Abstract
Background
Falls and fall-related injuries among older adults in Singapore are a serious health problem that require early intervention. In previous research, exercise interventions have been effective in improving functional outcomes and reducing falls for a broad group of older adults. However, results from multi-domain, multi-component falls prevention programs for high fall risk older adults in the community remain equivocal. One reason for these results is that there is significant heterogeneity in falls risk factors amongst high falls risk older adults which makes tailoring multicomponent interventions complex. The objective of the trial is to evaluate the effectiveness of an enhanced version of the predecessor program, SAFE. The Steps to Avoid Falls in the Elderly—a TECHnology enhanced intervention (SAFE-TECH) is designed for older adults in the community who are at high risk of falls, with candidate selection and program tailoring based on gait variables derived from wearable sensors and various questionnaire-based features.
Methods
SAFE-TECH is a 12-month randomized controlled trial involving 400 older adults at high risk of falling, who are randomly allocated to an intervention or control group in a 1:1 ratio. Participants will be assessed at baseline, 3rd-month and 12th-month for functional status, physical performance, cognitive status, quality of life, and medical history. Monthly phone calls will assess fall status, healthcare utilization, physical activity, and exercise self-efficacy. Participants in the intervention group will undergo a tailored, multi-domain, multi-component falls prevention program. The active intervention phase will last for 12-weeks with exercises focusing on strength, balance, coordination, flexibility, and aerobic endurance; and weekly educational sessions on falls risk with personalized feedback based on participant’s falls risk assessments and environmental checklist.
Discussions
SAFE-TECH seeks to evaluate enhanced existing falls prevention programs by addressing the heterogeneity of falls risk through rapid assessments and personalisation of exercise and education components while maintaining the efficiency of the group setting. Our findings will inform practical efforts to reduce falls and falls-related injuries among community-dwelling older adults.
Trial registration
ClinicalTrials.gov. Clinical Trial Number: NCT06102954|| 22–10-2023.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-024-20947-2.
Keywords: Older adults, Falls prevention intervention, Randomised clinical trial, Study protocols
Introduction
Falls among older adults result in significant adverse consequences such as injuries, prolonged hospitalization, reduced mobility, reduced quality of life, and death [1]. The prevalence of falls among older adults globally was 26.5% [95% CI: 23.4–29.8%], with the highest prevalence of falls among older adults in Oceania (34.4% [29.2%—40%]), America (27.9% [22.4%—34.2%], followed by Asia (25.8% [22.1%—29.9%]) [2]. In Singapore, 18.4% of community-dwelling older adults reported at least one fall in the past 12 months, with 4.6% of these individuals being recurrent fallers [3]. Moreover, falls accounted for 85% of all geriatric trauma cases presented to the emergency department [4]. With the population of Singapore continuing to age rapidly [5], there is an urgent need to develop and implement interventions to reduce the health and socio-economic burden of falls among older adults.
Studies on falls prevention continue to show some effectiveness of multi-component falls prevention interventions in improving functional outcomes and reducing falls compared to usual care [6–8]. We have previously conducted The Steps to Avoid Falls in the Elderly Study (SAFE), a multi-component falls intervention program for older adults in Singapore with falls-related trauma admitted to the emergency department [9]. The study found that this high fall risk population was heterogenous, with varied risk factors such as muscle weakness, balance impairments, environmental hazards, vision impairments, medications, fear of falling and cognitive decline. While SAFE resulted in an overall reduction in injurious falls, the effect on all falls risk and on cost-effectiveness depended on predisposing physical and cognitive features; participants with 2 or more comorbid conditions or suffering from cognitive impairment were less likely to benefit from intervention. As such, we revised SAFE to more directly address predisposing factors and to incorporate technology to quantify baseline falls risk. In addition, to enhance the reach of falls prevention, the resulting program, Steps to Avoid Falls in the Elderly – a TECHnology enhanced intervention (SAFE-TECH), identified candidates in the community.
