debate: ict can never replace an exercise...

DEBATE: ICT can never

replace an exercise instructor

Professor Dawn Skelton

Arguments

• Exercise Interventions to reduce falls (robust and massive evidence base over the last 15-20 years) WITHOUT ICT!

• Interventions to reduce Fear of Falling require people talking and NO EVIDENCE FOR ICT!

• ICT still not completely accepted by both therapists/instructors and older people (though this will change eventually!)

Interventions in the community

• Update of 2009 review • 159 trials with 79,193

participants • most common

interventions tested – exercise as a single

intervention (59 trials) – Multi-factorial programmes

(40 trials)

Conclusions: • Group and home-based exercise

programmes, and home safety interventions delivered by an occupational therapist reduce rate of falls and risk of falling.

• Multi-factorial assessment and intervention programmes reduce rate of falls but not risk of falling;

• Tai Chi reduces risk of falling. • Insufficient evidence that interventions

designed to prevent falls will also prevent hip or other fall-associated fractures.

Gillespie et al. Interventions for preventing falls in older people living in the community. Cochrane Library 2012

Support and Encouragement A programme is more than a series of exercises

• Examples from successful falls and exercise programmes • A range of strategies that support participants eg.

– Goal setting and self monitoring – Overcoming obstacles and difficulties – Educating the participant – Highlighting successes – Providing individual and group support

Can ICT provide all this? Possibly, but a PERSON DEFINITELY CAN!

• Interview data reveal that relationships with professionals, families and friends (the existence of social networks) during and after the rehabilitation process can impact on uptake and continuation of exercise

• Development of a relationship with the professional led to a sense of responsibility to stick with the programme

Health Education Journal 2009

Can ICT address this? Probably NO….. BUT A PERSON DEFINITELY CAN

Fear of Falling

• Fear and lack of confidence in balance predict – Deterioration in physical functioning

(Arfken 1994, Vellas 1997)

– Decreases in physical activity, indoor and outdoor (Arfken 1994, Finch 1997)

– Increase in fractures (Arfken 1994)

– Admission to Institutional Care (Cumming 2000, Vellas 1997)

• Avoid activity and may be non compliant or require additional support..


No evidence for ICT in FoF Evidence suggests that Fear of Falling is reduced through two mechanisms: • via group exercise delivered by an instructor

(Kendrick et al. Cochrane Review, 2014) and • through cognitive behavioural modification

with counseling (Zijlstra et al. JAGS. 2007) (ie. It needs a person!)


Interventions in nursing care and hospitals

• 41 trials with 25,422 participants • Nursing care facilities

– 7 trials testing supervised exercise interventions were inconsistent.

Cameron et al. Interventions for preventing falls in older people in nursing care facilities and hospitals. Cochrane Library 2010

?? Frailty / multiple co-morbidity of residents requires clinical judgement / adaptation – not ICT!

• In hospitals – Supervised exercise

interventions showed a significant reduction in risk of falling (RR 0.44; 3 trials).

Cameron et al. Interventions for preventing falls in older people in nursing care facilities and hospitals. Cochrane Library 2010

Interventions in nursing care and hospitals

?? ICT would not work in these settings – only face to face contact with a therapist

Maybe ICT can help identify risk…

? Better than a person

Are risk factor tools (delivered through ICT or not)

better than clinical judgment??

Major risk factors

All fallers = fell at least once during follow up Recurrent fallers = fell at least twice during follow up

All fallers (Odds Ratio)

Recurrent Fallers (Odds Ratio)

History of Falls 2.8 3.5

Gait Problems 2.1 2.2

Walking Aids Use 2.2 3.1

Vertigo 1.8 2.3

Parkinson’s Disease 2.7 2.8

Antiepileptic Drug Use 1.9 2.7

Physical Disability 1.6 2.4

Disability in Instrumental Activities in Daily Life 1.5 2.0

Fear of Falling 1.6 2.5

Deandrea S et al. Epidemiology. 2010;21: 658-668.

