ICT Solutions to Develop an Effective Motor and Cognitive Training

to Reduce Risk of Falls

The I-DONT-FALL Project

Francesco Barban

, Roberta Annicchiarico

, Alessia Federici

, Ilenia Debora Mazzù

Maria Giovanna Lombardi

, Simone Giuli

, Claudia Ricci

, Fulvia Adriano

, Ivo Griffini

Manuel Silvestri

, Massimo Chiusso

, Sergio Neglia

, Raquel Cuevas Perez

, Yannis Dionyssiotis

Georgios Koumanakos

, Milo Kovačeić

, Nuria Montero

, Oscar Pino

, Carmela Zincarelli

Niels Boye

, Cristian Barrué

, Peter Levene

, Stelios Pantelopoulos

, Roberto Rosso

Angelo Maria Sabatini

and Carlo Caltagirone

1,16

Clinical and Behavioral Neurology Laboratory, IRCCS Fondazione Santa Lucia, Rome, Italy

Engineering Ingegneria Informatica SpA, Rome, Italy

Hospital General de Granollers, Barcelona, Spain

Social Policy Center, Municipality of Kifissia, Greece

Frontida Zois Home Care Agency, Patras, Greece

Municipality of Stari Grad, Belgrade, Serbia

Hospital General Universitario Gregorio Marañón, Madrid, Spain

Benito Menni CASM, Sant Boi de Llobregat-Barcelona, Spain

IRCCS Fondazione Salvatore Maugeri, Telese Terme, Italy

Klinisk Informatik, Aarhus, Denmark

Knowledge Engineering & Machine Learning Group Computer Software Department,

Universitat Politècnica de Catalunya-Barcelona, Barcelona, Spain

Docobo Ltd, Bookham, Surrey, U.K.

Singular Logic, Athens, Greece

Tesan SpA, Vicenza, Italy

The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy

Systems Medicine Department, University of Rome “Tor Vergata”, Rome, Italy

Keywords: ICT, Risk of Falls, Training, RCT.

Abstract: This study shows preliminary results of the multicenter and international I-DONT-FALL (IDF) project, co-

funded by the European Union, aiming to offer an integrated Information and Communication Technologies

(ICT) solution for fall prevention and detection. Here we assessed the efficacy of a motor and a cognitive

treatment delivered through the IDF ICT solution, aiming to reduce the risk of falls through a randomized

controlled trial. The outcome was measured with the Falls Efficacy Scale-International (FES-I) and the

subscales of the Tinetti Performance Oriented Mobility Assessment for balance (POMA-B) and gait

(POMA-G). We compared the effect of a 24-sessions period of motor training delivered through an i-

Walker vs. a comparable period of non-motor training in terms of frequency and duration of sessions. The

same comparison was performed for a period of cognitive training delivered though a touch-screen

computer interface vs. a comparable period of non-cognitive training in terms of frequency and duration of

sessions. Results showed that motor treatment alone or mixed with cognitive training reduces significantly

the fear of falling and the risk of falls. Both cognitive and motor treatments showed a nonspecific positive

effect on balance performance of participants. These preliminary results are consistent with previous

evidences.

259

Barban F., Annicchiarico R., Federici A., Mazzù I., Lombardi M., Giuli S., Ricci C., Adriano F., Grifﬁni I., Silvestri M., Chiusso M., Neglia S., Cuevas

Perez R., Dionyssiotis Y., Koumanakos G., Kova

cei

c M., Montero N., Pino O., Zincarelli C., Boye N., Barrué C., Levene P., Pantelopoulos S., Rosso R.,

Sabatini A. and Caltagirone C..

ICT Solutions to Develop an Effective Motor and Cognitive Training to Reduce Risk of Falls - The I-DONT-FALL Project.

DOI: 10.5220/0005490802590263

In Proceedings of the 1st International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AgeingWell-

2015), pages 259-263

ISBN: 978-989-758-102-1

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

1 INTRODUCTION

It is estimated that about a third of community-

dwelling people over 65 years old fall each year

(Gillespie et al., 2012). Falls can have serious

physical consequences such as fractures and head

injuries (Peel et al., 2002) and psychological

consequences as well, particularly fear of falling and

loss of self-confidence, thus resulting in a restriction

in physical functions and social interactions

(Yardley et al., 2002). Moreover, as reported from

Peel and colleagues (2002), the rate of fall-related

injuries increases with age.

