THERAPY Magazin
Assistive technologies in fall prevention in the home environment
Discover how smart home systems, wearables, and exergames are transforming fall prevention for older adults. Learn about effectiveness, implementation, and ethical considerations—shaping safer, independent living.
Results of a Health Technology Assessment
Falls in old age represent a considerable health risk and result in high costs. Assistive technologies such as sensor systems and smart home solutions offer innovative approaches to fall prevention and could both improve the quality of life of older people and reduce the burden on the healthcare system. Discover how these technologies work, what the challenges are and what recommendations have been developed for their successful implementation.
Falls pose a considerable health risk for older people. Around a third of over-65s and half of over-80s fall at least once a year, with the majority of these falls occurring at home (Endress et al., 2023; Jansen et al., 2021). Fall-related injuries are among the most common causes of hospitalisation and the need for long-term care in old age (Schoene et al., 2023). In addition to the individual consequences for those affected, falls also represent an enormous economic burden for the healthcare system. In Germany alone, the total annual costs are expected to be in the billions (Jansen et al., 2021).
In view of the demographic ageing of society, fall prevention is becoming increasingly important. Numerous studies show that the fall rate can be significantly reduced through targeted measures, in particular through physical training and the elimination of fall hazards in the living environment (Becker & Bauer, 2023; Schoene et al., 2023).
Assistive technologies offer promising new approaches for recognising risk factors at an early stage and preventing falls (Moers, 2023). These
include, for example, sensor systems for fall detection, robotics-supported training programmes and smart home solutions for adapting the living environment. The widespread implementation of effective technology-supported prevention strategies could not only improve the quality of life of older people, but also contribute to considerable cost savings in the healthcare system.
The central research questions focus on:
1. The effectiveness of medical care compared to conventional measures and factors influencing acceptance.
2. Technological challenges and security aspects.
3. Health economics cost-benefit assessment.
4. Ethical, social and legal aspects such as data protection and privacy.
German and English-language studies from 2015 to July 2024 were selected from relevant databases for the systematic literature search. These include studies with seniors over 65 years of age in a home setting that investigated assistive technologies for fall prevention. The study quality was assessed using customised criteria. This article presents results from sub-project 4 “Assistive systems” of the CARE REGIO joint project.
Falls pose a considerable health risk for older people. Around a third of over-65s and half of over-80s fall at least once a year, with the majority of these falls occurring at home (Endress et al., 2023; Jansen et al., 2021). Fall-related injuries are among the most common causes of hospitalisation and the need for long-term care in old age (Schoene et al., 2023). In addition to the individual consequences for those affected, falls also represent an enormous economic burden for the healthcare system. In Germany alone, the total annual costs are expected to be in the billions (Jansen et al., 2021).
In view of the demographic ageing of society, fall prevention is becoming increasingly important. Numerous studies show that the fall rate can be significantly reduced through targeted measures, in particular through physical training and the elimination of fall hazards in the living environment (Becker & Bauer, 2023; Schoene et al., 2023).
Assistive technologies offer promising new approaches for recognising risk factors at an early stage and preventing falls (Moers, 2023). These
include, for example, sensor systems for fall detection, robotics-supported training programmes and smart home solutions for adapting the living environment. The widespread implementation of effective technology-supported prevention strategies could not only improve the quality of life of older people, but also contribute to considerable cost savings in the healthcare system.
The central research questions focus on:
1. The effectiveness of medical care compared to conventional measures and factors influencing acceptance.
2. Technological challenges and security aspects.
3. Health economics cost-benefit assessment.
4. Ethical, social and legal aspects such as data protection and privacy.
German and English-language studies from 2015 to July 2024 were selected from relevant databases for the systematic literature search. These include studies with seniors over 65 years of age in a home setting that investigated assistive technologies for fall prevention. The study quality was assessed using customised criteria. This article presents results from sub-project 4 “Assistive systems” of the CARE REGIO joint project.
Innovative approaches such as sensor systems and smart home solutions can reduce the risk of falls in older people.
