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Does the Use of Smart Watches Reduces the Risk of Falls in the Elderly?

Clinical Question

What is the evidence that smartwatches use is the most effective intervention compared to other interventions to reduce the risk of falls in the elderly?

Clinical Scenario

Injuries associated with falls in homes, healthcare facilities, and nursing homes have increased the cost of follow-up treatments (Martinho et al., 2019). Elderly individuals are highly prone to falls due to poor vision, muscle weakness, and body imbalances. Aging is the commonest cause of falls among these individuals because it causes loss of sight, hearing, and other implications. Most elderly individuals are also likely to suffer from long-term health conditions like dementia that increases the risks of falls (Kiburi, 2019). Hence, it is crucial to develop appropriate mitigative measures to enhance well-being and promote healthy living among the elderly.

There are multiple interventions to reduce the risks of falls among the elderly. The most common recommended interventions include guided exercise, education and training, environmental modifications, medication, and vitamin supplementations (Stavropoulos et al., 2020). However, smartwatches can be more reliable because they can assess an individual’s fall risks to help caregivers identify patients prone to high risks of falls to provide them with appropriate interventions (Beh et al., 2021). Smartwatches can apply to multiple environments. What is the effectiveness of this intervention in using smartwatches to reduce the risks of falls among the elderly, and is it more effective than other interventions like home modification and vitamin supplementations?

Summary of Key Findings

  • Twelve citations utilized in this report met the inclusion/exclusion criteria.
  • Three of the articles that met the inclusion/exclusion criteria were appraised.
  • Another review demonstrated that smartwatches offer low cost and the most effective ways of detecting falls among the elderly and summon for real-time help. However, the systematic review found that various sensors on wearable devices, especially on foot, lower back, or truck improved smartwatch accuracy. The study provides an overall performance of the wearable devices without discussing the efficacy of specific devices.
  • One of the systematic reviews predicted fall risks among the elderly using smartwatches and found that smartwatches effectively assess fall risks. Moreover, the article found that smartwatches effectively improve lifestyle among the elderly; hence, they can effectively reduce the chances of falls.
  • One article found smartwatches to be the most accurate intervention to reduce the risks of falls among the elderly. The article found smartwatches to effectively detect the use of assistive devices like walkers and canes. The study is limited by using small sample size, lack of validating the system in homes, and lack of standard walkers.
  • Neither systematic review addressed how different environments can affect the accuracy of the smartwatches in detecting falls in the elderly.

Clinical Bottom Line

Smartwatches are cost-effective and accurate interventions to reduce the risks of falls among the elderly. They improve lifestyle among the elderly and detect the use of assistive devices (Thakur & Malhotra, 2021). Smartwatches also notice the likelihood of falls and summon real-time help from caregivers. However, a combination of smartwatches with other digital wearable devices, especially on foot or truck, can improve the accuracy of smartwatches. The systematic reviews have not addressed the environmental implications on the accuracy of smartwatches in preventing risks of falls in the elderly.

Limitation of CAT

The major limitation of this study is the lack of a peer review. It is individuals prepared.


Search Strategy

The search strategy aimed at identifying reviews with the following designs:

  • Systematic reviews and meta-analyses of RCTs
  • Randomised Controlled Trials (RCTs)
  • Case-controlled trails
  • Systematic reviews and meta-analyses of Non-randomised controlled trials.

Search terms

Patient: Elderly or aged.

Intervention: Falls prevention, Falls management, smartwatches, wearable devices, wearable technology, wearable sensor.

Comparison: Home modifications, smartphones.

Outcomes: Cost-effectiveness, the accuracy of detecting falls, improved lifestyle, summon for real-time help, overall prevention of falls among the elderly.

Sites/Resources searched.

  • Four electronic databases were searched. The search was based on the most recent data from the databases from 2018 to December 2021. The databases included MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase database, and Cochrane Database of Systematic Reviews (CDSR).
  • Google search engine.
  • The reference lists from studies.
  • Science direct
  • Google scholar.

Inclusion criteria 

The inclusion of papers for this review was based on the originality of articles published not later than five years. The reports also were supposed to be published in English with unlimited access to the full text. Papers were also expected to focus on adults and interventions to reduce the risks of falls. The review included all the documents that assessed the reduction of elderly falls and those that evaluated the efficacy of wearable devices, especially smartwatches, in detecting and preventing falls among the elderly.

Exclusion criteria

Articles not published in English and with limited access to the full text were excluded. Second publications presenting similar results as the primary publication were also excluded.


