title: Cohort Study Comparing the Berg Balance Scale and the Mini-BESTest in People Who Have Multiple Sclerosis and Are Ambulatory Validity of the Dynamic Gait Index in People With Multiple Sclerosis Falls prevention and balance rehabilitation in multiple sclerosis: a bi-centre randomised controlled trial Falls in people with multiple sclerosis who use a walking aid: prevalence, factors, and effect of strength and balance interventions. Predicting accidental falls in people with multiple sclerosis — a longitudinal study CoDuSe group exercise programme improves balance and reduces falls in people with multiple sclerosis: A multi-centre, randomized, controlled pilot study Effects on Balance and Walking with the CoDuSe Balance Exercise Program in People with Multiple Sclerosis: A Multicenter Randomized Controlled Trial Mobility, Balance and Falls in Persons with Multiple Sclerosis Predicting falls among patients with multiple sclerosis: Comparison of patient-reported outcomes and performance-based measures of lower extremity functions. Development of a common outcome data set for fall injury prevention trials: the Prevention of Falls Network Europe consensus. Fall risk in people with MS: A Physiological Profile Assessment study Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration Three simple clinical tests to accurately predict falls in people with Parkinson's disease. Exploring Physical Activity in Women with Multiple Sclerosis: Associations with Fear of Falling and Underlying Impairments Risk factors for falls in multiple sclerosis: an observational study The Diagnostic Accuracy of Static Posturography in Predicting Accidental Falls in People With Multiple Sclerosis Fatigue, Physical Activity, Quality of Life, and Fall Risk in People with Multiple Sclerosis. Utility of Disease-Specific Measures and Clinical Balance Tests in Prediction of Falls in Persons With Multiple Sclerosis
abstract: Background The Berg Balance Scale ( BBS ) is a balance measure commonly used for people with multiple sclerosis ( MS ) . The Mini-BESTest is an alternative based on balance systems . Objective The study objective was to compare the BBS and the Mini-BESTest for sensitivity to change , likelihood ratios for walking aid use and falls , and associations with clinical variables in people who have MS and are ambulatory . Design This was a cohort study with measurements before and after exposure to 8 weeks of routine physical therapy intervention . Methods For 52 participants who had a primary diagnosis of MS and who were independently mobile , with or without an aid , demographic details and a history of falls and near falls were collected . Participants completed the  Mini-BESTest , Multiple Sclerosis Impact Scale-29 , Multiple Sclerosis Walking Scale-12 , BBS , Modified Fatigue Impact Scale  , and  Six-Minute Walk Test .  Results No participant started with a baseline  Mini-BESTest  maximum score of 28 , whereas 38.5 % ( n=20 ) started with a baseline BBS maximum score of 56 . Statistically significant changes in the  Mini-BESTest score ( X̅=5.31 , SD=3.5 ) and the BBS score ( X̅=1.4 , SD=1.9 ) were demonstrated . Effect sizes for the Mini-BESTest and the BBS were 0.70 and 0.37 , respectively ; st and ard response means for the Mini-BESTest and the BBS were 1.52 and 0.74 , respectively . Areas under the receiver operating characteristic curves for the Mini-BESTest and the BBS were 0.88 and 0.77 , respectively , for detecting mobility device use and 0.88 and 0.75 , respectively , for detecting self-reported near falls . The Mini-BESTest had a higher correlation for each secondary measure than did the BBS . Limitations This study involved a sample of convenience ; 61 % of the participants did not use a walking aid . The order of testing was not r and omized , and fall status was obtained through retrospective recall . Conclusions The Mini-BESTest had a lower ceiling effect and higher values on responsiveness tests . These findings suggest that the Mini-BESTest may be better at detecting changes in balance in people who have MS , are ambulatory , and have relatively little walking disability Background Evaluation of walking capacity and risk of falls in people with multiple sclerosis often are performed in rehabilitation . The Dynamic Gait Index ( DGI ) evaluates walking during different tasks , but the feasibility in identifying people at risk for falls needs to be further investigated . Objective The objective of this study was to investigate ( 1 ) the construct validity ( known groups , convergent , and discriminant ) of the DGI and ( 2 ) the accuracy of predicting falls and establishing a cutoff point to identify fallers . Design This trial was a multicenter , cross-sectional study . Methods A convenience sample was composed of 81 people with multiple sclerosis with subjective gait and balance impairment who were able to walk 100 m ( comparable to Exp and ed Disability Status Scale 1–6 ) . Mean age of the participants was 49 years ; 76 % were women . The  25-Foot Timed Walk Test , Timed “ Up & Go ” Test , Four Square Step Test , Timed Sit-to-St and Test , MS Walking Scale , Multiple Sclerosis Impact Scale , and self-reported falls during the previous  2 months were used for validation , to establish cutoff points for identifying fallers , and to investigate predictive values . Results Significantly lower DGI scores ( P≤.001 ) were found for participants reporting falls ( n=31 ) . High sensitivity ( 87 % ) in identifying fallers was found , with a cutoff score ≤19 . The positive predictive value was 50 % , and the negative predictive value was 87 % . The positive likelihood ratio was 1.77 , and the negative likelihood ratio was 0.26 . The convergent validity was moderate to strong ( ρ=0.58–0.80 ) , with the highest correlation coefficient found for the 25-Foot Timed Walk Test . Discriminant validity was shown with low correlation for the psychological subscale of the Multiple Sclerosis Impact Scale . Limitations The sample included ambulatory people participating in a r and omized controlled trial investigating balance training . Conclusions The DGI is a valid measure of dynamic balance during walking for ambulatory people with multiple sclerosis . With the cutoff point of ≤19 , sensitivity was high in identifying people at risk of falls Abstract Purpose : People with Multiple Sclerosis ( PwMS ) have a high incidence of accidental falls that have a potentially detrimental effect on their daily life participation . The effect of balance specific rehabilitation on clinical balance measures and frequency of falls in PwMS was studied . Method : A bi-centre r and omised rater-blinded controlled trial . Participants in both groups received 20 treatment sessions . Participants in the intervention group received treatment aim ed at improving balance and mobility .  Participants in the control group received treatments to reduce limitations at activity and body function level .  Primary measures were frequency of fallers ( > 1 fall in two months ) and responders ( > 3 points improvement ) at the Berg Balance Scale ( BBS ) . Data was analysed according to an intention to treat approach . Results : One hundred and nineteen participants were r and omised . Following treatment frequency of fallers was 22 % in the intervention group and 23 % in the control group , odds ratio ( OR ) and ( confidence limits ) : 1.05 ( 0.41 to 2.77 ) . Responders on the BBS were 28 % in the intervention group and 33 % in the control group , OR = 0.75 ( 0.30 to 1.91 ) . At follow up ORs for fallers and responders at BBS were 0.98 ( 0.48 to 2.01 ) and 0.79 ( 0.26 to 2.42 ) , respectively . Conclusions : Twenty sessions 2–3 times/week of balance specific rehabilitation did not reduce fall frequency nor improve balance suggesting the need for more frequent and challenging interventions . Implication s for Rehabilitation Programs for balance rehabilitation can improve balance but their effects in fall prevention are unclear . Twenty treatments sessions 2/3 times per week did not reduced frequency of falls in MS . The comparison with similar studies suggests that higher intensity of practice of highly challenging balance activities appears to be critical to maximizing effectiveness OBJECTIVES To investigate falls prevalence , factors associated with falling , and the effects of balance and strengthening interventions on falls in persons with multiple sclerosis ( MS ) . DESIGN Baseline and posttreatment data from a r and omized controlled trial . SETTING Community . PARTICIPANTS People with MS ( N=111 ) who use bilateral support for gait .  INTERVENTIONS Group and one-on-one physiotherapy . MAIN OUTCOME MEASURES Falls prevalence was assessed using retrospective recall . Demographic information was collected , impairments of body function were assessed , and results from the Berg Balance Scale , 6-minute walk test ( 6MWT ) , Multiple Sclerosis Impact Scale-29 version 2 physical and psychological scores , and the Modified Fatigue Impact Scale ( MFIS ) were obtained . RESULTS The prevalence of falls in a 3-month period was 50.5 % among participants with MS , of whom 28 % had more than 1 fall . Fallers had a greater physical ( mean difference , -3.9 ; P=.048 ) and psychological ( median difference , -4.5 ; P=.001 ) impact of MS and a greater impact of fatigue ( mean difference , -9.4 ; P=.002 ) . A logistic regression analysis found that the MFIS score made a unique , significant contribution to the model ( odds ratio=1.04 ; 95 % confidence interval , 1.018 - 1.079 ) , correctly identifying 68 % of fallers . A 10-week group physiotherapy intervention significantly reduced both the number of fallers ( 58.3 % before to 22.9 % after intervention , P=.005 ) and the number of falls ( 63 before to 25 after intervention , P=.001 ) . CONCLUSIONS The prevalence of falls is high in this population of persons with MS , and the impact of MS and of fatigue is greater in fallers . Development and evaluation of interventions to reduce falls risk and the transition to faller or multiple faller status are required Objective : To investigate accidental falls and near fall incidents in people with multiple sclerosis with respect to clinical variables and the predictive values of four tests . Design : A longitudinal , multi-centred cohort study with prospect ively collected falls . Procedures : Self-reported incidents during the three months following a st and ardized test procedure . Subjects : Seventy-six people with multiple sclerosis and an Exp and ed Disability Status Scale score between 3.5 and 6.0 . Main outcome measures : Berg Balance Scale , Timed Up and Go cognitive , Four Square Step Test ( FSST ) and 12-item Multiple Sclerosis Walking Scale . Results : Forty-eight