Our identification of individuals at high risk of falls leverage on novel wearable sensor technology and a statistical model that incorporates a machine learning approach [10–13]. The effectiveness of SAFE-TECH in reducing the number of fallers and injurious fallers in a 12-month study period will be evaluated in a randomized controlled trial (RCT). Beyond falls reduction, we also aim to evaluate additional outcomes including mobility and cost-effectiveness.
Materials and methods
This manuscript details the study protocol for SAFE-TECH according to the SPIRIT guidelines.
Study design
The study is a single-blinded, multisite, two-arm, parallel-group RCT involving 400 community-dwelling older adults aged 60–95 years old in Singapore. The interviewer conducting recruitment, outcome assessment, and monthly follow-up calls will be blinded to the group allocation.
Design process
SAFE-TECH exercises were modified from SAFE [9] and OTAGO [14, 15] and have been developed after two rounds of consultation with local experts. Clinicians (i.e., from specialties such as geriatrics, rehabilitation medicine and neurology) and allied healthcare professionals (i.e., physiotherapists and occupational therapists) were consulted to ensure that exercises designed are of sufficient intensity to elicit beneficial outcomes and are tailored to the health needs, physical capabilities, and cultural context of community-dwelling older adults in Singapore. The content and delivery plan of the education material were designed based on available literature [16–20] and were similarly reviewed by these local experts as well. A summary of the study design and logic model can be found in Fig. 1 and Table 1 respectively.
Fig. 1.
SAFE-TECH study design
Table 1.
Logic model
| Inputs and resources for the implementation of SAFE-TECH | Processes for intervention delivery | Outputs to demonstrate intervention effectiveness | Outcomes | Target Impact |
|---|---|---|---|---|
|
Manpower Resources and Support • Research study team • Community Provider team: Allied health professionals (PT/PT aides) and community health centers SAFE-TECH Toolkit • Selecting participants (Fall risk prediction tool) • 24-sessions lesson plan inclusive of: • Goal setting • Strength, balance, coordination, flexibility, and endurance training • Fall prevention education • Timely feedback and progression assessments • Instructor Training Materials Digital technology-enabled support • Target population selection • Monitoring of participant progress overtime |
SAFE-TECH falls prevention program plan • Tailored, multi-component falls prevention program • 5 domain exercise protocol • Goal setting with participants • 11 education components to manage falls risk Training Providers • Instructor Training Materials and support from study team • Internal training by study team to community-providers staff, physiotherapists, and physiotherapy assistants via Train-the-Trainer Model Communication to older adults • Develop communication strategy and tools for providers to communicate effectively with older adults • Emphasis on maintaining or building independence and mobility • Emphasis on shared decision-making and goal setting ML algorithm for target population identification • “Sweet spot” population best suited for community-based, tailored, progressive fall prevention program |
SAFE-TECH multi-component fall prevention program • Proof-of-Concept: feasibility assessment after SAFE-TECH pilot • Proof-of-Value: Effectiveness evaluation after study period Provider compliance and fidelity • Independent assessor for fidelity checks and observation at three-time points across all study sites and classes |
Behavioral Change • Older adults participate in recommended strength, balance and/or exercise programs during maintenance phase • Improvements in frequency of exercise after interventions • Changes in practicing falls prevention behaviors between control and intervention arm Functional Changes • Improvements in gait, strength, and balance as per Short Physical Performance Batter (SPPB) scores Changes in healthcare utilization • Fewer fall-related emergency department visits • Fewer fall-related hospitalization |
Primary outcome: Reduction in the number of fallers within group (intervention group) and between groups (intervention compared to control groups) Secondary outcomes: • Reduction in the number of injurious fallers within and between groups • Improvements in SPPB scores within and between groups • Improvements in falls efficacy within and between groups • Improvements in falls prevention behaviors within and between groups |
Hypothesis
We hypothesize that SAFE-TECH, a technology-enhanced, multi-domain, multi-component falls prevention program, will reduce the number of fallers among community-dwelling older adults in the intervention arm compared to the control arm (usual care) during the 12-month study period.