Efficacy and safety
Current evidence suggests that various assistive technologies can be effective in preventing falls in older people in the home environment. A meta-analysis by Lee et al. (2024) showed that telehealth programmes, exergames, smart home systems and wearable sensors were able to significantly reduce the risk of falling compared to control groups. Exergames in particular, which combine physical training with cognitive stimulation, appear to be very promising. Several meta-analyses show that exergaming can improve balance control and reduce falls in healthy seniors with similar or even better effects than conventional training (Chen et al., 2021; Cieślik et al., 2023). There is also evidence that virtual reality training improves balance and gait (Piech & Czernicki, 2021).
In the field of wearable sensors, studies suggest that algorithms that combine acceleration data from body sensors with questionnaire data can assess the risk of falling with high accuracy and thus represent an objective alternative to clinical assessment tools (Greene et al., 2021). The detection of near-falls using wearables to identify high-risk individuals at an early stage also appears promising (Pang et al., 2019). For smart home solutions such as intelligent lighting or fall sensors, there is also evidence of an effective reduction in the fall rate as a useful addition to active training programmes (Del Miranda-Duro et al., 2021).
Despite these positive results, the quality of evidence is still limited. Many studies have methodological limitations, such as small samples, short intervention durations or a lack of comparisons with established methods. In particular, there is a lack of controlled long-term studies that allow a comparison with conventional measures and an investigation of hard endpoints such as the actual incidence of falls (Del Miranda-Duro et al., 2021).
Current evidence suggests that various assistive technologies can be effective in preventing falls in older people in the home environment. A meta-analysis by Lee et al. (2024) showed that telehealth programmes, exergames, smart home systems and wearable sensors were able to significantly reduce the risk of falling compared to control groups. Exergames in particular, which combine physical training with cognitive stimulation, appear to be very promising. Several meta-analyses show that exergaming can improve balance control and reduce falls in healthy seniors with similar or even better effects than conventional training (Chen et al., 2021; Cieślik et al., 2023). There is also evidence that virtual reality training improves balance and gait (Piech & Czernicki, 2021).
In the field of wearable sensors, studies suggest that algorithms that combine acceleration data from body sensors with questionnaire data can assess the risk of falling with high accuracy and thus represent an objective alternative to clinical assessment tools (Greene et al., 2021). The detection of near-falls using wearables to identify high-risk individuals at an early stage also appears promising (Pang et al., 2019). For smart home solutions such as intelligent lighting or fall sensors, there is also evidence of an effective reduction in the fall rate as a useful addition to active training programmes (Del Miranda-Duro et al., 2021).
Despite these positive results, the quality of evidence is still limited. Many studies have methodological limitations, such as small samples, short intervention durations or a lack of comparisons with established methods. In particular, there is a lack of controlled long-term studies that allow a comparison with conventional measures and an investigation of hard endpoints such as the actual incidence of falls (Del Miranda-Duro et al., 2021).
Successful implementation could lead to considerable cost savings in the healthcare system.
In terms of technical aspects and safety, a number of challenges still need to be overcome to ensure reliable everyday use. These include a user-friendly design, sufficient battery life, stable data transmission and processing performance as well as the fulfilment of high quality and safety standards (Del Miranda-Duro et al., 2021; Zhao et al., 2021). Regular maintenance and reliable support are essential, as technical failures in fall prevention can have serious consequences (Merda et al., 2017). It is also crucial to protect the privacy and sensitive health data of users through suitable encryption and authentication procedures (Merda et al., 2017).
User acceptance and implementation
Older people often express reservations about new technologies due to fear of contact, fear of stigmatisation or concerns about surveillance and loss of autonomy (Peek et al., 2016). To break down these barriers, the systems must be easy to use, reliable and immediately recognisable as useful (Thordardottir et al., 2019). An intuitive user interface, sufficiently large displays and buttons, easy-to-understand instructions and a target group-orientated look are just as important as taking into account possible sensory and motor impairments of the user (Gaspar & Lapão, 2021).
It is also crucial to involve users in the development and testing of the technologies at an early stage. Their expectations, wishes and experiences provide valuable information for a needs-based design (Merda et al., 2017). For example, exergames should be both motivating and challenging through age-appropriate game content and difficulty levels (Mähs, 2021). Flexible adaptation to individual abilities and preferences, for example with regard to the range of functions or the wearability of sensors, can also increase acceptance (Chaccour et al., 2017).
