Abstract
Background
Optimistic fall risk screening has been recommended in recent global fall prevention guidelines. While fall screening mobile application (FallSA©) is acknowledged for its acceptance and reliability, its effectiveness in modifying fall prevention behaviors remains underexplored.
Objectives
In this study, we aimed to investigate the effects of FallSA© on fall prevention behavior and functional outcomes among community-dwelling older adults. The transition to its upgraded version, FallSA© 2.0 was also reported.
Methods
A six-month randomized controlled trial included 59 participants: the experimental group (n = 30, mean age 66.2 ± 5.3 years) and the control group (n = 29, mean age 69.2 ± 5.0 years). The experimental group received fall prevention education and used FallSA©, while the control group received the education only. Outcomes included fall prevention awareness, knowledge, balance confidence, physical function and physical activity levels.
Results
While no significant time × group interaction and group effects (p > .05), time effects were observed for improvements in FallSA© risk score (p = .03), balance confidence (p = .009), behavior (p = .001), and physical function (p = .008), the experimental group demonstrated a larger mean change in fall behavior, balance confidence, FallSA© risk score, and physical activity level compared to the control group (p < .05 for all parameters). Limitations in FallSA© were addressed in FallSA© 2.0 by incorporating an enhanced educational package, Sit-to-Stand test with normative values, and improved monitoring system for health professionals.
Conclusion
The findings suggest that FallSA© has the potential to enhance fall prevention behaviors and awareness, which has been successfully integrated in FallSA© 2.0. Future studies are needed for broader applicability of FallSA© 2.0 in fall prevention strategies among older adults.
Registry
Australian New Zealand Clinical Trials Registry.
Clinical trial number
ACTRN1262200112076.
Introduction
Falls are a major public health concern among older adults, often leading to serious injuries, loss of independence, and increased mortality risk. 1 Globally, the prevalence of falls in older adults ranges from 30% to 50%, with considerable variation depending on the population and setting studied, such as community-dwelling, hospitalized, or institutionalized older adults.2–4 In Malaysia, approximately 18% of community-dwelling older adults have reported experiencing at least one fall in the past 12 months, with 28% of these individuals identified as recurrent fallers at an 18-month follow-up. 5 This relatively lower prevalence may partly be attributed to the inclusion of more active older adults and the use of a younger age threshold (starting at 60 years) in this local study, compared to institutionalized populations and international research, which typically include individuals aged 65 and above. Nevertheless, falls remain a major public health concern in Malaysia, driven by a rapidly ageing population and the associated health and socioeconomic consequences. Early identification of fall risk factors is therefore essential to inform effective prevention strategies and reduce adverse outcomes.6,7 In line with international guidelines, there is growing recognition of the importance of timely and proactive fall risk screening, particularly in community settings. 8
Several fall risk assessment tools are available in clinical settings, primarily designed for use by trained healthcare professionals, such as physiotherapists, occupational therapists, and geriatricians. 9 Although these tools provide detailed assessments, they often require significant time and training, making them impractical for quick or self-screening, and large-scale community screening. Moreover, a shift toward community-based prevention models is encouraged over conventional clinical-centric approaches. 10 In order to bridge this gap, simple and accessible self-screening digital health tools are needed to enable early risk detection before a comprehensive clinical evaluation is conducted.
Several mobile applications for fall risk assessment have been developed and studied, including FallSA©, 11 the Aachen Fall Prevention App, 12 Steady, 13 and Lindera. 14 Despite these advancements, there is a notable gap in the literature concerning the effectiveness of these digital health tools in optimizing fall risk screening and prevention strategies among older adults. A key strength of FallSA© is its systematic development process, which was followed by rigorous testing for acceptance, concurrent validity, reliability, as well as discriminative and predictive validity. 11 Unlike many other applications that primarily rely on self-reported data or sensor-based analysis, FallSA© incorporates a validated physical performance measure, the Timed Up and Go (TUG) test alongside culturally tailored educational content. This dual approach positions FallSA© not only as a screening tool but also as a self-empowerment resource designed specifically for community-dwelling older adults in the Malaysian context.