Recruitment
Participant selection: recruiting individuals from the TARGET Cohort
SAFE-TECH participants will be recruited from an ongoing longitudinal observational study on falls and fracture risks, known as Targeted Assessment and Recruitment of Geriatrics for Effective Fall Prevention Treatments (TARGET). Participants in TARGET are recruited from a nationally representative random sample of community-dwelling older Singapore citizens or permanent residents aged 60 years and older, subject to meeting the following inclusion criteria: speak one or more of Singapore’s four official languages (English, Mandarin, Malay, or Tamil) or a Mandarin dialect, correctly answer 6 or more questions from the 10-item Abbreviated Mental Test – Singapore, able to walk with or without walking aids, able to see with or without glasses, able to hear with or without hearing aids, willing to walk for 5 min as a part of the assessment (described below), and at the time of the study not experiencing any chest discomfort, breathless, dizziness, or profuse sweating.
Participant selection: identifying individuals at high-risk of falling
The TARGET data collection includes information on demographics, cognitive status, functional, biomechanical, and psychosocial characteristics of community-dwelling older adults above 60 years old. During the TARGET assessment, six inertial measurement unit (IMU) sensors (ZurichMOVE) [12] were affixed to designated anatomical locations of participants, including the feet, trunk, wrists, and head, to measure acceleration and angular velocity at each site. The IMUs embedded within ZurichMOVE have a dynamic range of ± 16 g for the accelerometer and 2000°/s for the gyroscope at a sampling frequency of 200 Hz. Participants were instructed to perform a 5-min walking task within their home environment or in an adjacent corridor, with the freedom to walk at their preferred pace and navigate turns. The cohort is followed up for prospective falls and fractures for 2 years from baseline assessment.
Raw data from the IMU sensors is processed using algorithms designed to filter the signals and detect gait events [21], thereby identifying key phases in the gait cycle for each participant. These phases allow an evaluation of critical spatio-temporal parameters of gait providing information such as duration of the cycle and the different phases. Furthermore, inter-cycle variations are evaluated during the entire walking trial to assess variability in movement behaviour. Utilizing this information, critical spatial–temporal gait parameters, including stride time, gait speed, and double-limb support time, were extracted.
Gait parameters were augmented with falls risk predictors from the questionnaire to predict high falls-risk individuals for recruitment into SAFE-TECH. These questionnaire-based falls risk predictors include the participant’s age (with increasing age correlating with a higher risk of falling), fear of falling as measured from the Iconographical Falls Efficacy Scale [22] (with higher scores indicating greater fear of falling), physical activity as measured from the International Physical Activity Questionnaire [23] (with lower physical activity levels indicating higher risk of falling) and retrospective falls history (with previous falls increasing the likelihood of future falls). These features (gait and questionnaire-based fall-risk predictors) are used to assess a personalised fall-risk score for every individual.
The risk score is generated using a simplified decision tree-style function where parameters are first weighted based on their relevance to fall risk [24], and the distribution of the data. The weights for those parameters that exceed previously established thresholds [11, 24, 25] are then scored. The final risk score, in this manner, provides an individualized stratification of the fall risk. The cut-off risk score [13], based on Youden’s Index evaluated from 50% of the available data, is then used to classify individual as having low vs. high fall risk and used to support decision making on recruitment into the intervention.
Sample size considerations
A nominal risk score cut-off will be selected based on the Youden’s index (i.e., finding a balance between both sensitivity and specificity values), such that approximately 50% of older adults with high fall risk are likely to fall in the next one year. The threshold for recruitment can be modified as falls outcomes are collected to maximize study power without interfering with the integrity of the randomization. We expect to find that the multi-component falls prevention program reduces the proportion of fallers by 31% after the 12-month intervention. Based on these assumptions, at 5% level of significance and 80% power, the total sample size calculated is 156 per arm. Assuming a 20% drop-out rate, a total of 390 participants is required for this study. As such, a total of 400 participants (200 in each arm) will be recruited into this study.