Clinical judgment had a greater accuracy at predicting falls that two validated falls risk tools (Downton and Stratify) A trained and experienced person can differentiate risk better than a set of questions or tests People don’t always follow the rules, they may be at low risk as assessed by a set of questions but exhibit risky behaviour or lack of insight into movement patterns? Who/what can assess this risk?

Potentially ICT can help tailor interventions based on risk profile …

but

• BUT little proof any of it works well at the moment (only emerging evidence on falls risk, not falls .... And...)

• Most ICT evidence is in those at lowest risk (independent infrequent fallers) NOT those who are currently being admitted to hospital and suffering severe injuries

• Can ICT differentiate cognition and body awareness changes day to day?

• Can ICT provide safe, tailored interventions (exercise) to support the wide range of health and abilities of older people?


Wide range of abilities and needs

ICT to improve uptake and adherence to strength and balance exercise

• Allow people to exercise at home (preference or rurality) • Ability to offer progression of intensity/balance challenge • Ability to offer different options based on ability • Ability to maintain adherence to exercise • Ability to report back

– To Health Professionals – To Older People

• Ability to show improved outcomes – Adherence; Risk Factors such as strength, TUG, balance, falls?

• Many examples, including iStoppFalls, MIRA and other Exergames

✔ ?

✔ ✔ ?

✔

✔ ? ✔ ?

✔ ?

Overall (I-squared = 61.5%, p = 0.000)

Ebrahim, 1997

Barnett, 2003

Woo, Tai Chi, 2007

Luukinen, 2007

Campbell, 2005

Schoenfelder, 2000

Sihvonen, 2004

Lord, 2003

Buchner, 1997

Author,

Nowalk, Tai Chi, 2001

Mulrow, 1994

Day, 2002

Reinsch, 1992

Skelton, 2005

Wolf, Balance, 1996

Woo, Resistance, 2007

Wolf, Tai Chi, 1996

year

McMurdo, 1997

Korpelainen, 2006

Morgan, 2004

Campbell, 1999

Hauer, 2001

Voukelatos, 2007

Faber, Functional walking, 2006

Li, 2005

Lord, 1995

Schnelle, 2003

Steinberg, 2000

Faber, Tai Chi, 2006

Liu-Ambrose, Resistance, 2004

Lin, 2007

Bunout, 2005

Liu-Ambrose, Agility, 2004

Resnick, 2002

Latham, 2003

Madureira, 2007

Carter, 2002

Green, 2002

Toulotte, 2003

Wolf, 2003

Cerny, 1998

Sakamoto, 2006Rubenstein, 2000

Means, 2005

Protas, 2006

Suzuki, 2004

Campbell, 1997

Nowalk, Resist./Endurance, 2001

Robertson, 2001

0.83 (0.75, 0.91)

1.29 (0.90, 1.83)

0.60 (0.36, 0.99)

0.49 (0.24, 0.99)

0.93 (0.80, 1.09)

1.15 (0.82, 1.61)

3.06 (1.61, 5.82)

0.38 (0.17, 0.87)

0.78 (0.62, 0.99)

0.61 (0.40, 0.94)

Effect

0.77 (0.46, 1.28)

1.26 (0.90, 1.76)

0.82 (0.70, 0.97)

1.24 (0.77, 1.98)

0.69 (0.50, 0.96)

0.98 (0.71, 1.34)

0.78 (0.41, 1.48)

0.51 (0.36, 0.72)

size (95% CI)

0.53 (0.28, 0.98)

0.79 (0.59, 1.05)

1.05 (0.66, 1.68)

0.87 (0.36, 2.10)

0.75 (0.46, 1.25)

0.67 (0.46, 0.97)

1.32 (1.03, 1.69)

0.45 (0.33, 0.62)

0.85 (0.57, 1.27)

0.62 (0.38, 1.00)

0.90 (0.79, 1.03)