people ( 63 % ) registered 270 falls . Most falls occurred indoors during activities of daily life . We found a correlation of rs=0.57 between near falls and falls , and of rs = 0.82 between registered and retrospectively recalled falls . Fallers and non-fallers differed significantly regarding Exp and ed Disability Status Score ( odds ratio ( OR ) 1.99 , 95 % confidence interval ( CI ) 1.22 ; 3.40 ) , spasticity ( OR 1.14 , CI 1.02 ; 1.31 ) , proprioception ( OR 2.50 , CI 1.36 ; 5.12 ) and use of walking aids ( OR 2.27 , CI 1.23 ; 4.37 ) . Reported use of walking aids both indoors and outdoors increased the odds of falling fivefold while disturbed proprioception increased the odds 2.5—15.6 times depending on severity . The odds of falling were doubled for each degree of increased Exp and ed Disability Status Score and more than doubled for each degree of increased spasticity . The Berg Balance Scale , use of walking aids and Timed Up and Go cognitive best identified fallers ( 73—94 % ) and proprioception , Exp and ed Disability Status Score , 12-item Multiple Sclerosis Walking Scale and Four Square Step Test best identified non-fallers ( 75—93 % ) . Conclusions : In clinical practice , looking at the use of walking aids , investigating proprioception and spasticity , rating Exp and ed Disability Status Score and using Berg Balance Scale or Timed Up and Go cognitive all contribute when identifying fallers Background : Imbalance leading to falls is common in people with multiple sclerosis ( PwMS ) . Objective : To evaluate the effects of a balance group exercise programme ( CoDuSe ) on balance and walking in PwMS ( Exp and ed Disability Status Scale , 4.0–7.5 ) . Methods : A multi-centre , r and omized , controlled single-blinded pilot study with r and om allocation to early or late start of exercise , with the latter group serving as control group for the physical function measures . In total , 14 supervised 60-minute exercise sessions were delivered over 7 weeks . Pretest – posttest analyses were conducted for self-reported near falls and falls in the group starting late . Primary outcome was Berg Balance Scale ( BBS ) . A total of 51 participants were initially enrolled ; three were lost to follow-up . Results : Post-intervention , the exercise group showed statistically significant improvement ( p = 0.015 ) in BBS and borderline significant improvement in MS Walking Scale ( p = 0.051 ) , both with large effect sizes ( 3.66 ; −2.89 ) . No other significant differences were found between groups . In the group starting late , numbers of falls and near falls were statistically significantly reduced after exercise compared to before ( p < 0.001 ; p < 0.004 ) . Conclusion : This pilot study suggests that the CoDuSe exercise  improved balance and reduced perceived walking limitations , compared to no exercise . The intervention reduced falls and near falls frequency Background . Balance and walking impairments are frequent in people with multiple sclerosis ( MS ) . Objective . The aim was to investigate the effects of a group-based balance exercise program targeting core stability , dual tasking , and sensory strategies ( CoDuSe ) on balance , postural sway , walking , perceived walking limitations , and balance confidence . Design . A single-blinded r and omized multicenter trial . No intervention was given to controls . Participants . People with MS able to walk 100 meters but unable to maintain t and em stance ≥30 seconds . Eighty-seven participants were r and omized to intervention or control . Intervention . The 60-minute CoDuSe group program , twice weekly for seven weeks , supervised by physical therapists . Measurements . Primary outcome was dynamic balance ( Berg Balance Scale ( BBS ) ) . Secondary outcomes were postural sway , walking ( Timed-Up and Go test ; Functional Gait Assessment ( FGA ) ) , MS Walking Scale , and Activities-specific Balance Confidence ( ABC ) Scale . Assessment s were performed before and after ( week 8) the intervention . Results . 73 participants fulfilled the study . There were significant differences between the intervention and the control groups in change in the BBS and in the secondary measures : postural sway with eyes open , FGA , MS Walking Scale , and ABC scale in favor of the intervention . Conclusions . The seven-week CoDuSe program improved dynamic balance more than no intervention Background There is a lack of information concerning the relation between objective measures of gait and balance and fall history in persons with MS ( PwMS ) . This investigation assessed the relation between demographic , clinical , mobility and balance metrics and falls history in persons with multiple sclerosis ( MS ) . Methods 52 ambulatory persons with MS ( PwMS ) participated in the investigation . All persons provided demographic information including fall history over the last 12 months . Disease status was assessed with Exp and  ed Disability Status Scale ( EDSS ) . Walking speed , coordination , endurance and postural control were quantified with a multidimensional mobility battery . Results Over 51 % of the participants fell in the previous year with 79 % of these people being suffering recurrent falls . Overall , fallers were older , had a greater prevalence of assistive devices use , worse disability , decreased walking endurance , and greater postural sway velocity with