The Contribution of Level of Cognitive Complexity and Pubertal Timing to Behavioral Risk in Young Adolescents

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The

Contribution

of Level

of Cognitive

Complexity

and

Pubertal

Timing

to Behavioral

Risk

in Young

Adolescents

Donald P. On, MD* and Gary M. Ingersoll, PhD

ABSTRACT. Purpose. To determine the unique

con-tributions of cognitive complexity and pubertal timing to

participation in behavioral risk (substance use, sexual

activity, school and legal problems) among young

ado-lescents.

Design. Cross-sectional with cohort replication.

Methods. Two cohorts of middle school students in

grades eight and nine in a midwestern school district

completed a self-report questionnaire in 1987 and 1989.

Measures of behavioral and emotional risk, cognitive

complexity and pubertal timing relative to peers were

included.

Results. Complete data were available for 817 and 796

students in each cohort year. Gender, race, level of

cog-nitive complexity and pubertal timing each contributed significantly to the variance in behavioral risk indepen-dent of chronological age (P < .0001). Boys, whites, those at lower levels of cognitive complexity and those who began pubertal maturation earlier than peers, were at

significantly greater risk. Adolescents who demonstrated

higher levels of cognitive complexity and those who

began puberty later compared to peers participated in a smaller array of risk behaviors, independent of chrono-logical age.

Implications. Pediatricians should consider

adoles-cents at lower levels of cognitive complexity (concrete thinking) and those who begin puberty earlier at greater risk for participation in health risk behaviors. Preventive health counseling tailored to the needs of this group may be most beneficial. Pediatrics 199595:528-533;

adoles-cence, health risks, sexuality, cognitive development,

puberty.

ABBREVIATION. CL, conceptual level.

United States adolescents participate in a variety

of activities that are potentially harmful. A large

number report unprotected sexual activity,

experi-ence with tobacco, alcohol, and other drugs, and ride

in vehicles whose driver is under the influence of

alcohol or illegal substances.’4 Participation often

begins in middle school,”’4 is more common among

boys,”’4 varies with family factors,46 and is

associ-ated with academic underachievement and school

difficulties.3’4’6 Across multiple samples, health

en-From the *jpftflent of Pediatrics, Section ofAdolescent Medicine, School

of Medicine, School of Education, Indiana University, Indianapolis, IN.

Received for publication May 9, 1994; accepted Jul 7, 1994.

Reprint requests to (D.P.O.) Professor of Pediatrics, Section of Adolescent

Medicine, 5857, Indiana University Medical Center, 702 Barnhill Dr, India-napolis, IN 46202.

Acad-emy of Pediatrics.

dangering behaviors generally covary and

collec-tively have been called problem behaviors,6

risk-tak-ing behaviors,2 and behavioral risk.7 They increase

during adolescence, peak during late adolescent

years, and then decline.6

Irwin and Millstein2 have described biological,

de-velopmental, and social factors which they believe

contribute to adolescent risk-taking. Although

pu-berty has been the focus of much research, it is

unclear whether pubertal processes contribute

di-rectly via hormone actions or indirectly through

changes of adolescent and adult perceptions of

be-haviors and roles and/or peer group choice and peer

influences.8’ Much of the research has involved

small samples or relied on very indirect indicators of

puberty such as height/weight ratios or facial

ap-pearance or late pubertal events such as age of men-arche; the results are conflicting. For example, inves-tigators have reported variable gender differences

with respect to the association of timing of puberty

and participation in risk activities.’2’9 It is unlikely that the increased prevalence of these activities

dur-ing puberty is solely a reflection of increasing age.

Adolescence is also a period of increasing cogni-tive capacities. There is a general progression in

thinking from simple to complex, from concrete to

abstract, and from egocentric to decentered.2#{176} As ad-olescents’ cognitions become more complex, they be-gin to develop the capacity to plan ahead,

under-stand the longer-term consequences of behaviors,

appreciate the interrelationships of emotions and

be-haviors, and use hypothetical-deductive reasoning.

The level of cognitive development is believed to

affect the ways in which adolescents understand

their world and make decisions20; Irwin and

Mill-stein,2 who have included cognitive maturation in

their model of adolescent risk, argue that adolescents

at lower stages of cognitive development should be

at higher risk; however, little empirical support for

this hypothesis has been provided.

