Falls, commonly defined as “inadvertently coming to rest on the ground, floor or other lower level, excluding intentional change in position to rest in furniture, wall or other objects”,1(para.1) are a major public health concern. Worldwide, approximately 37.3 million falls require medical attention each year with 646,000 resulting in death.1 Fatal falls are more common among older people and non-fatal falls are a major cause of pain, disability and loss of independence.1 With the predicted increase in the proportion of the population aged 65 and over (e.g. approximately 25% in the United Kingdom by 20502 and nearing 2.1 billion globally by 20503), the rate of falls can be expected to increase, as can the associated personal, clinical and economic costs.
The economic cost of fall-related injuries are significant and range from USD3476 per faller to USD10,749 per injurious fall, to USD26,483 per fall requiring hospitalization.4 Prevention and management of falls therefore remains an important research priority.1
Several risk factors for falls have been reported in the literature including age, race, gender and history of chronic health conditions such as stroke, kidney disease, arthritis, depression and diabetes.1,5-7 In the hospital setting, risk factors such as muscle weakness, cardiovascular problems, dementia, delirium, toileting and medication contribute to in-patient falls; hence, guidelines recommend multifactorial falls risk assessments to be conducted8 using appropriate falls risk assessment tools.9 However, risk assessment does not in itself prevent falls from occurring.
A large body of evidence exists on falls prevention interventions for community-dwelling adults, particularly exercise-based and individually tailored multifactorial interventions.10-12 These can be considered primary prevention interventions,13 where a number of intrinsic and extrinsic risk factors are identified and interventions are designed to mitigate these risk factors to prevent future falls. Secondary prevention is also important, not least in the in-patient setting, and includes detecting a fall early and preventing/mitigating injury from a fall.13 This scoping review will be concerned with both primary and secondary prevention (detection) of falls. While prevention and detection of falls in the adult in-patient population has received relatively less attention to date in comparison to the adult community-dwelling population, there is a growing body of evidence that will be timely to review.
Technology is commonly thought of as scientific knowledge and increasingly as being related to computer hardware or software and other electronic devices. However, the definition of health technology is much broader, defined by the World Health Organization as “…the application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures and systems developed to solve a health problem and improve quality of lives”.14(p.106) Thus, settings of care and interventions are considered to be health technologies.15
Health technologies that have been utilized for the prevention of falls in the in-patient setting include falls prevention toolkits,16 personalized care plans,17 patient-centered education,17 intentional rounding,18 improving patients’ environments (including patient-pathways),19 increasing nursing staff vigilance (including provision of assistive devices or appropriate footwear),19 exercise-based interventions focusing on balance retraining20 and multi-component interventions (e.g. exercise and medication review/environmental modification/staff education),20 as well as devices such as alarms, sensors,21 microphones and cameras.22
Health technologies that have been used for the detection of falls in the in-patient setting are predominantly devices such as wearable motion-detectors,23,24 alarms, sensors, microphones and cameras.21,22
The literature cited above demonstrates that there is a body of evidence pertaining to technologies for the prevention and detection of falls in the in-patient setting, including primary quantitative16,18,19,23,24 and qualitative research,21 as well as evidence syntheses.17,20,22 In addition, a preliminary search indicates a wide range of other material on falls prevention and detection from sources such as government health departments, and the professional bodies for the medical, nursing and allied health professions. Given the range of evidence available, it might be challenging to make recommendations for policy makers and practitioners in relation to which falls prevention and detection technologies to implement on a local, national or international level. Since scoping reviews are ideal for examining a broad area in order to report on the types of evidence that address and inform practice,25 it is intended that this scoping review will map the evidence related to falls prevention and detection in the in-patient setting. In doing so, it will also identify specific questions that might be best addressed by future systematic reviews,26 for example, whether sufficient studies have been conducted for an economic evidence-synthesis, a qualitative synthesis of patients’ perceptions of the acceptability of technologies, or whether it might be appropriate to conduct a network meta-analysis27 to compare the relative effectiveness of different types of interventions. It is also intended that this scoping review will clarify key concepts28 and definitions related to technologies for falls prevention and detection.
A search of MEDLINE, CINAHL, JBI Database of Systematic Reviews and Implementation Reports, Cochrane Library (reviews; protocols), PEDro, EPPI (DoPHER) and Epistemonikos identified a number of systematic reviews on specific aspects of falls prevention and detection technologies, in specific populations and settings, mostly in relation to community-dwelling older adults. One recent scoping review was identified that mapped the literature on technologies for fall detection.29 The definition of technology used was restricted to “… information processing involving both computer hardware and software”30(p.38) and the authors reported on various types of ambient and wearable sensors. The findings from their scoping review28 will be a useful addition to the current proposed scoping review, which intends to conduct a much broader mapping exercise using a more inclusive definition of technologies for falls prevention and detection. The search of the databases listed above did not find evidence of any scoping reviews in progress on the topic of technologies for falls prevention and detection in adult in-patients.
