Effect of Treadmill Slip- and Trip-perturbation Training on Falls Among Community-dwelling Older Adults: A Randomized, Controlled Trial
Phase: NA
Conditions: Accidental Fall, Accident Prevention
Sponsor: Aalborg University Hospital

 
 
 
 
 
 
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STATISTICAL ANALYSIS PLAN (SAP) FOR: 
 
Effects of treadmill slip and trip perturbation-based balance training on falls in 
community-dwelling older adults (STABILITY): a randomised controlled trial 
 
 
TRIAL REGISTRATION IDENTIFIER:  
NCT04733222 
 
PROTOCOL ARTICLES PUBLISHED:  
Nørgaard, J. E., Andersen, S., Ryg, J., Stevenson, A. J. T., Andreasen, J., Danielsen, M. B., ... & Jørgensen, M. G. 
(2022). 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. BMJ Open, 12(2). 
 
Version: 1.0 
Date: September 30th, 2022 
 
 
Study group 
Jens Eg Nørgaard, MSc, Department of Geriatric Medicine, Aalborg University Hospital; De-
partment of Clinical Medicine, Aalborg University, DK-9000  Aalborg, Denmark 
Stig Andersen, Department of Geriatric Medicine, Aalborg University Hospital and Department 
of Clinical Medicine, Aalborg University, DK-9000 Aalborg, Denmark 
Jesper Ryg, Department of Geriatric Medicine, Odense University Hospital, DK-5000 Odense, 
Denmark; Geriatric Research Unit, Department of Clinical Research, University of Southern 
Denmark, DK-5000 Odense, Denmark 
Jane Andreasen, Department of Occupational Therapy and Physiotherapy, Aalborg University 
Hospital, DK-9000 Aalborg, Denmark; Public Health and Epidemiology Group, Department of 
Health, Science and Technology, Aalborg University, DK-9000 Aalborg, Denmark 
Anderson de Souza Oliveira, Department of Materials and Production, Aalborg University, Aal-
borg, Denmark. 
Andrew James Thomas Stevenson, Department of Health Science and Technology, Aalborg 
University, Aalborg, Denmark 
Mathias Brix Danielsen, Department of Geriatric Medicine, Aalborg University Hospital; De-
partment of Clinical Medicine, Aalborg University, DK-9000 Aalborg, Denmark 
Martin Gronbech Jorgensen, Department of Geriatric Medicine, Aalborg University Hospital; 
Department of Clinical Medicine, Aalborg University, DK-9000 Aalborg, Denmark 
 

 
 
 
 
 
 
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Table of contents 
1. 
Study Synopsis ........................................................................................................................ 3 
2. Study Objectives, Hypothesis, and Outcomes ........................................................................... 3 
2.1. Primary Objective and Outcome (if applicable) .................................................................. 3 
2.2. Secondary Objectives and Outcomes .................................................................................. 3 
2.3. Descriptive Outcomes.......................................................................................................... 3 
2.4. Specification of endpoints ................................................................................................... 4 
2.4.1. Primary Endpoint .......................................................................................................... 4 
2.4.2. Secondary Endpoints .................................................................................................... 4 
3. Study Design .............................................................................................................................. 4 
3.1. Sample Size ......................................................................................................................... 4 
3.2. Randomisation and Blinding ............................................................................................... 4 
4. Study Population ........................................................................................................................ 4 
4.1. Subject Disposition .............................................................................................................. 4 
5. Data handling ............................................................................................................................. 5 
5.1 Missing data .......................................................................................................................... 5 
6. Statistical Analysis ..................................................................................................................... 6 
Sensitivity analysis ................................................................................................................... 11 
Fall outcomes ........................................................................................................................ 11 
Fall-related risk factor outcomes .......................................................................................... 11 
6.3. Major Protocol Deviations ................................................................................................ 11 
7. Implementation of Analysis Plan ............................................................................................. 11 
STATA CODE ...................................................................................................................... 11 
8. References ................................................................................................................................ 13 
 