Therefore, in this study, we aim to investigate the effects of the FallSA© mobile application on fall prevention knowledge, behavior, balance confidence, fall risk, and physical performance in community-dwelling older adults. The current study utilizes the original version of FallSA©, which has undergone prior validation for usability, reliability, and predictive accuracy. In addition to assessing its impact, this study also incorporates observational insights and user feedback to systematically identify limitations and areas for enhancement. These findings will directly inform the development of FallSA© 2.0, an upgraded version in progress which aims to provide a more comprehensive, user-friendly, and scalable tool for self-screening, promoting early intervention, and supporting continuous fall risk management. This iterative, evidence-driven approach contributes to the advancement of digital health interventions and supports the integration of mobile technology into community-level fall prevention strategies.
Materials and methods
Study design
The study is conducted in two phases: Phase 1 is a site-level quasi-cluster randomized controlled trial (RCT) involving community dwelling older adults aged 60 years and above in Malaysia, designed to assess the efficacy of fall risk screening, conducted between October 2020 and March 2021. Phase 2 focuses on enhancing the FallSA© mobile application.
Recruitment
Participant selection and recruitment
Older adults identified as at risk of falling through the FallSA©, were recruited from six Senior Citizen Activity Centres (PAWE) located in Kuala Lumpur and Selangor, Malaysia. Eligibility criteria included: (1) aged 60 years or older; (2) at risk of falling, as defined by a FallSA© score of ≥5; (3) ambulatory, with or without the use of assistive devices; and (4) able to use mobile applications, either independently or with the assistance of a caregiver.
Participants were excluded if they: (1) were unable to comprehend or follow instructions; (2) had medical conditions that could pose safety concerns during balance and strength assessments, including musculoskeletal disorders (e.g. recent fractures, acute knee pain, or a history of hip or knee replacement), active malignancies, or other significant health conditions; (3) reported functional limitations such as an inability to stand without support or to walk without assistive devices; (4) had severe sensory impairments (e.g. vision or hearing loss); (5) were experiencing depressive symptoms, defined by a score of ≥5 on the Geriatric Depression Scale-15; (6) had moderate to severe cognitive impairment, indicated by a Mini-Mental State Examination score of ≤22; or (7) were unable to use mobile applications.
Written informed consent was obtained from all participants prior to their inclusion in the study. Participants were fully informed about the study's objectives, procedures, potential risks, and benefits, and were assured of their right to withdraw at any time without any consequences.
Sample size calculation
The sample size was calculated by using G-power 3.0.10. 15 In the RCT, for ANOVA repeated measures, within-between interaction, the effect size used = 0.30 (medium), α = 0.05 and power (1 − β) = 0.8, the total sample size was 24 participants. Considering a possibility of a 20% drop-out rate, a total of 58 participants (29 in each group) were recruited into this study.
Randomization and participant allocation
A cluster randomized controlled trial design was used, with community centers serving as the units of randomization (clusters). Centers were randomly assigned to either the intervention or control group using randomization software, stratified by location. Cluster randomization was adopted primarily for logistical convenience and to minimize contamination risk arising from participant interactions and dissatisfaction in shared community settings where only one group received the fall screening app. All eligible participants within each center received the group allocation assigned to their respective cluster. To minimize potential bias, the randomization process was carried out by an individual who was not involved in the research implementation or outcome assessments.
Intervention
One week after baseline assessments, participants allocated to the experimental group attended a face-to-face group orientation session facilitated by the principal researcher. The session was held in a quiet, designated room at the community center to ensure minimal distractions. During this session, the researcher installed the FallSA© mobile application on each participant's smartphone. Participants received detailed step-by-step instructions on how to use the application, including navigation of the interface, performing the in-app TUG physical performance test entering relevant personal and health information, and accessing embedded educational materials. Participants were given the opportunity to practice using the application under supervision, with individual guidance provided as necessary to ensure confidence and competence in its use.
