Lynn McInnes - New Dynamics of Ageing

THE NEW DYNAMICS OF AGEINGPROGRAMME –‘EARLY FINDINGS‘NDA Programme Meeting November 2009

Mapping a mobility profile of the'oldest-old' survivors of alongitudinal study of ageingDr Lynn McInnes (Northumbria University)Professor Pam Briggs (Northumbria University)Dr Linda Little (Northumbria University)Professor Lynn Rochester (Newcastle University)

Ageing• Ageing associated with:– Sensory changes– Physical changes– Cognitive changes• Impact on mobility• NDA grant examining mobility in an oldergroup of people

Effects of poor mobility• Restricted social lives/social interaction• Limited access to leisure and other activities• Limited access to good nutrition– Adults with restricted mobility found to be consuminginsufficient amounts of fruit and vegetables (Whylie,Copeman, & Kirk, 1999).

Effects of poor mobility• Loss of independenceOlder people have a desire to stay independent butreport that mobility interferes with this wish(Sidonvall, Nydahl, & Fjellstorm, 2001)• Loss of bone density• Physical health problemsIncreased risk of weight loss found inparticipants with a limitation in mobility(Bannerman et al, 2002)

Predictors of mobility?• Understanding predictors as well asconsequences of mobility important toguide interventions to reduce mobilityproblems associated with ageing.

Aim of study• To utilise innovative methods for mapping themobility of the oldest-old members of an existing25 year old longitudinal study of ageing.– Activity monitoring methods and state-of-the-artlocation aware technologies to develop new metrics.– These will then be used to describe the relationshipbetween mobility and physical and mental well-being– To identify predictors and consequences of mobility

North East Age Research• Longitudinal dataset (25 years)• Prof Pat Rabbitt• 3384 volunteers recruited / 300 survivors• Periodically assessed• Extensive information on– Cognitive ability– Health– SES– Activities– Lifestyle (alcohol & tobacco use etc)

4 Objectives of Study1. To establish a sophisticated mobilityprofile of the oldest old.

New technology for assessing mobility

New technology for assessing mobility• Accelerometry (activity monitors)small monitors strapped to thigh musclemap activityregister stepping movements of legsregister changes in body positionused to assess activity (Busse et al. 2004)but do not give context of activity

New technology for assessing mobility• Location based GPS (i-locate device)devices worn or carriedrelay location of wearernot used to track but to monitor and trace (i.e.plotting an individual’s geographicalmovements over both space and time)log the extent and variability of activity across daysand week.Gives some indication of context (e.g. location, speed)Concerns: accuracy, usability, privacy

Assessing mobility• Time use diariesSelf-ratedGives contextAccuracy?

All 3 measures together– Using location based technologies (i-locate,Trackaphone) to provide an objectivemeasure of activity but ‘at home’ or ‘in shop’– Using Accelerometer data (ActivPAL) toobjectively quantify intensity and type ofactivity e.g. ‘housework at home’, ‘sitting inshopping centre’– Using time use diaries to provide self-reportsof activities and context

2. To establish an accurate picture ofcurrent health and lifestyle of theoldest-old.Interviews, tests and questionnaires give current information on:• daily activities,• health• social engagement,• cognitive and sensory acuity• physiological indices of balance and gait (using gait analysis).• nutrition and diet• depression, anxiety, sleep• fear of fallingTaken together these measures will establish a reliablehealth and lifestyle profile of the current ‘oldest old’cohort.

3. Combine current data withlongitudinal data to model predictorsand consequences of mobility in theoldest old.Map mobility against indices of successfulageing.

Recruitment• 308 contact letters out.• 213 responses• 146 wished to take part• 100 invited to take part• 86 completed tests

Methodology• Initially participants completedquestionnaires.– Personal details update– Pittsburgh sleep quality index (PSQI)– Impact of new technologies (specificallylocation based services) on privacy.– Activity diary for 7 consecutive days

Gait assessed over a 10 meter walk using GaitRiteelectronic walking system.Single and dual tasks (auditory stroop).

Other measures• Timed get up and go• Height and weight

i-locate and ActivPAL• Following return of activity diaryparticipants visited in home to deliver andprovide instructions on how to wear theaccelerometer and the i-locate device.• Wear both for 7 consecutive days.

i-locate device

Second lab visit – cognitive testing• Cognitive status– CANTAB (the Cambridge neuropsychological automated testingbattery).– The Mini Mental State Exam (MMSE).• Fluid intelligence– Culture Fair test (Cattell, 1949)• Crystallised intelligence– The National Adult Reading Test (NART) (Nelson, 1982)– Mill Hill Vocabulary test (Raven, 1960)

Other Assessments• Psychological:• Health:Beck Depression InventoryState Trait Anxiety Scale– Cornell Medical Index to assess health.• CHAMPS: activities in older populations• FES-I: fear of falling• Life Events: stress• Privacy questionnaire

Profile of participants• Data for 86 ppts.

