-dwelling older adults (60+ years). Models had to be applicable for screening in a general population setting.Information source MEDLINE, EMBASE, CINAHL, The Cochrane Library, PsycINFO and Web of Science for studies published in English, Danish, Norwegian or Swedish until January 2020. Sources also included trial registries, clinical guidelines, reference lists of included papers, along with contacting clinical experts to locate published studies.Data extraction and risk of bias Two authors performed all review stages independently. Data extraction followed the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Risk of bias assessments on participants, predictors, outcomes and analysis methods followed Prediction study Risk Of Bias Assessment Tool.Results After screening 11 789 studies, 30 were eligible for inclusion (n=86 369 participants). Median age of participants ranged from 67.5 to 83.0 years. Falls incidences varied from 5.9% to 59%. Included studies reported 69 developed and three validated prediction models. Most frequent falls predictors were prior falls, age, sex, measures of gait, balance and strength, along with vision and disability. The area under the curve was available for 40 (55.6%) models, ranging from 0.49 to 0.87. Validated models’ The area under the curve ranged from 0.62 to 0.69. All models had a high risk of bias, mostly due to limitations in statistical methods, outcome assessments and restrictive eligibility criteria.Conclusions An abundance of prognostic models on falls risk have been developed, but with a wide range in discriminatory performance. All models exhibited a high risk of bias rendering them unreliable for prediction in clinical practice. Future prognostic prediction models should comply with recent recommendations such as Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis.PROSPERO registration number CRD42019124021