Following the application training, participants took part in a structured group falls prevention education session lasting approximately two hours. This session combined the use of printed booklets and short video presentations (10–15 min each) covering key topics such as medication use, home hazards, environmental safety strategies, importance of physical activity, and tips to improve strength and balance. The educational materials were culturally adapted and presented in the local language to ensure comprehension and relevance for the Malaysian older adult population. The printed booklet was distributed to all participants, and an open discussion was encouraged to clarify doubts and promote engagement.
Subsequently, participants were instructed to independently perform fall risk assessments on a monthly basis using the FallSA© application, either at the PAWE center or at home, based on their preference and capability. The application generated a fall risk score based on participant input and TUG test results. Participants were required to document these results monthly in a hardcopy “Falls Diary” provided by the research team, in addition to submitting the data electronically via the application, which synced to the researcher's database for monitoring and analysis. Participants were also instructed to record any fall incidents in the fall diaries provided.
To support adherence and reinforce the intervention content, the researcher conducted monthly follow-up phone calls throughout the six-month study period. During these calls, participants were reminded to complete their monthly assessments using the FallSA© app. The calls also provided a platform to troubleshoot any technical issues, address questions about the app, and offer verbal encouragement. Participants were asked to report any falls that occurred during the intervention period, and these events were documented using a standardized report form. The combined strategy of education, digital self-assessment, and regular researcher engagement was designed to support behavioral change and enhance functional outcomes related to fall prevention among community-dwelling older adults. Adherence was ensured through the frequency of participants’ monthly FallSA self-assessments and diary entries over six months, supported by researcher follow-up calls to encourage consistent engagement.
Control
Participants in the control group attended a separate, 2-h structured fall prevention education session facilitated by the same researcher. The session was comparable in both format and content to that of the experimental group, using the same culturally adapted printed booklets and short video presentations delivered in the local language. These materials covered the same key topics, including medication safety, home hazard reduction, environmental modifications, the importance of physical activity, and strategies to improve strength and balance. Printed booklets were provided as take-home resources, and open discussions were encouraged throughout the session to enhance understanding and foster active engagement. Participants in the control group were also provided with fall diaries to document any incidents on a monthly basis over the six-month period. They received monthly follow-up phone calls to reinforce diary completion and to capture any reported fall events. However, the FallSA© application was not utilized in this control group intervention.
Outcome measures
At baseline, participants provided sociodemographic and clinical information. All assessments were conducted in person by trained assessors who were blinded to group allocation. Follow-up assessments were conducted six months after baseline (changes over the six-month study period), during which participant diaries were collected, and both groups were reassessed using the same instruments. Outcome measures were assessed using validated instruments.
The behavioral outcomes included Fall Risk Assessment Questionnaire (FRAQ). FRAQ evaluates behavioral, physical, medication-related, and environmental risk factors. The FRAQ has demonstrated strong validity and agreement with clinical assessments (kappa = 0.875, p < .001). 16 Fall prevention behavior was measured using the Falls Behavior Scale (FaB), a 30-item tool designed to identify risky behaviors and environmental hazards in daily activities. The FaB has demonstrated high internal consistency (Cronbach's α = 0.84) and test–retest reliability (r = 0.94).17,18 Physical Activity was measured using MBPAQ, adapted for older adults to assess habitual activity patterns. The MBPAQ has demonstrated good test–retest reliability (Spearman's ρ = 0.89).19,20
The functional outcomes consisted of fall risk scores from the mobile application FallSA©, balance confidence, and physical performance. Fall risk scores were calculated using the FallSA© mobile app, 11 which employs a validated algorithm combining sociodemographic data (being female), self-reported fall risk indicators (having Cataract/Glaucoma, joint pain, falls history and fear of falls) and the TUG test (≥11.18 s). 3 Scores range from 1 to 11, with a cutoff of ≥5 indicating elevated fall risk in community-dwelling older adults. 11 Users are guided by an instructional video within the FallSA© app to perform the TUG test, which involves standing from a chair, walking 3 m, turning, returning, and sitting down. They start and stop the in-app timer during the test, and the recorded time is integrated into the app's algorithm to calculate the overall fall risk score.