In addition to user-friendly design, training and support play a key role in successful implementation. Both the older users and their relatives and carers need guidance and support to be able to use the systems effectively (Ohneberg et al., 2023). This calls for target group-specific, low-threshold approaches that gradually introduce the technology and include practical everyday exercises. Continuous support and the involvement of more tech-savvy caregivers can also help to solve any problems that arise and maintain motivation (Parzen et al., 2021).
Nursing staff also need to be sensitised to and trained in the use of assistive technologies. There are often still reservations that the systems could lead to additional work or replace interpersonal attention (Scorna et al., 2021). To counteract this, the technologies should be communicated as a supplement and to make work easier. Interprofessional training courses that combine nursing science and technical aspects can help to reduce fear of contact and strengthen the technical skills of carers (Braeseke et al., 2022).
Last but not least, assistive technologies must be sensibly integrated into existing care and nursing processes. To date, the needs of people requiring care and existing care routines have been given too little consideration in the development of technology (GKV-Spitzenverband, 2021). Stronger networking between manufacturers, research and care practice is necessary in order to develop solutions suitable for everyday use. It is also important to examine how the systems can be integrated into higher-level care structures such as GP practices, care support centres or hospitals (Braeseke et al., 2022). Cross-sector collaboration and interface management could promote acceptance among professional carers.
Older people often express reservations about new technologies due to fear of contact, fear of stigmatisation or concerns about surveillance and loss of autonomy (Peek et al., 2016). To break down these barriers, the systems must be easy to use, reliable and immediately recognisable as useful (Thordardottir et al., 2019). An intuitive user interface, sufficiently large displays and buttons, easy-to-understand instructions and a target group-orientated look are just as important as taking into account possible sensory and motor impairments of the user (Gaspar & Lapão, 2021).
It is also crucial to involve users in the development and testing of the technologies at an early stage. Their expectations, wishes and experiences provide valuable information for a needs-based design (Merda et al., 2017). For example, exergames should be both motivating and challenging through age-appropriate game content and difficulty levels (Mähs, 2021). Flexible adaptation to individual abilities and preferences, for example with regard to the range of functions or the wearability of sensors, can also increase acceptance (Chaccour et al., 2017).
In addition to user-friendly design, training and support play a key role in successful implementation. Both the older users and their relatives and carers need guidance and support to be able to use the systems effectively (Ohneberg et al., 2023). This calls for target group-specific, low-threshold approaches that gradually introduce the technology and include practical everyday exercises. Continuous support and the involvement of more tech-savvy caregivers can also help to solve any problems that arise and maintain motivation (Parzen et al., 2021).
Nursing staff also need to be sensitised to and trained in the use of assistive technologies. There are often still reservations that the systems could lead to additional work or replace interpersonal attention (Scorna et al., 2021). To counteract this, the technologies should be communicated as a supplement and to make work easier. Interprofessional training courses that combine nursing science and technical aspects can help to reduce fear of contact and strengthen the technical skills of carers (Braeseke et al., 2022).
Last but not least, assistive technologies must be sensibly integrated into existing care and nursing processes. To date, the needs of people requiring care and existing care routines have been given too little consideration in the development of technology (GKV-Spitzenverband, 2021). Stronger networking between manufacturers, research and care practice is necessary in order to develop solutions suitable for everyday use. It is also important to examine how the systems can be integrated into higher-level care structures such as GP practices, care support centres or hospitals (Braeseke et al., 2022). Cross-sector collaboration and interface management could promote acceptance among professional carers.
Health economics aspects
The health economics consideration of assistive technologies in home-based fall prevention is an important aspect in the assessment of their feasibility and dissemination. In the long term, these technologies could pay off economically if their use avoids the consequential costs of falls. In the USA alone, around 50 billion dollars are spent annually on fall-related injuries in older people, so effective prevention measures could make significant savings (Tanwar et al., 2022). The need for personal support could also be reduced by customised digital solutions (Hamm et al., 2016).
Braeseke et al. (2022) emphasise that although the costs of implementing technical assistance systems are high, long-term savings are possible due to the potential for reducing care times and relieving the burden on staff. Accordingly, age-appropriate technologies could be used to help older people or those in need of support to carry out everyday tasks and ensure their safety. This could reduce the workload for carers and caregivers and delay or even prevent admission to a care facility (Mähs, 2021).