 
 
 
 
 
 
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1. Study Synopsis 
Falls are common among older adults and can have severe consequences such as disability, de-
creased quality of life, and premature death [1–4]. Perturbation-based balance training (PBT) 
has recently gained interest as a potential brief, effective, and sustainable fall preventive strategy 
[5]. During PBT, participants are exposed to repeated slips and trips during walking while wear-
ing a safety harness in a laboratory. Two meta-analyses, looking at eight and four PBT studies, 
have shown a vaccination-like effect of almost 50% decreased fall rates after even small dosages 
(1-8 sessions) [6–8]. Nonetheless, more evidence is needed to evaluate the effects of PBT per-
formed on a treadmill [8]. This assessor-blinded, parallel-group, randomised, controlled trial 
will evaluate the effects of treadmill-PBT on falls and other relevant physical, cognitive and 
sociopsychological factors among community-dwelling older adults. 
2. Study Objectives, Hypothesis, and Outcomes 
2.1. Primary Objective and Outcome (if applicable) 
The primary objective of this study is to determine the effects of a four-session PBT intervention 
on fall rates (number of falls per person-year) in community-dwelling older adults aged 65 or 
older compared to treadmill walking without perturbations.  
The main hypothesis is that treadmill-PBT will decrease the fall rate by up to 50% in the 12 
months following the intervention compared to time-matched treadmill walking.   
2.2. Secondary Objectives and Outcomes 
The secondary objectives are to evaluate the effects on additional fall metrics and the potential 
transfer effects of PBT to other relevant physical, cognitive, and social-psychological risk fac-
tors.  
The secondary fall metrics include the proportion of fallers, the time to first fall, the proportion 
with at least one fall-related fracture, the rate of fall-related fractures, the proportion with at least 
one fall-related injury, the rate of fall-related injury, the proportion with at least one fall-related 
health-care contact, and the rate of fall-related health-care contact. It is expected that the propor-
tion of fallers is 50% lower in the PBT-group compared to the treadmill walking group.[6,9] 
However, we do not have enough evidence regarding the remaining fall-related outcomes to 
make hypothesis hereof; thus, these outcomes are considered exploratory. 
The secondary fall-related risk factors included are single- and dual-task gait speed, reaction 
time, single- and dual-task static balance, lower extremity performance, executive function, 
health-related quality of life, and fear of falling. These secondary outcomes were chosen as they 
all have been identified as fall risk markers [10–16]. However, there is insufficient information 
about such outcomes following PBT; therefore, we consider these outcomes exploratory.   
2.3. Descriptive Outcomes 
Descriptive data include height, weight, sex, physical and cognitive function, medication usage, 
Tilburg Frailty Indicator, highest education level, living arrangements, and fall history, includ-
ing associated injuries, everyday activity functionality (Vulnerable Elders Survey-13), physical 
activity levels, and home care usage. Information will be collected through a combination of 
self-reporting, measurements, questionnaires, and medical/municipality records. Descriptive 
data will be presented in a table stratified by intervention type as mean and standard deviation 
(normally distributed continuous variables), median and inter-quartile range (not normally dis-

 
 
 
 
 
 
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tributed continuous variables), or number and percentage (categorical variables). Descriptive 
data will be visually compared to evaluate any potential differences between groups. 
 
