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Trial registered on ANZCTR


Registration number
ACTRN12621001189819
Ethics application status
Approved
Date submitted
28/07/2021
Date registered
6/09/2021
Date last updated
6/09/2021
Date data sharing statement initially provided
6/09/2021
Type of registration
Prospectively registered

Titles & IDs
Public title
Testing the efficacy of motor analogies designed to promote safe landing by older adults who fall accidentally
Scientific title
Testing the efficacy of motor analogies designed to promote safe landing by older adults who fall accidentally: A randomized control study
Secondary ID [1] 305050 0
None
Universal Trial Number (UTN)
Trial acronym
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Fall related injuries 323020 0
Condition category
Condition code
Injuries and Accidents 320584 320584 0 0
Fractures
Injuries and Accidents 320820 320820 0 0
Other injuries and accidents

Intervention/exposure
Study type
Interventional
Description of intervention(s) / exposure
Brief Name:
Motor analogy

Measurements and instrumentation:
A 2D video camera (Canon, 25 frames per second) and 15 Delsys TrignoTM (Delsys Inc., Natrick, MA) inertial measurement units (IMU) will be used for data collection. The video camera will be positioned 3 meters from the side of the participants on a tripod (height 1.3 meters). The researcher will place 15 Delsys TrignoTM (Delsys Inc., Natrick, MA) inertial measurement unit (IMU) sensors on different body segments, described in the ‘sensor placement’ section. Acceleration data from the IMU sensors will be recorded at a frequency of 148.15 Hz using EMGworks Acquisition software (Version 4.5.4). A hand-held dynamometer (MyoMeter, M550; range: 0-50 kg) will be used to record the force applied when nudging each participant to fall.
Procedure:
Participants will be invited to a data collection session at the Human Performance Science Lab (TT 0.05) at the School of Health at the University of Waikato. Overall, the whole experiment will take approximately 1 hour and 30 minutes to 1 hour and 45 minutes. The procedure will be performed at the participant’s most comfortable pace and there will be an assistant standing by the participant at all times. The participant’s safety will be observed before performing each test and if there is any sign of potential harm the procedure will be immediately stopped. Furthermore, chairs will be placed close to the participants during all the procedures and they will be able to sit on them at any point during the experiment and participants will be provided with as many breaks as they want.

1. Sensor placement
Fifteen small IMU sensors (dimension: 27 x 37 x 13 mm) will be attached on the following body segments using double sided tape: head, chest (aligned with the sternum), lower back (aligned with L3), upper arms (dorsal), wrists (dorsal), hands (dorsal), hip (greater trochanter) thighs (lateral), lower legs (lateral).

2. Crossword puzzle
Participants in the analogy condition will be required to complete a three-word crossword puzzle designed to prime them about how feathers land on the ground: soft, slow, silent.

3. Falling onto a soft surface
Participants will stand on a surface surrounded by a fully padded area in the laboratory. The research assistant will unexpectedly apply a gentle impulse (nudge) to the shoulder of the participant in one of 4 different directions using a hand held dynamometer. Participants will be asked to fall onto a padded area in the direction in which the impulse (nudge) is applied. In order to avoid anticipation of the applied impulse (nudge), participants will be asked to engage in a secondary task – counting backwards in 3’s. To control for the nudge applied to the participants, the research assistant will be blinded to the conditions and a hand-held dynamometer will be used to record the force applied for each nudge. The participant will fall in four directions: backward, forward, left-side, and right-side. Participants in the analogy condition will be instructed to “land like a feather”. After the participants land on the soft mattress the assistant will help them to get up and they will have a 2 minutes interval between each fall. The order of the falls will be randomized using a random order generator. The experimental procedure will be repeated twice (with different order of falls each time); in total, each participant will fall eight times during the experimental procedure.
Intervention code [1] 321286 0
Prevention
Intervention code [2] 321445 0
Behaviour
Comparator / control treatment
Participants in the control condition will be asked to complete a crossword puzzle that uses name of birds as neutral primes and will be instructed to “land safely” during the falling procedure
Control group
Placebo

