Development of a Multivariable Prognostic PREdiction Model for 1-year Risk of FALLing in Community-dwelling Older Adults in a Non-clinical Setting
Conditions: Accidental Fall, Fall
Sponsor: Aalborg University Hospital

 
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PREFALL - Protocol 
Title:  
Development of a multivariable prognostic PREdiction model for 1-year risk of FALLing 
in community-dwelling older adults in a non-clinical setting (PREFALL). 
Registration: 
ClinicalTrials.gov Identifier: NCT03608709 
Protocol version: 
Issue date: 4th of November 2018. 
In the event of protocol amendments, this section will describe the date, changes and 
rationale of each amendment. Changes will not be incorporated into the protocol sections. 
All authors will be responsible for approving the amendments. Also, GS will be 
responsible for documentation and implementation of these.  
- 
4th of November 2018: 
o Change #1: 
 An extra recruitment site was added: The local hall of Hjørring. (page 
5 and 7) 
 Rationale: 
 To optimise recruitment.  
o Change #2: 
 End of follow-up was changed from April to August 2021 (page 13) 
 Rationale: 
 To optimise recruitment.  
- 
25th of June 2018:  
o Change: #1 

 
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 The number of recruiting senior activity centres was reduced from 
four to three.  
 Rationale: 
 A write error in the original protocol.  
o Change: #2 
 The time frame for the secondary outcome regarding time 
consumption was changed from “after enrolment of 250 participants” 
to “after 6 months”. 
 Rationale:  
 Registrational requirement at www.clinicaltrials.gov  
Funding: 
This research will be funded by The Department of Geriatrics, Aalborg University 
Hospital, Denmark, The Department of Clinical Medicine, Aalborg University, Denmark, 
The Department of Cardiology, Aalborg University Hospital, Denmark, the municipality 
of Hjørring, Denmark and by external funding. The external funding sources will have no 
role in the design of this study, during its execution, analyses, interpretation of data or 
decision to submit results.  
Authors: 
Corresponding author: 
Gustav Valentin Blichfeldt Sørensen (GS), MD 
Department of Geriatrics, Aalborg University Hospital, Denmark and 
Department of Clinical Medicine, Aalborg University, Denmark. 
E-mail: gustav.soerensen@rn.dk 
 
Stig Andersen (SA), MD, PhD, Professor. 
Department of Geriatrics, Aalborg University Hospital, Denmark and 
Department of Clinical Medicine, Aalborg University, Denmark. 
E-mail: lasa@rn.dk   

 
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Martin Grønbech Jørgensen (MGJ), MSc, PhD, Assistant Professor 
Department of Geriatrics, Aalborg University Hospital, Denmark.  
E-mail: mgj@rn.dk  
 
Jesper Ryg (JR), MD, PhD, Associated professor. 
Department of Geriatric Medicine, Odense University Hospital, Denmark and Institute of 
Clinical Research, University of Southern Denmark. 
E-mail: jesper.ryg@rsyd.dk  
 
Tahir Masud (TM), MD, PhD, Professor. 
School of Health and Social Care, University of Derby & Geriatric Medicine, Nottingham 
University Hospitals NHS Trust. 
E-mail: tahir.masud@nuh.nhs.uk    
 
Sam Riahi (SR), MD, PhD, Professor. 
Department of Cardiology, Aalborg University Hospital, Denmark and 
Department of Clinical Medicine, Aalborg University, Denmark. 
E-mail: sar@rn.dk  
Authors’ contributions: 
GS is the guarantor and drafted the manuscript for the protocol. GS, SA, MGJ, JR, TM 
developed selection criteria and chose predictors for the model.  
SR will provide hardware for arrhythmia-measurements, expertise on arrhythmias and 
support to GS in analysing arrhythmia-measurements.  
Sponsor contact information: 
Study sponsor:  
Department of Geriatrics, Aalborg University Hospital, Denmark.  
Contact name:  

 
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Stig Andersen, MD, PhD, Professor. E-mail: lasa@rn.dk  
Address:  
Geriatrisk afdeling, Gl. Rød bygn. 6, 2. Etage 
Aalborg Universitetshospital, Syd, Hobrovej 18-22, 9000 Aalborg 