2.4. Specification of endpoints 
2.4.1. Primary Endpoint 
The primary endpoint will be the fall rate 12 months after completion of the third training ses-
sion, and it will be collected using monthly fall calendars as recommended.[17] 
2.4.2. Secondary Endpoints 
Secondary outcomes and their endpoints are outlined in table 1 and 2. The secondary fall out-
comes will be collected using the fall calendars for 12 months. The fall-related risk factor out-
comes will be collected at the pre- and post-training test and the 26- and 52-week follow-up.  
3. Study Design 
This study is designed as an assessor-blinded, randomised, parallel-group (1:1 ratio), controlled 
trial 
3.1. Sample Size 
The sample size calculation was conducted in G*power (version 3.1.9.4, University of Kiel, 
Kiel, German) using a Poisson regression model. The calculation was made with certain as-
sumptions (80% power, 5% significance level, 50% difference in fall rate (favouring the PBT), 
and 20% dropout rate) and an expected average fall rate of 0.85.[18–21] This resulted in an es-
timated required sample size of 70 participants in each group.  
3.2. Randomisation and Blinding 
After the pre-training tests, participants will be randomly allocated to either the PBT or treadmill 
walking group using a permuted block randomisation module in REDCap to ensure similar 
group sizes (Research Electronic Data Capture; version 9.5.6). Random block sizes (two, four, 
six, or eight) will ensure that allocation concealment is maintained. The allocation sequence will 
be generated by a research staff member not involved in enrolling or assigning participants to 
groups. 
4. Study Population 
4.1. Subject Disposition 
One hundred forty community-dwelling older adults (70 in each group) living in and around 
Aalborg will be recruited via informal presentations about the trial, local and national newspa-
pers, radio and television spots, flyer hand-outs, and snowball sampling. Participants are eligible 
if they are 1) ≥65 years old, 2) community-dwelling, and 3) able to walk without a walking aid. 
Participants will be excluded if they 1) have any of the following self-reported conditions: or-
thopaedic surgery within the past 12 months, osteoporosis or history of osteoporosis-related 
fractures (low-impact hip, spine, and wrist fracture), or progressive neurological disease (e.g., 
Parkinson), 2) have an unstable medical condition that would prevent safe participation, 3) have 

 
 
 
 
 
 
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a severe cognitive impairment (a score <8 in The Short Orientation-Memory-Concentration 
Test)[22], or 4) are currently participating in another fall prevention trial.  
5. Data handling 
All data will be collected and managed using the secure, web-based software platform REDCap 
hosted in The Region of Northern Denmark.[23,24] The data collection forms in REDCap en-
sure strong data integrity by applying functions that check for mandatory information, data 
ranges, and alerts whenever data violates specific limits.[24] To ensure data quality, all out-
comes will be visually inspected for implausible values before the dataset is locked. Missing and 
out-of-range data in REDCap will also be assessed compared to original data files (paper docu-
ments for questionnaires and FysioMeter software for balance and reaction time) and corrected 
in cases of discrepancies. REDCap also logs every record activity, which will be used to monitor 
data validity.  
5.1 Missing data 
The number of missing observations and the associated reasons will be reported. For the primary 
outcome, fall rates, missing data will not be imputed; however, the analysis will be adjusted for 
follow-up time (days of follow-up will be used as an offset). Likewise, missing data regarding 
the secondary binary outcomes will neither be imputed, but the modified Poisson regression will 
be adjusted for person-years (days of follow-up will be used as an offset). For participants who 
do not return any fall calendars 0 falls and 0 person-years will be registered. However, if more 
than 10% of data is missing in any outcomes, a sensitivity analysis utilising multiple imputations 
will be conducted. 
Missing data in continuous outcome are expected to be missing at random; thus, they will not be 
imputed as it has shown that multiple imputations do not add any benefits to a linear mixed-
effects model.[25]  
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
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6. Statistical Analysis 
All statistical tests will be performed using an intention-to-treat approach. Moreover, a per-protocol analysis will also be performed, including only par-
ticipants who complete 75% or more of the training sessions. The secondary outcome will not be adjusted for multiple comparisons; thus, these results 
should be considered exploratory. 
Count data will be reported as incidence rate ratios (IRR) with 95% confidence intervals (95% CI). Binary outcomes will be reported as risk ratios (RR) 
and 95% CIs. When appropriate, continuous variables will be reported as either mean and standard deviation or median and interquartile range. 
 