However, the currently still high costs are a major obstacle to widespread use. As Scorna et al. (2021) state, many of the current systems for fall prevention are too cost-intensive, which is an argument against purchasing them. This applies in particular to the outpatient sector, where the investment costs often cannot be justified by a high frequency of use and rapid amortisation (Braeseke et al., 2022). The costs of clinical gait analysis tools are also often too high for use at home (Chaccour et al., 2017).
Another problem is the unresolved financing issue. Neither long-term care insurance nor health insurance companies have yet covered the costs of assistance systems to any great extent, which is slowing down the spread of such services (Merda et al., 2017). As Lee et al. (2024) show, lower household income goes hand in hand with lower technology use, so high costs can make it more difficult for lower-income groups to adopt technology.
To date, only a few high-quality studies are available on the health economics evaluation of age-appropriate assistive technologies for fall prevention. In order to improve the economic potential of these technologies, various criteria should be used in their evaluation. In addition to the acquisition costs, subsequent costs such as installation, maintenance, repairs, training and ongoing operating costs must also be included in the cost evaluation. This is offset by potential savings, e.g. by avoiding fall-related injuries and the need for care. A long-term perspective is important in order to be able to map sustainable effects (Mähs & Fachinger, 2022; Merda et al., 2017).
In addition, the evaluation should take into account different perspectives, such as those of the health insurer and the insured person as the beneficiary. For health insurance, the direct, tangible costs and benefits are primarily relevant in order to consider the potential reduction in expenditure. From the perspective of the insured, on the other hand, non-monetary benefits such as subjective health and quality of life are particularly important (Mähs & Fachinger, 2022).
The health economics consideration of assistive technologies in home-based fall prevention is an important aspect in the assessment of their feasibility and dissemination. In the long term, these technologies could pay off economically if their use avoids the consequential costs of falls. In the USA alone, around 50 billion dollars are spent annually on fall-related injuries in older people, so effective prevention measures could make significant savings (Tanwar et al., 2022). The need for personal support could also be reduced by customised digital solutions (Hamm et al., 2016).
Braeseke et al. (2022) emphasise that although the costs of implementing technical assistance systems are high, long-term savings are possible due to the potential for reducing care times and relieving the burden on staff. Accordingly, age-appropriate technologies could be used to help older people or those in need of support to carry out everyday tasks and ensure their safety. This could reduce the workload for carers and caregivers and delay or even prevent admission to a care facility (Mähs, 2021).
However, the currently still high costs are a major obstacle to widespread use. As Scorna et al. (2021) state, many of the current systems for fall prevention are too cost-intensive, which is an argument against purchasing them. This applies in particular to the outpatient sector, where the investment costs often cannot be justified by a high frequency of use and rapid amortisation (Braeseke et al., 2022). The costs of clinical gait analysis tools are also often too high for use at home (Chaccour et al., 2017).
Another problem is the unresolved financing issue. Neither long-term care insurance nor health insurance companies have yet covered the costs of assistance systems to any great extent, which is slowing down the spread of such services (Merda et al., 2017). As Lee et al. (2024) show, lower household income goes hand in hand with lower technology use, so high costs can make it more difficult for lower-income groups to adopt technology.
To date, only a few high-quality studies are available on the health economics evaluation of age-appropriate assistive technologies for fall prevention. In order to improve the economic potential of these technologies, various criteria should be used in their evaluation. In addition to the acquisition costs, subsequent costs such as installation, maintenance, repairs, training and ongoing operating costs must also be included in the cost evaluation. This is offset by potential savings, e.g. by avoiding fall-related injuries and the need for care. A long-term perspective is important in order to be able to map sustainable effects (Mähs & Fachinger, 2022; Merda et al., 2017).
In addition, the evaluation should take into account different perspectives, such as those of the health insurer and the insured person as the beneficiary. For health insurance, the direct, tangible costs and benefits are primarily relevant in order to consider the potential reduction in expenditure. From the perspective of the insured, on the other hand, non-monetary benefits such as subjective health and quality of life are particularly important (Mähs & Fachinger, 2022).
Ethical and social implications
One key aspect is the processing of personal and sensitive health data by the assistive systems. The requirements of the General Data Protection Regulation (GDPR) must be observed here, which is a challenge due to the complexity and diversity of the technologies. It is important to clearly define responsibilities, obtain consent, guarantee data security and transparency and take into account the rights of data subjects. There are special requirements for the use of cloud services, localisation functions and learning systems (GKV-Spitzenverband, 2021; Merda et al., 2017).