Balance Confidence was evaluated using the ABC-16, which measures confidence in performing various activities without losing balance. The ABC-16 has shown excellent test–retest reliability (r = 0.92, p < .001) and high internal consistency (Cronbach's α = 0.96).21,22 Physical Performance was assessed using the Short Physical Performance Battery (SPPB), which includes tests of balance, gait speed, and Sit-to-Stand (STS) performance. The SPPB has demonstrated high test–retest reliability (r = 0.83, p < .001), validity, and responsiveness in community-dwelling older adults.23–25
Statistical analysis
IBM Corp's Statistical Package for the Social Sciences version 25 was used for data analysis. Prior to statistical analysis, the distribution of each continuous variable was assessed for normality using tests such as Kolmogorov–Smirnov, Wilks Shapiro, skewness ratio, kurtosis, box-plot, stem and leaf, and histogram.
In order to identify significant differences in the effects of empowering community-dwelling older adults to self-screen for fall risk using FallSA©, a mixed-model ANOVA repeated measures (ANCOVA) was conducted.
Identification of limitations of FallSA©
Following the completion of Phase 1, which assessed the efficacy of fall risk screening using FallSA©, the methodology for upgrading the FallSA© mobile application was structured into three key stages: identifying limitations, conducting secondary data analysis, and implementing upgrades to develop FallSA© 2.0. In the initial stage, limitations were identified through field observations and informal verbal feedback provided by participants (during routine follow-up calls, postassessment interactions, and community sessions) as well as research team discussions. Insights from researchers were gathered via virtual meetings, including Google Meet and WhatsApp, to refine features such as the lower limb muscle strength test and the educational package for fall prevention. Subsequently, secondary data analysis was performed using 30-s STS data from the LRGS-TUA study, 26 enabling the establishment of normative values for different age groups (60–64, 65–69, 70–74, 75–79, and ≥80 years). Based on these findings, the app's features and design were enhanced, incorporating instructional videos and audio guidance for physical performance tests. Professional developers executed these upgrades under the guidance of the research team to optimize the application for future use among older adults.
Results
Baseline characteristics of participants
A total of 64 community dwelling older adults aged 60 and above within several senior citizens centers in Selangor state, Malaysia were screened in the present study. The flow of participants throughout the study is illustrated in the CONSORT diagram shown in Figure 1, with the CONSORT checklist included in the Supplemental Material.
CONSORT diagram of the participants.
There were 29 individuals in the control group (average age of 69.21 ± 5.0) and 30 in the experimental group (average age of 66.23 ± 5.3). About 34% of the participants reported experiencing falls in the previous 12 months. Comparison of baseline sociodemographic data of the control and experimental groups showed no statistically significant differences (p > .05) across all variables, except race, education level and having chronic illnesses (hypertension) (p < .05) (Table 1). No statistically significant differences were found between both groups for baseline for FallSA score, SPPB, FaB, ABC-16, FRAQ and MBPAQ (p > .05) (Table 1).
Baseline sociodemographic data of community-dwelling older adults classified based on control and intervention groups.
Note: MMSE: Mini Mental State Examination; GDS: Geriatric Depression Scale; PASE: Physical Activity Scale for the Elderly; FallSA©: Fall Screening Application; SPPB: Short Physical Performance Battery; FaB: Falls Behavioral Scale; ABC: Activity Specific Balance Confidence Scale; FRAQ: Fall Risk Awareness Questionnaire; MBPAQ: Modified Baecke's Physical Activity Questionnaire.
Significant at *p < .05, **p < .001 using independent t-test.
Intervention effects on behavioral outcomes
Table 2 demonstrates the intervention effects on behavioral outcomes from baseline and six months of study between the control and experimental groups. An intention-to-treat approach was used, with the two participant dropouts included in the analysis. There were no significant time × group interaction effects on all variables of behavioral outcomes. In the subscale analysis of FaB score, a significant interaction effect was observed in the mean score of the “changes in level” subscale following the six-month intervention (p < .05).
Time, group and interaction effects of behavioral and functional outcomes.
Note: η2p: partial eta squared; FaB: Falls Behavioral Scale; FRAQ: Fall Risk Awareness Questionnaire; ABC: Activity Specific Balance Confidence Scale; FallSA©: Fall Screening Application; SPPB: Short Physical Performance Battery; MBPAQ: Modified Baecke's Physical Activity Questionnaire.