The objective of this review is therefore to map the available evidence to provide an overview of the evidence on technologies used for falls detection and prevention in adult hospital in-patients.
This review will consider literature that includes adult (aged 18 and over) in-patients, defined as being admitted to a setting for patient care activity that takes place in a hospital setting. These settings include elective, non-elective (emergency admission/accident and emergency), day-case and secondary care (community hospital) care settings and long-stay rehabilitation units.31 Literature that includes residential settings will be excluded from this review as this area has been included in a recent systematic review.32
This review will consider literature that reports on the use of falls prevention or detection technologies and also literature that reports the clinical effectiveness, cost-effectiveness, acceptability and feasibility of falls prevention or detection technologies in the adult in-patient setting. Literature that reports on one or more of these aspects will be considered for inclusion. For the purpose of this scoping review, the World Health Organization definition of technology will be used: “A health technology is the application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures and systems developed to solve a health problem and improve quality of lives.”14(p.106)
This review will consider literature that reports on falls prevention and detection in adult patients in any hospital ward setting. This might include large secondary care or small community rehabilitation facilities, and any area of clinical specialism. For the results of this review to inform practice in the United Kingdom, as well as other countries, literature conducted within countries demonstrating very high human development (according to the Human Development Index [HDI])33 will be included. The HDI is a composite index that measures three dimensions of human development: a long and healthy life, knowledge and a decent standard of living.33 By including countries listed as having very high human development (i.e. the top 51 countries), this review ensures nations comparable to the United Kingdom are included, enabling an international comparison. Studies that are identified to be in developing countries or countries defined as low, medium or high human development will not be included.
Types of studies
This review will consider a broad range of published and unpublished literature including primary research studies, systematic reviews, reports and expert opinion. Quantitative study designs including experimental, quasi-experimental, descriptive and observational studies in which any information on clinical or cost-effectiveness outcomes is reported will be considered. We will also consider studies that focus on qualitative data including, but not limited to, designs such as phenomenology, grounded theory, ethnography and action research, to report on feasibility and acceptability outcome measures used, as they may report healthcare staff and patients’ views and experiences of using health technologies for falls prevention and detection. Systematic reviews that have synthesized evidence on any aspect of falls prevention and detection relevant to the review objectives will also be considered for inclusion. Finally, we will also consider government reports, expert opinion, discussion papers, position papers and other forms of text, as they may be relevant to the review objectives.
This scoping review will be conducted according to Joanna Briggs Institute (JBI) methodology for scoping reviews.26
The search strategy will aim to find both published and unpublished articles. An initial limited search of MEDLINE and CINAHL has been undertaken followed by analysis of the text words contained in the title and abstract, and of the index terms used to describe articles. This informed the development of a search strategy which will be tailored for each information source. A full search strategy for MEDLINE is detailed in Appendix I. The reference list of all studies selected for inclusion will be screened for additional studies.
The databases to be searched include: MEDLINE, CINAHL, Embase, EPPI-Centre (DoPHER and TRoPHI), AMED, JBI Database of Systematic Reviews and Implementation Reports, Cochrane Library (controlled trials and systematic reviews), PEDro, and Epistemonikos. The trial registers to be searched include: ClinicalTrials.gov, ISRCTN Registry, The Research Registry, European Union Clinical Trials Registry (EU-CTR), and Australia New Zealand Clinical Trials Registry (ANZCTR). The search for unpublished studies will include: OpenGrey, MedNar, The New York Academy Grey Literature Report, Ethos, CORE, and Google Scholar. In addition, the following government health department websites and websites of professional bodies will be searched for information relating to falls prevention and detection: the Department of Health and Social Care (UK), Scottish Government, The U.S. Department of Health and Human Services, Health Resources and Services Administration (USA), Australian Government Department of Health, Royal College of General Practitioners (UK), Australian Medical Association, American Medical Association, Royal College of Nursing, American Nurses Association, and the Chartered Society of Physiotherapy (UK). A research librarian will be consulted in order to tailor the search strategy to each database appropriately.
Due to time and resource limitations, only studies published in English will be considered.
Due to the manageable numbers of studies identified in preliminary searching, and the aim of providing a broad and comprehensive overview of the topic, no lower date limit will be applied.