 
 
Table 1      Variables, measures, and methods of analysis for fall outcomes. 
Primary outcome 
Variable/outcome 
Mode of assessing 
Time frame 
Variable type 
Assumption 
Methods of analysis 
Fall rate (falls per 
person-year) 
Fall calendars 
Continuously for  
12 months 
Count 
Equal mean and  
variance * 
Poisson regression 
Alternative: Bootstrapping† 
Secondary fall outcomes 
Variable/outcome 
Mode of assessing 
Time frame 
Variable type 
Assumption 
Methods of analysis 
Proportion of fallers 
Fall calendars 
Continuously for  
12 months 
Binary 
Equal mean and  
variance * 
Poisson regression with robust error vari-
ance 
Time to first fall 
Fall calendars 
Continuously for  
12 months 
Survival 
Linear relationship 
between log hazard 
and covariateα 
Cox proportional hazard 
Fall-related fracture 
rate 
Fall calendars 
Continuously for  
12 months 
Count 
Equal mean and  
variance * 
Poisson regression 
Alternative: Bootstrapping† 
The proportion with a 
fall-related fracture 
Fall calendars 
Continuously for  
12 months 
Binary 
Equal mean and  
variance * 
Poisson regression with robust error vari-
ance 
Fall-related injury rate 
(other injuries than 
fractures) 
Fall calendars 
Continuously for  
12 months 
Count 
Equal mean and  
variance * 
Poisson regression 
Alternative: Bootstrapping† 

 
 
 
 
 
 
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The proportion with 
fall-related injuries 
(other injuries than 
fractures) 
Fall calendars 
Continuously for  
12 months 
Binary 
Equal mean and  
variance * 
Poisson regression with robust error vari-
ance 
Fall-related hospital 
contact rate 
Fall calendars 
Continuously for  
12 months 
Count 
Equal mean and  
variance * 
Poisson regression 
Alternative: Bootstrapping† 
The proportion with a 
fall-related fracture 
Fall calendars 
Continuously for  
12 months 
Binary 
Equal mean and  
variance * 
Poisson regression with robust error vari-
ance 
All-cause fracture 
rates 
Fall calendars 
Continuously for  
12 months 
Count 
Equal mean and  
variance * 
Poisson regression 
Alternative: Bootstrapping† 
The proportion with an 
all-cause fracture 
Fall calendars 
Continuously for  
12 months 
Binary 
Equal mean and  
variance * 
Poisson regression with robust error vari-
ance 
Patient Global Impres-
sion of Change 
7-item question-
naire and 11-point 
Likert scale; pro-
portion “4 – some-
what better”) and 0-
11 points on the 
Likert scale (lower 
score indicates 
better performance) 
52-week follow-up 
7-item ques-
tionnaire:  
Binary 
 
VAS: 
Continuous 
(Ordinal) 
VAS-scale: 
Normal distributionᶲ 
Homogeneity of var-
ianceᶲ 
7-item questionnaire: 
Fisher’s Exact 
 
VAS-scale: 
Unpaired t-test 
Alternative: Unpaired two-sample Wil-
coxon test‡ 
Laboratory-induced 
overall fall rate 
Visual inspection 
of video recording 
of a level 1 slip and 
trip perturbation; 
fall (1) or no fall 
(0) 
Pre-training test; 
post-training test; 
26- week follow-up; 
52-week follow-up 
Count 
Equal mean and  
variance * 
Poisson regression 
Alternative: Bootstrapping† 

 
 
 
 
 
 
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Laboratory-induced 
slip falls 
Visual inspection 
of video recording 
of a level 1 slip 
perturbation; fall 
(1) or no fall (0) 
Pre-training test; 
post-training test; 
26- week follow-up; 
52-week follow-up 
Binary 
- 
Fisher’s Exact 
Laboratory-induced 
trip falls 
Visual inspection 
of video recording 
of a level 1 trip 
perturbation; fall 
(1) or no fall (0) 
Pre-training test; 
post-training test; 
26- week follow-up; 
52-week follow-up 
Binary 
- 
Fisher’s Exact 
* Similarity of the calculated mean and variance; † If mean is not equal to variance; α Visual inspection of residual plots; ᶲ Examined by visual inspection of 
histograms and QQ-plots; ‡ If data is not normal-distributed; Ο Participants ID as randoms effect - only participants, who did not fall during the perturbation 
at pre-training test was included 
 