0.96 (0.76, 1.22)

1.80 (0.67, 4.85)

0.67 (0.32, 1.41)

1.22 (0.70, 2.14)

1.03 (0.36, 2.98)

0.71 (0.04, 11.58)

1.08 (0.87, 1.35)

0.48 (0.25, 0.93)

0.88 (0.32, 2.41)

1.34 (0.87, 2.07)

0.08 (0.00, 1.37)

0.75 (0.52, 1.08)

0.87 (0.17, 4.29)

0.82 (0.64, 1.04)0.90 (0.42, 1.91)

0.41 (0.21, 0.77)

0.62 (0.26, 1.48)

0.35 (0.14, 0.90)

0.68 (0.52, 0.89)

0.96 (0.63, 1.46)

0.54 (0.32, 0.91)

100.00

2.64

1.88

1.22

3.85

2.74

1.40

0.98

3.38

2.21

%

1.88

2.75

3.80

2.04

2.81

2.86

1.41

2.67

Weight

1.48

3.05

2.04

0.88

1.89

2.56

3.31

2.87

2.38

1.98

3.97

3.34

0.72

1.13

1.67

0.65

0.11

3.46

1.34

0.70

2.21

0.10

2.58

0.31

3.341.11

1.40

0.88

0.80

3.13

2.27

1.84

0.83 (0.75, 0.91)

1.29 (0.90, 1.83)

0.60 (0.36, 0.99)

0.49 (0.24, 0.99)

0.93 (0.80, 1.09)

1.15 (0.82, 1.61)

3.06 (1.61, 5.82)

0.38 (0.17, 0.87)

0.78 (0.62, 0.99)

0.61 (0.40, 0.94)

Effect

0.77 (0.46, 1.28)

1.26 (0.90, 1.76)

0.82 (0.70, 0.97)

1.24 (0.77, 1.98)

0.69 (0.50, 0.96)

0.98 (0.71, 1.34)

0.78 (0.41, 1.48)

0.51 (0.36, 0.72)

size (95% CI)

0.53 (0.28, 0.98)

0.79 (0.59, 1.05)

1.05 (0.66, 1.68)

0.87 (0.36, 2.10)

0.75 (0.46, 1.25)

0.67 (0.46, 0.97)

1.32 (1.03, 1.69)

0.45 (0.33, 0.62)

0.85 (0.57, 1.27)

0.62 (0.38, 1.00)

0.90 (0.79, 1.03)

0.96 (0.76, 1.22)

1.80 (0.67, 4.85)

0.67 (0.32, 1.41)

1.22 (0.70, 2.14)

1.03 (0.36, 2.98)

0.71 (0.04, 11.58)

1.08 (0.87, 1.35)

0.48 (0.25, 0.93)

0.88 (0.32, 2.41)

1.34 (0.87, 2.07)

0.08 (0.00, 1.37)

0.75 (0.52, 1.08)

0.87 (0.17, 4.29)

0.82 (0.64, 1.04)0.90 (0.42, 1.91)

0.41 (0.21, 0.77)

0.62 (0.26, 1.48)

0.35 (0.14, 0.90)

0.68 (0.52, 0.89)

0.96 (0.63, 1.46)

0.54 (0.32, 0.91)

100.00

2.64

1.88

1.22

3.85

2.74

1.40

0.98

3.38

2.21

%

1.88

2.75

3.80

2.04

2.81

2.86

1.41

2.67

Weight

1.48

3.05

2.04

0.88

1.89

2.56

3.31

2.87

2.38

1.98

3.97

3.34

0.72

1.13

1.67

0.65

0.11

3.46

1.34

0.70

2.21

0.10

2.58

0.31

3.341.11

1.40

0.88

0.80

3.13

2.27

1.84

Favours exercise Favours control

1.25 .5 1 2 4

RR = 0.83 95%CI 0.75-0.91 P<0.001

17% reduction in falls

Sherrington et al., JAGS 2008 and NSWPHB 2011

All study interventions

delivered by PEOPLE

What makes the difference? • Greatest effects of exercise on fall rates from

interventions including:

– Highly challenging balance training

– High dose (50+ hours)

– No walking program

Sherrington et al., JAGS 2008, NSWPHB 2011

Exercise instructors can ensure adherence

to these principles over time by building

relationships and constant clinical

judgement assessments on what is best

for that individual

ICT delivered home exercise: • Do people challenge themselves or take the

easy option? • Do people understand the risks and could we

see an increased risk (cognitive ability) • Can people self identify safe progression and

adaptation?

Can ICT offer this?

• A trained exercise instructor is able to: – Adapt exercise for an individual (not based on age or

risk factors) – Tailor to how the person is THAT DAY – Offer support if someone is fearful to let go of their

support – Risk assess the safety of the person within their

environment (home or group) – Notice changes in level of cognition / dehydration /

changing function with new medications / presence of a urinary tract infection

– Etc…

Get rid of the personal touch?

• Robot teaches exercise to older people https://www.youtube.com/watch?v=TStB3HH4pZk

https://www.youtube.com/watch?v=TStB3HH4pZk


Improved outcomes

• Retirement village residents – good vision and cognition

• No adverse events • Good compliance • Improved outcomes in choice step reaction time,

falls risk (PPA), dual task ability (TUG) • But… FALLS not assessed In the past many exercise studies have shown reduced falls risk but not a reduction in falls

• Reduced falls risk (PPA) • Better outcomes in adherers vs non-adherers • DID NOT assess actual falls • And participants:

– Able to walk 20m without walking aid – Good vision and cognition

What might ICT solutions be missing? • Safe Progression • Adaptation to ability • Preferences/Choice • Ability to check

– Good technique / movement quality

• Ability to Feedback – To Health Professional

• Ability to Interact – With a Health Professional – With their peers

Would ICT be able to determine if cognition was declining and whether the participant would be ‘at risk’ to exercise unsupervised?

• LLT Inspire Video – https://player.vimeo.com/video/119397743

https://player.vimeo.com/video/119397743

Transitioning onto other exercise opportunities

• Important – to encourage an active lifestyle beyond

rehabilitation – to ensure a change in exercise habits and continue

to improve social involvement – to ensure the opportunities continue to improve

strength and balance and offer different opportunities dependent on needs, preferences and abilities

Can ICT provide all this? NO…..

Counter argument - Why the therapist using ICT will replace the one who

doesn’t… Easily managed to produce the robust and

massive evidence base for exercise to reduce falls over the last 15-20 years WITHOUT ICT!

There will ALWAYS be people who avoid / are cynical about ICT and so we NEED instructors prepared/able to work without!

Conclusions • Huge Potential of ICT but… • The personal touch and expertise of an exercise

instructor has KNOWN positive outcomes to: – Falls and Fear of falling reduction – Improved outcomes – self efficacy, locus of control, social

engagement, reuse of public transport…. • Need long term RCTs to look at:

– safety and cost effectiveness of ICT to deliver exercise to the full range of function, health and ability

– Comparisons of traditional delivered exercise with addition of ICT to support instructor/participants

– with outcomes of falls prevented (not risk factor reduction!)

Questions

[email protected]

Videos

• Robot teaches exercise to older people – go to at least 35 secs in to see exercise instructor showing those who cannot see the robot! – https://www.youtube.com/watch?v=TStB3HH4pZ

k

• LLT Inspire Video – just over 1 min – https://player.vimeo.com/video/119397743



https://player.vimeo.com/video/119397743

ICT to improve risk assessment and offer advice?

• Address multifactorial nature of falls • Ability to provide advice based on self report OR objective

risk factors • Ability to tailor information based on responses

– To health professionals – To older people

• Ability to allow a health professional without distinct expertise to offer advice

• Ability to provide tailored advice in the form of video clips, leaflets, onward referrals

• Many examples, including iStoppFalls

Is there a history of any fall in the previous year? How assessed? Ask the person.