Following the search, all identified citations will be collated and uploaded into Refworks (ProQuest LLC, Ann Arbor, USA) and duplicates removed. Titles and abstracts will then be screened by two independent reviewers for assessment against the inclusion criteria for the review. Studies that may meet the inclusion criteria will be retrieved in full and their details imported into the JBI System for Unified Management, Assessment and Review of Information (JBI SUMARI) (Joanna Briggs Institute, Adelaide, Australia). The full text of selected studies will be retrieved and assessed in detail against the inclusion criteria by two independent reviewers. Full-text studies that do not meet the inclusion criteria will be excluded, and reasons for exclusion will be provided in an appendix in the final scoping review report. The results of the search will be reported in full in the final report and presented in a PRISMA flow diagram.34 Any disagreements that arise between the reviewers will be resolved through discussion or with a third reviewer.
Data relevant to the review questions will be extracted from the included studies by two independent reviewers using methods recommended by Peters et al.26 The data extracted will include: authors, publication year, source, study or article type, description of falls prevention and/or detection technology reported, population, setting and outcomes reported. Where relevant, authors of included studies will be contacted for clarification or missing information. A draft data extraction form is available in Appendix II; this will be tested on three articles and may be subsequently refined depending on the data available for extraction.
The results will be presented as a map of the data extracted from the included studies in tabular form for each review question. Each table will present the different results for each review question with a narrative summary to accompany the tabulated results. Each table will include author, date of publication, country of origin, as well as data relevant to the review questions. Appendix III details draft results tables; as with the data extraction tool, these will be piloted and may be subject to amendment during the review process.
The authors acknowledge the funding for this review provided by the NHS Grampian Endowments Fund grant (17/033).
Appendix I: Search strategy for MEDLINE (EBSCO host)
- 1. mh hospitals OR kw hospital∗
- 2. mh Accidental falls OR kw “fall∗ prevention” OR kw “fall∗ detection” OR kw fall∗
- 3. mh Delivery of healthcare OR mh Biomedical technology OR kw Technolog∗ OR kw device∗ OR kw intervention∗ OR kw strateg∗ OR kw program∗ OR kw system∗ OR kw organiz∗ OR kw organis∗
- 4. 1 AND 2 AND 3
Limits: Adults; English language
Appendix II: Draft data extraction form
Appendix III: Draft results tables
Technologies for falls prevention/detection
Outcomes for assessing falls prevention/detection technologies
III: Acceptability and Feasibility
1. World Health Organization. Global Report on Falls Prevention in Older Age [Internet]. Geneva: World Health Organization; 2007.
2. Cracknell R. The Ageing Population, Briefing Papers. United Kingdom: House of Commons Library; 2010.
3. United Nations Department of Economic and Social Affairs Population Division. World Population Aging 2015 [Internet]. United Nations, New York, 2015 [cited 2018 Apr 2]. Available from: http://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2015_Report.pdf
4. Davis JC, Robertson MC, Ashe MC, Liu-Ambrose T, Khan KM, Marra CA. International comparison of cost of falls in older adults
living in the community: A systematic review. Osteoporosis Int
2010; 21 8:1295–1306.
5. Stevens JA, Mack KA, Paulozzi LJ, Ballesteros MF. Self-reported falls and fall-related injuries among persons aged ≥ 65 years–United States, 2006. J Safety Res
2008; 39 3:345–349.
6. Bergland A. Fall risk factors in community-dwelling Elderly People. Norsk Epidemiologi
2012; 22 2:151–164.
7. Paliwal Y, Slattum PW, Ratliff SM. Chronic health conditions as a risk factor for falls among the community-dwelling US older adults
: A zero-inflated regression modelling approach. BioMed Res Int
2017; 2017: 5146378.
8. National Institute for Health and Care Excellence. Falls – Risk Assessment [Internet]. 2014 Jan [cited 2018 Apr 2]. Available from: https://cks.nice.org.uk/falls-risk-assessment#!scenariorecommendation
9. Matarese M, Dhurata I. Falls risk assessment in older patients in hospital. Nurs Stand
2016; 30 48:53–63.
10. Lipardo DS, Aseron AMC, Kwan MM, Tsang WW. Effect of exercise and cognitive training on falls and fall-related factors in older adults
with mild cognitive impairment: A systematic review. Arch Phys Med Rehabil
2017; 98 10:2079–2096.
11. Tricco AC, Thomas SM, Veroniki AA, Hamid JS, Cogo E, Strifler L, et al. Comparisons of interventions for preventing falls in older adults
: A systematic review and meta-analysis. JAMA
2017; 318 17:1687–1699.
12. Stubbs B, Brefka S, Denkinger MD. What works to prevent falls in community-dwelling older adults
? Umbrella review of meta-analyses of randomized controlled trials. Phys Ther
2015; 95 8:1095–1110.