The purpose of this study was to investigate the

unique contributions of the timing of the onset of

puberty and cognitive complexity to participation in

risk behaviors (independent of other identifiable fac-tors) in a population of young adolescents. We

hy-pothesized that adolescents at lower levels of

cogni-tive complexity and who began puberty earlier than

peers would engage in a greater problem behavior.

Conversely, adolescents at more advanced levels of

cognitive complexity and those beginning puberty

at Viet Nam:AAP Sponsored on September 1, 2020 www.aappublications.org/news

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later than peers would be less likely to participate in

behaviors potentially harmful to their health.

METHODS

We surveyed eighth and ninth grade students attending two

junior high schools in 1987 and 1989. The schools were located in

working class neighborhoods; urban students are bused into this

district. Students in each school completed the questionnaire on a

single day during a mandatory class. Parents and students had

been informed in writing that participation was optional and that

information was confidential. Written parental permission was

waived with the approval of the Indiana University Committee for the Protection of Human Subjects and the school administration.

Students provided standard sociodemographic information

and completed a health behaviors questionnaire reflecting the

extent of participation in multiple activities and endorsement of

selected emotions.37 Students responded on a 4-point ordinal scale (1 (never) to 4 (about once a week)). Several questions, eg, “I have

attempted suicide,” “I have been pregnant/gotten someone

preg-nant”) were dichotomous. (The complete questionnaire is

avail-able from the authors.)

Previous research had demonstrated that the questions tapped two factor-analytic dimensions that are stable across several pop-ulations of younger and older boys and girls.7 Weighted (based on

the factor analytic loadings), summed standard scores (mean = 50;

S.D. = 10) were created. Behavioral risk (Cronbach’s a = 0.83)

reflects alcohol and drug use, sexual activity, minor delinquency, and suicide attempt. Four-month test-retest reliability of 0.75 was deemed acceptable. Emotional risk (Cronbach’s a 0.79) reflects

feelings of sadness, loneliness, sleep disturbance, suicidal

thoughts, and anxiety.7 Four-month test-retest reliability was 0.56.

Emotional risk scores correlated with the Depression (R 0.46; P = .001), Self-image (R = 0.46; P< .001), and Family relationships (R = 0.34; P = .009) subscales of the Offer Self-Image

Question-nair&1; Behavioral and Emotional Risk are modestly correlated (R - 0.28; P < .001).’

Cognitive complexity was measured using the Hunt Paragraph Completion Method task,24 a measure of general social cognition.#{176} This semiprojective instrument, designed to measure conceptual level (CL) uses six open-ended topic stems to assess the individ-uals conceptions and attitudes about authority, uncertainty, rules, and conflict. Values range from 0.0, reflecting immature, egocen-tric orientations through 3.0, reflecting complex, independent, and

abstract orientations. Interrater reliability for scoring the Hunt was

0.86, similar to that reported by Hunt.24 For purposes of these analyses, CL scores were reclassified into three ordered ordinal levels based on dividing the scores into thirds around the median value of 1 (lower cognitive complexity < 1, middle cognitive complexity = 1, higher cognitive complexity > 1).#{176}These

catego-ries represent relative levels for purposes of statistical analysis (see

Results).

Pubertal timing was assessed by asking the students to recall the timing of physical events, not to self-assess their level of sexual maturation.6 Specifically they were asked to indicate if specific physical signs of puberty are present and if so, at what age they were first noticed. Girls were asked about breast development, pubic hair, and menarche; boys were asked about the appearance of pubic hair and genital growth. We used recall of age of events instead of self-assessment of pubertal status because of some con-cern that there may be age-related bias associated with use of the

self-assessment method.28 In previous researcW”#{176} using our method with junior high school students, we observed the

follow-ing 4-month test-test correlations for the age of these events: boys’

pubic hair (R = 0.74; P < .001) and genital growth (R 0.71; P <

.001); girls’ pubic hair (R 0.78; P < .001), breast growth (R 0.72;

P < .001), and menarche (R = 0.69; P < .00l).There were no

significant differences in the strength of the correlations for

younger and older adolescents (unpublished data). These findings

are similar to those reported by Gilger and co-workers.31

Because we were interested in the students’ pubertal

develop-ment relative to peers (and not in identifying students who were

precocious or delayed in the clinical sense), we defined timing stages of early, average, or late by classifying approximately 50%

of the sample as average maturers.32 This provided cell sizes

sufficiently large for multivariate analyses. Boys were classified as early maturers if they reported genital growth or pubic hair at age 10 years and late maturers if they reported no development by age

13. Early maturing girls were those who reported the appearance

of pubic hair or breast growth by age 9 or menarche by age 10.

