Marta Gimunová,1 Martin Sebera,1 Mario Kasović,1,2 Lenka Svobodová,3,4 Tomáš Vespalec1
1Department of Kinesiology, Faculty of Sport Studies, Masaryk University, Brno, Czech Republic; 2Department of General and Applied Kinesiology, Faculty of Kinesiology, University of Zagreb, Zagreb, Croatia; 3Department of Gymnastics and Combatives, Faculty of Sport Studies, Masaryk University, Brno, Czech Republic; 4Incubator of Kinanthropology Research, Faculty of Sport Studies, Masaryk University, Brno, Czech Republic
Correspondence: Marta Gimunová, Department of Kinesiology, Faculty of Sport Studies, Masaryk University, Brno, Czech Republic, Tel +420 549 49 8398, Email [email protected]
Purpose: The aim of this study was to analyze factors affecting spatio-temporal gait parameters in elderly people of both genders and different ages with different risks of fall, fall history, and medications.
Patients and Methods: A total of 210 community-dwelling older adults (156 females, 54 males; mean age 72.84± 6.26 years) participated in this study. To assess the risk of falls, the Downton Fall Risk Index was used. An additional question about medication intake (all prescribed drugs) was asked. To assess the spatio-temporal gait parameters, the Zebris FDM platform was used. Gait parameters and Downton Fall Risk Index, stratified by participants’ history of falls, multiple medication use (0/1/2+), gender, age, and medication categories, were statistically analyzed using the Mann–Whitney U-test and Kruskal–Wallis test.
Results: When comparing different medication categories, a Downton Fall Risk Index score indicating a high risk of falls was observed in the psychotropic medication category (3.56± 1.67). A gait velocity suggesting a higher risk of falls (≤ 3.60 km/h) was observed in the psychotropic (2.85± 1.09 km/h) and diabetes (2.80± 0.81 km/h) medication categories, in the age groups 70– 79 years (3.30± 0.89 km/h) and 80+ years (2.67± 0.88 km/h), and in participants using two or more medications (3.04± 0.93 km/h).
Conclusion: The results of this study confirm previous observations and show that higher age and multiple medication negatively affect the gait, and that the higher risk of falls is associated with psychotropic and diabetes medication use. These results provide important information for future fall preventive programs for the elderly that would be especially beneficial for elderly people taking psychotropic and diabetes medication.
Falls are one of the most common causes of injuries, hospitalization, and deaths among the elderly.5,17 As the majority of falls occur during locomotion,18 gait evaluation is recommended in current fall prevention guidelines for older people.19 With higher age, the medio-lateral margin of stability, pace, and base of support change.47 New technologies such as deep brain stimulation implants have been introduced to treat the motor symptoms typical of some diseases (eg, Parkinson’s disease).48 Still, the association between medications and abnormalities in gait pattern in older people is unclear.
Medication use is one of the risk factors associated with falling in elderly people owing to its adverse effects, such as unsteadiness, impaired alertness, or dizziness.1 The risk of falling and its negative consequences increase with the number of consumed medications,2 especially when an antidepressant or a benzodiazepine is included,3 and with prolonged use of the medication.4 Drugs that increase the risk of a fall include medication for cardiovascular diseases, such as digoxin and type 1a antiarrhythmics, and diuretics, benzodiazepines, antidepressants, antiepileptics, antipsychotics, antiparkinsonian drugs, opioids, and urological spasmolytics.5 Psychotropic and cardiovascular medications have been most strongly associated with an increased risk of falling in elderly people in previous studies.1,4–10 Compared to their healthy peers, the gait of elderly people with higher depressive symptoms is characterized by slower velocity, and increased stride and swing time variability.11 Similarly, cardiovascular diseases have been observed to impair the gait velocity in the elderly.12 Both low and high thyroid function are associated with alterations in gait velocity and the base of support.13 Diabetes mellitus has also been observed to alter the gait pattern in the elderly by decreasing the gait velocity and cadence and increasing the stride time.14 Previous studies show that changes in spatio-temporal gait parameters, eg, a gait velocity below 3.60 km/h, or in step length, increase the fall risk in the elderly.15,16,46 Furthermore, elderly fallers have been observed to show a longer stance phase, with increased double-stance phase during the gait.46
Understanding the gait strategies of elderly people using different medications and with different risk of falls may lead to effective interventions (eg, preventive exercise programs) aimed at preventing falls in the elderly. The aim of this study was to analyze the spatio-temporal gait parameters in elderly people of both genders and different ages with different fall risk, fall history, and medication use.