 
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Introduction: 
Falls in community-dwelling older adults are common with an incidence of 30 % in over-
65s and 50 % in over-80s.1 Incidences are expected to increase significantly in the future 
due to population aging.2,3 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.2 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.4 Furthermore, falls are associated 
with elevated morbidity, mortality, poorer physical functioning and early admission to 
long-term care facilities5–7. 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.3,8 This is substantiated by more than 400 risk factors of falling that have now been 
identified.9 These spread across different domains including socio-demographics, medical 
conditions (e.g. atrial fibrillation10), medication, physical performance (e.g. reduced lower 
extremity strength6,11 or reaction time12), psychology (e.g. depression13,14 or fear of 
falling12,15) and cognition (e.g. global cognitive impairment or reduced executive 
functioning16–20).  
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.21 This emphasised the need for development of a validated prediction 
model to be used in a municipality environment to identify older adults at risk of falling. 

 
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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).  
Objective: 
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 refer citizens with high risk of falls to fall-preventive 
interventions.  
Secondary: 
1. To estimate time-consumption for the final prediction model.  
2. To describe the prevalence of arrhythmias in community-dwelling older adults.  
Methods: 
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. 

 
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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.  
Preventive home visits (PHV): 
The primary aims of PHVs are to empower the citizens to help themselves, support them 
in utilising own resources and preserve their functional capacity. The target group of 
PHVs is community-dwelling older adults primarily +75 years old. When turning 75 years 
old, a letter is sent to the citizen inviting him/her to an information meeting on senior life 
and the option to receive a PHV in their own homes. If they accept, a PHV will be 
scheduled. When turning 80 years old, the citizen will receive an annual letter offering a 
prescheduled PHV. In this case, the citizen has to actively decline the offer in order to not 
receive the PHV. Also, certain predefined groups at risk are included in the target group. 
PHVs are not offered to citizens already receiving local authority home help except for 
those receiving help with cleaning. As of 2017, the target group of PHVs in the 
municipality of Hjørring consisted of approximately 4,800 community-dwelling older 
adults of which 2,053 received a PHV. We expect to include 400 participants from PHVs. 
However, in case of economical, administrative or timing problems, this may alter. This 
will be noted in the amendments section to this protocol. PHVs are performed by trained 
nurses. These will collect data on participants in this setting. Each citizen in the target 
group will consecutively have a visit scheduled approximately 1-2 months beforehand. In 
connection with this scheduling, the participant information for this study will be mailed 
to the citizen. At the visit, citizens will be given oral information on the study in order to 
give consent for participation and will be assessed by data collectors according to in- and 
exclusion criteria. Afterwards, baseline tests will be performed. Within approximately 7 
working days, participants will receive a phone call from the municipality. Here they will 
be assisted in completing the questionnaire and have the Orientation-Memory-
Concentration test performed over the phone.  
Senior activity centres (SAC): 
The primary aim of SACs is to enhance quality of life of its attending citizens. This is done 
through empowerment of the citizen to preserve own resources and physical, 

 
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psychological and social abilities through different activities. The target group of SACs is 
primarily retirees (+65 years old), but also early retirees (+60 years old) with reduced 
physical, psychological or social functional capacity. In order to attend the activities, a 
small user charge (2 €) is required. Transportation to the SACs is either done through 
community transport schemes or on one’s own. As of 2018, 318 citizens of the target group 
attend SACs in the municipality of Hjørring weekly. We expect to include 100 participants 
from SACs. However, in case of economical, administrative or timing problems, this may 
alter. This will be noted in the amendments section to this protocol. The activity centres 
are staffed with health care workers. These will collect data on participants in this setting. 
There are five SACs in the municipality of Hjørring of which three will be recruited from. 
These are located in the city of Hjørring, Hirtshals and Sindal. This was chosen due to 
economical and administrative reasons. Participants will be recruited at occasions where 
citizens are gathered at the SACs. At these occasions, a video of GS giving oral 
information on the study will be presented. If interested in participating, citizens will be 
given written information in order to give consent for participation and assessed by data 
collectors according to in- and exclusion criteria. This recruitment method for the SACs 
was chosen due to practical reasons since attendance at the SACs is varying. Afterwards, 
baseline tests will be performed. Within approximately 7 working days, participants will 
receive a phone call from the municipality. Here they will be assisted in completing the 
questionnaire and have the Orientation-Memory-Concentration test performed over the 
phone.  
The Local Hall of Hjørring: 
The local hall of Hjørring constitutes a meeting point for 84 general- and patient 
associations within the municipality22. Furthermore, citizens from the municipality attend 
the hall for social events. Participants will be recruited at occasions where citizens are 
gathered at the local hall. An employee of the municipality, or GS, will frequently visit the 
local hall to recruit these. At these occasions, a video of GS giving oral information on the 
study will be presented or the study will be presented in-person by GS. If interested in 
participating, citizens will be given written information in order to give consent for 
participation and assessed by data collectors according to in- and exclusion criteria. This 

