Development of a Multivariable Prognostic PREdiction Model for 1-year Risk of FALLing in Community-dwelling Older Adults in a Non-clinical Setting

Study Overview
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Brief Summary
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Falls in community-dwelling older adults is a frequent problem with an incidence of 30 % in over-65s and 50 % in over-80s. Incidences are expected to increase significantly in the future due to population aging. For instance, as of 2017, the global population older than 65 years is estimated to be 962 million and will increase to 1.4 and 2.1 billion in 2030 and 2050 respectively. In Denmark, falls are the most common accidents among older adults with around 36,000 fall accidents seen annually by the Danish health services and approximately 680 deaths yearly. This high frequency of fall accidents may also support the fact that falls in Denmark are the fourth most common reason for years lived with disability, thereby giving rise to reduced quality of life. Also, falls are associated with elevated morbidity, mortality, poorer physical functioning and early admission to long-term care facilities. Thus, this frequent and escalating problem of fall accidents is of major concern. Fall prevention is therefore highly relevant. It is recognised that fall-preventive strategies should take on a multifaceted approach due the multifactorial aetiology of falls. This is substantiated by more than 400 risk factors of falling that have now been identified. These spread across different domains including socio-demographics, medical conditions (e.g. atrial fibrillation), medication, physical performance (e.g. reduced lower extremity strength or reaction time), psychology (e.g. depression or fear of falling) and cognition (e.g. global cognitive impairment or reduced executive functioning). In order to aid health care professionals in targeting fall-preventive interventions, individual assessments of fall risk are imperative. In Denmark, municipalities are obliged to perform preventive initiatives to preserve the physical, mental and social health along with the functional capacity and quality of life of their older adults. The aim of these initiatives is to enable the older adults to live an independent and meaningful life for as long as possible. Recently, The Danish Health Authority released an updated manual to support this work. This emphasised the need for development of a validated prediction model to be used in a municipally environment to identify older adults at risk of falling. This is due to the abovementioned consequences of falls. To the knowledge of the authors, this is in line with literature being sparse on prognostic prediction models on falls in community-dwelling older adults with data collected outside a clinical environment (i.e. hospitals, GPs and screening or assessment centres). Objectives: Primary: To develop and internally validate a multifactorial prognostic prediction model on fall risk in community-dwelling older adults in a non-clinical setting. The intended use of the model is, for municipalities, to identify and refer citizens with high risk of falls to fall-preventive interventions. Secondary: To estimate time-consumption for the final prediction model. To describe the prevalence of arrhythmias in community-dwelling older adults.