Participant recruitment
Although all TARGET participants are classified into either a high or low risk of falls category, they are ineligible from SAFE-TECH if they meet any of the following exclusion criteria:
- Have any significant morbidity:
- ◦ Congestive heart failure in the past 6 months
- ◦ Myocardial infarction in the past 6 months
- ◦ Stroke (Intracranial hemorrhage) in the past 6 months
- ◦ Concussion or head injury in the past 6 months
- ◦ End stage renal disease requiring dialysis
- ◦ Severe asthma or chronic obstructive pulmonary disease (COPD), experiencing symptoms at rest or with mild activity
- ◦ Lower limb fractures in the past 6 months
Amputation of any part of the lower limbs except toes, or had an amputation of any toes in the last 30 days
Currently living in a long-term institution for health- or non-health reasons
Currently participating in any randomized clinical or controlled trial that involves physical exercise
Completed fewer than 3 min of the 5-min gait assessment
Randomization and participant allocation
After providing consent and completing the baseline assessment, participants will be randomly allocated to intervention and control groups in a 1:1 ratio using a computer-generated random number schedule with a fixed block size of 4. Participants will then be informed of their assigned groups by the study team within 6 weeks of baseline measurement. Allocations will be performed by an investigator blinded to the recruitment and assessments and group allocations will be inaccessible to the study team and interviewers conducting outcome assessments and follow-up calls. However, due to the nature of the intervention, it is not possible to blind participants and the research team overseeing the intervention. Only interviewers conducting baseline, outcome assessments and follow-up calls will be blinded to participant’s group allocations. Participants will be instructed to not reveal their allocations to interviewers conducting outcome assessments and/or follow-up calls.
A detailed data collection timeline can also be found in Table 2.
Table 2.
Data collection timeline
Intervention
Participants assigned to the intervention group will be invited to participate in the 12-month SAFE-TECH falls prevention program. The study is divided into two phases: a 3-month active, supervised, SAFE-TECH intervention phase; and 9-month unsupervised, maintenance phase (Fig. 1).
Active intervention phase
The SAFE-TECH falls prevention program is a structured, physiotherapist-supervised, group program conducted in the community. It consists of a total of 24 sessions conducted twice a week, for three consecutive months. Each session is expected to be 120 min long. Participants will be invited to their nearest available community health center for sessions and will be provided the option for free transportation. Caregivers will also be invited to facilitate the participants in the intervention program on a voluntary basis with their informed consent.
SAFE-TECH sessions will cover a range of exercise and education components. The 5 exercise domains include: strength, balance, flexibility, coordination, and endurance. Education focuses on the management of fall risk factors such as polypharmacy, nutrition, pain, orthostatic hypotension, poor vision, and environmental hazards. Exercise will be personalized according to participant’s physical function (across 4 different levels of progression) and education sessions will be tailored according to participant’s risk factors (i.e., presence of pain, polypharmacy, etc.) to educate participants on better adaptations or management of these risk factors. SAFE-TECH will also include several goal setting and action planning activities, to ensure that participants are able to identify specific behaviors to change and how to go about doing so.
A typical class size will range from 15–20 participants with PT and PT aides facilitating the session. Each PT and PT aide will facilitate a maximum of 5 participants in a smaller group. Each class will begin and end with a 10-min safety check. A typical session would involve 70-min of exercise, comprising of 5–10-min warm-up (aerobic dancing or 6-min walk), strengthening, balance training, coordination training and 10-min flexibility cool-down at every alternate session. The remaining 30-min will be dedicated to education. Education sessions consist of goal setting, evaluating individual falls risk through self-assessment, evaluating environmental hazards through environmental checklist, and managing various falls risk factors such as pain, orthostatic hypotension, and polypharmacy. Information about the strategies to maintain adherence to the program can be found in Appendix 1.
Maintenance phase
After the active intervention phase, participants will enter a 9-month maintenance phase in which they will be encouraged to continue with the exercise on their own (Fig. 1). The study team will also provide a list of group exercise programs available in their neighborhoods and advise participants to enroll in these programs.