Patients and Methods
A total of 210 community-dwelling older adults (156 females, 54 males) from the city of Brno, Czech Republic, and its rural surroundings participated in this study. The inclusion criteria for participating in this study consisted of being >60 years of age and being able to walk at least 10 m without aid. The exclusion criteria consisted of dementia or severe cognitive impairment, wearing a limb prosthesis, hip or knee arthroplasty, and using a walking aid, as using a walking aid limits gait analysis testing.38 Informed consent was provided by all participants before participating in the study. The study was performed according to the Declaration of Helsinki and was approved by the Research Ethical Committee of Masaryk University (EKV-2020-007).
Participants’ characteristics can be found in Table 1. The mean age of participants was 72.94±5.92 and 73.07±6.18 years for females and males, respectively. The mean weight and height were significantly different between females and males (p<0.001); however, no statistically significant difference in body mass index (BMI) was observed (29.95±5.85 and 28.57±4.31 kg/m2 for females and males, respectively). The number of medications used differed significantly between females and males (p=0.001). The mean number of medications used was higher in females (1.65±1.11) compared to males (1.09±0.83). The mean Downton Fall Risk Index did not differ with statistical significance between genders.
Table 1 Participants’ Characteristics, Divided by Gender
The Downton Fall Risk Index was used to assess the risk of falls. A trained kinesiologist (MG, LS, MS) performed the Downton Fall Risk Index assessment. The Downton Fall Risk Index consists of 11 risk factors divided into five categories: 1) previous falls (yes; no; reported by participants themselves), 2) medication (none; sedatives; diuretics; antihypertensives; antiparkinsonian drugs; antidepressants; other medication), 3) sensory deficit (none; visual impairment; hearing impairment; limb impairment), 4) mental state (oriented; confused), and 5) gait (safe without walking aids; safe with walking aids; unsafe; unable).39 The score ranges between 0 and 11, with a higher score indicating a higher risk of falls. A score of 3 and more indicates a high risk of falls. Additional question about medication intake (all prescribed drugs) was asked. The Downton Fall Risk Index was used to assess the risk of falls in previous studies by Rosendahl et al20 and Štefan et al.21
The information about medication obtained from the Downton Fall Risk Index and the additional question about prescribed medication was sorted out into three categories by the number of medications used (none; one; two or more) and into five categories of medication: 1) none, 2) cardiovascular, 3) psychotropic, 4) diabetes, and 5) thyroid function. These five categories covered most of the medications used by our participants.
Spatio-Temporal Gait Parameter Assessment
To assess the spatio-temporal gait parameters, the Zebris platform (FDM; Zebris Medical GmbH, Munich, Germany) was used. The Zebris force distribution measurement platform (sensor area: 149×54.2 cm) with built-in capacitive sensors (number of sensors: 11,264; sampling rate: 100 Hz) enables the measurement and analyses of the force distribution under the feet. All participants were asked to walk barefoot across the platform at their natural speed. The platform was placed in a custom-designed 10-meter-long walkway to provide a level walking surface. Each participant was instructed to walk toward the end of the walkway, then to turn 180° around and continue walking over the platform. This cycle was repeated several times to obtain five step cycles across the platform. This methodology was used also in previous studies by Kasović et al22 and Štefan et al.21
Zebris software generated the data regarding gait velocity (km/h), foot rotation (°), step length (cm; % of body height), step width (cm), stance phase (% of the gait cycle), double-stance phase (% of the gait cycle), swing phase (% of the gait cycle), and cadence (steps/min).