Late maturing girls were those who indicated that pubic hair or

breast growth had not occurred by age 12 or menarche by age 13.

Assessment of the role of pubertal timing and cognitive

com-plexity on behavioral risk is complicated by their common

corre-lation with age. That is, for example, the youngest adolescents

could not be classified as late maturers. To compensate for this

source of confounding, behavioral risk scores were regressed on

age.n Each residual score was then transformed into a

standard-ized (M = 50; SD 10) score. The residual behavioral risk score

thus operates independent of age.

The adapted behavioral risk scores were then subjected to a

five-way analysis of covariance with each cohort (1987, 1989)

serving as a replicate for gender (male, female) by race (white,

African-American) by cognitive complexity (lower, middle,

higher) by pubertal timing (early, average, late) comparison)

Be-cause of the covariation of emotional and behavioral tisk,7’ we

included emotional risk as a covariate. Emotional risk does not

covary with age and thus was treated in its standardized form.3’723

Three-way and higher interactions were collapsed into the error

term because of the limited power with the resultant small cell

sizes. Pairwise differences in mean behavioral risk scores for

pu-bertal timing and cognitive complexity were assessed using the

Bonferroni thterion.’ We report standard deviations (SD) for

mean values (NO that were not standardized (cognitive

complex-ity, age) and standard error of the mean (SEM) for variables

standardized to a mean of 50 and standard deviation of 10

(be-havioral risk).

RESULTS

After eliminating the few students who had

corn-pleted the survey in 1987 and 1989 because they had been detained 2 years in the same grade,

question-naires were available for 918 students in 1987 and

922 in 1989 (approximately 85% of the student body;

each year 12 or fewer parents requested that their

adolescents not participate in the survey). Analysis

of variance procedures using listwise deletion

elim-mated subjects who had missing data for any

mea-sur&; thus complete data were available for 816 and

796 students, respectively. Adolescents with

corn-plete data did not differ significantly from the total

sample when compared by age, gender, race,

puber-tal timing, cognitive maturity, behavioral, or emo-tional risk scores (data not shown).

The distributions of subjects used in the analysis for each year are shown in Table I.The 1989 sample

was slightly younger (M = 14.2; SD = 0.8 vs 14.0;

SD = 0.8; P = .0001) and of slightly lower cognitive

complexity (M = 0.98; SD = 0.31 and M = 0.85; SD =

0.57; P = .001) than the 1987 group. The average level

of cognitive complexity was low, indicative of the

young ages of the subjects. The mean values for each

of the levels were as follows: lower (M = 0.49; SD =

0.25), middle (M = 1 .0; SD = 0), and higher (M =

1.43; SD = 0.33; P .0001). Females had slightly

higher levels of cognitive complexity (M = 0.96;

SD = 0.47) compared to males (M = 0.86; SD = 0.46;

P = .001). White students were slightly more

corn-plex (M = 0.92; SD = 0.48) than African-Americans

(M = 0.86; SD = 0.4; P = .03). There were no

IThese samples are not totally independent (same school in different years).

Therefore it may also be appropriate to perform separate analyses of

co-variance for each year (cohort). We have chosen to calculate a single

five-way ANCOVA approach for ease of presentation and space consider-ations. The separate analyses yielded results statistically indistinguishable from those presented and are available from the authors.

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TABLE 1. Distribution of 1987 and 1989 Samples

Variables

--- N

1987

(%) N

1989

(%)

Race

White 666 (81.6) 660 (82.9)

African-American 150 (18.4) 136 (17.1)

Age*

13 years 160 (19.6) 213 (26.8)

14 years 373 (45.7) 402 (50.5)

15 years 242 (29.7) 158 (19.8)

16 years 41 (5.0) 23 (2.9)

Gender

Male 429 (52.6) 413 (51.8)

Female 387 (47.4) 383 (48.2)

Cognitive complexity

Low 320 (39.2) 415 (52.1)

Middle 250 (30.6) 146 (18.3)

High 246 (30.1) 235 (29.6)

Pubertal timing

Early 133 (16.3) 138 (17.4)

Average 417 (51.1) 421 (52.8)

Late 266 (32.6) 237 (29.7)

* x2= 69.34; df = 3; P < .001.