 
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recruitment method was chosen due to practical reasons since attendance is varying. Also, 
the video of GS will frequently be broadcasted at the local information-channel on TV 
informing about the project taking place in the local hall.  
 
Eligibility criteria: 
The following in- and exclusion criteria were chosen by an expert panel consisting of GS, 
SA, MGJ, JR, and TM. Reasons are stated in the underlying bullets of the criteria.   
Inclusion criteria: 
1. Community-dwelling older adults, i.e. not living in nursing homes.  
o This was decided due to this being the target group of prophylactic 
initiatives in the municipality of Hjørring.  
2. 75 years old or above. 
o This was decided due to the PHVs are primarily targeted towards citizens at 
this age category. 
Exclusion criteria: 
1. 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. 
 
a. This was decided in order to assume the measurements of the predictors 
being based on the potentially best performance of each participant.  
2. Unable to understand Danish evaluated by the data collectors. 
a. This was decided in order to ensure compliance with instructions for 
predictor measurements.  
3. Diagnosed with dementia. 
a. This was decided for ethical reasons in terms of achieving informed consent 
for participation.  
4. Unable to stand up for 60 seconds without support. Support is defined by any 
assistive devices or help from another person.  

 
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a. This was decided since it is a requirement in order to perform the physical 
tests using the Nintendo Wii Balance Board.  
Outcome:  
Primary outcome: 
The primary outcome is number of falls. These will be monitored using monthly prepaid 
fall calendars and validated by a phone call if a fall is registered.23 Also, circumstances of 
the fall will be ascertained in the phone call.  
Blinding:  
Assessors of the outcome will be 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. 
Secondary outcome: 
Time consumption for final prediction model: 
After 6 months, GS will perform a field trip to the SACs and PHVs to observe and record 
time-consumption in each setting. 
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. Due to 
practical reasons, recordings will be analysed for occurrence of arrhythmias after follow-
up has been completed. Both brady- and tachyarrhythmias will be considered.  
Predictors: 
Data collection of predictors will be performed at baseline. 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 on the inclusion date and questionnaires filled out by study participants 
in cooperation with a health care worker at the municipality over the telephone. All results 
from tests and questionnaires will be typed into REDCap (Research Electronic Data 
Capture, Vanderbilt University, Nashville, USA) electronic data capture tool hosted at 
Region Nordjylland, Denmark. The following predictors were chosen by an expert panel 
consisting of GS, SA, MGJ, JR, and TM. Reasons are stated in the underlying bullets of the 
criteria. First, a brief summary on the process of how predictors were selected will be 
given followed by a short description of each predictor.  