eyes closed compared to non-fallers . Additionally , fallers had greater impairment in cerebellar , sensory , pyramidal , and bladder/bowel subscales of the EDSS . Conclusions The current observations suggest that PwMS who are older , more disabled , utilize an assistive device , have decreased walking coordination and endurance and have diminished balance have fallen in the previous year . This suggests that individuals who meet these criteria need to be carefully monitored for future falls . Future research is needed to determine a prospect i ve model of falls specific to PwMS . Additionally , the utility of interventions aim ed at reducing falls and fall risk in PwMS needs to be established BACKGROUND Accurate fall screening tools are needed to identify those multiple sclerosis ( MS ) patients at high risk of falling . The present study aim ed at determining the validity of a series of performance-based measures ( PBMs ) of lower extremity functions and patient-reported outcomes ( PROs ) in predicting falls in a sample of MS patients ( n = 84 ) , who were ambulatory independent . METHODS Patients were assessed using the following PBMs : timed up and go ( TUG ) , timed 25-foot walk ( T25FW ) , cognitive T25FW , 2-min walk ( 2MW ) , and cognitive 2MW . Moreover , a series of valid and reliable PROs were filled in by participants including the activities-specific balance confidence ( ABC ) , 12-item multiple sclerosis walking scale ( MSWS-12 ) , fall efficacy scale international ( FES-I ) , and modified fatigue impact scale ( MFIS ) . The dual task cost ( DTC ) of 2MW and T25FW tests were calculated as a percentage of change in parameters from single to dual task conditions . Participants were classified as none-fallers and fallers ( ⩾1 ) based on their prospect i ve fall occurrence . RESULTS In the present study , 41(49 % ) participants recorded ≥ 1 fall and were classified as fallers . The results of logistic regression analysis revealed that each individual test , except DTC of 2MW and T25FW , significantly predicted future falls . However , considering the area under the curves ( AUCs ) , PROs were more accurate compared to PBMs . In addition , the results of multiple logistic regression with the first two factors extracted from principal component analysis revealed that both factor 1 ( PROs ) and factor 2 ( PBMs ) significantly predicted falls with a greater odds ratio ( OR ) for factor 1 ( factor 1 : P = < 0.0001 , OR = 63.41 ( 6.72 - 597.90 ) ) than factor 2 ( P < 0.05 , OR = 5.03 ( 1.33 - 18.99 ) ) . CONCLUSIONS The results of this study can be used by clinicians to identify and monitor potential fallers in MS patients The prevention of injury associated with falls in older people  is a public health target in many countries around the world . Although there is good evidence that interventions such as multifactorial fall prevention and individually prescribed exercise are effective in reducing falls , the effect on serious injury rates is unclear . Historically , trials have not been adequately powered to detect injury endpoints , and variations in case definition across trials have hindered meta- analysis . It is possible that fall-prevention strategies have limited effect on falls that result in injuries or are ineffective in population s who are at a higher risk of injury . Further research is required to determine whether fall-prevention interventions can reduce serious injuries . Prevention of Falls Network Europe ( ProFaNE ) is a collaborative project to reduce the burden of fall injury in older people through excellence in research and promotion of best practice ( www.profane.eu.org ) . The European Commission funds the network , which links clinicians , members of the public , and research ers worldwide . The aims are to identify major gaps in knowledge in fall injury prevention and to facilitate the collaboration necessary for large-scale clinical research activity , including clinical trials , comparative research , and prospect i ve meta- analysis . Work is being undertaken in a 4-year program . As a first step , the development of a common set of outcome definitions and measures for future trials or meta- analysis was considered Introduction The Physiological Profile Assessment ( PPA ) is used in research and clinical practice for assessing fall risk . We compared PPA test performance between people with multiple sclerosis ( MS ) and healthy controls , determined the fall-risk profile for people with MS and developed a reference data base for people with MS . Methods For this study , 416 ambulant people with MS ( 51.5 ± 12.0 years ) and 352 controls ( 52.8 ± 12.2 years ) underwent the PPA ( tests of contrast sensitivity , proprioception , quadriceps strength , reaction time and sway ) with composite fall-risk scores computed from these measures . MS participants were followed prospect ively for falls for 3 months . Results The MS participants performed significantly worse than controls in each PPA test . The average composite fall-risk score was also significantly elevated , indicating a “ marked ” fall risk when compared with controls . In total , 155 MS participants ( 37.3 % ) reported 2 + falls in the follow-up period . Frequent fallers performed significantly worse than non-frequent fallers in the contrast sensitivity , reaction time and sway tests and had higher PPA composite