Results of search for articles about intervention effectiveness
Database Search Terms Used Limits Used in Search No. of Search Results No. of relevant search results that met the criteria
Databases (MEDLINE, CINAHL, CDSR, and Embase). Elderly, aged, falls prevention, effectiveness of smartwatches. Publication date, language. 15 2
Google Search Elderly, aged, falls prevention, efficacy of wearable devices, effectiveness of smartwatches. Publication date, language, unlimited access to full text. 49 2
Google scholar Elderly, aged, falls prevention, efficacy of wearable devices, effectiveness of smartwatches. Unlimited access to full text, publication date, language, originality. 66 4
Science direct Elderly, aged, falls prevention, efficacy of wearable devices, effectiveness of smartwatches. Unlimited access to full text, publication date, language, originality. 58 4

The search strategy was effective and provided adequate and relevant information needed for assessing the efficacy of smartwatches in preventing the risks of falls among the elderly. The limits were critical in delivering only suitable materials with adequate information on the topic. However, it is noticeable that there were numerous articles and papers with relevant information with unlimited publication dates. Therefore, I would want to increase the publication date limit to not later than ten years in future research.

Summary and Appraisal of Each Study

Description and Appraisal for SR by Antos et al. (2019)

The objective of the review

The objective of this review was to assess whether smartwatches or smartphones can effectively detect whether the elderly used an assistive device to prevent falling or not. The study aims at evaluating the effectiveness of smartwatches in detecting walkers or canes.


The review collected data from adults aged 60 years and older who used assistive devices like a cane or a walker.

Inclusion/Exclusion Criteria

Elderly individuals were aged 6o years and above and were using assistive devices. The participants also would have the ability to walk at least 10 meters without using assistive devices. The review excluded all participants who had increased pains while walking without assistive devices (Antos et al., 2019).

Data Extraction

The review extracted sensor data from elderly walking with or without assistive devices. The participants wore smartwatches during the 10 meters walk with and without assistive devices. There was random data collection to prevent bias. The physical therapist walked along with the participants to collect data and prevent falls. The smartwatches and smartphones had both accelerometer and gyroscope sensors.

Data Analysis

The review interpolated sensor signals linearly to 30 Hz, separated the labels and calculated the features for each tag. The reviewer then classified the algorithms, tested the classifiers in python, then carried out cross-validation to evaluate classifiers. The reviewers also used a combination of data from smartphones and smartwatches to assess the efficacy of classifiers while using multiple sensors. They compared smartwatches and smartphones using Wilcoxon signed ranked tests (α = 0.05 and p<0.05) to assess the accuracy of the classifiers.


Fourteen elderly individuals participated in the study because they met the inclusion/exclusion criteria. All the fourteen walked using assistive devices, and eight used walkers while six used canes.


Smartwatch classifiers were the most distinctive and provided superior results compared to smartphone classifiers in all cross-validation types (p<0.01) (Antos et al., 2019). Furthermore, the smartwatch system recorded 99.7% accuracy while smartphones recorded 92.9% accuracy for detecting assistive device use by a similar individual in a day. Smartwatches also recorded 99.6% accuracy, and smartphones recorded 64.2% accuracy for detective assistive device use by an individual for different days. Smartwatches recorded 99.2% accuracy, while smartphones recorded 57.6% accuracy for detecting assistive device use in a single session. Smartwatches recorded 98.2%, while smartphones had 54.4% accuracy for detecting assistive device use for new individuals (Antos et al., 2019).

Author’s Conclusion

Smartwatches provide high quality and accurate data for detecting assistive devices than smartphones. Hence, smartwatches are the most effective in detecting falls and daily activities among the elderly. The devices can remind the elderly of their assistive devices or notify caregivers when an elderly is not using the device, potentially preventing the risks of falls. Furthermore, caregivers can use smartwatches to monitor the elderly to ensure they use their assistive devices everywhere to prevent falls. The author also recommends a combination of various sensors and other wearable devices to enhance accuracy. Vargemidis et al. (2020) provided similar recommendations. Overall, the reviewer concluded that smartwatches are the most accurate compared to other interventions in preventing falls among the elderly.

Appraisal of the Study’s Internal Validity

The clinical question that the reviewer addressed was focused. The reviewer also provided detailed data collection, extraction, and analysis methods. There was statistical data analysis, and the author presented all the results in detail. However, the reviewers did not assess the papers’ validity and the heterogeneity among the studies. In addition, the reviewer did not validate the system’s accuracy in multiple environments. The study was based on a single domain, and individuals may likely have different home layouts that can affect the system’s efficacy. Small sample size may also implicate the outcomes. The review did not test the accuracy of smartwatches with standards walkers.

Description and Appraisal of SR for Warrington et al. (2021)

The objective of the review

The objective of this review was to do a literature search to evaluate the effectiveness of wearable electronic devices on the reduction and prevention of the risks of falls among the elderly. It aimed to measure the efficacy of wearable devices, including smartwatches, in detecting, preventing, and assessing the risk of falls and reducing the costs of living by reducing hospital admissions due to falls.


