Long Beach Longitudinal Study

Elizabeth Zelinski, PhDRita and Edward Polusky Chair in Aging

and EducationUSC Davis School of Gerontology

Funded by US National Institute on Aging Grants R01-AG10569 and T32-AG00037

Friday Harbor Psychometrics Workshop 2010

Acknowledgements

• Funded in part by Grant R13AG030995-01A1 from the National Institute on Aging

• The views expressed in written conference materials or publications and by speakers and moderators do not necessarily reflect the official policies of the Department of Health and Human Services; nor does mention by trade names, commercial practices, or organizations imply endorsement by the U.S. Government.

Collaborators• Kristin Antonio• Josette Bowers• Megan Braziel• Lisa Breen• Kerry Burnight, PhD• Sarah Canetti• Grace Sit Chan• Kami Chin• Althea De Pietro• Robin Engberg• Elena Estrada• Michael Gilewski, PhD• Amber Hall, PhD• Shoshana Hindin• George Holman• Patricia Housen, PhD

• Robert Kennison, PhD• Deanah Kim• Shirley Kirksey• Christianne Lane• Kayan Lewis, PhD• Jack McArdle, PhD• Kevin Petway• Joyce Riley• Mariette Salama• K. Warner Schaie, PhD• Aisha Shaheen• Marc Simpao, MD• Susan Stewart, PhD• Erin Westphal

Purposes of the Long Beach Longitudinal Study

• to document cognitive change in healthy older adults

• to identify mechanisms of change with individual differences models

• extend models of change to a relatively large sample of the oldest-old: 50% of sample is currently over age 80

Age

HealthContext

SocialContext

Cognition

Model Effects of aging on the social and health-related environment. These affect cognition in older adults, though cognitive status may affect some aspects of health and social functioning.

Data• Each panel retested every 3-5 years• Goal--development of growth models of cognitive change and its correlates throughout adulthood

• Measures are of• STAMAT:

– INDUCTIVE REASONING: letter series & word series– SPACE: figure rotation & object rotation– Word fluency (EXECUTIVE)– VOCABULARY (STAMAT & 2 ETS advanced vocabulary)

LIST RECALL: 2 lists TEXT RECALL: 3 short passagesWORKING MEMORY: 3 measuresSPEED: pattern, number, letter comparisonRARE WORD DEFINITIONDisourse production

• Lifestyle: Life Complexity Inventory: Social networks, neighborhood, educational & cultural activities, exercise • Personality: NEO-PI-R (5 factors)• Memory Functioning Questionnaire: Frequency of Forgetting• Health: Seattle Health Behaviors (+ specific medical conditions)

• IN PROCESS (oldest participants):• Blood samples for DNA/RNA analysis; blood lipids, markers for vascular & inflammatory risk• Imaging: Brain: brain volume & specific structures, WMH, cortical thickness; • Carotid intima media thickness, • Retinal photography

• All participants:• HRS physical function measures (BP, BMI, tandem walk, gait speed), lung capacity, grip strength• HRS cognitive measures

2007-10

22-year birth cohort difference from Panel 1

6-year birth cohort difference from Panel 2

1978 1981 1994-95 1997-98 2000-02 2003-05

Panel 1/Cohort 1N = 583

Panel 1/Cohort 1N = 264

Panel 1/Cohort 1N = 106

Panel 1/Cohort 1

N = 42

Panel 1/Cohort 1

N = 15

Panel 2/Cohort 2N = 630

Panel 2/Cohort 2N = 352

Panel 2/Cohort 2N = 173

Panel 2/Cohort 2N = 133

Panel 3/Cohort 3N = 911

Panel 3/Cohort 3N = 513

16 year birth cohort difference from Panel 1

Panel 1/Cohort 1

N = 20

Panel 2/Cohort 2N = 102

Panel 3/Cohort 3N = 296

Panel 1/Cohort 1

N = 0

IQ Subtests, Cohort, and Change (Zelinski & Kennison,

2007)• Compared age changes in people 55-82• Compared 2 16-year birth cohorts• Average birth years

– Cohort 1: 1906 (1897-1923)– Cohort 2: 1922 (1912-1939)

• Recalibration of test scores into the same interval metric via Rasch scaling to compare relative age and cohort differences

• Hypothesis: cohort differences in more fluid abilities; no differences in more crystallized

Zelinski & Kennison, 2007

20

30

40

50

60

70

80

56 59 62 65 68 71 74 77 80 83 86

Age

C1 Vocabulary

C2 Vocabulary

C1 Space

C2 Space

C1 List

C2 List

C1 Text

C2 Text

C1 Reasoning

C2 Reasoning

Longitudinal Age Effects by Cohort

Growth model over age; Intercept age 72

More recently born cohort better performance at intercept for more fluid like abilities

Larger cohort differences for reasoning & recall; but age declines on average

Zelinski & Kennison, 2007

Do accelerations of age slopes vary by cohort?