Control
Participants assigned to the control group (usual care) will not undergo any education sessions. They will be given educational materials (i.e., falls prevention booklet) and advised to continue their current lifestyle that is the current standard of care for older adults.
Study measures
Baseline, outcome assessments and monthly follow-up calls
All SAFE-TECH participants will undergo a baseline assessment, collecting their socio-demographic characteristics, functional status, physical performance, fall history, cognitive impairment, quality of life, healthcare utilization and medical history. Interviews will be carried out in all four official languages of Singapore (English, Mandarin, Malay, and Tamil). Where available, validated scales in non-English languages will be used. The same questionnaire will also be administered at the 3rd-month, and 12th-month after the first intervention session. These assessments will be conducted either at their place of residence or at a place of their preference.
Monthly follow-up calls will also be conducted to collect information on participants’ fall status, including whether they have fallen in the past month, the circumstances of any of the falls (e.g., injuries, places, causes and any resulting activity restrictions), healthcare utilization due to falls, as well as their physical activity, and exercise frequency. Additional physical performance assessments, including the Short Physical Performance Battery (SPPB) Test and Ankle Mobility Test, will also be conducted among participants in the intervention group at every 8th session (once a month) during the active intervention phase.
A summary of the data collection schedule can be found in the session plan in Table 3.
Table 3.
Session plan
| Week | Session | Lesson Plan | Participant Progress Assessment (PPA) | Research Data Collection Plan | Implementation Outcome (Theoretical Framework of Acceptability (TFA) |
|---|---|---|---|---|---|
| 1 | 1 | Get Active Questionnaire | |||
| Ice breaker | |||||
| Education: Introduction and Briefing | |||||
| Participant Progress Assessment | Assessment 0 | Assessment (Pre) | |||
| 2 | Exercise: Warm-up (Aerobic Training), Strengthening, Balance Training and Cool-down (Flexibility exercise) | ||||
| Participants to receive their Participant Progress Report (PPR) | |||||
| 2 | 3 | Exercise: Warm-up (Aerobic Training), Strengthening and Cool-down (Flexibility exercise) | |||
| Education: Goal setting: StayingSAFE Map | |||||
| Education: Environmental checklist and homework briefing | |||||
| 4 | Exercise: Warm-up (Aerobic Training), Strengthening, Balance Training, Coordination Training and Cool-down (Flexibility exercise) | ||||
| 3 | 5 | Exercise: Warm-up (Aerobic Training), Balance Training, Coordination Training and Cool-down (Flexibility exercise) | |||
| Education: Environmental Hazards workshop and goal setting (SAFE@Home) | |||||
| 6 | Exercise: Warm-up (Aerobic Training), Strengthening, Balance Training and Cool-down (Flexibility exercise) | ||||
| Community walk, Homework briefing on goal settings (SAFE@Community) | |||||
| 4 | 7 | Exercise: Warm-up (Aerobic Training), Strengthening, Balance Training and Cool-down (Flexibility exercise) | |||
| Education: Check on goal settings (SAFE@Community) homework | |||||
| 8 | Exercise: Warm-up (Aerobic Training), Strengthening and Cool-down (Flexibility exercise) | Assessment 1 | Data collection 1 | ||
| 5 | 9 | Exercise: Warm-up (Aerobic Training), Strengthening and Cool-down (Flexibility exercise) | |||
| Education: Risk Factors Workshop and goal settings (SAFE & Independent) | |||||
| Participants to receive Participant Progress Report (PPR) | |||||
| 10 | Exercise: Warm-up (Aerobic Training), Strengthening, Balance Training, Coordination Training and Cool-down (Flexibility exercise) | ||||
| 6 | 11 | Exercise: Warm-up (Aerobic Training), Balance Training and Cool-down (Flexibility exercise) | |||
| Education: Risk Factors Workshop, goal settings (SAFE & Independent) and Fall Risk Self-Assessment | |||||
| 12 | Exercise: Warm-up (Aerobic Training), Strengthening, Balance Training, Coordination Training and Cool-down (Flexibility exercise) | ||||
| 7 | 13 | Exercise: Warm-up (Aerobic Training), Strengthening and Cool-down (Flexibility exercise) | |||
| Education: Recap session and goal setting review | |||||
| 14 | Exercise: Warm-up (Aerobic Training), Strengthening, Balance Training, Coordination Training and Cool-down (Flexibility exercise) | ||||
