Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Showing posts with label fall prediction. Show all posts
Showing posts with label fall prediction. Show all posts

Wednesday, September 23, 2026

Early gait segments may be sufficient: fall risk assessment does not require steady-state walking

 What is your competent? doctors EXACT FALL PREVENTION PROTOCOL? Doesn't have one I bet; A FIREABLE OFFENSE! We have to forcefully clean out a lot of incompetent dead wood in stroke.

Early gait segments may be sufficient: fall risk assessment does not require steady-state walking

    We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.

    Abstract

    Falls remain a significant health concern for older adults, highlighting the need for efficient and accurate fall risk screening. Although wearable inertial measurement units provide accessible gait analysis, it remains unclear whether fall-history classification requires gait parameters computed over fully stabilized walking sequences or whether discriminative information may already be present in the early portion of the walking sequence before parameters converge. This study analyzes foot-mounted IMU data from two independent cohorts: the publicly available GSTRIDE dataset and a private dataset collected by our team. After preprocessing, the analytical samples included 147 GSTRIDE participants (71 fallers and 76 non-fallers) and 95 participants from our dataset (16 fallers and 79 non-fallers) recruited from senior living facilities. Faller status was defined using retrospective fall-history labels. Across cumulative and sliding window feature extraction strategies, variability-based gait parameters required a large number of strides to achieve stable reliability, particularly among fallers. Nevertheless, strong discriminative potential was consistently observed using gait segments obtained prior to full parameter stabilization. Window-based statistical analyses further showed that significant early-window variability differences were present in the GSTRIDE dataset but not in our dataset, despite comparable classification trends. These findings indicate that full parameter stabilization is not a prerequisite for effective fall-history classification. Instead, gait segments from the early portion of walking sequences can provide useful discriminative information, offering a practical alternative to conventional approaches that rely on prolonged steady-state walking recordings.

    Wednesday, November 9, 2022

    Motor Cognitive Dual-Task Testing to Predict Future Falls in Multiple Sclerosis: A Systematic Review

    WHOM will be doing this testing on stroke survivors to see if it will predict falls in them? AND THEN CREATE EXACT PROTOCOLS TO PREVENT THOSE FALLS?  That is what great stroke leadership would be doing if there was ANY stroke leadership at all. Alas, there is NO stroke leadership doing one fucking thing to help survivors. You're on your own to solve stroke before your children and grandchildren have strokes.

    Motor Cognitive Dual-Task Testing to Predict Future Falls in Multiple Sclerosis: A Systematic Review

    Abstract

    Background

    Mobility and cognitive impairments are often associated with increased fall risk among people with multiple sclerosis (PwMS). However, evidence on the concurrent assessment of gait or balance and cognitive tasks (dual-task) to predict falls appears to be inconsistent.

    Objective

    To summarize the ability of gait or balance dual-task testing to predict future falls among PwMS.

    Methods

    Seven databases including PubMed, Embase, Web of Science, Scopus, CINHAL, SPORTDiscuss, and PsycINFO were searched from inception to May 2022. Two independent reviewers identified studies that performed a dual-task testing among adults with multiple sclerosis and monitored falls prospectively for at least 3 months. Both reviewers also evaluated the quality assessment of the included studies.

    Results

    Eight studies with 484 participants were included in the review. Most studies (75%) indicated that dual-task testing and dual-task cost did not discriminate prospective fallers (⩾1 fall) and non-fallers (0 fall) and were not found as predictors of future falls. However, dual-task cost of walking velocity (OR = 1.23, 95% CI 0.98-4.45, P = .05) and dual-task of correct response rate of serial 7 subtraction (OR = 1.34, 95% CI 1.04-3.74, P = .02) were significantly associated with increased risk of recurrent falls (≥2 falls). Pattern of cognitive-motor interference was also associated with an increased risk of falling. All studies presented with strong quality.

    Conclusion

    The scarce evidence indicates that dual-task testing is not able to predict future falls among PwMS. Further research with more complex motor and cognitive tasks and longer-term fall monitoring is required before dual-task testing can be recommended as a predictor of future falls in this population.

    Get full access to this article


     

    Thursday, December 23, 2021

    Abnormal Gait Movements Prior to a Near Fall in Individuals After Stroke

    By not specifying anything for suggested interventions you made this completely useless.