In addition, all participants reported their age, and their body height and weight were measured by a portable stadiometer and scale.
Most of the variables did not meet the assumptions of the normal distribution, as tested by the Kolmogorov–Smirnov, Lilliefors, and Shapiro–Wilk tests. The Mann–Whitney U-test was used to compare gender differences (participants’ characteristics, gait parameters) and history of falls (fallers, non-fallers). To compare the multiple medication use (none, one, two or more drugs), medication category (none, cardiovascular, psychotropic, diabetes, thyroid function), and age groups (60–69, 70–79, 80+ years of age), the Kruskal–Wallis test was used. An alpha level of 0.05 was used to define statistical significance. The statistical analysis was performed using Statistica 14 by TIBCO Software.
In Table 2, spatio-temporal gait parameters, Downton Fall Risk Index, and number of medications used, stratified by history of falls, multiple medication, gender, and age, are shown. Gait parameters for participants with one or more fall history (fallers) and participants with no history of falls (non-fallers) in the past year did not differ with statistical significance. These two groups of participants differed significantly only in the Downton Fall Risk Index (p<0.001), which was higher in fallers. Participants divided by multiple medication use differed with statistical significance in most of the spatio-temporal gait parameters, including gait velocity, step length, cadence, and swing phase, which were lower in people using two or more medications. Gender differences were observed with statistical significance in foot rotation and step width, which were higher in males, and in cadence, which was higher in females. Age groups differed with statistical significance in most of the spatio-temporal gait parameters, as the participants in the 80+ group walked more slowly, with fewer steps per minute and with prolonged double-stance and stance phases of the gait.
Table 2 Gait Parameters Stratified by History of Falls, Multiple Medication, Gender, and Age
A slower mean gait velocity than the cut-off gait velocity of 3.60 km/h, widely used when identifying elderly fallers,15,16 was observed in the age groups 70–79 years and 80+ years, in the group using two or more medications, and in both females and males and fallers and non-fallers.
To analyze differences between the five categories of medication, participants using more than one medication were included in all categories of medication that they used. In Table 3, descriptive statistics, and the results of the comparison of the Downton Fall Risk Index and gait parameters in the different medication categories are shown.
Table 3 Comparison of Downton Fall Risk Index and Gait Parameters in Five Categories of Medication
When comparing the five categories of medication, a statistically significant difference was observed in the Downton Fall Risk Index between the no-medication (none) category and the cardiovascular, psychotropic, and diabetes medication categories (Figure 1). Gait velocity and step length differed significantly between the none and psychotropic, and none and diabetes categories (Figures 2 and 3). Both gait velocity and step length were higher in the none category compared to psychotropic and diabetes. The mean gait velocity in the cardiovascular, psychotropic, diabetes, and thyroid function categories was slower than the widely used cut-off gait velocity of 3.60 km/h when identifying elderly fallers.15,16
Figure 1 Downton Fall Risk Index. Score ranges from 0 to 11.39
Figure 2 Downton Fall Risk Index grouped by medication categories.
Figure 3 Gait velocity grouped by medication categories.
The main aim of this study was to analyze factors affecting spatio-temporal gait parameters in elderly people with different fall risk, fall history, and medication use. The results of this study confirm previous observations and show that higher age and multiple medication use negatively affect the gait. However, a higher risk of falls was associated solely with a specific medication use in this study.