• Data in Zelinski & Kennison modeled at the average age of the sample

• Measures differed at the intercept; do age declines accelerate at the same point across measures?– Do the cohorts have similar age breakpoints?– Are cohort differences observed at the average

intercept observed at the best-fitting age breakpoints for each of the measures ?

Kennison & Zelinski, in preparation

Multiple Adaptive Regression Splines of Change Points over Age by Cohort

0

10

20

30

40

50

60

70

80

90

100

60 62 64 66 68 70 72 74 76 78 80 82 84 86

Age

Rasch score (0-100)

Reasoning cohort 1 Reasoning cohort 2

Space cohort 1 Space cohort 2

List cohort 1 List cohort 2

Text cohort 1 Text cohort 2

Vocabulary (both cohorts)

The advantage enjoyed by Cohort 2 at the first turning point is reduced or eliminated by very old age

• The initially greater cognitive reserve enjoyed by the later-born cohort may be more limited late in life due to age related declines or less selection at older ages compared to Cohort 1.

• The oldest Cohort 1 members may have had greater cognitive reserve due to selective survival (their Time 1 scores were higher than those of Cohort 2)

Kennison & Zelinski, in preparation

Cohort differences in Activities as a predictor of change

Mean/Proportiona (SD) Threshold SE Loadings SE

Factor Cohort/

Panel 1

Cohort/

Panel 2Invariant over Cohorts/Panels

Baseline N 399 521

Mental Fitness

Educational .28 (.45) .42 (.49) 0.94 .16 =1 0

Cultural .30 (.46) .41 (.49) 0.88 .13 0.89 3.96

Going out to Movies .12 (.32) .31 (.47) 1.28 .11 0.31 3.06

Self-Improvement .28 (.45) .39 (.49) 0.87 .10 0.62 4.44

Volunteering .32 (.47) .41 (.49) 0.62 .08 0.39 3.98

Writing/

Correspondence

.59 (.49) .68 (.47) -0.22 .07 0.30 3.20

Physical Fitness

Fitness .40 (.49) .60 (.49) 0.43 .11 =1 0

Participant Sports .28 (.45) .24(.43) 0.69 .08 0.31 2.99

Walking .67 (.47) .81 (.40) 0.47 .09 0.80 3.46

Outdoor Hobbies .38 (.49) .50 (.50) 0.35 .07 0.46 3.62

Means, Thresholds, and Factor Loadings of Mental And Physical Fitness Activities aMeans also represent the proportion of people reporting any participation in the activity because of categorical (0,1) coding.

• Strict (strong) invariance of a categorical cognitive activity factor across 16-year cohorts and over 3 years (2 cohorts x 2 measurement occasions each)

Longitudinal Self-Reported Activities by Cohort

Mental Physical

Zelinski, Lewis, Kennison & Watts, 2008

“new” 1994+ measures

.74

list

.74

text

.74

workingmemory

.77

speed

.95

vocab

3

.78 (1.0)

2

11 (.93)

6

.88 (1.0)

5

4 .94 (.92)

9

1.0 (1.0)

8

7.73 (.80)

12

1.0 (.86)

11

10 .88 (1.0 )

14

13 .94 (1.0)

.41

.52

.80

.61

.73

.63

.62

.47.43

.50

1.0 (.85)

Zelinski & Lewis, 2003

Multiple Group Factor Analysis Results (3 age x 2 occasions)

FactorSD

Factor Correlations

Spd WM Txt Lst

Full Information Data

Voc .92 .41 .70 .60 .46

Spd .76 .51 .45 .51

WM .67 .81 .64

Txt .70 .65

Lst .79

Retested Subjects Only

Voc .92 .42 .71 .60 .49

Spd .79 .46 .45 .51

WM .68 .80 .66

Txt .68 .66

Lst .78

Age

Working Memory

Speed

Vocabulary

Text RecallList Recall

Age

Working Memory

Speed

Vocabulary

Text RecallList Recall

Models of List and Text recall as Related but Separate OutcomesReplication of model across two panels at Time 1

Replication of model within panels at Time 1 comparing retested subjects and Time 2 dropouts

Replication of model within 1994-95 panel over time 1 & time 2 comparing time 3 dropouts

Attrition did not markedly change results in the models, even over samples and over more than one retest.

This implies that relatively similar underlying patterns of cross-sectional interindividual differences in these measures across adulthood hold.

Lewis & Zelinski, 2010

Findings

• No evidence of increased factor SDs or correlations– Over test occasions– Over retestee/dropout status– Between Panels 1 & 2 compared to Panel 3

• Conclusion: Structural relationships in LBLS remain invariant under a wide variety of cross sectional data samples– Measurement of resource constructs stable– No evidence for dedifferentiation

ONE REASON WHY I HAVE HOPE FOR THE FUTURE OF LONGITUDINAL RESEARCH IN AGING

Older Adults desperate for WiFi, Death Valley Visitor Center, March 19, 2009