    Abnormal Gait Movements Prior to a Near Fall in Individuals After Stroke

    https://doi.org/10.1016/j.arrct.2021.100156Get rights and content
    Under a Creative Commons license
    open access

    Highlights

    •

    We analyzed movements that occurred 1 cycle before a near fall after stroke.

    •

    We compared the usual gait and the last cycle before the near fall in each patient.

    •

    Decreased gait speed was found in the cycle before the near fall.

    •

    Excessive lateral center of mass movement was found in the pre–near fall cycle.

    Abstract

    Objective

    To investigate the abnormal kinematic and kinetic movements in the last gait cycle before a near fall in individuals poststroke, where a near fall is defined as a physical therapist feeling the need to stabilize a patient.

    Design

    Retrospective study.

    Participants

    Twenty-five adults (22 men, 3 women; N=25) with an average age of 66.3 years and mean duration from stroke of 4 months who required manual assistance for a sudden imbalance during routine 3-dimensional motion analysis.

    Interventions

    Not applicable.

    Main Outcome Measures

    We compared the averaged usual gait cycle and the last cycle before the near-falling gait cycle (pre–near-falling gait cycle). We obtained the following spatiotemporal parameters: gait velocity, gait cycle duration, mediolateral center of mass displacement, step length, step width, joint moments, and angular displacement of the trunk in a cycle. Peak values of joint moments and trunk angle displacement were calculated.

    Results

    Etiology for near falls included toe trip, mediolateral perturbation, and knee collapse. We found the following significant differences in the pre–near-falling gait cycle compared with the usual gait cycle: decreased gait velocity, prolonged total cycle time, and excessive mediolateral center of mass displacement.

    Conclusions

    Decreased gait velocity, prolonged cycle time, and excessive mediolateral center of mass displacement may be a sign of an impending fall in people with impaired gait after stroke.

     

    Saturday, May 23, 2020

    Cognition assessments to predict inpatient falls in a subacute stroke rehabilitation setting

    Wrong, wrong, wrong objective. We don't need predictions. WE NEED EXACT FALL PREVENTION PROTOCOLS. Not this lazy crapola. Until we get survivors in charge we will never get the rehab research we need to 100% recover.

    Cognition assessments to predict inpatient falls in a subacute stroke rehabilitation setting


    Received 19 Dec 2019, Accepted 19 Apr 2020, Published online: 20 May 2020

    ABSTRACT

    Background: Stroke-related falls occur at especially high rates in rehabilitation settings. Inpatient-hospital falls have been identified as one of the most common medical complications after stroke, negatively influencing recovery, nevertheless, the role of cognition in relation to falls during inpatient rehabilitation is largely unexplored.
    Objective. We aim to predict inpatient falls in a subacute stroke rehabilitation setting using previously reported variables such as stroke severity, gender, age, ataxia, hemiparesis, and functionality in activities of daily living, further extending them with specific cognition variables assessing memory, verbal fluency, attention, and orientation.
    Methods: This observational study included 158 stroke patients admitted to a rehabilitation center between 2007 and 2019, with less than 30 days since stroke onset to admission. Stroke severity was assessed using the National Institutes of Health Stroke Scale (NIHSS). Four logistic regressions were performed including NIHSS, age, sex, ataxia, and hemiparesis plus one of the following: (1) Functional Independence Measure cognitive (C-FIM) and motor (M-FIM) subtests. (2) individual C-FIM items, (3) Ray Auditory Verbal Memory Test (RAVLT) and (4) verbal fluency test (PMR), Digit Span from Wechsler Adult Intelligence Scale (WAIS III), and Orientation from Test Barcelona.
    Results: Neither NIHSS, age, sex, ataxia nor hemiparesis predicted falls. C-FIM was a significant predictor (AUC:0.891), but not M-FIM. The problem solving C-FIM item (AUC:0.836), the RAVLT learning subtest (AUC:0.879), and PMR verbal fluency (AUC:0.871) were significant predictors for each model, respectively.
    Conclusions: Cognition assessments, i.e., one FIM item, one RAVLT item, or a one-minute verbal fluency test are significant falls predictors.

    Additional information

    Funding

    This research was partially funded by EU H2020 PRECISE4Q - Personalized Medicine by Predictive Modeling in Stroke for better Quality of Life [Grant Agreement 777107 – Research and Innovation Action].