scores . Conclusions In line with poor PPA test performances , falls incidence in people with MS was high . This study provides comprehensive reference data for the PPA measures for people with MS that could be used to inform future research and clinical practice In medicine , numerous decisions are made by care providers , often in shared decision making , on the basis of an estimated probability that a specific disease or condition is present ( diagnostic setting ) or a specific event will occur in the future ( prognostic setting ) in an individual . In the diagnostic setting , the probability that a particular disease is present can be used , for example , to inform the referral of patients for further testing , to initiate treatment directly , or to reassure patients that a serious cause for their symptoms is unlikely . In the prognostic context , predictions can be used for planning lifestyle or therapeutic decisions on the basis of the risk for developing a particular outcome or state of health within a specific period ( 13 ) . Such estimates of risk can also be used to risk-stratify participants in therapeutic intervention trials ( 47 ) . In both the diagnostic and prognostic setting , probability estimates are commonly based on combining information from multiple predictors observed or measured from an individual ( 1 , 2 , 810 ) . Information from a single predictor is often insufficient to provide reliable estimates of diagnostic or prognostic probabilities or risks ( 8 , 11 ) . In virtually all medical domains , diagnostic and prognostic multivariable ( risk ) prediction models are being developed , vali date d , up date d , and implemented with the aim to assist doctors and individuals in estimating probabilities and potentially influence their decision making . A multivariable prediction model is a mathematical equation that relates multiple predictors for a particular individual to the probability of or risk for the presence ( diagnosis ) or future occurrence ( prognosis ) of a particular outcome ( 10 , 12 ) . Other names for a prediction model include risk prediction model , predictive model , prognostic ( or prediction ) index or rule , and risk score ( 9 ) . Predictors are also referred to as covariates , risk indicators , prognostic factors , determinants , test results , ormore statisticallyindependent variables . They may range from demographic characteristics ( for example , age and sex ) , medical historytaking , and physical examination results to results from imaging , electrophysiology , blood and urine measurements , pathologic examinations , and disease stages or characteristics , or results from genomics , proteomics , transcriptomics , pharmacogenomics , metabolomics , and other new biological measurement platforms that continuously emerge . Diagnostic and Prognostic Prediction Models Multivariable prediction models fall into 2 broad categories : diagnostic and prognostic prediction models ( Box A ) . In a diagnostic model , multiplethat is , 2 or morepredictors ( often referred to as diagnostic test results ) are combined to estimate the probability that a certain condition or disease is present ( or absent ) at the moment of prediction ( Box B ) . They are developed from and to be used for individuals suspected of having that condition . Box A. Schematic representation of diagnostic and prognostic prediction modeling studies . The nature of the prediction in diagnosis is estimating the probability that a specific outcome or disease is present ( or absent ) within an individual , at this point in timethat is , the moment of prediction ( T= 0 ) . In prognosis , the prediction is about whether an individual will experience a specific event or outcome within a certain time period . In other words , in diagnostic prediction the interest is in principle a cross-sectional relationship , whereas prognostic prediction involves a longitudinal relationship . Nevertheless , in diagnostic modeling studies , for logistical reasons , a time window between predictor ( index test ) measurement and the reference st and ard is often necessary . Ideally , this interval should be as short as possible without starting any treatment within this period . Box B. Similarities and differences between diagnostic and prognostic prediction models . In a prognostic model , multiple predictors are combined to estimate the probability of a particular outcome or event ( for example , mortality , disease recurrence , complication , or therapy response ) occurring in a certain period in the future . This period may range from hours ( for example , predicting postoperative complications [ 13 ] ) to weeks or months ( for example , predicting 30-day mortality after cardiac surgery [ 14 ] ) or years ( for example , predicting the 5-year risk for developing type 2 diabetes [ 15 ] ) . Prognostic models are developed and are to be used in individuals at risk for developing that outcome . They may be models for either ill or healthy individuals . For example , prognostic models include models to predict recurrence , complications , or death in a certain period after being diagnosed with a particular disease . But they may also include models for predicting the occurrence of an outcome in a certain period in individuals without a specific disease : for example , models to predict the risk for developing type 2 