Among the several different definitions of fall, a

consensus definition has been suggested by Lamb

(2005). According to the author, fall should be

defined as ‘an unexpected event in which the

participants come to rest on the ground, floor, or

lower level’. The difficulty to formulate a consensus

definition of fall possibly derives from the

complexity of evaluating the risk factors for falls.

Indeed, risk factors are various and a few

comprehensive syntheses of them have been

provided (Campbell and Robertson, 2006; Deandrea,

2010). Particularly, it seems that only 15% of falls

have a single identifiable cause (e.g., syncopal falls

with cardiac pacing or falls related to neurological

disease; Campbell and Robertson, 2006) and a

similar percentage of falls results from an external

event that would cause falling, especially in younger

and intellectually able people (Campbell et al.,

1989). Interestingly, over 60% of falls result not just

from the additive effects of multiple pathologies but

from multiple interacting aetiological factors

(Fairweather and Campbell, 1991; Campbell and

Robertson, 2006). Research on risk factors for falls

has received an increasing attention as the evaluation

and detection of these are a keypoint to develop

effective intervention programs aimed to prevent

falls (Gillespie et al., 2012). Indeed, over the last 10

years, several attempts using Information and

Communication Technologies (ICT) aimed at falls

prevention and detection (Hawley-Hague et al.,

2014). Some of these studies delivered ICT-based

motor trainings for falls prevention and suggest

positive messages about the benefits.

Several studies investigated the possible link

between cognition and gait (Verlinden et al., 2014)

and its implication in falls (Amboni et al., 2013).

Indeed, gait is a complex motor behavior and

presents many different measurable facets besides

proper motor facets (e.g., velocity), such as an

important relationship to different aspects of

cognition (Holtzer et al., 2006). Particularly, pace

seems to be associated with attention and executive

functions and with general cognitive decline and

incident dementia as well (Verghese et al., 2007),

whereas rhythm seems to be associated to

information processing speed (Verlinden et al.,

2014). As suggested by Shumway-Cook and

Woollacott (2000), indeed, attentional demands for

postural control increase with aging whereas sensory

information decreases. Moreover, the declines in the

ability to allocate attention to postural control under

multi-task conditions might furtherly contribute to

increase the risk of falls.

Therefore, the development of effective

prevention programs should take into account not

only motor factors but also cognitive factors.

Particularly, it is agreeable that training programs

aimed to prevent risk of falls and to reduce number

of falls should be focused also on cognitive domains

such as attentional-executive functions, thus

providing effective results on motor behavior and

particularly in pace and rhythm of gait.

The findings that we report in this paper are

partial results of the I-DONT-FALL project which is

a multicenter and international project co-funded by

the European Union. This project aims to offer an

integrated system for fall management solution, both

in prevention and detection strategies. Moreover, the

project aims to assess the efficacy of a motor and of

a cognitive intervention and their combination to

reduce the risk of falls through an European

multicenter randomized controlled trial (RCT). The

assessment of treatment effects combined standard

scales and ICT assessment tools such as WIMU

(Mannini and Sabatini, 2014) and i-Walker (Cortés

et al., 2008).

The main aim of the present study was to assess

the differential effect of motor training and of a

cognitive training on risk of falls measured with the

Falls Efficacy Scale-International (FES-I) (Yardley

et al., 2005) and the subscales of the Tinetti

Performance Oriented Mobility Assessment for

balance (POMA-B) and gait (POMA-G) (Tinetti,

1986). Therefore, we compared the effect of a 24 –

sessions (twice-a-week) period of motor training vs.

a comparable period of non-motor training in terms

of frequency and duration of sessions. The same

comparison was performed for a period of cognitive

training vs. a comparable period of non-cognitive

training in terms of frequency and duration of

sessions.