Is the patient / client on four or more medications per day? How assessed? Identify number of prescribed medications.

Does the patient / client have a diagnosis of stroke or Parkinson's Disease? How assessed? Ask the person.

Does the patient / client report any problems with their balance? How assessed? Ask the person.

Is the patient/client unable to rise from a chair of knee height? How assessed? Ask the person to stand up from a chair of knee height without using their arms.

FRAT – Falls Risk Assessment Tool Multi - professional guidance for use by the primary health care team,

hospital staff, and social care workers

Score >3 = high risk of falls

Nandy et al, J Publ Health. 2004

Case Study – High risk or low risk?

• Mr Moss – Age 65 – Married and lives in a flat on 2nd floor (with no lift!) – Retired builder, does voluntary work twice a week in a

charity shop and has an allotment – On 5 meds (to keep blood thin, cholesterol and bp down) – One fall 2 years ago whilst rambling, hairline fracture in

patella – Severe knee pain (from old injury) when squatting or

getting up from chair, not painful on walking – Moderately deaf – Normal weight

QIS Case Finding Workshop 9/9/09






FRAT – Falls Risk Assessment Tool Multi - professional guidance for use by the primary health care

team, hospital staff, and social care workers

So, Mr Moss would be considered a HIGH RISK faller………. But is he more important to identify early, rather than Mrs Ferguson……..

Case Study – High risk or low risk?

• Mrs Ferguson – Age 93 – Lives alone and has home help – Cannot manage the garden or house cleaning anymore – Walks with a stick and only with company, concerned

about falling – On 3 meds (for high bp and osteoporosis) – previous falls - minor injuries but cannot get up from floor

unaided – Poor vision, wears bifocals – Big toe amputation on right foot – Urinary urge and frequency problems – Low weight

QIS Case Finding Workshop 9/9/09






FRAT – Falls Risk Assessment Tool Multi - professional guidance for use by the primary health care

team, hospital staff, and social care workers

So, Mrs Ferguson would be considered a LOW RISK faller !!!! Yet, at high risk of fracture, hospital admission and would benefit from further assessment and intervention

Exploring and designing tools to enhance falls rehabilitation in the home

envisage

Set up

Laboratory studies Home studies

Intended environment

Limited space

Controlled environment

Ample space

Exergames

User chooses character

Speed of movement controlled by game

Objects collected have different scores

- Score improves as technique improves - Feedback to participant if too fast/slow or not an appropriate range of movement

Uzor, Skelton, Baillie. Trials 2013; Uzor, Baillie, Skelton, Fairlie. In Proc. INTERACT 2011; Uzor, Baillie, Skelton. In Proc. CHI 2012.

What do the experts want from home

rehabilitation games?

Participatory design with

older adults at risk of falling

Sensor task

Average time

- 30 seconds first attempt

- 20 seconds on next

Easy to use

- Placement

- Switching on and off

Acceptable

- Happy to use given the

benefits

User error

- Instructions were not

always observed

Exergame showed best adherence to exercises over a 12 week period and with guaranteed good movement quality

Uzor, Skelton, Baillie. Trials 2013

“I only hope I’ll be able to stop playing because

it is fun”

“To get a better score I had to move better – I had

a sense of achievement”

“This could give me the

confidence and discipline that I

need”

BUT…

• Falls not assessed as an outcome • No idea if this would work over a longer

period of time • No feedback to therapist • No feedback to participant, except better

score • No progression of balance challenge

• Group-based exercise is effective for falls prevention, quality-of-life enhancement, and balance improvements in the older adults, comparable with traditional home exercise programs.

• Group-based exercise promotes greater patient satisfaction and exercise adherence.

It is important to consider patient satisfaction for adherence!! Will ICT provide this?

debate: ict can never replace an exercise...

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