diabetes ( 16 ) or cardiovascular events in middle-aged nondiseased individuals ( 17 ) , or the risk for preeclampsia in pregnant women ( 18 ) . We thus use prognostic in the broad sense , referring to the prediction of an outcome in the future in individuals at risk for that outcome , rather than the narrower definition of predicting the course of patients who have a particular disease with or without treatment ( 1 ) . The main difference between a diagnostic and prognostic prediction model is the concept of time . Diagnostic modeling studies are usually cross-sectional , whereas prognostic modeling studies are usually longitudinal . In this document , we refer to both diagnostic and prognostic prediction models as prediction models , highlighting issues that are specific to either type of model . Development , Validation , and Updating of Prediction Models Prediction model studies may address the development of a new prediction model ( 10 ) , a model evaluation ( often referred to as model validation ) with or without updating of the model [ 1921 ] ) , or a combination of these ( Box C and Figure 1 ) . Box C. Types of prediction model studies . Figure 1 . Types of prediction model studies covered by the TRIPOD statement . D = development data ; V = validation data . Model development studies aim to derive a prediction model by selecting predictors and combining them into a multivariable model . Logistic regression is commonly used for cross-sectional ( diagnostic ) and short-term ( for example 30-day mortality ) prognostic outcomes and Cox regression for long-term ( for example , 10-year risk ) prognostic outcomes . Studies may also focus on quantifying the incremental or added predictive value of a specific predictor ( for example , newly discovered ) ( 22 ) to a prediction model . Quantifying the predictive ability of a model on the same data from which the model was developed ( often referred to as apparent performance [ Figure 1 ] ) will tend to give an optimistic estimate of performance , owing to overfitting ( too few outcome events relative to the number of c and i date predictors ) and the use of predictor selection strategies ( 2325 ) . Studies developing new prediction models should therefore always include some form of internal validation to quantify any optimism in the predictive performance ( for example , calibration and discrimination ) of the developed model and adjust the model for overfitting . Internal validation techniques use only the original study sample and include such methods as bootstrapping or cross-validation . Internal validation is a necessary part of model development ( 2 ) . After developing a prediction model , it is strongly recommended to evaluate the performance of the model in other participant data than was used for the model development . External validation ( Box C and Figure 1 ) ( 20 , 26 ) requires that for each individual in the new participant data set , outcome predictions are made using the original model ( that is , the published model or regression formula ) and compared with the observed outcomes . External validation may use participant data collected by the same investigators , typically using the same predictor and outcome definitions and measurements , but sample d from a later period ( temporal or narrow validation ) ; by other investigators in another hospital or country ( though disappointingly rare [ 27 ] ) , sometimes using different definitions and measurements ( geographic or broad validation ) ; in similar participants , but from an intentionally different setting ( for example , a model developed in secondary care and assessed in similar participants , but selected from primary care ) ; or even in other types of participants ( for example , model developed in adults and assessed in children , or developed for predicting fatal events and assessed for predicting nonfatal events ) ( 19 , 20 , 26 , 2830 ) . In case of poor performance ( for example , systematic miscalibration ) , when evaluated in an external validation data set , the model can be up date d or adjusted ( for example , recalibrating or adding a new predictor ) on the basis of the validation data set ( Box C ) ( 2 , 20 , 21 , 31 ) . R and omly splitting a single data set into model development and model validation data sets is frequently done to develop and vali date a prediction model ; this is often , yet erroneously , believed to be a form of external validation . However , this approach is a weak and inefficient form of internal validation , because not all available data are used to develop the model ( 23 , 32 ) . If the available development data set is sufficiently large , splitting by time and developing a model using data from one period and evaluating its performance using the data from the other period ( temporal validation ) is a stronger approach . With a single data set , temporal splitting and model validation can be considered intermediate between internal and external validation . Incomplete and Inaccurate Reporting Prediction models are becoming increasingly abundant in the medical literature ( 9 , 33 , 34 ) , and policymakers are Falls are a major cause of morbidity in Parkinson 's disease ( PD ) . The objective of this study was to identify predictors of falls in PD and develop a simple prediction tool that would be useful in routine patient care . Potential predictor variables ( falls history , disease severity , cognition , leg muscle strength , balance , mobility , freezing of gait [ FOG ] , and fear of falling ) were collected for 205 community-dwelling people with PD .  