:I:x2= 39.13;df= 2;P< .001.

significant differences in the mean level of cognitive

complexity by age.

Table 2 shows the mean ages for each pubertal

event for the students who indicated that the finding

was present. The ages reported by the boys are

sim-ilar to published British norms. The reported mean

age of menarche is about I year younger than the

American average,37 however the ages reported for

breast development and appearance of pubic hair are

similar to British values. The lower mean age of

menarche reflects the fact that subjects who had not

yet menstruated (all 13 years of age and older) could

not be included in the calculation of this average.

There are no significant differences in mean ages of

events between the 1987 and 1989 samples.

The analyses of covariance demonstrated that

gen-der (F = 14.42; DF = 1; P = .0002), race (F = 13.30;

DF = 1; P = .0003), cognitive complexity (F = 24.73;

DF = 2; P = .0001), and pubertal timing (F = 21.12;

DF = 2;P = .0001) independently explained

signifi-cant variance in behavioral risk after controlling for

emotional risk. There was no cohort effect (F = 0.98;

DF = 1; P = .32). Boys reported significantly more

risk (M = 49.9; SEM = 0.4) than girls (M = 48.7; SEM

TABLE 2. Mean Age (S.D.) of Pubertal Events for Subjects

Reporting That Event Had Occurred

1987 1989

White males

Genital 11.9(1.3) 11.7(1.2)

Pubic hair 11.8(1.3) 11.6 (1.2)

African-American males

Genital 11.4(1.3) 11.4 (1.5)

Pubichair 11.3(1.2) 11.6(1.2)

White females

Breast 10.9 (1.3) 10.8 (1.3)

Pubichair 11.0(1.2) 11.0(1.2)

Menarche 11.8(1.2) 11.8 (1.1)

African-American females

Breast 10.9 (1.5) 10.6 (1.4)

Pubic hair 10.7 (1.3) 10.9 (1.5)

Menarche 11.7(1.1) 11.8(1.2)

= 0.4; P < .01). African-Americans (M = 47.3; SEM =

0.5) were less involved in risk activities than whites

(M = 49.8; SEM = 0.3; P < .01). There were no

significant two-way interactions between gender or

race and cognitive complexity and behavioral risk.

Behavioral risk scores varied with level of

cogni-tive complexity and timing of puberty relative to

peers. Adolescents at the lower level of cognitive

complexity were at highest risk (M = 51.2; SEM =

0.4), those at higher levels at least risk (M = 47.2;

SEM = 0.4); subjects at middle level of complexity

were between the two in terms of risk (M = 48.4;

SEM = 0.5). Post hoc comparison using the

Bonfer-rom Procedure for multiple comparisons

demon-strated that those at the lower level of cognitive

complexity were significantly different (P < .01) from

the other two groups. There were significant

differ-ences in behavioral risk for each of the levels of

pubertal timing (P < .01). Early maturing adolescents

were at greatest risk (M = 51.8; SEM 0.7). Those

beginning puberty latest were at least risk (47.1; SEM

= 0.4) with the average group reporting middle level

risk (M = 49.9; SEM = 0.4). There was no significant

two-way interaction between levels of cognitive

complexity and pubertal timing. The additive effect

is demonstrated in Table 3 which shows behavioral

risk scores cross-classified by levels of cognitive

corn-plexity and timing of puberty. The behavioral risk

scores decrease both with increasing cognitive

corn-plexity and later onset of puberty relative to peers.