 
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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.  
Tests: 
Arrhythmias  
Our 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 4.5-5 
days of 2-lead continuous heart rhythm monitoring (E-patch system, BioTelemetry Inc, 
Denmark).24  
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).25  
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.6,11 Measurements will be performed using a 
Nintendo Wii Balance Board with appropriate software Fysiometer (Bronderslev, 
Denmark).26 
Grip strength: 

 
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This was chosen since a poor grip strength was found in earlier studies to increase the risk 
of frailty,27 which is associated to increased fall risk.28 Measurements will be performed 
using a Nintendo Wii Balance Board with appropriate software Fysiometer (Bronderslev, 
Denmark).29 
Balance with dual-tasking: 
This was chosen since a poor dual-tasking ability was found in earlier studies to an 
increased fall risk.17,30–32 Measurements will be performed using a Nintendo Wii Balance 
Board with appropriate software Fysiometer (Bronderslev, Denmark).33 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.34 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.6 
 Gender.35 
 Comorbidities.36  
 Medication.35 
 Educational level.15 
 Status of living.35 
 Prior falls.12,14,15,35,37 
 Walking aids.14 
 Alcohol consumption.15 
 Using multifocal lenses.38 
 Having dogs or cats in the house-
hold.15 
 Health-related quality of life using 
EQ-5D-3L by the EuroQol group.39  
 Nutrition status.40 
 Symptoms 
of 
urinary 
incontinence37, pain when walking14 
and dizzi-ness.12,14,15  

 
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 Patient self-assessment as a measure 
of self-awareness of fall risk: Do you 
think that you could fall during the 
next year?41 
 Fear of falling using Short FES-I 7-
item. 12,15 
 Depression using Geriatric Depres-
sion Scale 4 item.13,14 
 Frailty using the Tilburg Frailty 
Indicator28,42  
 Activities of Daily Living using the 
Vulnerable Elders Survey43. 
 Orientation-Memory-Concentration 
test 
performed 
over 
the 
telephone.44–46  
 
Blinding: 
Due to nature of the study design, all assessments of predictors for the outcome to be 
predicted, future falls, will be blinded.   
Statistical methods: 
A description of the flow of participants through the study will be given including 
number of participants with and without the outcome. Baseline characteristics of non-
fallers, single fallers and recurrent fallers (>2 falls) together with follow-up time will be 
summarised using descriptive statistics. A summary on the number of participants with 
missing data for predictors and the outcome will be given. Missing data will afterwards be 
handled using multiple imputation methods. The relevant predictors for falls will be 
selected using shrinkage methods and cross-validation47 on a multivariable Cox regression 
including relevant predictors. The full prediction model will be presented to allow for 
prediction of individuals together with an explanation on how to apply it. In continuation 
hereof, participants will be classified as “at high, medium, or low-risk of falling”. This 
categorisation should improve the likelihood of implementing the model in clinical 
practice. Furthermore reporting of results will follow the Critical Appraisal and Data 
Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS 
Checklist.48 Also, further mathematical modelling will be performed. Statistical analyses 
will be performed using statistical software. Also, an analysis will be made to adjust for 
fall-preventive initiatives prompted by the municipality.  

 
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Ethics: 
Written informed consent will be obtained from all patients by the data collectors. Consent 
will also be asked for regarding future/ancillary studies on the cohort. The study was 
approved by The Danish Data Protection Agency (number 2018-82). The Ethics Committee 
in the Region of North Jutland, Denmark, stated that no approval was necessary as the 
study included testing and a questionnaire, but no intervention. When data has been 
analysed, participants will be contacted in case of abnormal findings on their heart rhythm 
analysis.  
Time schedule: 
Baseline data collection is expected to start in June 2018 and proceed until 500 participants 
have been recruited. Follow-up will end in August 2020 at the latest.  
Access to data: 
All authors will have access to the final dataset. However, if data are to be used outside 
the institution of Region Nordjylland, a processor agreement has to be made according to 
the Danish Data Protection Agency.  
Declaration of interests: 
The authors declare having no conflicts of interest. However, MGJ developed and holds a 
patent on the software used for grip strength, unilateral lower extremity strength, grip 
strength and balance measures in the study. Due to this, MGJ was left out of the final 
decision on predictors for the study.    
Dissemination and reporting: 
We plan to publish studies in peer-reviewed journals in English. The reporting of the 
study will follow guidelines for Transparent Reporting of a multivariable prediction 
model for Individual Prognosis Or Diagnosis (TRIPOD). Also, reporting of outcome 
measures on the prediction model will comply with according to the Critical Appraisal 
and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) 
check list.  

 
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