    Acknowledgments

    Special thanks to Toni Ustrell from Institut Guttmann’s Nursery Department for his support with falls protocols and to Jaume Lopez from Institut Guttmann's Research and Innovation Department for data access.

    Supplementary material

    Supplemental data for this article can be accessed here.

    Saturday, January 25, 2020

    Association between performance on an interdisciplinary stroke assessment battery and falls in patients with acute stroke in an inpatient rehabilitation facility

    Oh god, more prediction stupidity. Survivors want EXACT STROKE PROTOCOLS LEADING TO 100% RECOVERY. When the hell will you get there? After you are the 1 in 4 per WHO that has a stroke?

    Association between performance on an interdisciplinary stroke assessment battery and falls in patients with acute stroke in an inpatient rehabilitation facility

    Archives of Physical Medicine and Rehabilitation , Volume 100(11) , Pgs. 2089-2095.

    NARIC Accession Number: J82546.  What's this?
    ISSN: 0003-9993.
    Author(s): Eikenberry, Megan; Ganley, Kathleen J.; Zhang, Nan; Kinney, Carolyn L..
    Publication Year: 2019.
    Number of Pages: 7.
    Abstract: Study explored the association between demographic factors and functional performance measures of 139 patients with acute stroke in an inpatient rehabilitation facility (IRF) and falls during the IRF stay and determined the diagnostic accuracy of functional outcome measures in identifying fallers. Odds ratios were used to examine the relationship between fall frequency and functional outcome measures: National Institute of Stroke Scale, neglect (Item #11); Berg Balance Scale; Stroke Rehabilitation Assessment of Movement (STREAM) mobility and STREAM lower-extremity (STREAM-LE) subscales; Montreal Cognitive Assessment; Dynamic Gait Index; and Stroke Impact Scale. Receiver operator characteristic analysis with area under the curve, sensitivity, specificity, and diagnostic odds ratio were used to assess the diagnostic accuracy of each functional outcome measure to distinguish patients who fell vs those who did not fall in the IRF. A total of 23 patients (16.2 percent) fell during the IRF hospitalization. Patients who did and did not fall did not differ in terms of age, sex, stroke type, or stroke location. Only the STREAM-LE was associated with falls. Area under the curve was 0.67. With a positivity cutoff point of 12, sensitivity and specificity were 73.3 percent and 50.0 percent, respectively. The diagnostic odds ratio was 3.4. The findings suggest that the STREAM-LE score at admission to IRF may identify patients with acute stroke who are more likely to fall during their stay. However, the search for measures with greater diagnostic accuracy should continue.(No it shouldn't. We need protocols that deliver recovery, that will prevent falls.)
    Descriptor Terms: BODY MOVEMENT, DEMOGRAPHICS, EQUILIBRIUM, FUNCTIONAL EVALUATION, INTERDISCIPLINARY ACTIVITIES, MEASUREMENTS, MOBILITY, OUTCOMES, PERFORMANCE STANDARDS, POSTURE, REHABILITATION FACILITIES, STROKE.


    Can this document be ordered through NARIC's document delivery service*?: Y.

    Citation: Eikenberry, Megan, Ganley, Kathleen J., Zhang, Nan, Kinney, Carolyn L.. (2019). Association between performance on an interdisciplinary stroke assessment battery and falls in patients with acute stroke in an inpatient rehabilitation facility.  Archives of Physical Medicine and Rehabilitation , 100(11), Pgs. 2089-2095. Retrieved 1/25/2020, from REHABDATA database.
     

    Tuesday, August 20, 2019

    Do clinical assessments, steady-state or daily-life gait characteristics predict falls in ambulatory chronic stroke survivors?

    There is not a survivor alive who cares about predictions. Just deliver protocols that will prevent falls. 

    Do clinical assessments, steady-state or daily-life gait characteristics predict falls in ambulatory chronic stroke survivors?

     Journal of Rehabilitation Medicine (formerly the Scandinavian Journal of Rehabilitation Medicine) , Volume 49(5) , Pgs. 402-409.