Significant at *p < .05, **p < .001 using Mixed model ANOVA.
Intervention effects on functional outcomes
Table 3 demonstrates the intervention effects on functional outcomes from baseline and six months of study between the control and experimental groups. Similarly, there were no significant time × group interaction effects on all variables of functional outcomes. Subscales analysis on intervention effect of SPPB test showed a significant interaction effect in the mean score of “lower limb strength” subscale (p < .001).
Time, group and interaction effects of functional outcomes.
Note: η2p: Partial eta squared; ABC: Activity Specific Balance Confidence Scale; FallSA©: Fall Screening Application; SPPB: Short Physical Performance Battery.
Significant at *p < .05, **p < .001 using mixed model ANOVA.
Percentage change on behavioral and functional outcomes
While not statistically significant (p > .05), the experimental compared to the control group showed greater improvements in all the behavioral and functional outcomes except for fall risk knowledge (Figure 2). The experimental group showed a 7% mean increase in FaB score following the six-month intervention, compared to 4% in the control group. Despite improvements in fall risk knowledge among both groups, the percentage of mean change was 4% for the control group and 3% for the experimental group), with no significant difference observed (p > .05).
Percentage change in behavioral (FaB and FRAQ score) and functional (ABC-16, FallSA© score, SPPB and MBPAQ score) outcomes between experimental and control groups.
Experimental group demonstrated a 9% mean change in ABC-16 score, while the control group showed a 6% change (p > .05). Although there was no statistically significant difference found in FallSA© mean score (p > .05), the improvement in fall risk score among participants in experimental and control group were 11% and 2% respectively. Comparing the percentage change of SPPB test between control and experimental groups, the decline in physical performance among participants in control group was 10% (p > .05). Participants in the experimental group demonstrated a 5% increase in physical activity level compared to a 1% increase observed in the control group (p > .05).
Identification of limitations of FallSA©
The limitations and suggestions for FallSA© are presented in Table 4. With regard to the fall risk score, the newly identified cutoff score (>5) from earlier stage of study was modified in the upgraded FallSA 2.0. Furthermore, the TUG test normative graph report was revised to include three different percentiles (25th, 50th, and 75th) in the upgraded FallSA 2.0 (Figure 3).
TUG test normative graph reports based on three different percentiles (25th, 50th, and 75th).
Identified limitations of FallSA© and upgrade in FallSA 2.0.
Note: TUG: Timed Up and Go; STS: Sit-to-Stand.
Upgraded FallSA© features
The features and design of FallSA 2.0 (Figure 4) were developed and refined based on the previous version, FallSA©. 11 The apps developers also created the Graphic User Interface and initial mockup features of FallSA 2.0, incorporating input from the research team and the original FallSA©. 11
Features and design of FallSA 2.0.
Discussion
In this study, we evaluated the impact of the FallSA© application on behavioral and functional outcomes among community-dwelling older adults and introduced its upgraded version, FallSA© 2.0. The findings demonstrated that both groups showed positive trends in fall-related awareness and behaviors, while the experimental group experienced a relatively smaller decline in physical performance and balance confidence compared to the control group. The improvements observed, coupled with the limitations noted, informed the development of FallSA© 2.0, which incorporates enhanced educational content and improved fall risk assessment tools for broader applicability.
Our study's findings indicated that fall risk knowledge improved across both groups following the educational sessions, emphasizing the critical role of education in enhancing awareness and understanding of fall prevention. 27 Consistent with previous research, falls prevention education has been shown to increase fall risk knowledge and promote preventive behaviors among older adults.28–30 However, the lack of a significant differential effect in the experimental group using FallSA© may be due to the high baseline knowledge levels and the provision of similar educational content to both groups. Community-based health education remains vital for motivating older adults to adopt healthy behaviors and attitudes.