Falls were monitored prospect ively for 6 months using monthly falls diaries . In total , 125 participants ( 59 % ) fell during follow-up . A model that included a history of falls , FOG , impaired postural sway , gait speed , sit-to-st and , st and ing balance with narrow base of support , and coordinated stability had high discrimination in identifying fallers ( area under the receiver-operating characteristic curve [ AUC ] , 0.83 ; 95 % confidence interval [ CI ] , 0.77 - 0.88 ) . A clinical tool that incorporated 3 predictors easily determined in a clinical setting ( falling in the previous year : odds ratio [ OR ] , 5.80 ; 95 % CI , 3.00 - 11.22 ; FOG in the past month : OR , 2.39 ; 95 % CI , 1.19 - 4.80 ; and self-selected gait speed < 1.1 meters per second : OR , 1.86 ; 95 % CI , 0.96 - 3.58 ) had similar discrimination ( AUC , 0.80 ; 95 % CI , 0.73 - 0.86 ) to the more complex model ( P = 0.14 for comparison of AUCs ) . The absolute probability of falling in the next 6 months for people with low , medium , and high risk using the simple , 3-test tool was 17 % , 51 % , and 85 % , respectively . In people who have PD without significant cognitive impairment , falls can be predicted with a high degree of accuracy using a simple , 3-test clinical tool . This tool enables individualized quantification of the risk of falling . © 2013 Movement Disorder Society Objective The aim of this study was to conduct an exploratory analysis of fear of falling ( FoF ) , balance , gait , and strength impairments and future physical activity in women with multiple sclerosis . Design This prospect i ve study followed a convenience sample of 99 women with multiple sclerosis for 1 yr . The participants were assessed on  FoF and perceived mental health by question naire . Objective measures included Limits of Stability , the Sensory Organization Test , and the Functional Ambulation Profile . Strength was quantified by knee extensor power asymmetry . Activity-specific metabolic equivalent values were used to determine minutes per week of moderate and vigorous physical activity . Results Future physical activity most strongly associated with baseline FoF ( R2 = 0.09 , P < 0.01 ) , and baseline FoF associated with limits of stability and lower extremity strength asymmetry ( R2 = 0.21 , P < 0.001 ) . Follow-up FoF is best predicted by initial levels of FoF independent of intervening falls ( & bgr ; = 3.26 , P < 0.001 ) . Conclusions Future physical activity of women with multiple sclerosis was best predicted by FoF independent of physical and mental functioning . Increased FoF was associated with greater lower extremity strength asymmetry and decreased limits of stability rather than with the experience of falls Background : People with multiple sclerosis ( MS ) experience frequent falls , which are associated with impairments and limitations to activities and participations . Objective : The objective of this paper is to evaluate falls risk factors using robust clinical measures . Methods : A total of 150 individuals ( confirmed MS diagnosis , Exp and ed Disability Status Scale ( EDSS ) 3.5–6.5 ) were recruited , with 148 participants included in the final analysis . Demographic data were collected and performance assessed in eight predictor measures  ( Physiological Profile Assessment ( PPA ) , Brief Ataxia Rating scale , Ashworth scale ( Ashworth ) , Modified Falls Efficacy scale , Symbol Digit Modalities Test , dual-task interference , lying/st and ing blood pressure , static/dynamic visual acuity )  . Participants prospect ively recorded falls over three months using a daily diary . People were classified as “ fallers ” based on reports of ≥ two falls . Results : A total of 104 participants recorded 672 falls ; 78 ( 52.7 % ) reported ≥ two falls . Continence issues , previous falls history and use of prescribed medications were each associated with increased risk of being a “ faller ” . Ashworth and PPA risk score contributed significantly to a logistic regression model predicting faller/non-faller classification . The reduced model ( Ashworth , PPA , EDSS ) showed fair-to-good predictive ability ( ROC c-statistic 0.73 , sensitivity 70 % , specificity 69 % ) . Conclusion : This study confirms the high prevalence of falls in ambulant people with MS . Important potentially modifiable risk factors are identified , suggesting aspects to target in falls interventions Background . Quantitative posturography has been reported as a reliable tool to measure balance in people with multiple sclerosis ( MS ) . However , data on its diagnostic accuracy in predicting the occurrence of falls are lacking . Objective . To determine sensitivity , specificity , predictive values , and accuracy of  posturography  in detecting falls  in MS subjects over a 3-month follow-up period . Methods . One hundred consecutive patients with MS were tested by the  Berg Balance Scale ( BBS ) and by  static posturography on a monoaxial platform . Participants recorded the occurrence of accidental falls for the next 3 months . Abnormal cutoff values for static st and ing balance measures were set at 2 st and ard deviations above the mean values obtained from 50 healthy controls ( HC ) . The diagnostic accuracy of the BBS and static posturography was analyzed with respect to the prospect ively collected data on the occurrence of falls . Results . Posturometric measures in participants with MS were significantly worse than in HC ( all P values < .0001 ) ; however , only the center of pressure ( COP ) path with open eyes condition had substantial test – retest reliability .  