Outcomes
Primary outcome [1] 328406 0
Maximum acceleration (Impact force normalized by mass) of head by assessing IMU sensor data attached to participant's head
Timepoint [1] 328406 0
After receiving instructions and falling onto a soft surface at data collection session.
Primary outcome [2] 328407 0
Maximum acceleration (Impact force normalized by mass) of chest by assessing IMU sensor data attached to participant's chest
Timepoint [2] 328407 0
After receiving instructions and falling onto a soft surface at data collection session.
Primary outcome [3] 328624 0
Maximum acceleration (Impact force normalized by mass) of lower back by assessing IMU sensor data attached to participant's lower back
Timepoint [3] 328624 0
After receiving instructions and falling onto a soft surface at data collection session.
Secondary outcome [1] 399724 0
Maximum acceleration (Impact force normalized by mass) of arms by assessing IMU sensor data attached to participant's arms
Timepoint [1] 399724 0
After receiving instructions and falling onto a soft surface at data collection session.
Secondary outcome [2] 399725 0
Maximum acceleration (Impact force normalized by mass) of wrists by assessing IMU sensor data attached to participant's wrists
Timepoint [2] 399725 0
After receiving instructions and falling onto a soft surface at data collection session.
Secondary outcome [3] 399726 0
Maximum acceleration (Impact force normalized by mass) of hands by assessing IMU sensor data attached to participant's hands
Timepoint [3] 399726 0
After receiving instructions and falling onto a soft surface at data collection session.
Secondary outcome [4] 399727 0
Maximum acceleration (Impact force normalized by mass) of hips by assessing IMU sensor data attached to participant's hips
Timepoint [4] 399727 0
After receiving instructions and falling onto a soft surface at data collection session.
Secondary outcome [5] 399728 0
Maximum acceleration (Impact force normalized by mass) of thighs by assessing IMU sensor data attached to participant's thighs
Timepoint [5] 399728 0
After receiving instructions and falling onto a soft surface at data collection session.
Secondary outcome [6] 399729 0
Maximum acceleration (Impact force normalized by mass) of legs by assessing IMU sensor data attached to participant's legs
Timepoint [6] 399729 0
After receiving instructions and falling onto a soft surface at data collection session.
Secondary outcome [7] 399730 0
Fracture risk ratio of hips
Fracture risk ratio is defined as the ratio of force at impact divided by the load necessary to cause a fracture. To calculate the force applied to the hips, the acceleration data from the hip sensors at impact will be multiplied by the scaling factors for the femoral head mass (%mass) and then multiplied by 9.807 (convert g to m/s^2). Finally, the force applied to the participant’s hip sensors will be divided by the load required to fracture the radius and femur bones based on cadaveric studies.
Timepoint [7] 399730 0
After receiving instructions and falling onto a soft surface at data collection session.
Secondary outcome [8] 399731 0
Fracture risk ratio of wrists
Fracture risk ratio is defined as the ratio of force at impact divided by the load necessary to cause a fracture. To calculate the force applied to the wrists, the acceleration data from the wrist sensors at impact will be multiplied by the scaling factors for the forearm mass (%mass) and then multiplied by 9.807 (convert g to m/s^2). Finally, the force applied to the participant’s wrist sensors will be divided by the load required to fracture the radius and femur bones based on cadaveric studies.
Timepoint [8] 399731 0
After receiving instructions and falling onto a soft surface at data collection session.

Eligibility
Key inclusion criteria
• Age: 65 years and older
• Able to stand without help for 1 minute
• Able to walk without a walking aid for 6 meters
• Able to communicate in English, with no psychiatric or neurological impairments prohibiting participation
• Able to score above 3 on the Mini-Cog test*
• Able to pass the PARQ+ criterion**


* The Mini-Cog test (mini cognition test) has been validated for dementia screening; the score of 1 to 3 is considered “possibly impaired”, and a score above 3 is considered "probably normal” (Borson et al., 2003).

** The PARQ+ (physical activity readiness questionnaire) offers safe screening of older adults prior to engaging in exercise or physical activity (Cardinal & Cardinal, 1995; Cardinal, Esters, & Cardinal, 1996); it is sensitive to underlying conditions such as osteoporosis, cardiovascular conditions, respiratory disease, previous surgery, arthritis, chronic conditions, high blood pressure, back problem, etc. Therefore, if a participant is not in a healthy physical condition they will be excluded.