The review sourced data from literature from multiple databases and other search engines. The databases included MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase database, and Cochrane Database of Systematic Reviews (CDSR). The authors explored the databases from their inception until 2019. Moreover, the authors focused on only systematic reviews to generate data. The authors also utilized google search and reference lists of various papers for more data.

Inclusion criteria

Original meta-analyses of systematic reviews were eligible in this review. The study had no publication date limits. However, all the articles were supposed to be published in English and have open access to the full content. All the papers with wearing electronic devices interventions against elderly falls were acceptable in the review. In addition, all the articles that assessed the effectiveness of wearable electronic devices in reducing and preventing elderly falls were included in the study.

Data extraction

The first and second reviewers extracted data, and the third reviewer assessed data accuracy. The reviewers extracted publication year for the articles, objectives, sample size, population characteristics, types of wearable devices under review, and outcomes. The third review made the final decisions regarding the accuracy of data.

Data analysis

There was considerable heterogeneity between the articles utilized in the review that hindered additional meta-analysis. The reviewers assessed the methodologies of all reports to check for bias.


The authors found 12 papers from databases and 16 from other sites aforementioned. However, 11 articles met the inclusion criteria and were utilized in the review.


The review did not perform a meta-analysis due to the heterogeneity of the studies. However, the authors reported 93.1% or greater sensitivity based on three systematic reviews. Data accuracy was heterogeneous (Warrington et al., 2021). The comparison of locations for wearing electronic devices found that lower back, trunk, or foot were the most appropriate locations. Hence, the smartwatches with accelerometer sensors would be fixed in these locations for accuracy. Another systematic review found that utilizing multiple sensors on wearable electronic devices would increase efficacy (Casilari et al., 2020).

Authors’ Conclusions

Warrington et al. (2021) concluded that wearable electronic devices effectively prevent falls among the elderly, and they are cost-effective. They are accurate in detecting falls and summoning real-time help from caregivers. Mrozek et al. (2020) reported similar outcomes. They don’t rely on the user to press the button, and they will summon for help automatically; thus, they are the most effective interventions. A combination of accelerometers and gyroscopes will enhance the efficacy of the smartwatches, and the body location is also vital in improving the performance of these devices.

Appraisal of the Study’s Internal Validity

The reviewer adequately addressed a focused clinical question. The methodology is well-structured and adequate. The reviewer also detailed the steps of extracting data enough. However, the systematic reviews were of low quality based on the authors’ analysis. For instance, the authors state that only two of the articles were of moderate quality, the rest of the reviews were of low quality. None of the articles was of high quality. In addition, the study did not perform a risk of bias assessment for the papers and the meta-analysis. There was no statistical assessment of the heterogeneity of the reviews. The sample size was also inadequate. The authors used a small number of studies that would not provide adequate data on the efficacy of wearable devices. Furthermore, the review was general regarding the wearable devices and did not assess individual devices to evaluate their effectiveness in preventing falls among the elderly.

Description and Appraisal for SR by Haescher et al. (2018)

Objectives of the Review

To evaluate fall risk among the elderly using smartwatches. The review aims at assessing fall risks and providing caregivers with fall scores to determine individuals with special needs for the prevention of falls.


Data sources

Not specified

Study Design

The study was based on various standard tests on sleep quality, various environmental factors, balance, body strength, and patients’ history.

Inclusion/exclusion criteria

Not specified

Data extraction

The reviewers collected data through standard tests based on sleep quality, various environmental factors, balance, body strength, general fitness, and patients’ history. They calculated the total fall risk score to assess fall risks among the elderly. Posture and transitions test provided information on physical fitness. The number of positions changes threshold test provided information on the elder’s sleep quality. Other tests provided information on balance, elders’ strength, medication history, and environmental factors. The reviewer utilized the data to calculate the total fall risk score. The reviewers evaluated the system in a pilot study with 30 elderly.


Data synthesis for this review is limited. The author did not report on the validity of the included reviews. There is also no testing for heterogeneity of the included studies.


The tests aided the calculation of the total fall risks score. The smartwatch-based approach proved effective in predicting changes in behavior and vitality among the elderly. The reviewer did not weigh each fall risk in the pilot study.

Authors’ Conclusion

The smartwatch-based approach effectively evaluates fall risks among the elderly to prevent falls. Based on the total falls risk score, caregivers can determine the best course to monitor the elderly and prevent falls.

Appraisal of the Study’s Internal Validity

The reviewers addressed a focused clinical question. However, there are no details of the methods and criteria for inclusion and exclusion. There is no statistical analysis of data, and the authors did not assess the validity of the articles included in the review. Moreover, the authors did not provide detailed results of the pilot study.