Profile of participants: SEX• 73% female, 27% male• Typical of panel 73% female, 27% male

Profile of participants: AGE• Range 72 to 92 years• Mean: 79.73 years• SD: 4.99

Profile of participants: SESSES1 = Prof. occupationsDoctors, dentists, lawyers2 = Intermediate prof.occsSchool teachers, managers3N= Skilled e.g. office workers3M = Manual skilled e.g.mechanics4 = Partly skilled e.g. drivers5 = Unskilled, cleaners

Profile of participants: SESOriginal panelNDA ppts1 2% 9%2 27% 41%3N 38% 38%3M 12% 9%4 10% 3%5 0.05% 0%DK 11% 0%

Profile of participants: fluid intelligenceAH4 verbal IQ Max score 65Mean score when joined panel= 39.75 sd = 9.95Low ability: 0-20Medium ability: 21-40High ability: 41+% scores at panel recruitment NDA pptsLow 19% 6%Med 61% 46%High 20% 49%

Profile of participants: fluid intelligenceCulture Fair IQ Max score = 46Mean score when joined panel=28.75 sd = 6.08Low ability: 0-15Medium ability: 16-30High ability: 31-46Mean score currently= 25.36 sd = 6.03

Profile of 86 participants:changes over timeCulture Fair gf1 st test n=91Gf2 nd test n=873 rd test n=46Current n = 86292827262524231st test2ndtest3rd test C urrentGf

Profile of participants: gcMill Hill vocabulary test IQMax score = 33Mean score when joined panel = 23.82sd = 4.17Low ability: 0-11Medium ability: 12-22High ability: 23-33Mean score currently = 23.58s.d = 3.88

Profile of 86 participants:changes over timeMHA vocabulary1 st test n=912 nd test n=913 rd test n=664 th test n=43

MMSEMMSENART (IQ)Max score = 30Mean = 27.8 Mean = 119.99sd = 2.14 sd = 4.95

Profile of 86 participants: depressionAt entry to panelCurrentMean = 5.29 Mean = 7.01sd = 3.71 sd = 4.83Max = 6310-16 = mild dep.16+ more severe

Profile of participants : anxietyState anxietyTrait anxietyMax score = 80 Max score =80Mean =29.20 Mean = 31.43sd = 8.77 sd = 8.13

Profile of participants: healthNumber of illnessesMean = 1.97Sd = 1.73Range = 0-6Number of medicationsMean = 3.93Sd = 3.02

Profile of participants: healthCVDcancerdiabetesstrokearthritishbpbreathingnervescirculatoryno illness

What has been done so far . . .

Aim: to provide a profile ofmobility in the oldest-old• Each device provides numerous measuresof mobility• Need to ‘condense’ to produce profile ofmobility• Link i-locate, activpal and diary todemonstrate mobility – note problems

Aim: to provide a profile ofmobility in the oldest-old• Calculating the following measures perparticipant

i-locate measures• Total distance travelledPer weekTotal distance travelled per day (for each day)Mean distance travelled per week = day• Maximum distance travelled away from homePer weekMaximum distance travelled away from the home per day (foreach day)Mean distance travelled away from the home per week = perday• Total number of journeys outside of homePer weekTotal number of journeys outside home per day (for each day)Mean number of journeys outside home per week = per day• Total distance per journey• Maximum distance per journey• Mean distance per journey

i-locate (n = 27)FURTHESTTRAVELLEDAWAY FROMHOMETOTALJOURNEYDISTANCEAVERAGEDISTANCEPER DAYTOTALNUMBER OFJOURNEYSPER WEEKOn average, furthestdistance travelledaway from home.On average, the totalnumber of km /milestravelled per week isOn average, peopletravel . . .per dayOn average, thenumber of journeysper week isMean =6.79 km4.22 milesMean =36.64 km22.77 milesMean=5.23 km3.25 milesMean =5.63sd = 8.27 sd = 35.62 sd = 6.29 sd = 2.96

Extra measures to collect in connection with diariesDiaries can be used to provide info on types of places visited. This howeverinvolves manual data extraction.• Number of journeys made on foot per week• Number of journeys in transport per week• Number of visits to:FamilyFriendsSocial placesMedical placesShopping• Timing of journeysNumber of journeys starting from home from 7 am to 12.59 pmNumber of journeys starting from home from 1 pm to 6.59 pmNumber of journeys starting from home after from 7pm to 10.59 pmNumber of journeys starting from home after 11 pm

Activity and energy expenditure

Activity and energy expenditure (n = 78)SUMMARY MEASURES (Mean per day based on a 6am to 12 pm (18 hours) day• Mean time spent seated/lying: 12.66 hours (70%)• Mean time spent standing: 3.88 hours (22%)• Mean time spent walking: 1.22 hours (7%)• Mean number of steps taken: 5755.35• Mean metabolic expenditure: 25.03 MET.h

Time use activity diaries.SUMMARY MEASURES FOR EACH PPT.Summary data of time spent on:• static indoor activities• dynamic indoor activities• static outdoor activities• dynamic outdoor activities• Summary data of time spent on each of 30activities• Per day, 7 day total and mean