Minimum age
65 Years
Maximum age
No limit
Gender
Both males and females
Can healthy volunteers participate?
Yes
Key exclusion criteria
• Leg and/or foot amputation
• Need external support other than walking aids to stand
• Cannot stand without help for 1 minute
• Cannot walk without walking aid for 6 meters
• Cannot communicate in English, and have psychiatric or neurological impairments prohibiting participation
• Score below 3 on the Mini-Cog test
• Answer yes to 2 or more of the questions in the PARQ+ and require a doctor consultation for physical activity

Study design
Purpose of the study
Educational / counselling / training
Allocation to intervention
Randomised controlled trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
Each participant will be randomly assigned to the analogy condition or the control condition, using a random generator computer program. The randomization procedure will only be available to the researcher who will not share this information with the participants or the research assistant.
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
Simple randomization using a randomization table created by computerized sequence generator
Masking / blinding
Blinded (masking used)
Who is / are masked / blinded?
The people receiving the treatment/s
The people administering the treatment/s

Intervention assignment
Parallel
Other design features
Phase
Not Applicable
Type of endpoint(s)
Efficacy
Statistical methods / analysis

The demographic, trait, and outcome measures will be used to compare the 2 conditions (analogy vs control). The means (M) and standard deviation (SD) values of variables will be calculated and differences between conditions will be examined using IBM SPSS Statistics 25 (IBM® SPSS Statistics Software). A 2-way between-groups multivariate analysis of variance (MANOVA) will be conducted to explore the impact of condition (analogy, control) and fall direction (forward, backward, left-side, right-side) on the following variables of interest: Free fall duration (s); Impact duration (s); max acceleration (g) of the 15 sensors located on the head , chest, lower back, arms, wrists, hands, hips, thighs, and legs. Significant main and/or interaction effects will be followed up with ANOVA procedures on the relevant variables. The strength of the effect size (partial eta squared values or Cohen’s d) will be considered trivial when less than or equal to 0.01, small when between 0.01 and 0.06, moderate when between 0.06 and 0.14, large when greater than or equal to 0.14. To control for the multiplicity problem caused by conducting multiple statistical tests, the Benjamini–Hochberg (B-H) method will be used. This procedure controls the a level using successive modified Bonferroni corrections (Benjamini & Hochberg, 1995).

We calculated the sample size for this study based on our previous research with young adults with impact force and fracture risk ratio as our primary outcomes. The power was set at beta equal to .80, the significance level at alpha equal to .05, and standard deviation of 0.49 m/s2. The calculations resulted in a minimum group size of 32 participants per condition. To account for 20% attrition rate, this study aims to recruit 38 participants per condition.

Recruitment
Recruitment status
Not yet recruiting
Date of first participant enrolment
Anticipated
Actual
Date of last participant enrolment
Anticipated
Actual
Date of last data collection
Anticipated
Actual
Sample size
Target
Accrual to date
Final
Recruitment outside Australia
Country [1] 24006 0
New Zealand
State/province [1] 24006 0
Waikato

Funding & Sponsors
Funding source category [1] 309264 0
University
Name [1] 309264 0
University of Waikato
Address [1] 309264 0
TT Building Hillcrest Road, Hillcrest, Hamilton 3240.
Country [1] 309264 0
New Zealand
Funding source category [2] 309449 0
Other
Name [2] 309449 0
Freemason's foundation
Address [2] 309449 0
285 Grey Street, Hamilton East, Hamilton 3216
Country [2] 309449 0
New Zealand
Primary sponsor type
University
Name
University of Waiakto
Address
TT Building Hillcrest Road, Hillcrest, Hamilton 3240.
Country
New Zealand
Secondary sponsor category [1] 310238 0
None
Name [1] 310238 0
None
Address [1] 310238 0
None
Country [1] 310238 0

Ethics approval
Ethics application status
Approved
Ethics committee name [1] 309105 0
University of Waikato Human Research Ethics Committee
Ethics committee address [1] 309105 0
University of Waikato, Te Whare Wananga o Waikato, Private Bag 3105, Hamilton 3240
Ethics committee country [1] 309105 0
New Zealand
Date submitted for ethics approval [1] 309105 0
27/05/2021
Approval date [1] 309105 0
28/06/2021
Ethics approval number [1] 309105 0
HREC(Health)2021#45