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2 METHODS

2.1 Subjects

The results reported in this paper come from the first

subset of 49 participants enrolled in the RCT study

of I-DONT-FALL project that completed the

assessment at T0 (pre-training) and T1 (post-

training). All participants were elderly (mean age 79

years, range 65-96 years), with formal education

(mean years 8.8, range 5-18), with high risk of falls

(POMA total score ≤ 20 and/or at least one previous

fall in the last year – mean score 19, range 10-28;

mean number of previous falls 1.2, range 0-9) and

without or with only a mild cognitive deficit (mean

MMSE 26.3, range 20-30). Moreover, they were free

of major behavioural disturbances and not receiving

any rehabilitative treatment. All participants gave

their written informed consent approved by local

ethics committees.

All participants were randomly enrolled in four

different kinds of training: a motor training, a

cognitive training, a mixed motor and cognitive

training and a placebo activity. The randomization

was double and stratified for pilot site: a first

randomization was done between cognitive

intervention or not. After that, a second

randomization was done between the motor

intervention or not. In this way, those receiving the

cognitive training might receive it mixed with the

motor (i.e., mixed training) or not (i.e., cognitive

training alone), whereas those not receiving the

cognitive treatment might receive the motor (i.e.,

motor training alone) or not (i.e., placebo). This

resulted in the four after mentioned conditions (see

figure 1). This kind of randomization was adopted to

balance the factors that were tested during the

analysis, i.e., cognitive (group A) vs. non-cognitive

(group B) and motor (group C) vs. non-motor (group

D).

2.2 Training and Placebo Activities

Each kind of training (cognitive, motor, mixed) and

placebo activity were executed through 2 sessions

per week for 12 weeks (24 sessions). Each session

lasted 1 hour for a total of 24 hours training. Motor

training was administered with an i-Walker (Cortés

et al., 2008) designed to help and support a user with

some mobility impairment. Specifically, it provides

assistance to compensate unbalanced muscle force

and lack of muscle force on climbs and descendents.

Motor training consisted in a set of warm-up

procedures followed by exercises dedicated for 1/2

of the session to balance and for 1/2 of the session to

gait. Cognitive training sessions consisted of a set of

exercises covering all the cognitive functions and it

was supported by surface computing (touchscreen-

enabled) equipment. Touchscreen computers could

be either large-format screens that could be used on

tables or standard PCs with touchscreen monitors.

Cognitive exercises were dedicated for the 2/3 of the

whole session to executive functions and attention

exercises and for 1/3 to other cognitive functions

(i.e., declarative memory, orientation, language,

constructional praxis, abstract reasoning). Executive

functions training consisted in exercises practicing

abstraction and planning such as sorting cards and

grouping them according with a covered criterion or

setting up a menu according with some rules and

working memory exercises. Attention was trained

with exercises of focused attention with distracters

or with exercises of sustained attention. Difficulty

level of exercises was increased according to

participant’s performance. Mixed training consisted

in the combination of 30 minutes of motor exercises

and 30 minutes of cognitive exercises during the

same training session. Placebo activity consisted in

entering data (i.e., words, names, codes) into a file

on the same computer used during the cognitive

training.

2.3 Outcome Measures

The risk of falls was measured with the Falls

Efficacy Scale-International (FES-I) (Yardley et al.,

2005) and the POMA-B and POMA-G subscales

(Tinetti, 1986).

We performed a total of 6 analyses of variance

ANOVA, resulting from each outcome measure (i.e.,

FES-I, POMA-B, POMA-G) by each kind of

treatment (i.e., motor, cognitive ). We used mixed

ANOVA with time (T0 vs. T1) as within factor, and

the kind of treatment, i.e., motor vs. non-motor and

cognitive vs. non-cognitive, as between factor. More

specifically, cognitive treatment was obtained

collapsing data from cognitive and mixed training

(group A) and non-cognitive training was obtained

collapsing data form motor and placebo treatment

Figure 1: Randomization of participants.

ICTSolutionstoDevelopanEffectiveMotorandCognitiveTrainingtoReduceRiskofFalls-TheI-DONT-FALLProject

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Table 1: Results.