Static posturography  was more sensitive ( 88 % vs 37 % ) and accurate ( 75 % vs 63 % ) , but slightly less specific ( 67 % vs 81 % ) , than the BBS in predicting accidental falls . A logistic regression analysis revealed that the worse the COP path , the greater the risk for accidental falls ( odds ratio = 1.08 ; P < .0001 ) , even after adjusting for sex , age , disease duration , body mass index , MS subtype , Exp and ed Disability Status Scale , and BBS score . Conclusion . The COP path measurement in static position is a sensitive and accurate tool to identify people with MS who are at risk of accidental falls Background Fatigue , inactivity , and falls are major health issues for people with multiple sclerosis ( MS ) . We examined the extent to which fatigue and low walking activity are associated with quality of life and increased fall risk in people with MS . Methods People with MS ( N = 210 , aged 21 - 74 years ) were categorized as having either high or low reported fatigue and walking activity levels  and were then followed up for falls using monthly fall diaries for 6 months . Results A high level of fatigue was significantly associated with higher MS Disease Steps scores , worse balance , high composite physiological ( Physiological Profile Assessment ) fall risk scores , greater fear of falling , lower World Health Organization Disability Assessment Schedule ( WHODAS ) quality of life scores , and more prospect ively recorded falls . Low walking activity was significantly associated with higher MS Disease Steps scores , reduced proprioception , worse st and ing and leaning balance , slow stepping , slow gait speed , worse fine motor control , high Physiological Profile Assessment fall risk scores , more fear of falling , and lower WHODAS quality of life scores . Conclusions Increased fatigue and low walking activity levels were significantly associated with increased fall risk and lower quality of life in people with MS . Interventions aim ed at addressing fatigue and inactivity may have multiple benefits for this group Background and Purpose : The sensory and motor deficits associated with multiple sclerosis ( MS ) contribute to falls with the majority of persons with MS falling at least once annually . To appropriately direct treatment , accurate fall prediction measures are needed . In this study of community-dwelling individuals with MS followed for 12 months , we sought to determine frequency of falls , utility of clinical balance tests to predict falls , and accuracy of participants ' retrospective recall of fall events . Methods : Independently ambulatory persons with MS underwent 5 clinical balance tests including Activities-specific Balance Confidence , Berg Balance Scale , Functional Reach , Timed Up and Go , and Dynamic Gait Index , and one disease-specific measure of disability  ( Exp and ed Disability Status Scale ) and then were followed for 1 year . Participants were queried monthly by phone to determine the number of fall events that had occurred . Accuracy of fall prediction was determined by receiver operating characteristic curve analysis and comparison of balance test performance between fallers and nonfallers . Results : Sixty-one percent of the 38 participants were classified as fallers at 12-month follow-up . Only the Berg Balance Scale , Dynamic Gait Index , and the Activities-specific Balance Confidence demonstrated clinical ly useful levels of accuracy . In addition , participants were generally poor in their accurate recall of fall events relative to their monthly fall reports . Discussion and Conclusions : The majority of participants fell during a 1-year prospect i ve follow-up . Only 2 balance performance measures and 1 balance confidence measure accurately distinguished between fallers and nonfallers as well as possessed clinical ly useful levels of sensitivity and specificity . These results also emphasized the inaccuracy of retrospective fall history in an MS sample . Video Abstract available ( see Supplemental Digital Content 1 , http://links.lww.com/JNPT/A55 ) for more insights from the author
target: Meta- analysis demonstrated fallers perform significantly worse than non-fallers on all measures analysed except the Timed Up and Go Cognitive ( p < 0.05 ) , but discriminative ability of the measures is commonly not reported .
  Of those reported , the Activities-specific Balance Confidence Scale had the highest area under the receiver operating characteristic curve value ( 0.92 ) , but without reporting corresponding measures of clinical utility .
  Conclusion : Clinical measures of balance differ significantly between fallers and non-fallers but have poor predictive ability for falls risk in people with multiple sclerosis
background: Objective : To determine the ability of clinical measures of balance to distinguish fallers from non-fallers and to determine their predictive validity in identifying those at risk of falls .