    NARIC Accession Number: J81435.  What's this?
    ISSN: 1650-1977.
    Author(s): Punt, Michiel ; Bruijn, Sjoerd M. ; Wittink, Harriet ; van de Port, Ingrid G. ; van Dieën, Jaap H..
    Publication Year: 2017.
    Number of Pages: 8.
    Abstract: Study investigated the extent to which gait characteristics and clinical physical therapy assessments predict falls in chronic stroke survivors. Steady-state gait characteristics were collected from 40 participants while walking on a treadmill with motion capture of spatiotemporal, variability, and stability measures. An accelerometer was used to collect daily-life gait characteristics during 7 days. Six physical and psychological assessments were administered. Fall events were determined using a “fall calendar” and monthly phone calls over a 6-month period. Participanmts who experienced no falls during the 6-month follow-up were classified as non-fall-prone stroke survivors; the participants who experienced at least one fall were classified as fall-prone stroke survivors. After data reduction through principal component analysis, the predictive capacity of each method was determined by logistic regression. Thirty-eight percent of the participants were classified as fallers. Laboratory-based and daily-life gait characteristics predicted falls acceptably well, with an area under the curve of, 0.73 and 0.72, respectively, while fall predictions from clinical assessments were limited. The results suggest that, Independent of the type of gait assessment, qualitative gait characteristics are better fall predictors than clinical assessments. Clinicians should therefore consider gait analyses as an alternative for identifying fall-prone stroke survivors.
    Descriptor Terms: AMBULATION, EQUILIBRIUM, MEASUREMENTS, OUTCOMES, PHYSICAL EVALUATION, PHYSICAL THERAPY, POSTURE, PREDICTION, PSYCHOLOGICAL EVALUATION, STROKE.


    Can this document be ordered through NARIC's document delivery service*?: Y.
    Get this Document: https://www.medicaljournals.se/jrm/content/abstract/10.2340/16501977-2234.

    Citation: Punt, Michiel , Bruijn, Sjoerd M. , Wittink, Harriet , van de Port, Ingrid G. , van Dieën, Jaap H.. (2017). Do clinical assessments, steady-state or daily-life gait characteristics predict falls in ambulatory chronic stroke survivors?.  Journal of Rehabilitation Medicine (formerly the Scandinavian Journal of Rehabilitation Medicine) , 49(5), Pgs. 402-409. Retrieved 8/20/2019, from REHABDATA database.

    Tuesday, January 8, 2019

    Dynamic balance and instrumented gait variables are independent predictors of falls following stroke

    Fuck, fuck, fuck. More useless predictions rather than coming up with recovery solutions that would prevent such falls. Does no one understand that survivors don't give a shit about fall prediction? They want 100% recovery. GET THERE!

    Dynamic balance and instrumented gait variables are independent predictors of falls following stroke 


    Journal of NeuroEngineering and Rehabilitation201916:3
    • Received: 3 September 2018
    • Accepted: 19 December 2018
    • Published:

    Abstract

    Background

    Falls are common following stroke and are frequently related to deficits in balance and mobility. This study aimed to investigate the predictive strength of gait and balance variables for evaluating post-stroke falls risk over 12 months following rehabilitation discharge.

    Methods

    A prospective cohort study was undertaken in inpatient rehabilitation centres based in Australia and Singapore. A consecutive sample of 81 individuals (mean age 63 years; median 24 days post stroke) were assessed within one week prior to discharge. In addition to comfortable gait speed over six metres (6mWT), a depth-sensing camera (Kinect) was used to obtain fast-paced gait speed, stride length, cadence, step width, step length asymmetry, gait speed variability, and mediolateral and vertical pelvic displacement. Balance variables were the step test, timed up and go (TUG), dual-task TUG, and Wii Balance Board-derived centre of pressure velocity during static standing. Falls data were collected using monthly calendars.

    Results

    Over 12 months, 28% of individuals fell at least once. The faller group had increased TUG time and reduced stride length, gait speed variability, mediolateral and vertical pelvic displacement, and step test scores (P < 0.001–0.048). Significant predictors, when adjusted for country, prior falls and assistance (i.e., physical assistance and/or gait aid use) were stride length, step length asymmetry, mediolateral pelvic displacement, step test and TUG scores (P < 0.040; IQR-odds ratio(OR) = 1.37–7.85). With comfortable gait speed as an additional covariate, to determine the additive benefit over standard clinical assessment, only mediolateral pelvic displacement, TUG and step test scores remained significant (P = 0.001–0.018; IQR-OR = 5.28–10.29).

    Conclusions

    Reduced displacement of the pelvis in the mediolateral direction during walking was the strongest predictor of post-stroke falls compared with other gait variables.(Well, then create a protocol that has correct pelvis placement. ) Dynamic balance measures, such as the TUG and step test, may better predict falls than gait speed or static balance measures.