A Downton Fall Risk Index score of 3 or more indicates a high risk of falls.20 In a previous study, the Downton Fall Risk Index was confirmed to be a useful tool for predicting the risk of falls in elderly people living in a residential care facility.20 The predictive accuracy of this index has also been confirmed in other populations, such as stroke patients41 and older hospitalized patients after discharge from hospital.42 A mean score on the Downton Fall Risk Index of 3 or more was observed in this study only when stratifying participants into five categories by the medication that they used. A score indicating a high risk of falls was observed in the psychotropic category. In addition, the psychotropic and diabetes categories were observed to be related to the greatest impairment of the gait, characterized by slower gait velocity and shorter step length, compared to the other medication categories in this study. The gait velocity in these categories was slower than the cut-off forward gait velocity of 3.60 km/h that is widely used to identify elderly fallers.15,16
Psychotropics are used as a standard treatment in mental disorders in elderly patients, whereas psychotherapy is rarely provided.23 Psychotropics are a significant factor that increases the risk of falls.24–27 Psychotropics increase the risk of falls by various mechanisms, including sedation, confusion, vision changes, drowsiness, and neuromuscular incoordination.28 Elderly people with diabetes are at higher risk of falls, with the possible risk factors including retinopathy, the most common cause of vision loss in adult age, peripheral neuropathy, which increases postural instability, and hypoglycemia, resulting in dizziness.29 People taking medication in either of these categories should especially benefit from exercise interventions designed to prevent falls. Exercise interventions in elderly people have been observed to reduce the risk of falls. Especially effective were observed to be multiple component group exercises, Tai Chi as a group exercise, and individually prescribed multiple-component exercises for the home.30,31 These exercise interventions usually contain balance and strength training, and besides reducing falls they appear to reduce fall-related fractures.31 Furthermore, regular exercise has considerable health benefits for people with type 1 diabetes (eg, improved insulin sensitivity) and type 2 diabetes (eg, improved insulin sensitivity and glucose control).43 In previous studies, an association between physical activity and reduced risk of antidepressant, sedative, and sleep medication use was observed.44,45 Higher volumes of physical activity showed further benefits for mental health.44
The use of multiple medications, ie, taking two or more medications, was observed to negatively affect the gait in this study, decreasing the gait velocity, step length, and cadence. The gait velocity of this group suggests a higher risk of falls.15,16 Polypharmacy was also associated with a higher risk of falls in previous studies.2,3,18
The incidence of gait abnormalities increases with advancing age; however, some gait parameters, such as slow gait velocity or short steps, have been recognized as disease markers.32,33 An abnormal gait is prevalent in 60% of elderly people at the age of 80 and older.32 This was confirmed by the results of our study. The gait velocity, step length, and cadence were especially affected in the oldest age category in this study. Recognition and monitoring of frailty by a simple test measuring gait speed were suggested in a previous study for the effective care for the elderly,33 as gait disorders are associated with a greater risk of institutionalization and death.32
The gender differences observed in this study are similar to those observed in young adults. Shorter stride length, higher cadence, and narrower step width were observed in women compared to men in gait studies in young adults.34–36 A higher cadence and narrower step width were also observed in women in this study. In addition, a higher foot rotation angle was observed in males in this study. The foot progression angle and step width are balance-related gait variables and an increase in these variables helps to increase the stability of the gait.37
There are several limitations to this study. The number of participants was limited because of COVID-19 restrictions. Therefore, the number of participants in each category when stratifying the participants by age, gender, history of falls, and medication number and category is not equally distributed. The cross-sectional design of this study represents another limitation, as it does not allow us to discern the cause-and-effect relationship in the analysis of the factors impairing the gait and increasing the risk of falls. The retrospective assessment of the history of falls during the past year represents another limitation, as monthly monitoring of daily reports of falls has been suggested for the standardization of fall events.31,40 Future studies focused on prospective recording of falls and gait abnormalities in relationship to medication use will provide more detailed knowledge on this topic.
This study analyzed the spatio-temporal gait parameters in elderly people of both genders and different ages with different risk of falls, fall history, and medication use. The results of this study confirm previous observations and show that higher age and the use of multiple medications negatively affect the gait. Our results when stratifying the participants by medication category show that a higher risk of falls is associated with psychotropic and diabetes medication use. These results provide important information for future fall-prevention programs for the elderly that would be especially beneficial for elderly people taking psychotropic and diabetes medication.