The protection of privacy is a critical issue. Many older people express reservations about this, especially when cameras or tracking systems are used. There is a risk of surveillance and restriction of self-determination here. On the other hand, monitoring can also increase safety. An appropriate balance must be found between protection and autonomy and individual preferences must be taken into account (Del Miranda-Duro et al., 2021; Madara Marasinghe, 2016).
In addition to data protection and privacy, the effects on social relationships and care structures must also be considered. On the one hand, assistive technologies can relieve and support carers and professionals. On the other hand, there is concern that human attention and care will be pushed back as a result. This calls for models that see technology as a supplement to human care rather than a substitute for it. The GKV-Spitzenverband (2021) emphasises that technical innovations must not lead to a substitution of personal care, but must be seen as supplements.
It is also important to maintain motivation over a longer period of time (Lee et al., 2024; Mähs, 2021). In addition, socio-cultural reservations about the use of technology in care must be taken seriously, especially among carers. Targeted information and training programmes are needed to bring about change (Braeseke et al., 2022).
The debate about the benefits of digital applications for people in need of care remains central to the ongoing discussion in this area. This is because in the area of health and care provision for older people, it is about criteria that are not only focussed on medical benefits, but also take into account aspects that are aimed at self-determination, independence and quality of life (GKV-Spitzenverband, 2021).
One key aspect is the processing of personal and sensitive health data by the assistive systems. The requirements of the General Data Protection Regulation (GDPR) must be observed here, which is a challenge due to the complexity and diversity of the technologies. It is important to clearly define responsibilities, obtain consent, guarantee data security and transparency and take into account the rights of data subjects. There are special requirements for the use of cloud services, localisation functions and learning systems (GKV-Spitzenverband, 2021; Merda et al., 2017).
The protection of privacy is a critical issue. Many older people express reservations about this, especially when cameras or tracking systems are used. There is a risk of surveillance and restriction of self-determination here. On the other hand, monitoring can also increase safety. An appropriate balance must be found between protection and autonomy and individual preferences must be taken into account (Del Miranda-Duro et al., 2021; Madara Marasinghe, 2016).
In addition to data protection and privacy, the effects on social relationships and care structures must also be considered. On the one hand, assistive technologies can relieve and support carers and professionals. On the other hand, there is concern that human attention and care will be pushed back as a result. This calls for models that see technology as a supplement to human care rather than a substitute for it. The GKV-Spitzenverband (2021) emphasises that technical innovations must not lead to a substitution of personal care, but must be seen as supplements.
It is also important to maintain motivation over a longer period of time (Lee et al., 2024; Mähs, 2021). In addition, socio-cultural reservations about the use of technology in care must be taken seriously, especially among carers. Targeted information and training programmes are needed to bring about change (Braeseke et al., 2022).
The debate about the benefits of digital applications for people in need of care remains central to the ongoing discussion in this area. This is because in the area of health and care provision for older people, it is about criteria that are not only focussed on medical benefits, but also take into account aspects that are aimed at self-determination, independence and quality of life (GKV-Spitzenverband, 2021).
Further research is needed to ensure the effectiveness, safety and acceptance of these technologies.
Conclusion and outlook
The present analysis shows that assistive technologies offer promising approaches for improving the prevention of falls in older people at home. Telehealth, exergames, assistance technologies for movement training, smart homes and wearables can supplement or even surpass the effectiveness of conventional measures by specifically addressing risk factors such as balance and gait disorders. However, the evidence base is still limited and there is a need for further research, particularly on long-term effects and comparisons with established methods.
In addition to effectiveness, aspects such as economics, user acceptance and ethical and legal implications must also be taken into account for successful implementation in practice. There are still various barriers that need to be broken down through user-orientated development, training, financial incentives and education. It is crucial to consistently focus on the needs and preferences of the target group, which is often far removed from technology, in order to reduce fear of contact and promote adherence. In addition, data protection and self-determination must be safeguarded and technology must always be seen as a supplement rather than a substitute for human attention.