    Thursday, July 12, 2018

    Wearable Device Can Predict Older Adults' Risk of Falling

    Useless unless you already have gone thru a fall prevention protocol with your therapists. How much have your therapists perturbed your walking and had you recover? If they haven't done that they have done NOTHING to prevent your falling. I'm sure my walking would show an extreme chance of falling, but I'm very good at recovering. I'm now more likely to trip with my good foot rather than my bad foot. Which makes it easier to recover since I can quickly and forcefully get that foot where it needs to go to be safe.

    Wearable Device Can Predict Older Adults' Risk of Falling

    Every year, more than one in three individuals aged 65 and older will experience a fall.

    Falls are the most common cause of injury in older adults, and can create ongoing health problems. But treatment and awareness of falling usually happens after a fall has already occurred.

    As a part of the NIH's Women's Health Initiative, researchers wanted to see if they could predict an individual's risk of falling so that preventative measures could be taken to reduce this risk.

    New analysis has now made this prediction a reality.

    The study involved 67 women, all over the age of 60, who were tested on their walking ability and asked about the number of falls they had experienced in the past year. Participants also wore a small device with motion sensors that measured their walking patterns for one week.

    Bruce Schatz, head of the Department of Medical Information Science in the University of Illinois College of Medicine at Urbana-Champaign and faculty member of the IGB's Computing Genomes for Reproductive Health research theme, was asked to analyze the data from the study. He worked with colleagues from the Women's Health Initiative, including David Buchner from the Department of Kinesiology & Community Health, while supervising Illinois graduate students Andrew Hua and Zachary Quicksall, associated with the University of Illinois College of Medicine.

    They found that data extracted automatically from the devices could accurately predict the participants' risk of falling, as measured by physical examinations of unsteadiness in standing and walking. Their findings were published in Nature Digital Medicine.

    "Our prediction showed that we could very accurately tell the difference between people that were really stable and people that were unstable in some way," Schatz said.

    Studies have shown that older individuals fall differently than younger individuals. Younger people fall if they misjudge something, such as a slippery surface. But older adults fall because their bodies are unstable, causing them to lose balance when walking or become unsteady when standing up and sitting down.

    This difference gave researchers the idea that they might be able to measure this instability. The device they used, called an accelerometer, was able to measure the user's walking patterns and how unsteady they were. They combined this measurement with the individual's fall history to determine the risk of falling in the future.

    Being able to predict the fall risk is significant because many older adults often don't pay attention to the fact that they are unstable until after they fall. But if they know they're at risk, they can do rehabilitation exercises to increase their strength and reduce their chance of falling.

    Schatz sees the successful outcome of this research as a sign that, in the future, more wearable devices, or even smartphone apps, will be able to measure walking patterns and warn users of their fall risk.

    Most cellphones today already have an accelerometer, the same sensor that was used in this study. Schatz envisions a future where everyone over 60 would have a phone app that constantly records their motion, requiring no input from the user. If the user's walking becomes unstable, the app could notify the user or their doctor, and they could begin preventative exercises.

    "I work a lot with primary care physicians, and they love this (idea), because they only see people after they start falling," Schatz said. "At that point, it's already sort of too late."

    This research relates to the larger idea of preventative medicine -- health care that can warn patients about health problems so they can take action and better manage the problem.

    Predictions like these are difficult to make, but research experiments like this one make Schatz hopeful that progress is being made. More federally funded studies monitoring larger populations are being conducted more often, so predictive models developed for existing studies, such as the Women's Health Initiative, are important for future research. Additionally, wearable devices like those used in this study are becoming cheaper and more widely available.

    These developments give Schatz hope that a future with successful predictive medicine is coming.

    "The question is: is it known how to take the signal, how to take whatever comes out of (a device), and predict something that's useful?" he said. "I believe strongly the answer is yes."

    Schatz sees value in doing fundamental research that could solve major health problems, like falls in older adults. Most people are aware that it's a common problem, but Schatz said there is a sense of hopelessness about this issue -- if it happens to so many older adults, then what can be done?

    "There is a solution which is completely workable and isn't very expensive, but requires different behavior," Schatz said. "That message is not getting out."

    He predicts that the quality of life among older adults will improve as medicine and health care become more predictive and effective.

    "The future is different," Schatz said. "And it's because of projects like this."