Data Sharing Statement
All the data can be accessed upon reasonable request from the corresponding author.
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
This research was written at Masaryk University as part of project number MUNI/A/1082/2019 with the support of the Specific University Research Grant provided by the Ministry of Education, Youth and Sports of the Czech Republic.
The authors report no conflicts of interest in this work.
1. Tinetti ME, Kumar C. The patient who falls: “It’s always a trade-off.”. JAMA. 2010;303(3):258–266. doi:10.1001/jama.2009.2024
2. Tinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community. N Engl J Med. 1988;319(26):1701–1707. doi:10.1056/NEJM198812293192604
3. Richardson K, Bennett K, Kenny RAN. Polypharmacy including falls risk-increasing medications and subsequent falls in community-dwelling middle-aged and older adults. Age Ageing. 2015;44(1):90–96. doi:10.1093/ageing/afu141
4. Ziere G, Dieleman JP, van der Cammen TJM, Hofman A, Pols HAP, Stricker BHC. Selective serotonin reuptake inhibiting antidepressants are associated with an increased risk of nonvertebral fractures. J Clin Psychopharmacol. 2008;28(4):411–417. doi:10.1097/JCP.0b013e31817e0ecb
5. Huang AR, Mallet L, Rochefort CM, Eguale T, Buckeridge DL, Tamblyn R. Medication-related falls in the elderly. Drugs Aging. 2012;29(5):359–376. doi:10.2165/11599460-000000000-00000
6. Ensrud KE, Blackwell TL, Mangione CM, et al. Central nervous system–active medications and risk for falls in older women. J Am Geriatr Soc. 2002;50(10):1629–1637. doi:10.1046/j.1532-5415.2002.50453.x
7. Woolcott JC, Richardson KJ, Wiens MO, et al. Meta-analysis of the impact of 9 medication classes on falls in elderly persons. Arch Intern Med. 2009;23:1952–1960. doi:10.1001/archinternmed.2009.357
8. Berdot S, Bertrand M, Dartigues J-F, et al. Inappropriate medication use and risk of falls – a prospective study in a large community-dwelling elderly cohort. BMC Geriatr. 2009;9(1). doi:10.1186/1471-2318-9-30
9. Quach L, Yang FM, Berry SD, et al. Depression, antidepressants, and falls among community-dwelling elderly people: the Mobilize Boston study. J Gerontol a Biol Sci Med Sci. 2013;68(12A):1575–1581. doi:10.1093/gerona/glt084
10. van Strien AM, Koek HL, van Marum RJ, Emmelot-Vonk MH. Psychotropic medications, including short acting benzodiazepines, strongly increase the frequency of falls in elderly. Maturitas. 2013;74(4):357–362. doi:10.1016/j.maturitas.2013.01.004
11. Brandler TC, Wang C, Oh-Park M, Holtzer R, Verghese J. Depressive symptoms and gait dysfunction in the elderly. Am J Geriatr Psychiatry. 2012;20(5):425–432. doi:10.1097/JGP.0b013e31821181c6
12. Busch TDA, Duarte YA, Pires Nunes D, et al. Factors associated with lower gait speed among the elderly living in a developing country: a cross-sectional population-based study. BMC Geriatr. 2015;15(1). doi:10.1186/s12877-015-0031-2
13. Bano A, Chaker L, Darweesh SKL, et al. Gait patterns associated with thyroid function: the Rotterdam Study. Sci Rep. 2016;6. doi:10.1038/srep38912
14. Allet L. Gait and balance characteristics in patients with diabetes type 2: evaluation and treatment efficacy; 2009. doi:10.26481/dis.20091216la
15. Kyrdalen IL, Thingstad P, Sandvik L, Ormstad H. Associations between gait speed and well-known fall risk factors among community-dwelling older adults. Physiother Res Int. 2019;24(1):e1743. doi:10.1002/pri.1743
16. Giladi N, Herman T, Reider-Groswasser II, Gurevich T, Hausdorff JM. Clinical characteristics of elderly patients with a cautious gait of unknown origin. J Neurol. 2005;252(3):300–306. doi:10.1007/s00415-005-0641-2
17. Weir E, Culmer L. Fall prevention in the elderly population. CMAJ. 2004;171(7):724. doi:10.1503/cmaj.1041381
18. Thaler-Kall K, Peters A, Thorand B, et al. Description of spatio-temporal gait parameters in elderly people and their association with history of falls: results of the population-based cross-sectional KORA-Age study. BMC Geriatr. 2015;15(1). doi:10.1186/s12877-015-0032-1
19. Society AG, Society G, Of AA, On Falls Prevention OSP. Guideline for the prevention of falls in older persons. J Am Geriatr Soc. 2001;49(5):664–672. doi:10.1046/j.1532-5415.2001.49115.x.