Summary
Brief summary

Falling is associated with several adverse effects on the heath of the older adult population. The majority of research on falls among older people has focused on prevention, with little attention to ‘how to fall’ as safely as possible. Previous research has shown that motor analogies can be used to promote safe-landing in the younger population (Oladi, Uiga, Hébert-Losier, & Masters, 2019; Oladi, Uiga, Hébert-Losier, & Masters, 2020). However, the efficacy of using this technique in older adults is unknown. The aim of this study is to determine whether a motor analogy can be used to promote safe landing in the older adult population.
People 65 years and older will be eligible for participation, if they can walk without assistance for at least 6 meters, are able to stand without help for 1 minute, and pass the Physical Activity Readiness Questionnaire. We will attach multiple sensors to different body segments (head, trunk, hips, upper and lower extremities) of the participants and they will be randomly allocated to a control or analogy condition. Participants in the analogy condition will be required to complete a three-word crossword puzzle designed to prime them about how feathers land on the ground: soft, slow, silent. Participants in the control condition will be asked to complete a similar crossword puzzle that uses name of birds as neutral primes: swallow, shag, swan. They will then stand by a fully padded area and will be given an unexpected nudge (forward, backward or sideways) and will land on soft mattresses in the direction that the nudge is applied (i.e., a simulation of an unexpected fall). Participants in the analogy condition will be instructed to use a motor analogy: ‘land like a feather: soft, silent, slow’ while participants in the control condition will be instructed to: ‘land safely’. Data recorded from the sensors will be used to extract biomechanical parameters that characterise a safe fall, such as force applied to different body segments and fracture risk ratio of the wrists and hips. These parameters will be used to compare differences between the control and analogy groups. Based on our previous studies, we hypothesise that: using a motor analogy will result in safer landing (less fracture risk ratio, less impact force applied to body segments).

References:
Oladi, S., Uiga, L., Hébert-Losier, K., Masters, R.S.W., (2019). Using inertial measurement units to determine the potential efficacy of a motor analogy for improving landing from self-initiated falls. The Journal of Sport and Exercise Science, 3(1), 1-39.
Oladi, S., Uiga, L., Hébert-Losier, K., Masters, R.S.W., (2020). Using a motor analogy to promote safe-landing from unexpected falls. The Journal of Sport and Exercise Science, 4(3), 1-42.

Trial website
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 112994 0
Prof Rich Masters
Address 112994 0
Te Huataki Waiora School of Health, University of Waikato
TT Building Hillcrest Road, Hillcrest, Hamilton 3240
Country 112994 0
New Zealand
Phone 112994 0
+64 7 838 6206
Fax 112994 0
Email 112994 0
rich.masters@waikato.ac.nz
Contact person for public queries
Name 112995 0
Ms Sana Oladi
Address 112995 0
Te Huataki Waiora School of Health, University of Waikato
TT Building Hillcrest Road, Hillcrest, Hamilton 3240
Country 112995 0
New Zealand
Phone 112995 0
+64 21436130
Fax 112995 0
Email 112995 0
ro59@students.waikato.ac.nz
Contact person for scientific queries
Name 112996 0
Ms Sana Oladi
Address 112996 0
Te Huataki Waiora School of Health, University of Waikato
TT Building Hillcrest Road, Hillcrest, Hamilton 3240
Country 112996 0
New Zealand
Phone 112996 0
+64 21436130
Fax 112996 0
Email 112996 0
ro59@students.waikato.ac.nz

Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
Yes
What data in particular will be shared?
All datasets used or analyzed during this study will be available after de-identification
When will data be available (start and end dates)?
From immediately following publication until 5 years following main results publication

Available to whom?
Whoever asks for it on a reasonable request
Available for what types of analyses?
To achieve the aims in the approved proposal and for IPD meta-analyses
How or where can data be obtained?
Access subject to approvals by Principal Investigator
Either email Professor Rich Masters (rich.masters@waikato.ac.nz) or Sana Oladi (ro59@students.waikato.ac.nz)
What supporting documents are/will be available?
Study protocol
Statistical analysis plan
Informed consent form
Ethical approval
How or where can supporting documents be obtained?
Type [1] 12686 0
Ethical approval
Citation [1] 12686 0
Link [1] 12686 0
Email [1] 12686 0
ro59@students.waikato.ac.nz
Other [1] 12686 0
Attachment [1] 12686 0
Type [2] 12687 0
Informed consent form
Citation [2] 12687 0
Link [2] 12687 0
Email [2] 12687 0
ro59@students.waikato.ac.nz
Other [2] 12687 0
Attachment [2] 12687 0
Type [3] 12688 0
Statistical analysis plan
Citation [3] 12688 0
Link [3] 12688 0
Email [3] 12688 0
ro59@students.waikato.ac.nz
Other [3] 12688 0
Attachment [3] 12688 0
Type [4] 12690 0
Study protocol
Citation [4] 12690 0
Link [4] 12690 0
Email [4] 12690 0
ro59@students.waikato.ac.nz
Other [4] 12690 0
Attachment [4] 12690 0
Summary results
No Results