Time use diaries (6 am to 10 pm, 16 hrs)static indooractivitiesTime 1 Time 28.7 hours 8.0 hoursdynamic indooractivities3.3 hours 3.3 hoursstatic outdooractivities1.2 hours 1.2 hoursdynamic outdooractivities2.0 hours 2.1 hours

Aim: to provide a profile ofmobility in the oldest-old• Need to ‘condense’ to produce profile ofmobility• Factor analyses on mobility measures

Aim: to provide a profile ofmobility in the oldest-old• Linking the measures• Noting problems

i-locate and time use diariesExample 1• 74 year old lady• 10.29am she left her house by car to visit friends 2.38miles away.• Picked up her friend and continued a further mile in thecar, where she continued on foot doing shopping andthen having lunch.• At 1.06pm she travelled the 3.36 miles back home whereshe spent the afternoon reading.• After cooking the evening meal participant and husbandthen spent the evening entertaining friends.• Distance travelled that day = 5.93 miles.

i-locate dataTotal distanceMean distanceTotal no ofMean number oftravelled in 7 Daystravelled per dayjourneys outsidejourneys outside(miles)(miles)of home over 7home per daydays24.93 3.56 18 2.57

i-locate and time use diariesExample 2• 81 year old man• The participant left the house at approximately 10.45 amand travelled 2.5 miles by car from home to do someshopping and carry out some appointments.• Then travelled a further 0.5 miles and visited friends forlunch between 12 and 1pm.• At approximately 1.10pm he then travelled homearriving at 1.21pm where he remained for the rest of theday doing odd jobs around the house followed by anevening of reading and watching TV.• During this day he travelled a total journey distance of6.28 miles.

i-locate dataTotal distancetravelled in 7 Days(miles)Mean distancetravelled per day(miles)Total no ofjourneys outsideof home over 7Mean number ofjourneys outsidehome per daydays15.58 2.23 5 0.71

i-locate and time use diariesExample 3• 91 year old lady• Left her house at 11:25 am for shopping• At 11:47 am the participant was backhome where she spent the rest of the daydoing house work, reading and watchingTV.

i-locate dataTotal distancetravelled in 7 Days(miles)Mean distancetravelled per day(miles)Total no ofjourneys outsideof home over 7Mean number ofjourneys outsidehome per daydays0.28 0.037 1 0.13

2. To establish an accurate picture ofcurrent health and lifestyle of theoldest-old.Interviews, tests and questionnaires give current information on:• daily activities,• health• social engagement,• cognitive and sensory acuity• physiological indices of balance and gait (using gait analysis).• nutrition and diet• depression, anxiety, sleep• fear of fallingTaken together these measures will establish a reliable health andlifestyle profile of the current ‘oldest old’ cohort.

3. Combine current data withlongitudinal data to model predictorsand consequences of mobility in theoldest old.Map mobility against indices of successfulageing.

Longitudinal data• demographic information,• health & pathology• cognitive ability• functional ability• physical and intellectual activities• social involvement / social networks• alcohol and tobacco consumption• depression• personality

ModellingPredictorsLongitudinalmeasuresPredictorsCurrentmeasurese.g.AgeSexBalanceGaitSocialnetworkDepressFear offallingHealthCognitionMobilityConsequencesCurrent measuresSocial networkDepressionFear of fallingAnxietySleepHealthCognitionDiet

4. Assess utility and acceptability of devicesas healthcare interventions.Identification of individuals at risk• To understand the utility and acceptability of bothaccelerometry and location-based tracking technologiesfor the health monitoring of older adults.• To assess the trade-off between privacy concerns andhealth benefits.• To discuss viability of using location-based data totrigger health interventions.

Privacy re. location based services• People generally unaware of benefits of LBS• Research tends to focus on use by young people• Very little research with regard to the olderpopulation’s attitude towards use of LBS• We explored older adults attitudes pre and postuse towards adoption of LBS

Main findings• Very little change pre and post usage of devices• Majority did not mind people knowing where they were• Routines• Don’t do anything they are ashamed of• ‘We are a very laid back bunch’• Don’t see benefit as perceive themselves as ‘healthy’• Do see benefit for others e.g. Alzheimer's• If changes were noted:• More concerned over personal privacy post use via response toquestionnaire – but same concern did not emerge in interviews• Worried over losing independence and control• Post use rate system effective resource

Conclusions: the next 2 months• Modelling of data– to understand activity and mobility patterns ofolder people living in the community.– to investigate how physical and mental wellbeingrelate to this mobility, and alsodetermine the consequences of good or poormobility among the oldest-old members of thepopulation.

Conclusions• Product development– ease of use, acceptability and privacy issuessurrounding locating devices will beexamined.– factors that need to be considered to ensureease of utility and acceptability.– examine the viability of commercialdevelopment of mobility tracking technologyas a means of identifying those ‘at risk’.

Thank-you