Detailed Description
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Study design and sample size: A prospective cohort study with 1-year follow-up will be performed in collaboration with the municipality of Hjørring, Denmark. A total sample size of 500 participants is expected. This was chosen due to economical and administrative reasons of the municipality contributing with personnel to include participants and collect data on predictors. Data for the development and internal validation of the model will originate from the same cohort. Study setting, participants, data collectors, process of recruitment and data collection: According to Danish legislation on health and social services, Danish municipalities are responsible for developing and initiating prophylactic and health-promoting initiatives for their senior citizens. This is done through different authorities in the municipality (e.g. preventive-home-visits, senior activity centres). Also, the municipality of Hjørring administers a local hall for citizens together with general- and patient associations. Therefore, data collection will be performed in participants' own homes through preventive-home-visits, at senior activity centres and at the local hall in the municipality of Hjørring, Denmark. Predictors: Data collection of predictors will be performed at baseline. The following predictors were chosen by an expert panel. Reasons are stated at each predictor. First, a brief summary on the process of how predictors were selected will be given followed by a short description of each predictor. - Predictor selection process: A feasibility study. This model is intended for health care professionals in a non-clinical setting, in this case the municipality of Hjørring with a setting consisting of homes and activity centres. Therefore, it needs to be time-efficient, low-cost and practicable. In order to ease implementation if the model is successful in accuracy, the predictors for data collection was chosen by an expert panel on the basis of scientific value and experiences from a feasibility study performed as a precursor for this study. The feasibility study investigated the feasibility of measuring a set of predictors, selected by the expert panel, with regards to time-consumption and user experiences both from participants and data collectors in order to ensure participant and public involvement. These predictors constituted the basis from which final selection for the prospective cohort study was performed. In order to collect data on predictors in a time-efficient way. It was decided to collect these both by tests performed by data collectors and questionnaires filled out by study participants. All results from tests and questionnaire will be typed into REDCap (Research Electronic Data Capture, Vanderbilt University, Nashville, USA) electronic data capture tool hosted at Region Nordjylland, Denmark. Tests: • Arrhythmias: The investigator's study will be the first to investigate the prevalence of arrhythmias in a Danish population of older adults (+75 years old) by performing data collection in participants' own environments (i.e. own homes and activity centres). All participants will receive 5 days of 2-lead continuous heart rhythm monitoring (E-patch system, BioTelemetry Inc, Denmark). • Lower limb reaction time: This was chosen since a slow reaction time was found in earlier studies to increase the risk of falling.12 Measurements will be performed using a Nintendo Wii Balance Board with appropriate software Fysiometer (Bronderslev, Denmark). • Unilateral lower extremity strength: This was chosen since a poor lower strength in lower extremities was found in earlier studies to increase the risk of falling. Measurements will be performed using a Nintendo Wii Balance Board with appropriate software Fysiometer (Bronderslev, Denmark). • Grip strength: This was chosen since a poor grip strength was found in earlier studies to increase the risk of frailty, which is associated to increased fall risk. Measurements will be performed using a Nintendo Wii Balance Board with appropriate software Fysiometer (Bronderslev, Denmark). • Balance with dual-tasking: This was chosen since a poor dual-tasking ability was found in earlier studies to an increased fall risk. Measurements will be performed using a Nintendo Wii Balance Board with appropriate software Fysiometer (Bronderslev, Denmark).31 Simultaneously, participants will be instructed mention as many things one can buy in a supermarket, while attempting to stand still on the board. • Walking speed: This was chosen since a poor gait speed was found in earlier studies to an increased fall risk. Participants will be instructed to do a 4-meter walk at regular speed. Time spent will be noted using a stop watch. The fastest of the two measurements will be selected for further analysis. • Physical activity: This will be measured using an accelerometer incorporated in the hearth rhythm monitoring device. Questionnaire: The following list specifies predictors of falls and participant characteristics that will be included in the questionnaire. Age. Gender. Comorbidities. Medication. Educational level. Status of living. Prior falls. Walking aids. Alcohol consumption. Using multifocal lenses. Having dogs or cats in the household. Health-related quality of life using EQ-5D-3L by the EuroQol group Nutrition status. Symptoms of urinary incontinence, pain when walking and dizziness. Patient self-assessment as a measure of self-awareness of fall risk: Do you think that you could fall during the next year? Fear of falling using Short FES-I 7 item. Depression using Geriatric Depression Scale 4 item. Frailty using Tilburg Frailty Indicator. Activities of Daily Living using Vulnerable Elders Survey Orientation-Memory-Concentration test performed over the telephone Blinding: Due to nature of the study design, all assessments of predictors for the outcome to be predicted, future falls, will be blinded.

Official Title
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Development of a Multivariable Prognostic PREdiction Model for 1-year Risk of FALLing in Community-dwelling Older Adults in a Non-clinical Setting

Conditions
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Fall, Accidental Fall

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Community-dwelling older adults 75 years old or above Exclusion Criteria: Presence of acute illness defined by the presence of a participant-reported experience of illness arisen within 7 days prior to inclusion impairing their everyday functioning in such a way that they opt out of social activities outside their homes while this state is present. Unable to understand Danish evaluated by the data collectors. Diagnosed with dementia. Unable to stand up for 60 seconds without support and visually fixate on an object at the same time. Support is defined by any assistive devices or help from another person.

Ages Eligible for Study
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Minimum Age: 75 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of falls | Falls will be monitored using monthly prepaid fall calendars and validated by a phone call if a fall is registered. Also, circumstances of the fall will be asked about in the phone call. Blinding: Assessors of the outcome will be naturally blinded towards the predictors due to test results not being available before end of follow-up. Also, assessors of the outcome will be blinded to the questionnaire results by not having access to these in REDCap | 1 year follow up | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Time consumption for the final prediction model | Time consumption for both the tests and questionnaire | After 6 months | 
| Arrhythmias | Prevalence of arrhythmias will be calculated as the proportion of participants having arrhythmias in the study population at the time of the baseline measurements. | After 1 year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Accidental falls, Prediction model