 
 
 
 
 
 
Table 2      Variables, measures, and analysis methods for fall-related risk factors.  
Variable/outcome 
Mode of assessing 
Time frame 
Variable 
type 
Assumption 
Methods of analysis 
Single- and dual-task 
gait speed 
6-meter walking test;  
walking speed (m/s) 
Pre-training test; 
post-training test;  
26- week follow-up; 
52-week follow-up 
Continuous 
(ratio) 
Normal 
distribution 
of residuals and ran-
dom effects*ᶲ 
Homogeneity of var-
ianceꚚᶲ 
Linear mixed-effects model Ο 

 
 
 
 
 
 
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Single- and dual-task 
sway 
30-second balance 
test on WBB‡; 
centre of pressure 
area (mm2) and ve-
locity (mm/s) 
Pre-training test; 
post-training test;  
26- week follow-up; 
52-week follow-up 
Continuous 
(ratio) 
Normal 
distribution 
of residuals and ran-
dom effects*ᶲ 
Homogeneity of var-
ianceꚚᶲ 
Linear mixed-effects model Ο 
Choice stepping  
reaction time (CSRT) 
CSRT on WBB‡;  
reaction time (ms) 
Pre-training test; 
post-training test;  
26- week follow-up; 
52-week follow-up 
Continuous 
(ratio) 
Normal 
distribution 
of residuals and ran-
dom effects*ᶲ 
Homogeneity of var-
ianceꚚᶲ 
Linear mixed-effects model Ο 
Short physical  
performance battery  
2x4 meter walking 
time, 3x10 second 
static balance with 3 
different foot posi-
tions, and 5 chair 
raises; score from 0-
12 (higher score in-
dicates better per-
formance) 
Pre-training test; 
post-training test;  
26- week follow-up; 
52-week follow-up 
Continuous 
(ordinal) 
Normal 
distribution 
of residuals and ran-
dom effects*ᶲ 
Homogeneity of var-
ianceꚚᶲ 
Linear mixed-effects model Ο 
Trial-making-test  
Part A and B; time and 
error 
Part A and Part B of 
the Trail-making-
test; time (s) and 
errors (n). 
Difference in time-
to-complete between 
Part A and Part B; 
time (s) 
Pre-training test; 
post-training test;  
26- week follow-up; 
52-week follow-up 
Continuous 
(ratio) 
Normal 
distribution 
of residuals and ran-
dom effects*ᶲ 
Homogeneity of var-
ianceꚚᶲ 
Linear mixed-effects model Ο 
Short Falls Efficacy 
Scale 
7-item questionnaire; 
score from 7-28 
(lower score indi-
cates better perfor-
mance) 
Pre-training test; 
post-training test;  
26- week follow-up; 
52-week follow-up 
Continuous 
(ordinal) 
Normal 
distribution 
of residuals and ran-
dom effects*ᶲ 
Homogeneity of var-
ianceꚚᶲ 
Linear mixed-effects model Ο 

 
 
 
 
 
 
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EuroQoL 5D-5L 
5-item questionnaire 
and visual analogue 
scale; index from 0-1 
(higher score indi-
cates better perfor-
mance) 
Visual analogue 
scale from 0-100 
(higher score indi-
cates better perfor-
mance) 
Pre-training test; 
post-training test;  
26- week follow-up; 
52-week follow-up 
Continuous 
(ordinal) 
Normal 
distribution 
of residuals and ran-
dom effects*ᶲ 
Homogeneity of var-
ianceꚚᶲ 
Linear mixed-effects model Ο 
* Examined by visual inspection of histograms and QQ-plots; Ꚛ Examined by visually inspecting residuals plotted against fitted values; † If data is not nor-
mal-distributed; ᶲ Violations of assumptions will be noted; however, no alternative method will be used, as the linear mixed-effects model is robust against 
such violations; Ο Participant ID as the random effect 
 

