LudoFit for Preventing Falls

Overseen By Shirley Huang, MD, FRCPC, MSc, MSc (HQ)

Age: Any Age

Sex: Any

Trial Phase: Academic

Sponsor: Shirley Huang

Disqualifiers: Cognitive impairment, Berg Balance Scale, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 2

Duration: 12 weeks

80 Participants Needed

Trial Summary

What is the purpose of this trial?

The goal of this mixed methods cohort study is to explore how an innovative game-based, technology-enabled home exercise software compares to a conventional home-based exercise program in older adults at risk for falls. The main question\[s\] it aims to answer are: * Does the exercise software lead to better or similar adherence to exercise recommendations compared to the conventional exercise program? * Does the exercise software improve user strength and balance, as measured by relevant physical function scores? * Do participants find the exercise software enjoyable and acceptable? Participants will be approached during their initial fall risk assessment to see if they are interested in taking part. Those who agree to participate will choose either the exercise software or the conventional exercise program and will be instructed to: * Perform their respective exercise program for a minimum of 3 times a week for 3 months. * Return to clinic in 3 months to see our C-FAST physiotherapist for reassessment of their relevant physical functional tests. * For conventional exercise program only - Document exercise compliance in an exercise log. * For software program only - Complete a survey regarding the use of the technology. Researchers will compare software-use cohort versus conventional exercise program cohort to see if adherence and physical function scores are comparable.

Do I have to stop taking my current medications for this trial?

The trial protocol does not specify whether you need to stop taking your current medications. It seems focused on exercise programs, so it's likely you can continue your medications, but please confirm with the trial coordinators.

What data supports the idea that LudoFit for Preventing Falls is an effective treatment?

The available research does not provide specific data on LudoFit for Preventing Falls. However, it mentions the use of smartphone technology and game-based approaches for fall prevention in older adults. These methods, like the Nintendo Wii Fit program, have been explored for their potential to assess fall risk and support exercise adherence. While these studies do not directly evaluate LudoFit, they suggest that technology-based interventions can be effective in promoting exercise adherence and potentially reducing fall risk. ^{1 2 3 4 5}

What safety data exists for LudoFit treatment?

The iStoppFalls study provides relevant safety data, as it assessed the feasibility, including safety, of an ICT-based exercise program for fall prevention in older adults. Although not directly named LudoFit, similar technology-based exercise interventions have been evaluated for safety and effectiveness in preventing falls. ^{1 5 6 7 8}

Is LudoFit a promising treatment for preventing falls?

Yes, LudoFit is a promising treatment for preventing falls. It uses fun exercise games that can be done at home, which makes it easy and enjoyable for older adults to stay active. This kind of exercise can help improve balance and strength, which are important for preventing falls. ^{1 5 7 9 10}

Eligibility Criteria

This trial is for older adults at risk of accidental falls who are interested in a home-based exercise program. They must speak English or French, be independent with daily activities, and agree to return for a follow-up after 3 months.

Inclusion Criteria

I can take care of my daily needs without help.

I agree to return for a check-up in 3 months.

I am interested in exercising at home.

Exclusion Criteria

I cannot safely bend down to touch the floor and stand back up on my own.

My balance score is 45 or lower.

I can't safely reach 5 inches to the side and forward by myself.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants perform their chosen exercise program (either LudoFit software or conventional exercises) at least 3 times a week for 3 months

12 weeks

1 visit (in-person) at the start

Follow-up

Participants return to the clinic for reassessment of their physical function tests and to complete surveys

1 week

1 visit (in-person)

Treatment Details

Interventions

• LudoFit

Trial Overview The study compares LudoFit, an innovative game-based exercise software, with conventional home exercises. Participants will do their chosen program three times weekly for three months and have their strength and balance reassessed.

Participant Groups

Group I: Game-based exercise Experimental Treatment 1 Intervention

Strength and balance exercises using the LudoFit game-based exercise software. Participants will be registered on the platform. Exercise is recommended to be done at least 3 times per week.

Group II: Conventional exercise Active Control 1 Intervention

Normally physiotherapist-recommended, tailored strength and balance exercises. These exercises are given in printed handouts and demonstrated by our physiotherapist with the patients during their initial clinic visit. Exercise is recommended to be done at least 3 times per week.

Who Is Running the Clinical Trial?

Shirley Huang

Lead Sponsor

Trials

1

Recruited

80+

Jintronix

Collaborator

Trials

1

Recruited

80+

Findings from Research

A board game designed for fall prevention in older adults was developed and tested, achieving a high Concordance Index of 93.22% from experts and 99% from older women, indicating strong approval and effectiveness.

The game incorporates elements of the Health Promotion Model, focusing on individual behaviors, benefits, and barriers to prevent falls, making it a suitable educational tool for the community.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development and testing of the Prev'Quedas game for older adults in the community: a descriptive study. Diniz, JL., Coutinho, JFV., Marques, MB., et al.[2022]

The 'FITTSBALL' tool integrates the technical aspects of exercise prescription (FITT: Frequency, Intensity, Time, Type) with cognitive behavioral theories to enhance adherence to exercise, addressing a significant challenge in managing chronic diseases.

This comprehensive framework provides a structured approach to exercise supervision, demonstrating how technical and behavioral components can work together to improve long-term adherence and support behavior change in patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

FITTSBALL - a dynamic tool for supervision of clinical exercise prescription. Ranasinghe, C., King, NA., Arena, R., et al.[2020]

A novel smartphone app for exercise training demonstrated high accuracy and precision in measuring movement, with mean differences of less than 1°/s compared to a gold standard dynamometer across various speeds.