While no significant intervention effects were observed on the behavioral outcomes measured using FRAQ and FaB, the subscale analysis yielded noteworthy findings. The subscale analysis of the FRAQ showed a notable increase in the “medication awareness” scores, with a 22% improvement in the experimental group compared to 9% in the control group. This finding supports prior research linking educational interventions to enhanced medication-related knowledge among older adults.29,31 Additionally, both groups showed improvements in falls prevention behaviors, with a greater increase observed in the experimental group (7%) compared to the control group (4%). This may be attributed to heightened awareness and consistent self-screening in the experimental group, which likely maintained participants’ engagement and fall risk awareness. Enhanced awareness could be a driver for adopting safer behaviors. 32 The FaB subscale analysis revealed a significant change in the “changes in level” subscale, where the experimental group achieved a 94% improvement in managing challenging activities, such as navigating stairs. This aligns with previous local research that found a correlation between “changes in level” and TUG test scores, 33 potentially due to increased engagement or a learning effect from the repeated TUG tests in the FallSA© group.
Similarly, participants in both groups demonstrated a reduction in fall risk scores, likely due to the fall prevention education received. While education is a vital component of falls prevention, it is not effective as a standalone intervention in significantly reducing fall risk. 7 Previous research highlights that integrating education with other interventions, such as physical exercise, yields better outcomes in lowering fall risks. 34 Although there were no significant group differences, those using FallSA© showed an 11% decrease in fall risk compared to 3% in the control group, indicating the potential clinical utility of self-screening. No improvement in balance confidence was observed, which aligns with findings that education alone has limited impact in this domain. 35 Additionally, physical activity and physical performance did not significantly improve, likely due to the lack of exercise components. However, the FallSA© group experienced a smaller decline in physical performance (2% vs 10%), possibly due to consistent engagement in self-monitoring and TUG test. This suggests that adding physical exercise information in FallSA© could further enhance its effectiveness. The FallSA 2.0 mobile application, an upgraded fall risk assessment tool, integrates multifactorial questions with two physical performance tests, the TUG test and the 30-s STS test, both identified as key risk factors and predictors of falls among older adults in Malaysia.4,5 This enhancement was based on recent findings linking lower limb muscle strength to fall risks. 5 The inclusion of normative values for the 30 s STS test, derived from secondary data analysis of the LRGS-TUA study, 26 added further value to the application. Unlike similar apps such as the Aachen Fall Prevention App, 12 Steady, 13 and Lindera, 14 which lack clarity in their selection of assessment tools. Moreover, FallSA 2.0 was designed with comprehensive, dual-language instructions for both physical tests to enhance accessibility for older adults.
Additionally, FallSA 2.0 integrates fall prevention education to address knowledge gaps identified in earlier stages of the study, where older adults demonstrated limited awareness of fall risks. Prior research suggests that single group-based education sessions are insufficient for improving fall-related knowledge, 30 hence the decision to embed educational content within the app itself. The inclusion of quizzes before and after the education modules helps assess users’ knowledge and awareness of fall risks. Feedback from participants of the initial FallSA© version informed improvements in FallSA 2.0, such as larger font sizes, a high-contrast color interface, and bilingual content enhancements to support better user experience. Other features, like auto-fill from identity cards, an OTP-based login system, and audible “beep” sounds for the tests, were introduced to accommodate age-related cognitive and motor function declines, 36 ensuring the app's usability among older adults.
While educational training alone showed comparable benefits in some outcomes, the FallSA© app provides added value as a client-centered, self-management tool that encourages consistent engagement and empowers older adults to proactively monitor their fall risk. Aligned with the world guidelines for falls 8 recommendations for opportunistic and routine screening, FallSA© facilitates regular self-assessment and generates actionable data to support personalized fall prevention strategies. Its potential for integration into community and primary care settings offers a user-friendly approach to early detection, ongoing monitoring, and enhanced patient-provider communication. Study findings suggest that self-assessments using FallSA© are feasible for motivated older adults, particularly with structured follow-up, though broader adoption would require addressing digital literacy, app usability, and compatibility with clinical workflows.
This study demonstrated several key strengths, notably the randomization of participants into experimental and control groups, along with blinded outcome assessments, which helped reduce bias and improve the reliability of findings. The use of FallSA© 2.0, a culturally adapted mobile application, introduced an innovative and context-specific method for promoting fall prevention among community-dwelling older adults in Malaysia.