13. Rajendran P, Corcoran A, Kinosian B, Alwan M. Alwan M, Felder RA. Falls, fall prevention
, and fall detection
technologies. Eldercare Technology for Clinical Practitioners. Aging Medicine
. New Jersey, USA: Humana Press, Totowa; 2008. 187–202.
14. World Health Organization. Resolution on Health Technologies (WHA60.29) [Internet]. Geneva: The World Health Organization; 2007.
15. National Institute for Health Research. Health Technology
Assessment [Internet]. United Kingdom, National Institute for Health Research; 2018 [cited 2018 Apr 2]. Available from: https://www.nihr.ac.uk/funding-and-support/funding-for-research-studies/funding-programmes/health-technology-assessment/
16. Dykes PC, Carroll DL, Hurley A, Lipsitz S, Benoit A, Chang F, et al. Fall prevention
in acute care hospitals: A randomized trial. JAMA
2010; 304 17:1912–1918.
17. Avanecean D, Calliste D, Contreras T, Lim Y, Fitzpatrick A. Effectiveness of patient-centred interventions on falls in the acute care setting compared to usual care: A systematic review. JBI Database System Rev Implement Rep
2017; 15 12:3006–3048.
18. Morgan L, Flynn L, Robertson E, New S, Forde-Johnston C, McCulloch P. Intentional rounding: A staff-led quality improvement intervention in the prevention of patient falls. J Clin Nurs
2017; 26 (1/2):115–124.
19. Tzeng H-M, Yin C-Y. A multihospital survey on effective interventions to prevent hospital falls in adults
. Nurs Econ
2017; 35 6:304–313.
20. Lee SH, Kim HS. Exercise interventions for preventing falls among older people in care facilities: A meta-analysis. Worldviews Evid Based Nurs
2017; 14 1:74–80.
21. Vandenburg AE, van Beijnum B-J, Overdevest VGP. US and Dutch nurse experiences with fall prevention
technology within nursing home environments: A qualitative study. Geriatr Nurs
2017; 38 4:276–282.
22. Chaudhuri S, Demiris Thompson H. Fall detection
devices and their use with older adults
: A systematic review. J Geriatr Phys Ther
2014; 37 4:178–196.
23. Lipsitz LA, Tchalia AE, Iloputaife I, Gagnon M, Su ZZ, Klickstein L. Evaluation of an automated falls detection device in nursing home residents. J Am Geriatr Soc
2016; 64 2:365–368.
24. Ferrari M, Harrison B, Rawashdeh O, Hammond R, Avery Y, Rawashdeh M, et al. Clinical feasibility trial of a motion detection system for fall prevention
in hospitalized older adult patients. Geriatr Nurs
2012; 33 3:177–183.
25. Decaria J, Sharp C, Petrella R. Scoping review report: Obesity in older adults
. Int J Obesity
2012; 36 9:1141–1150.
26. Peters MDJ, Godfrey C, McInerney P, Baldini Soares C, Khalil H, Parker D. Chapter 11: Scoping reviews. In: Aromataris E, Munn Z, editors. Joanna Briggs Institute Reviewer's Manual. The Joanna Briggs Institute; [internet]. 2017. [cited 2018 Apr 2]. Available from https://reviewersmanual.joannabriggs.org/
27. Tonin FS, Rotta I, Mendes AM, Pontarolo R. Network meta-analysis: A technique to gather evidence from direct and indirect comparisons. Pharm Pract
2017; 15 1:943.
28. de Chavez AC, Backett-Milburn K, Parry O, Platt S. Understanding and researching wellbeing: Its usage in different disciplines and potential for health research and health promotion. Health Educ J
2005; 64 1:70–87.
29. Lapierre N, Neubauer N, Miguel-Cruz A, Rincon AR, Liu L, Rousseau J. The state of knowledge on technologies and their use for fall detection
: A scoping review. Int J Med Inform
30. Thompson TG, Brailer DJ. The Decade of Health Information Technology: Delivering Consumer-centric and Information-rich Health Care [Internet]. US Department of Health and Human Services, Washington D.C.; [internet]. 2004. p38. [cited 2018 Apr 2]. Available from: http://www.providersedge.com/ehdocs/ehr_articles/the_decade_of_hit-delivering_customer-centric_and_info-rich_hc.pdf
32. Francis-Coad J, Etherton-Beer C, Burton E, Naseri C, Hill AM. Effectiveness of complex falls prevention interventions in residential aged care settings: A systematic review. JBI Database System Rev Implement Rep
2018; 16 4:973–1002.
33. United Nations Development Fund. Human Development Index and its components. United Nations, New York; [internet] 2016. [cited 2018 Apr 02]. Available from: http://hdr.undp.org/en/content/human-development-index-hdi
34. Moher D, Liberati A, Tetzlaff J, Altman DG. The PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med
2009; 6 7:e1000097.