MOTOR/NON MOTOR COGNITIVE/NON COGNITIVE

Scale TIME EFFECT TIME X TREATMENT TIME EFFECT TIME X TREATMENT

Fear of falling scale (FES-I) ns p< 0.012 ns ns

Tinetti Balance (POMA-B) p< 0.043 ns p< 0.047 ns

Tinetti Gait (POMA-G) ns ns ns ns

(Group B). Conversely, motor treatment was

obtained collapsing data from motor and mixed

training (Group C) whereas non-motor was obtained

collapsing data from cognitive and placebo training

(Group D) (Figure 1).

3 RESULTS

3.1 Fear of Falling (FES-I)

We found a significant reduction of the fear of

falling by the motor treatment alone or mixed with

cognitive training (Table 1). This was showed by the

significant interaction between time (T0 vs. T1) and

kind of treatment (motor vs. non-motor) on the FES-

I scores [F(1,47)= 6.772, p< 0.012] (Figure 2). Post-

hoc comparisons with paired t-test showed a

significant effect between T0 and T1 only for the

motor treatment (t(23)= 2.946, p< 0.007) and not for

the non-motor (t(24)= -.921, p< 0.366). The same

interaction between time and cognitive treatment

was not significant [F(1,47)= .751, p< 0.391]. Main

effects of time and group were not significant for

motor and cognitive treatments.

Figure 2: Effect of motor treatment on FES-I mean scores.

3.2 Balance and Gait (POMA-B,

POMA-G)

We found a general nonspecific effect of treatment

on balance. This was showed by a main effect of

time for both motor [F(1,47)= 4.340, p< 0.043] and

cognitive [F(1,47)= 4.158, p< 0.047] treatment on

the POMA-B subscale. Neither significant

interactions nor group effects emerged for both

treatments in both POMA subscales.

4 DISCUSSION

This study aimed at assessing the efficacy in

reducing the risk of falls of an ICT solution

providing a motor and cognitive treatment in a

sample of elderly participants at risk of falls. These

preliminary data showed that motor treatment alone

or mixed with cognitive training reduces

significantly the fear of falling and by consequence

the risk of falls. This was not the case of the

cognitive training focused on attentional-executive

functions when administered alone or mixed with

the motor one. However, both cognitive and motor

treatments showed a nonspecific positive effect on

balance performance of participants. These

preliminary results accord with the previous

published evidence (Huang et al., 2011; Segev-

Jacubovski et al., 2011; van het Reve and de Bruin,

2014) about the effect of the motor training in

combination with behavioral interventions on fear of

falling. To our knowledge, at present this study is

the first attempt to evaluate the reduction of risk of

falls through a cognitive training focused on

attentional-executive functions performed alone or

in association with a motor training. Previous

evidences (Smith-Ray et al., 2013) partially accord

with our results showing a positive effect of

cognitive training in elderly on balance when

compared with a rest period. Our preliminary data

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show that this effect is not specific of the cognitive

training.

5 CONCLUSIONS

Our preliminary results agree with previous

evidences (Huang et al., 2011; Segev-Jacubovski et

al., 2011; van het Reve and de Bruin, 2014) and are

motivating at pursuing with this study enlarging the

sample in order to better investigate the specific role

of the cognitive training alone or mixed with motor

training in the reduction of the risk of falls.

ACKNOWLEDGEMENTS

This study was co-funded by the EC funded project

I-DONT-FALL “Integrated prevention and

Detection sOlutioNs Tailored to the population and

Risk Factors associated with FALLs” (CIP-ICT-

PSP-2011-5-297225).

REFERENCES

Amboni, M., Barone, P., Hausdorff, J.M., 2013.

Cognitive contributions to gait and falls: evidence and

implications. Mov Disord. Sep 15;28(11):1520-33.

Campbell, A.J., Borrie, M.J., Spears, G.F., 1989. Risk

factors for falls in a community-based prospective

study of people 70 years and older. J Gerontol. 44:

M112–7.

Campbell, A.J., Robertson, M.C., 2006 Implementation of

multifactorial interventions for fall and fracture

prevention. Age Ageing. Sep;35 Suppl 2:ii60-ii64.

Cortés, U., Martínez-Velasco, A., Barrué, C., et al., 2008.

A SHARE-it service to elders’ mobility using the i-

Walker. Gerontechnology.7:95.