The app's performance was validated with a coefficient of variation under 3% for most velocities, indicating it can reliably track exercise movements, making it a promising tool for clinicians to monitor patient performance.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Accuracy and Precision of an Accelerometer-Based Smartphone App Designed to Monitor and Record Angular Movement over Time. Bittel, AJ., Elazzazi, A., Bittel, DC.[2017]

This feasibility trial will assess whether smartphone technology can improve adherence to a falls rehabilitation exercise program among 72 patients aged 50 and older, comparing standard service with and without a motivational app.

The primary focus is on the feasibility of the intervention and study procedures, while secondary outcomes will evaluate falls, function, and adherence using both traditional and smartphone-based measures over a 6-month period.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Can smartphone technology be used to support an effective home exercise intervention to prevent falls amongst community dwelling older adults?: the TOGETHER feasibility RCT study protocol. Hawley-Hague, H., Tacconi, C., Mellone, S., et al.[2023]

The Nintendo Wii Fit program, specifically the 'Basic Step' module, effectively differentiates between older adults at risk of falling and those who are not, with a significant score threshold of 111 points indicating fall risk.

The study, involving 45 community-dwelling older women, found that the Basic Step module correlated well with traditional fall risk assessments, suggesting it could be a practical tool for evaluating fall risk in this population.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The reliability and preliminary validity of game-based fall risk assessment in community-dwelling older adults. Yamada, M., Aoyama, T., Nakamura, M., et al.[2022]

Exercise programs that include balance training can reduce the risk of falls in older adults by 40%, but many participants find these programs boring and often drop out.

The development of a Kinect-based system for step training aims to make exercise more engaging while also measuring stepping performance, which could help predict and prevent falls in older individuals.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Exergames for the elderly: towards an embedded Kinect-based clinical test of falls risk. Garcia, JA., Felix Navarro, K., Schoene, D., et al.[2012]

The iStoppFalls exercise program, delivered through an ICT-based system, significantly reduced physiological fall risk in older adults, with no adverse events reported during the 16-week trial involving 153 participants.

Higher adherence to the exercise program (over 90 minutes per week) led to greater improvements in balance, cognitive function, and quality of life compared to those with lower adherence and the control group.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

ICT-based system to predict and prevent falls (iStoppFalls): results from an international multicenter randomized controlled trial. Gschwind, YJ., Eichberg, S., Ejupi, A., et al.[2020]

The Dance! Don't Fall (DDF) mobile application allows older adults to self-assess their fall risk and engage in exercises designed to reduce that risk, promoting proactive health management.

The development of the app was informed by a literature review and observational sessions, ensuring that it effectively combines fun with functional exercises to help prevent falls.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Dance! Don't Fall - preventing falls and promoting exercise at home. Kerwin, M., Nunes, F., Silva, PA.[2012]

The pilot study involving 21 community-dwelling older adults (15 using WiiFit and 6 receiving standard care) showed that 80% of participants attended most exercise sessions, indicating high acceptability of the intervention.

Participants using WiiFit demonstrated significant improvements in balance, as measured by the Berg Score at 4 weeks and Wii Age at 12 weeks, while those receiving standard care showed no improvement.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

EXercising with Computers in Later Life (EXCELL) - pilot and feasibility study of the acceptability of the Nintendo® WiiFit in community-dwelling fallers. Williams, MA., Soiza, RL., Jenkinson, AM., et al.[2023]

The iStoppFalls program, an ICT-based system, aims to reduce fall risk in older adults by providing a tailored 16-week home exercise program that focuses on balance and strength, with 160 participants aged 65 and older involved in the study across Germany, Spain, and Australia.

The program is expected to improve not only physical outcomes like balance and muscle strength but also enhance quality of life and cognitive function, making it a promising model for fall prevention in older adults who prefer exercising at home.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

ICT-based system to predict and prevent falls (iStoppFalls): study protocol for an international multicenter randomized controlled trial. Gschwind, YJ., Eichberg, S., Marston, HR., et al.[2022]

References

1. Brazil pubmed.ncbi.nlm.nih.gov

Development and testing of the Prev'Quedas game for older adults in the community: a descriptive study. [2022]

2. England pubmed.ncbi.nlm.nih.gov

FITTSBALL - a dynamic tool for supervision of clinical exercise prescription. [2020]

3. United States pubmed.ncbi.nlm.nih.gov

Accuracy and Precision of an Accelerometer-Based Smartphone App Designed to Monitor and Record Angular Movement over Time. [2017]

4. England pubmed.ncbi.nlm.nih.gov

Can smartphone technology be used to support an effective home exercise intervention to prevent falls amongst community dwelling older adults?: the TOGETHER feasibility RCT study protocol. [2023]

5. United States pubmed.ncbi.nlm.nih.gov

The reliability and preliminary validity of game-based fall risk assessment in community-dwelling older adults. [2022]

6. Netherlands pubmed.ncbi.nlm.nih.gov

Exergames for the elderly: towards an embedded Kinect-based clinical test of falls risk. [2012]

7. Germany pubmed.ncbi.nlm.nih.gov

ICT-based system to predict and prevent falls (iStoppFalls): results from an international multicenter randomized controlled trial. [2020]

8. Netherlands pubmed.ncbi.nlm.nih.gov

Dance! Don't Fall - preventing falls and promoting exercise at home. [2012]

9. England pubmed.ncbi.nlm.nih.gov

EXercising with Computers in Later Life (EXCELL) - pilot and feasibility study of the acceptability of the Nintendo® WiiFit in community-dwelling fallers. [2023]

10. England pubmed.ncbi.nlm.nih.gov

ICT-based system to predict and prevent falls (iStoppFalls): study protocol for an international multicenter randomized controlled trial. [2022]