Limitations of the Review

The review was based on studies not later than five years old. It is noticeable that there were numerous studies with relevant information with unlimited publication dates. Hence, limiting the publication date to five years would have limited the adequacy of data n the effectiveness of smartwatches in preventing falls among the elderly. The review also lacks adequate data on the comparison of various devices to determine the efficacy of smartwatches.

Critical Considerations for Delivering this Treatment

To deliver this treatment plan, there are various critical considerations. For instance, environmental factors are vital in determining the accuracy of the treatment plan. Environmental factors may have implications for the efficacy of smartwatches. The treatment plan is cost-effective. However, there is a need to provide adequate training and support to the elderly to help them comprehend the functionality of the treatment plan.

Need for Any Other Evidence to Inform Decision-Making

Smartwatches are effective interventions in preventing the risks of falls among the elderly. However, limited power autonomy, tiny screens, and tiny connectors limit the accuracy of the interventions, especially among the elderly who may have vision and hearing challenges. Therefore, assessing these factors and establishing proper mitigative measures will inform decision-making regarding the smartwatch approach to prevent elderly falls.

What Qualitative Study added to the topic

The qualitative and prognostic studies provided more information to inform the treatment of elderlies prone to fall risks. The studies provided information on the most relevant interventions to prevent falls among these individuals. The data is suitable for caregivers to decide on the most appropriate treatment plans to enhance their quality of life.

Results of the Review

The review results found that smartwatches are the most effective interventions for preventing falls among the elderly. They effectively detect falls and summon immediate help from caregivers. Smartwatches are will also detect the use of assistive devices and inform caregivers whether or not the elderly are adhering to the use of assistive devices (Van Thanh et al., 2019). In essence, the results reveal that smartwatches have high efficacy in preventing elderly falls. However, more research on the comparison of smartwatches and other devices will increase the accuracy of the outcomes.


Antos, S. A., Danilovich, M. K., Eisenstein, A. R., Gordon, K. E., & Kording, K. P. (2019). Smartwatches can detect walker and cane use in older adults. Innovation in Aging3(1), 1-10. https://doi:10.1093/geroni/igz008

Beh, P. K., Ganesan, Y., Iranmanesh, M., & Foroughi, B. (2021). Using smartwatches for fitness and health monitoring: the UTAUT2 combined with threat appraisal as moderators. Behaviour & Information Technology40(3), 282-299.

Casilari, E., Álvarez-Marco, M., & García-Lagos, F. (2020). A Study of the use of gyroscope measurements in wearable fall detection systems. Symmetry12(4), 1-22.

Haescher, M., Matthies, D. J., Srinivasan, K., & Bieber, G. (2018, September). Mobile assisted living: smartwatch-based fall risk assessment for elderly people. In Proceedings of the 5th international workshop on sensor-based activity recognition and interaction. 1-10.

Kiburi, P. (2019). Is Technology the key to prevention of falls among the elderly in rapidly aging societies? A case study of Kunming, China. 1-36.

Martinho, D., Carneiro, J., Novais, P., Neves, J., Corchado, J., & Marreiros, G. (2019). A conceptual approach to enhance the well-being of elderly people. In EPIA Conference on Artificial Intelligence. 50-61.

Mrozek, D., Koczur, A., & Małysiak-Mrozek, B. (2020). Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge. Information Sciences537, 132-147.

Sharma, S., Thakur, E., & Malhotra, I. (2021). A Review on Smartwatch for Paralytic and Critically Aged Persons. In 2021 6th International Conference on Communication and Electronics Systems (ICCES).1380-1389. http://doi.10.1109/ICCES51350.2021.9489120

Stavropoulos, T. G., Papastergiou, A., Mpaltadoros, L., Nikolopoulos, S., & Kompatsiaris, I. (2020). IoT wearable sensors and devices in elderly care: A literature review. Sensors20(10), 1-22.

Vargemidis, D., Gerling, K., Spiel, K., Abeele, V. V., & Geurts, L. (2020). Wearable physical activity tracking systems for older adults—a systematic review. ACM Transactions on Computing for Healthcare1(4), 1-37.

Van Thanh, P., Tran, D. T., Nguyen, D. C., Duc Anh, N., Nhu Dinh, D., El-Rabaie, S., & Sandrasegaran, K. (2019). Development of a real-time, simple and high-accuracy fall detection system for elderly using 3-DOF accelerometers. Arabian Journal for Science and Engineering44(4), 3329-3342.

Warrington, D. J., Shortis, E. J., & Whittaker, P. J. (2021). Are wearable devices effective for preventing and detecting falls: an umbrella review (a review of systematic reviews). BMC public health21(1), 1-12.


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