In order to utilise the potential of assistive technologies in fall prevention, the following recommendations for research, development and practice can be derived:
• Methodologically high-quality studies with sufficiently large samples, longer time periods and hard endpoints are necessary to prove the effectiveness beyond doubt in comparison to conventional measures.
• Technology development should be carried out in a participatory manner with the early involvement of people in need of care, relatives and carers in order to increase needs and acceptance. User motivation and adherence must be promoted through target group-specific design.
• Technical improvements in terms of energy efficiency, reliability and sensor fusion should be sought and supported through standardisation. In addition to effectiveness, intervention studies should also investigate long-term implementation in everyday care.
• Health economics analyses, taking into account direct and indirect costs, long-term effects and different perspectives, are necessary to evaluate costs and benefits and develop viable financing concepts.
• Data protection, ethics and user autonomy must take top priority during development and use. Technical innovations must not replace personal care, but complement it in a targeted manner. All stakeholders must be closely involved.
If these aspects can be implemented consistently, age-appropriate assistance technologies can make a valuable contribution to preventing falls, promoting independence and improving the quality of life of people in need of care. This opens up new perspectives for overcoming the challenges of demographic change and ensuring needs-based, dignified care in the long term.
The present analysis shows that assistive technologies offer promising approaches for improving the prevention of falls in older people at home. Telehealth, exergames, assistance technologies for movement training, smart homes and wearables can supplement or even surpass the effectiveness of conventional measures by specifically addressing risk factors such as balance and gait disorders. However, the evidence base is still limited and there is a need for further research, particularly on long-term effects and comparisons with established methods.
In addition to effectiveness, aspects such as economics, user acceptance and ethical and legal implications must also be taken into account for successful implementation in practice. There are still various barriers that need to be broken down through user-orientated development, training, financial incentives and education. It is crucial to consistently focus on the needs and preferences of the target group, which is often far removed from technology, in order to reduce fear of contact and promote adherence. In addition, data protection and self-determination must be safeguarded and technology must always be seen as a supplement rather than a substitute for human attention.
In order to utilise the potential of assistive technologies in fall prevention, the following recommendations for research, development and practice can be derived:
• Methodologically high-quality studies with sufficiently large samples, longer time periods and hard endpoints are necessary to prove the effectiveness beyond doubt in comparison to conventional measures.
• Technology development should be carried out in a participatory manner with the early involvement of people in need of care, relatives and carers in order to increase needs and acceptance. User motivation and adherence must be promoted through target group-specific design.
• Technical improvements in terms of energy efficiency, reliability and sensor fusion should be sought and supported through standardisation. In addition to effectiveness, intervention studies should also investigate long-term implementation in everyday care.
• Health economics analyses, taking into account direct and indirect costs, long-term effects and different perspectives, are necessary to evaluate costs and benefits and develop viable financing concepts.
• Data protection, ethics and user autonomy must take top priority during development and use. Technical innovations must not replace personal care, but complement it in a targeted manner. All stakeholders must be closely involved.
If these aspects can be implemented consistently, age-appropriate assistance technologies can make a valuable contribution to preventing falls, promoting independence and improving the quality of life of people in need of care. This opens up new perspectives for overcoming the challenges of demographic change and ensuring needs-based, dignified care in the long term.
Funding information: The CARE REGIO project and the research described in this article were made possible by funding from the Bavarian State Ministry of Health, Care and Prevention.