However, some limitations must be acknowledged. The study's sample size was calculated at the individual level without adjusting for cluster randomization, which may have underestimated the required number of participants. However, this impact was likely minimal due to the small number of clusters and their relative homogeneity, which would reduce intracluster correlation. 36 Additionally, some degree of imbalance can still occur due to chance, especially in a small cluster-randomized trial such as this one. While the study demonstrated adequate power for key functional outcomes such as balance confidence and physical function, the relatively small sample size may have limited the statistical power for other comparisons. In particular, behavioral outcomes, including physical activity, falls behavior, and falls risk awareness, as well as the falls risk score, showed low to moderate power. This may account for some of the nonsignificant findings and should be considered a limitation. These results should therefore be interpreted with caution, and future studies with larger sample sizes are recommended to confirm and expand upon these findings. Recruitment during the COVID-19 pandemic posed challenges that may have impacted participation and outcomes, including reduced physical activity levels. Also, insights from field observations and follow-up reports may have introduced interpretative bias. The study's generalizability is limited by its setting in only two Malaysian states and recruitment from senior citizens’ activity centers, which may not represent the broader older adult population. Additionally, the six-month intervention period may have been too short to capture long-term effects, and the monthly fall risk assessments used may not align with routine clinical practice, where less frequent monitoring is more feasible.
Despite these limitations, the study highlights the promising role of mobile self-screening tools like FallSA© 2.0 in enhancing fall prevention efforts. Further research is needed to evaluate the app's cost effectiveness, long-term effectiveness and its integration not only in the community settings but also into routine healthcare services, telehealth platforms, and hybrid care models. These advancements could contribute to a more accessible, scalable, and proactive approach to fall prevention in ageing populations.
Conclusion
In this study, we evaluated the effectiveness of FallSA© 2.0 in promoting fall prevention among community-dwelling older adults. Although there are no statistically significant results in the primary and secondary outcome measures, the overall trend in fall prevention knowledge, behavior, and physical performance supports a positive functional trajectory. These findings highlight the potential of mobile self-screening tools like FallSA© 2.0 to serve as valuable components within comprehensive fall prevention strategies.
Supplemental Material
sj-pdf-1-dhj-10.1177_20552076251390931 - Supplemental material for Transition from MHealth FallSA© to FallSA© 2.0: A randomized trial on enhancing behavioral and functional outcomes in community-dwelling older adults
Supplemental material, sj-pdf-1-dhj-10.1177_20552076251390931 for Transition from MHealth FallSA© to FallSA© 2.0: A randomized trial on enhancing behavioral and functional outcomes in community-dwelling older adults by Jing Wen Goh, Suzana Shahar, Siok Yee Tan and Devinder Kaur Ajit Singh in DIGITAL HEALTH
Footnotes
Acknowledgements
The authors would like to thank the main developers (Omar Faruk and Dewan Muhtasim) and participants in this study.
Ethical considerations
The Secretariat for Research and Ethics of Universiti Kebangsaan Malaysia (UKM PPI/111/8/JEP-2018-559) approved this study. The study was also retrospectively registered with the Australian New Zealand Clinical Trials Registry under the Registration No. ACTRN1262200112076.
Consent to participate
All participants who met the inclusion criteria were informed about the study, both verbally and using written information.
Authors’ contributions
GJW and DKAS are joint first authors. GJW, DKAS, SS, and TSY designed and developed the study protocol; GJW supervised and administered the project; DKAS and SS provided supervision and scientific oversight; TSY provided guidance and oversight for all technical aspects of the mobile application, ensuring its effective management and functionality; GJW wrote the original draft and DKAS edited this manuscript; and SS and TSY reviewed and edited the manuscript.
Funding
The work presented in this article was supported by grants (grants. Inovasi-2024-004 and Inovasi-2019-001) from Universiti Kebangsaan Malaysia as well as LRGS TUA (Grant. LRGS/BU/2012UKM/L/01) awarded by Ministry of Higher Education Malaysia.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Supplemental material
Supplemental material for this article is available online.
References
Supplementary Material
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