20. Rosendahl E, Lundin-Olsson L, Kallin K, Jensen J, Gustafson Y, Nyberg L. Prediction of falls among older people in residential care facilities by the Downton index. Aging Clin Exp Res. 2003;15:142–147. doi:10.1007/BF03324492
21. Štefan L, Kasović M, Zvonar M. Gait speed as a screening tool for foot pain and the risk of falls in community-dwelling older women: a cross-sectional study. Clin Interv Aging. 2020;15:1569–1574. doi:10.2147/CIA.S260931
22. Kasović M, Štefan L, Zvonař M. Domain-specific and total sedentary behavior associated with gait velocity in older adults: the mediating role of physical fitness. Int J Environ Res Public Health. 2020;17(2):593. doi:10.3390/ijerph17020593
23. Alvidrez J, Areán PA. Physician willingness to refer older depressed patients for psychotherapy. Int J Psychiatry Med. 2002;32:21–35. doi:10.2190/J26T-YQMJ-BF83-M05D
24. Bloch F, Thibaud M, Dugué B, Brèque C, Rigaud AS, Kemoun G. Psychotropic drugs and falls in the elderly people: updated literature review and meta-analysis. J Aging Health. 2011;23(2):329–346. doi:10.1177/0898264310381277
25. Leipzig RM, Cumming RG, Tinetti ME. Drugs and falls in older people: a systematic review and meta-analysis: i. psychotropic drugs. J Am Geriatr Soc. 1999;47(1):30–39. doi:10.1111/j.1532-5415.1999.tb01898.x
26. Seppala LJ, van de Glind EMM, Daams JG, et al. Fall-risk-increasing drugs: a systematic review and meta-analysis: III. others. J Am Med Dir Assoc. 2018;19(4):
27. Landi F, Onder G, Cesari M, Barillaro C, Russo A, Bernabei R Psychotropic medications and risk for falls among community-dwelling frail older people: an observational study. J Gerontol A Biol Sci Med Sci . 2005;60(5):622–626.
28. Howland HR. Prescribing psychotropic medications for elderly patients. J Psychosoc Nurs Ment Health Serv. 2022;47(11):17–20. doi:10.3928/02793695-20090930-06
29. Berlie HD, Garwood CL. Diabetes medications related to an increased risk of falls and fall-related morbidity in the elderly. Ann Pharmacother. 2010;44:712–719. doi:10.1345/aph.1
30. Tiedemann A, Sherrington C, Close JCT, Lord SR. Exercise and sports science Australia position statement on exercise and falls prevention in older people. J Sci Med Sport. 2011;14(6):489–495. doi:10.1016/j.jsams.2011.04.001
31. Gillespie LD, Robertson MC, Gillespie WJ, et al. Interventions for preventing falls in older people living in the community. In: editor, Gillespie LD. Cochrane Database of Systematic Reviews. John Wiley & Sons, Ltd; 2009. doi:10.1002/14651858.CD007146.pub2
32. Verghese J, LeValley A, Hall CB, Katz MJ, Ambrose AF, Lipton RB. Epidemiology of gait disorders in community-residing older adults. J Am Geriatr Soc. 2006;54(2):255–261. doi:10.1111/j.1532-5415.2005.00580.x
33. Schoon Y, Bongers K, van Kempen J, Melis R, Olde Rikkert M. Gait speed as a test for monitoring frailty in community-dwelling older people has the highest diagnostic value compared to step length and chair rise time. Eur J Phys Rehabil Med. 2014;50(6):693–701.