| 8 | 15 | Exercise: Warm-up (Aerobic Training), Balance Training, Coordination Training and Cool-down (Flexibility exercise) | |||
| Education: What to do in the event of a fall | |||||
| 16 | Exercise: Warm-up (Aerobic Training), Strengthening and Cool-down (Flexibility exercise) | Assessment 2 | Data collection 2 | ||
| 9 | 17 | Exercise: Warm-up (Aerobic Training), Balance Training and Cool-down (Flexibility exercise) | |||
| Education: Overall Recap | |||||
| Participants to receive PPR | |||||
| 18 | Exercise: Warm-up (Aerobic Training), Strengthening, Balance Training, Coordination Training and Cool-down (Flexibility exercise) | ||||
| 10 | 19 | Exercise: Warm-up (Aerobic Training), Strengthening, Balance Training and Cool-down (Flexibility exercise) | |||
| Education: Quiz | |||||
| 20 | Exercise: Warm-up (Aerobic Training), Strengthening, Balance Training, Coordination Training and Cool-down (Flexibility exercise) | ||||
| 11 | 21 | Exercise: Warm-up (Aerobic Training), Strengthening and Cool-down (Flexibility exercise) | |||
| Education: Family and Caregiver workshop | |||||
| 22 | Exercise: Warm-up (Aerobic Training), Strengthening, Balance Training, Coordination Training and Cool-down (Flexibility exercise) | ||||
| 12 | 23 | Exercise: Warm-up (Aerobic Training), Strengthening, Balance Training and Cool-down (Flexibility exercise) | Assessment 3 | ||
| 24 | Exercise: Aerobic exercise | ||||
| Education: Overall recap for SAFE-TECH, Debrief and Graduation | |||||
| Participants to receive PPR | |||||
| Education: Goal checking in | TFA Assessment (Post) |
Primary outcome
The primary outcome of interest is the difference in proportion of participants who fell at least once between control and intervention groups during the 12-month study period. A fall is defined as “an event which results in a person coming to rest inadvertently on the ground or floor or other lower level” [26]. Falls will be assessed through monthly phone calls, where participants will be asked whether they have fallen once in the past month (Yes/No). Should the participant report falls in the past month, the frequency and cause of falls will be recorded. Secondary outcome measures can be found in Table 4. Other variables of interest can be found in Appendix 2.
Table 4.
Secondary outcomes of interest
| Outcome measures | |
|---|---|
| Fallers | The difference in proportion of participants who fell at least once within group before and after intervention |
| Injurious Fallers |
The difference in proportion of participants who fell and had an injurious fall at least once within group before and after intervention The difference in proportion of participants who fell and had an injurious fall at least once between control and intervention groups during the study period |
| Short Physical Performance Battery (SPPB) scores | A change in SPPB scores within group before and after intervention |
| A change in SPPB scores between control and intervention groups during the study period | |
| Falls Efficacy |
A change in falls efficacy, as measured by the iconography falls efficacy scale (ICON-FES). within group before and after intervention A change in falls efficacy between control and intervention groups during the study period |
| Falls Prevention Behaviors | A change in practices of falls prevention behaviors within group before and after intervention |
| A change in their practices of falls prevention behaviors between control and intervention groups during the study period, as measured by the Fall Behavioral Scale (FaB) | |
Statistical analysis plan
All primary analyses will be conducted using the intention-to-treat (ITT) principle. A secondary per protocol analysis (PPA) will also be performed. Descriptive statistics will be presented to characterize participants in this study. Categorical data will be presented in frequencies (percentage), while continuous data will be presented using mean (standard deviation) for variables with parametric distributions, and interquartile ranges for non-parametric distributions. Group comparisons of outcome measures will be performed using the Fisher’s Exact Test or Chi-Squared test for categorical data; and independent t-test or Mann–Whitney U test for continuous data. Univariate and multivariate logistic regressions will be used to evaluate the effect of SAFE-TECH intervention on the possibilities of fallers (primary outcome) and injurious fallers (secondary outcome). A 5% level of significance will be used in this study.