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References:
- Becker, C. & Bauer, J. M. (2023). Leitlinien und Leitplanken für die Sturzprävention [Guidelines and guiding principles for the prevention of falls]. Zeitschrift fur Gerontologie und Geriatrie, 56(6), 445–447. https://doi.org/10.1007/s00391-023-02234-8
- Braeseke, G., Pflug, C., Lingott, N. & Pörschmann-Schreiber, U. (2022). Technische Assistenzsysteme in der pflegerischen Versorgung. In E.-W. Luthe, S. V. Müller & I. Schiering (Hrsg.), Gesundheit. Politik - Gesellschaft - Wirtschaft. Assistive Technologien im Sozial- und Gesundheitssektor (S. 649–667). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-34027-8_26
- Chaccour, K., Darazi, R., El Hassani, A. H. & Andres, E. (2017). From Fall Detection to Fall Prevention: A Generic Classification of Fall-Related Systems. IEEE Sensors Journal, 17(3), 812–822. https://doi.org/10.1109/JSEN.2016.2628099
- Chen, Y., Zhang, Y., Guo, Z., Bao, D. & Zhou, J. (2021). Comparison between the effects of exergame intervention and traditional physical training on improving balance and fall prevention in healthy older adults: a systematic review and meta-analysis. Journal of neuroengineering and rehabilitation, 18(1), 164. https://doi.org/10.1186/s12984-021-00917-0
- Cieślik, B., Mazurek, J., Wrzeciono, A., Maistrello, L., Szczepańska-Gieracha, J., Conte, P. & Kiper, P. (2023). Examining technology-assisted rehabilitation for older adults’ functional mobility: a network meta-analysis on efficacy and acceptability. NPJ digital medicine, 6(1), 159. https://doi.org/10.1038/s41746-023-00907-7
- Del Miranda-Duro, M. C., Nieto-Riveiro, L., Concheiro-Moscoso, P., Groba, B., Pousada, T., Canosa, N. & Pereira, J. (2021). Occupational Therapy and the Use of Technology on Older Adult Fall Prevention: A Scoping Review. International journal of environmental research and public health, 18(2). https://doi.org/10.3390/ijerph18020702
- Endress, C., Schwenk, M., Werner, C., Becker, C. & Jansen, C.‑P. (2023). Lebensstilintegriertes funktionelles Training zur Sturzprävention : Wie und warum verändert sich das Gehverhalten? [Lifestyle-integrated functional exercise for fall prevention : How and why do walking characteristics change?]. Zeitschrift fur Gerontologie und Geriatrie, 56(6), 464–469. https://doi.org/10.1007/s00391-023-02230-y
- Gaspar, A. G. M. & Lapão, L. V. (2021). eHealth for Addressing Balance Disorders in the Elderly: Systematic Review. Journal of medical Internet research, 23(4), e22215. https://doi.org/10.2196/22215
- GKV-Spitzenverband. (2021). Forschung für die Pflege: Impulse zur Weiterentwicklung der Pflegeversicherung.
- Greene, B. R., McManus, K., Ader, L. G. M. & Caulfield, B. (2021). Unsupervised Assessment of Balance and Falls Risk Using a Smartphone and Machine Learning.Sensors (Basel, Switzerland), 21(14). https://doi.org/10.3390/s21144770
- Hamm, J., Money, A. G., Atwal, A. & Paraskevopoulos, I. (2016). Fall prevention intervention technologies: A conceptual framework and survey of the state of the art.Journal of biomedical informatics, 59, 319–345. https://doi.org/10.1016/j.jbi.2015.12.013
- Jansen, C.‑P., Gross, M., Kramer-Gmeiner, F., Blessing, U., Becker, C. & Schwenk, M. (2021). Empfehlungspapier für das körperliche Gruppentraining zur Sturzprävention bei älteren, zu Hause lebenden Menschen : Aktualisierung des Empfehlungspapiers der Bundesinitiative Sturzprävention von 2009 [Group-based exercise to prevent falls in community-dwelling older adults : Update of the 2009 recommendations of the German Federal Initiative to Prevent Falls]. Zeitschrift fur Gerontologie und Geriatrie, 54(3), 229–239. https://doi.org/10.1007/s00391-021-01876-w
- Lee, K., Yi, J. & Lee, S.‑H. (2024). Effects of community-based fall prevention interventions for older adults using information and communication technology: A systematic review and meta-analysis. Health informatics journal, 30(2), 14604582241259324. https://doi.org/10.1177/14604582241259324