34. Cho SH, Park JM, Kwon OY. Gender differences in three dimensional gait analysis data from 98 healthy Korean adults. Clin Biomech. 2004;19(2):145–152. doi:10.1016/j.clinbiomech.2003.10.003
35. Laufer Y. Age- and gender-related changes in the temporal-spatial characteristics of forwards and backwards gaits. Physiother Res Int. 2003;8(3):131–142. doi:10.1002/pri.281
36. Di Nardo F, Mengarelli A, Maranesi E, Burattini L, Fioretti S. Gender differences in the myoelectric activity of lower limb muscles in young healthy subjects during walking. Biomed Signal Process Control. 2015;19:14–22. doi:10.1016/j.bspc.2015.03.006
37. Stolze H, Kuhtz-Buschbeck JP, Mondwurf C, et al. Gait analysis during treadmill and overground locomotion in children and adults. Electroencephalogr Clin Neurophysiol. 1997;105(6):490–497. doi:10.1016/S0924-980X(97)00055-6
38. Schwenk M, Schmidt M, Pfisterer M, Oster P, Hauer K. Rollator use adversely impacts on assessment of gait and mobility during geriatric rehabilitation. J Rehabil Med. 2011;43(5):424–429. doi:10.2340/16501977-0791
39. Downton J. Falls in the Elderly. London: Edward Arnold; 1993:128–130.
40. Lamb SE, Jørstad-Stein EC, Hauer K, Becker C. Development of a common outcome data set for fall injury prevention trials: the prevention of falls network Europe consensus. J Am Geriatr Soc. 2005;53:1618–1622. doi:10.1111/j.1532-5415.2005.53455.x
41. Nyberg L, Gustafson Y. Using the Downton index to predict those prone to falls in stroke rehabilitation. Stroke. 1996;27(10):1821–1824. doi:10.1161/01.STR.27.10.1821
42. Mojtaba M, Alinaghizadeh H, Rydwik E. Downton Fall Risk Index during hospitalization is associated with fall-related injuries after discharge: a longitudinal observational study. J Physiother. 2018;64(3):172–177. doi:10.1016/j.jphys.2018.05.005
43. Colberg SR, Sigal RJ, Yardley JE, et al. Physical activity/exercise and diabetes: a position statement of the American Diabetes Association. Diabetes Care. 2016;39(11):2065–2079. doi:10.2337/dc16-1728
44. Lahti J, Lallukka T, Lahelma E, Rahkonen O. Leisure-time physical activity and psychotropic medication: a prospective cohort study. Prev Med. 2013;57:173–177. doi:10.1016/j.ypmed.2013.05.019
45. Lampinen P, Heikkinen RL, Ruoppila I. Changes in intensity of physical exercise as predictors of depressive symptoms among older adults: an eight-year follow-up. Prev Med. 2000;30(5):371–380. doi:10.1006/pmed.2000.0641
46. Kwon MS, Kwon YR, Park YS, Kim JW. Comparison of gait patterns in elderly fallers and non-fallers. Technol Health Care. 2018;26(S1):427–436. doi:10.3233/THC-174736
47. Herssens N, van Criekinge T, Saeys W, et al. An investigation of the spatio-temporal parameters of gait and margins of stability throughout adulthood. J R Soc Interface. 2020;17(166):20200194. doi:10.1098/rsif.2020.0194
48. Panero E, Digo E, Dimanico U, et al. Effect of deep brain stimulation frequency on gait symmetry, smoothness and variability using IMU.