    Sunday, June 25, 2017

    Factors predicting falls and mobility outcomes in patients with stroke returning home after rehabilitation who are at risk of falling

    It would be much better to come up with fall prevention protocols than this piece of laziness. 
    http://www.archives-pmr.org/article/S0003-9993(17)30408-2/abstract
    DOI:

    Abstract





    Objective

    To identify factors predicting falls and limited mobility in people with stroke at 12 months after returning home from rehabilitation.




    Design

    Observational cohort study with 12 month follow-up.




    Setting

    Community.




    Participants

    People with stroke (n=144) and increased falls risk discharged home from rehabilitation.




    Interventions

    Not applicable.




    Main Outcome Measures

    Falls were measured using monthly calendars completed by participants, and mobility was assessed using gait speed over five metres (high mobility (>0.8m/s) versus low mobility (≤0.8m/s). Both measures were assessed at 12 months post-discharge. Demographics and functional measures including balance, strength, visual or spatial deficits, disability, physical activity level, executive function, functional independence and falls risk were analysed to determine factors significantly predicting falls and mobility levels after 12 months.




    Results

    Those assessed as being at high falls risk (Falls Risk for Older People in the Community (FROP-Com) score ≥19) were 4.5 times more likely to fall by 12 months (OR:4.506, 95% CI:1.71-11.86, p-value:0.002). Factors significantly associated with lower usual gait speed (<0.8m/s) at 12 months in the multivariable analysis were age (OR:1.07, 95% CI=1.01–1.14, p-value=0.033), physical activity (OR:1.09, 95% CI =1.03-1.17, p-value=0.007) and functional mobility (OR:0.83, 95% CI =0.75-0.93, p-value=0.001).




    Conclusion

    Several factors predicted falls and limited mobility for patients with stroke 12 months after rehabilitation discharge. These results suggest that clinicians should include assessment of falls risk (FROP-Com), physical activity, and dual task Timed Up and Go during rehabilitation to identify those most at risk of falling and experiencing limited mobility outcomes at 12 months, and target these areas during in-patient and out-patient rehabilitation to optimise long term outcomes.

    Monday, December 12, 2016

    Brain Activity May Predict Risk of Falls in Older People

    I know this is in healthy people but if we had ANY stroke leadership at all a research project would be set up to see what brain activity in stroke survivors could predict falls. That will never occur until all the current stroke associations are destroyed.
    http://dgnews.docguide.com/brain-activity-may-predict-risk-falls-older-people?overlay=2&nl_ref=newsletter&pk_campaign=newsletter
    MINNEAPOLIS, Minn -- December 7, 2016 -- Measuring the brain activity of healthy, older adults while they walk and talk at the same time may help predict their risk of falls later, according to a study published in the December 7, 2016, online issue of Neurology.
    “In older people who had no signs of disease that would make them prone to falls, higher levels of activity in the prefrontal cortex were associated with a higher risk of falls later in life,” said Joe Verghese, Albert Einstein College of Medicine, Bronx, New York. “This suggests that these people were increasing their activation of brain cells or using different parts of the brain to compensate for subtle changes in brain functioning.”
    For the study, researchers looked at 166 people with an average age of 75 years who had no disabilities, dementia or problems with walking. They then used brain imaging to measure changes in oxygen in the blood in the front of the brain as each person walked, recited alternate letters of the alphabet, and then did both tasks at the same time. The researchers then interviewed participants every 2 to 3 months over the next 4 years to see if they had fallen.
    Over that time, 71 people in the study reported 116 falls; 34 people fell more than once. Most falls were mild with only 5% resulting in fractures.
    The study found higher levels of brain activity while both walking and talking were associated with falls, with each incremental increase of brain activity associated with a 32% increased risk of falls. Such an association was not found when looking at brain activity levels during just walking or talking. The speed of the walking and naming letters did not help predict who was more likely to fall.
    The relationship between brain activity and falls risk was the same after researchers accounted for other factors that could affect a person’s risk of falling, such as slow walking speed, frailty, and previous falls.
    “These findings suggest that there may be changes in brain activity before physical symptoms like unusual gait appear in people who are more prone to falls later,” said Verghese. “More research needs to be done to look at how brain and nerve diseases associated with falls impact brain activity in their earliest phases. We also know there are other areas of the brain which may play a role in increasing fall risk, so those too should be studied.”
    SOURCE: American Academy of Neurology