 
 
 
 
 
 
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Sensitivity analysis 
Fall outcomes 
For the primary outcome (fall rates) and secondary fall outcomes, a sensitivity analysis adjusting 
for known confounders (age, sex, and previous falls) will be conducted to evaluate the robust-
ness of the results. Furthermore, if the count variable data is over-dispersed, a poisons regression 
with bootstrapping will be performed; however, a sensitivity analysis without bootstrapping will 
also be carried out. These analyses were planned before the commencement of data collection. 
Additional fall rate sensitivity analyses 1) only including participants with no prior history of 
falls 12 months before study commencement and 2) only including participants with a history of 
falls 12 months before study commencement will also be conducted. These sensitivity analyses 
were planned after data collection began. 
Fall-related risk factor outcomes 
Before data collection commenced, it was determined to conduct a sensitivity analysis on the 
secondary fall-related risk factors adjusting for age, sex, and previous falls will be carried out. 
 
6.3. Major Protocol Deviations 
Major protocol deviations will be reported in the trial registration at ClinicalTrials.gov 
(NCT04733222), the local ethics committee, and the SAP. 
7. Implementation of Analysis Plan 
The data will be exported from REDCap to the statistical program STATA. An external statisti-
cian not involved in the study will assist with the statistical test.  
 
STATA CODE 
Poisson regression (example of code for fall rate):  
poisson fallrate ib1.group, irr exposure(personyear) 
 
Poisson regression with bootstrapping (example of code for fall rate):  
poisson fallrate ib1.group, irr exposure(personyear) vce(bootstrap, reps(1000)) 
 
Poisson regression adjusting for age, sex, and fall history (example of code for fall rate):   
poisson fallrate ib1.group age i.sex i.prev_faller, irr exposure(personyear) 
 
Poisson regression with robust error variance (example of code for proportion of fallers):   
glm faller ib1.group, fam(poisson) link(log) vce(robust) eform 
 
Poisson regression with robust error variance adjusting for age, sex, and fall history (example 
of code for proportion of fallers): 
glm faller ib1.group age i.sex i.prev_faller, fam(poisson) link(log) vce(robust) eform 
 
 
Cox survival analysis (example of code for time to first fall): 
stset firstfall, failure(faller==1) 
stcox group   

 
 
 
 
 
 
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Cox survival analysis adjusted for age, sex, and fall history (example of code for time to first 
fall):   
stset firstfall, failure(faller==1) 
stcox group age i.sex i.prev_faller 
 
unpaired t-test (example of code for Global Patient Impression of Change (gpic)): 
ttest gpic, by(group) unpaired 
 
Unpaired two-sample Wilcoxon test (example of code for Global Patient Impression of Change): 
ranksum gpic, by(group)   
 
Fisher’s exact (example of code for the proportion of fallers following slip perturbation at pre-
training (t1)): 
tabulate lab_slip_t1 group, exact  
 
Linear mixed-effects model (example of code for single-task gait speed):  
mixed gaitspeed_st group time || record_id:, var reml 
In case of significant interaction effect, a posthoc analysis adjusted using the Bonferroni method 
is employed using the following code: 
contrast rb1.time#group, mcompare(bonferroni) 
 
Linear mixed-effects model adjusting for age, sex, and fall history (example of code for single-
task gait speed): 
mixed gaitspeed_st age i.sex i.prev_faller group##time || record_id:, var reml 
In case of a significant interaction effect, posthoc tests was conducted using the following code: 
contrast rb1.time#group 

 
 
 
 
 
 
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