Deandrea, S., Lucenteforte, E., Bravi, F., Foschi, R., La,

V.C., Negri, E., 2010. Risk factors for falls in

community-dwelling older people: a systematic review

and meta-analysis. Epidemiology. 21(5):658–68.

Fairweather, D.S., Campbell, A.J., 1991. Diagnostic

accuracy. The effects of multiple aetiology and the

degradation of information in old age. J R Coll

Physicians Lond. 25: 105–10.

Gillespie, L.D., Robertson, M.C., Gillespie, W.J., et al.,

2012. Interventions for preventing falls in older people

living in the community. Cochrane Database Syst Rev.

Sep 12;9:CD007146.

Hawley-Hague, H., Boulton, E., Hall, A., Pfeiffer, K.,

Todd, C., 2014. Older adults' perceptions of

technologies aimed at falls prevention, detection or

monitoring: a systematic review. Int J Med Inform.

Jun;83(6):416-26.

Holtzer, R., Verghese, J., Xue, X., Lipton, R.B., 2006.

Cognitive processes related to gait velocity: results

from the Einstein Aging Study. Neuropsychology.

Mar;20(2):215-23.

Huang, T.T., Yang, L.H., Liu, C.Y., 2011. Reducing the

fear of falling among community-dwelling elderly

adults through cognitive behavioural strategies and

intense Tai Chi exercise: a randomized controlled trial.

J Adv Nurs. May;67(5):961-71.

Lamb, S.E., Jorstad-Stein, E.C., Hauer, K., Becker, C.,

Prevention of Falls Network Europe and Outcomes

Consensus Group, 2005. Development of a common

outcome data set for fall injury prevention trials: the

Prevention of Falls Network Europe consensus. J Am

Geriatr Soc. 53(9):1618–22.

Mannini, A., Sabatini, A.M., 2014. Walking speed

estimation using foot-mounted inertial sensors:

comparing machine learning and strap-down

integration methods. Med Eng Phys. 36(10):1312-21.

Peel, N.M., Kassulke, D.J., McClure, R.J., 2002.

Population based study of hospitalised fall related

injuries in older people. Inj Prev. 8(4):280–3.

Segev-Jacubovski, O., Herman, T., Yogev-Seligmann, G.,

Mirelman, A., Giladi, N., Hausdorff, J.M., 2011. The

interplay between gait, falls and cognition: can

cognitive therapy reduce fall risk? Expert Rev

Neurother. 11(7):1057-1075.

Shumway-Cook, A., Woollacott, M., 2000. Attentional

demands and postural control: the effect of sensory

context. J Gerontol A Biol Sci Med SciJan;55(1):M10-6.

Smith-Ray, R.L., Hughes, S.L., Prohaska, T.R., Little,

D.M., Jurivich, D.A., Hedeker, D., 2013. Impact of

Cognitive Training on Balance and Gait in Older

Adults. J Gerontol B Psychol Sci Soc Sci. Nov 5.

Tinetti, M.E., 1986. Performance-oriented assessment of

mobility problems in elderly patients. J Am Geriatr

Soc. Feb;34(2):119-26.

van het Reve, E., de Bruin, E.D., 2014. Strength-balance

supplemented with computerized cognitive training to

improve dual task gait and divided attention in older

adults: a multicenter randomized-controlled trial. BMC

GeriatrDec 15;14:134.

Verghese, J., Wang, C., Lipton, R.B., Holtzer, R., Xue, X.,

2007. Quantitative gait dysfunction and risk of

cognitive decline and dementia. J Neurol Neurosurg

Psychiatry. Sep;78(9):929-35.

Verlinden, V.J., van der Geest, J.N., Hofman, A., Ikram,

M.A., 2014. Cognition and gait show a distinct pattern

of association in the general population. Alzheimers

Dement. May;10(3):328-35.

Yardley, L., Beyer, N., Hauer, K., Kempen, G., Piot-

Ziegler, C., Todd, C., 2005. Development and initial

validation of the Falls Efficacy Scale-International

(FES-I). Age Ageing. Nov;34(6):614-9.

Yardley, L., Smith, H., 2002. A prospective study of the

relationship between feared consequences of falling

and avoidance of activity in community-living older

people. Gerontologist. 42(1):17–23.

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