Study management
Details on study management can be found in Appendix 4.
Discussion
In this paper, we present the study protocol for a randomized controlled trial to determine the effectiveness of a technology-enhanced, multi-domain, multi-component falls intervention program for community-dwelling older adults compared to usual care.
This technology-enhanced multi-component falls prevention program was developed through a rigorous design process that gathered insights from multiple perspectives to identify key success factors to include in our intervention. SAFE-TECH is unique as it recognizes the variety of falls risk factors and is precisely designed to deal with these complexities by targeting the physiological, psychological, and social aspects of falls in a multi-component program.
In Singapore, there are no simple and effective approaches to address heterogenous falls-risk factors in community dwelling older adults. The current guidelines for falls prevention in the community are inconsistently implemented and not rigorously evaluated [16]. Furthermore, few studies evaluate the cost-effectiveness of multi-component falls prevention programs. This study hopes to build confidence in adopting a systematic approach addressing the heterogeneity of falls-risk factors in community-dwelling older adults [27].
This study has some limitations. The recruitment of SAFE-TECH participants is based on an existing national study of community-dwelling older adults. There may be selection bias as participants opt-in to be recontacted for future studies. However, these bias effects would be alleviated due to the random assignment to the intervention and control arm during allocation. Moreover, SAFE-TECH will be implemented by different physiotherapists and physiotherapy aides at different sites across Singapore. This may contribute to inconsistencies when implementing the program, which are nonetheless realistic should SAFE-TECH be scaled up. To reduce such inconsistencies in implementation, the study team will train physiotherapists and physiotherapists aides to ensure that they meet a minimum standard of core competencies and maintain consistency across all sites. More information about ensuring consistent quality can be found in Appendix 3.
In conclusion, this RCT will determine the effectiveness of SAFE-TECH, a technology-enhanced, multi-domain, multi-component falls prevention program for community dwelling older adults, in reducing fallers among community-dwelling older adults. We hope that these findings will provide insights into the effectiveness of this unique program, along with its potential for scalability in the future.
Supplementary Information
Acknowledgements
The research was conducted at the Future Health Technologies at the Singapore-ETH Centre, which was established collaboratively between ETH Zurich and the National Research Foundation Singapore. This research is supported by the National Research Foundation Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.
Authors’ contributions
*LWX and VK are joint first authors. LWX, VK, GJW, TKY designed and developed the study protocol. TKZ, NBS, SP developed the machine learning falls risk prediction algorithm. TKY and GJW will supervise and administer the project. LWX, VK and GJW wrote the original draft and edited this manuscript. TKY, TKZ, SP, AV, NBS, AWMC and DBM reviewed and edited the manuscript. With Prof. P. Duncan, DBM developed the original SAFE protocol and is Principal Investigator of SAFETECH, which is part of a Swiss/Singapore-funded collaboration, with TKY, GJW, TKZ, SP, WRT, AV, NBS, AWMC and DBM providing supervision and scientific oversight.
Funding
This research is supported by the National Research Foundation Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. The funders did not have a role in study design, data collection, analysis or preparation of this manuscript.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
The National University of Singapore Research Ethics Committee has approved this clinical trial. All participants will also provide written informed consent prior to randomisation. The study is also registered with the U.S. Clinical Trials Registration at clinicaltrials.gov as NCT06102954.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Lai Wei Xuan and Vanessa Koh are joint first authors.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
No datasets were generated or analysed during the current study.