- Madara Marasinghe, K. (2016). Assistive technologies in reducing caregiver burden among informal caregivers of older adults: a systematic review. Disability and rehabilitation. Assistive technology, 11(5), 353–360. https://doi.org/10.3109/17483107.2015.1087061
- Mähs, M. (2021). Anforderungen an die Evaluation von altersgerechten Assistenztechnologien aus gesundheitsökonomischer Sicht. In D. Frommeld, U. Scorna, S. Haug & K. Weber (Hrsg.), Gute Technik für ein gutes Leben im Alter? (S. 317–340). transcript Verlag. https://doi.org/10.1515/9783839454695-014
- Mähs, M. & Fachinger, U. (2022). Die Analyse ökonomischer Potentiale assistierender Technologien im Pflege- und Gesundheitssektor – Zur Problematik einer adäquaten Kosten-Nutzen-Bewertung. In E.-W. Luthe, S. V. Müller & I. Schiering (Hrsg.),Gesundheit. Politik - Gesellschaft - Wirtschaft. Assistive Technologien im Sozial- und Gesundheitssektor (S. 527–545). Springer Fachmedien Wiesbaden https://doi.org/10.1007/978-3-658-34027-8_21
- Merda, M., Schmidt, K. & Kähler, B. (2017). Pflege 4.0 – Einsatz moderner Technologien aus der Sicht professionell Pflegender. Moers, S. (2023). Für Sie analysiert – Globale Leitlinie zur Sturzprävention. physiopraxis, 21(03), 14–17. https://doi.org/10.1055/a-1976-0076
- Ohneberg, C., Stöbich, N., Warmbein, A., Rathgeber, I., Mehler-Klamt, A. C., Fischer, U. & Eberl, I. (2023). Assistive robotic systems in nursing care: a scoping review. BMC nursing, 22(1), 72. https://doi.org/10.1186/s12912-023-01230-y
- Pang, I., Okubo, Y., Sturnieks, D., Lord, S. R. & Brodie, M. A. (2019). Detection of Near Falls Using Wearable Devices: A Systematic Review. Journal of geriatric physical therapy (2001), 42(1), 48–56. https://doi.org/10.1519/JPT.0000000000000181
- Parzen, M., O’Keefe-McCarthy, S., Salfi, J. & Taplay, K. (2021). Perceptions of Informal Caregivers Use of Smart Technology in Caring for an Older Adult. Occupational Health, 230–239.
- Peek, S. T. M., Wouters, E. J. M., Luijkx, K. G. & Vrijhoef, H. J. M. (2016). What it Takes to Successfully Implement Technology for Aging in Place: Focus Groups With Stakeholders. Journal of medical Internet research, 18(5), e98. https://doi.org/10.2196/jmir.5253
- Piech, J. & Czernicki, K. (2021). Virtual Reality Rehabilitation and Exergames—Physical and Psychological Impact on Fall Prevention among the Elderly—A Literature Review.Applied Sciences, 11(9), 4098. https://doi.org/10.3390/app11094098
- emmers, H. (2019). Pflege und Technik. Stand der Diskussion und zentrale ethische Fragen.Ethik in der Medizin, 31(4), 407–430. https://doi.org/10.1007/s00481-019-00545-2
- Schoene, D., Gross, M., Stengel, S. von, Kohl, M., Kladny, B., Gosch, M., Sieber, C. C., Peters, S., Kiesswetter, E., Becker, C. & Kemmler, W. (2023). Empfehlungen für ein körperliches Training zur Sturzprävention bei älteren, selbständig lebenden Menschen.Osteologie, 32(03), 183–195. https://doi.org/10.1055/a-2110-7105
- Scorna, U., Frommeld, D [Deborah], Haug, S. & Weber, K. (2021). Digitale Technik in der Pflege als Generallösung? Neue Perspektiven auf altersgerechte Assistenzsysteme. In C. Freier, J. König, A. Manzeschke & B. Städtler-Mach (Hrsg.), Perspektiven Sozialwirtschaft und Sozialmanagement. Gegenwart und Zukunft sozialer Dienstleistungsarbeit (S. 301–314). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-32556-5_21
- Tanwar, R., Nandal, N., Zamani, M. & Manaf, A. A. (2022). Pathway of Trends and Technologies in Fall Detection: A Systematic Review. Healthcare (Basel, Switzerland), 10(1). https://doi.org/10.3390/healthcare10010172
- Thordardottir, B., Malmgren Fänge, A., Lethin, C., Rodriguez Gatta, D. & Chiatti, C. (2019). Acceptance and Use of Innovative Assistive Technologies among People with Cognitive Impairment and Their Caregivers: A Systematic Review. BioMed research international, 2019, 9196729. https://doi.org/10.1155/2019/9196729
- Zhao, G., Chen, L. & Ning, H. (2021). Sensor-Based Fall Risk Assessment: A Survey.Healthcare (Basel, Switzerland), 9(11). https://doi.org/10.3390/healthcare9111448