DISCUSSION

Our data indicated that levels of cognitive

corn-plexity and the age of onset of puberty relative to

peers were independently associated with

participa-tion in behaviors that are potentially health

endan-gering. The effects were linear and additive. With

increasing cognitive complexity and later onset of

puberty relative to peers, adolescents reported

par-ticipating in fewer negative behaviors. Although the

differences in risk activities between those in the

middle and upper levels of cognitive complexity

were not statistically significant in a post hoc

corn-parison, the lessened risk was present across all

cat-egories of pubertal timing (see Table 3). This

sug-gests that the failure to detect a significant difference

may be related to the generally low level of cognitive

complexity in our sarnple.20’ Our categorization into

three levels based on the distribution of values,

TABLE 3. Behavioral Risk Corrected for Age by Levels of

Cognitive Complexity and Pubertal Timing

Cognitive complexity Pubertal timing (mean (SEM))

Early Average Late Total

Lower 53.2 (1.1) 51.2 (0.6) 50.1 (0.8) 51.2(0.4)

(N = 110) (N = 416) (N = 208) (N = 734)

Middle 51.5 (1.5) 49.2 (0.8) 45.8 (0.8) 48.4 (0.5)

(N = 67) (N = 186) (N = 141) (N = 394)

Higher 50.3 (1.0) 47.1 (0.6) 44.1 (0.5) 47.2(0.4)

(N = 93) (N = 235) (N = 152) (N = 480)

Total 51.8 (0.7) 49.9 (0.4) 47.1 (0.4)

(N = 270) (N = 837) (N =501)

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useful for statistical analysis, should not be

con-strued to indicate that low, middle, and high levels of

cognitive complexity as discussed in other research

were represented in our sample.

Cognitive Complexity and Risk

Our results are supported by other studies8’#{176}”#{176}45

which suggest that adolescents operating at higher

levels of cognitive complexity may participate in

fewer risk activities, because they are better able to

understand the potentially negative consequences,

begin to understand the interdependence of

psycho-logical factors and behaviors, are more independent,

and thus are better able to resist social and peer

pressures (particularly of closer friends). As

articu-lated by Hunt,39 conceptual level describes

develop-ment in terms of increasing complexity in processing

information and increasing self-responsibffity and is

reflected in more mature social cognition. The shift in

conceptual level (CL) to higher levels (becoming

more complex) is associated with increasing ability

to consider alternatives and to direct behavior. This

shift is age-related but not age-dependent.8 More

complex individuals demonstrate more ernpathy,#{176}

more autonomy (Phillips M, unpublished), more

independent styles (Rathbone C, unpublished),

better information processing (Reid R, unpublished),

and less field dependence. Although cognitive

complexity and intelligence are correlated,’45

there is unique variance in CL not explained by

intelligence.20’45

Bruch and co-workers found that more

cogni-tively complex college students were more assertive

in difficult situations, ie, interaction with close

friends, reflecting greater abilities to resolve

con-flicts. Enhanced conflict resolution results then from

having the capacities to view situations from

multi-ple perspectives and to rely on internal standards of

appropriate action. This suggests that individuals of

greater complexity posses a more internal locus of

control (King R, unpublished). Our findings are

con-sistent with the theory and previous suggestions that

adolescents who are less cognitively mature are an

especially vulnerable group,2 perhaps because they

are less able to resist peer social pressures.

How cognitive complexity might be related to

other health protective behaviors is unknown. We

have reported that adolescents with

insulin-depen-dent diabetes mellitus who demonstrate higher

1ev-els of cognitive complexity engage in more

seif-man-agement behaviors and subsequently have better

glycemic control49 independent of chronological age.

Sexually active adolescent females who are less

cog-nitively complex report less use of condoms for AIDS

protection than those at higher levels,#{176}however, less

complex males at high risk for STD are more likely to

report condom use.51 We could find no ernpirical

data about how cognitive complexity might relate to

the larger domain of health protective activities such

as exercise, diet, stress reduction, contraception, seat

belt use, and dental hygiene; this may represent an

important area for future research.

Pubertal Timing and Risk

Our finding that the relative timing of pubertal

onset is related to participation in behavioral risk

activities generally confirrns or extends the findings

of others.’3”6”7’2 Duncan and colleagues32

demon-strated that early maturing boys were engaged in

more deviant behaviors than late maturers. They

observed no consistent findings among girls.

Mag-nusson and co-worker&3 demonstrated, however,

that norm violation (breaking rules, smoking,

drink-ing, stealing) increased with earlier age of menarche,

especially for those having older peers. The later

maturing girls caught up in their experiences with

alcohol by age 15, and by age 25 were no different in their drinking behaviors between those with early

age of menarche. The authors believed that these

findings supported the hypothesis that early

matur-ers were involved in an older peer culture that was

more supportive of deviant behaviors (norm viola-tion) and less supportive of education. Simmons and

Blyth16”7 found early maturing girls to be more likely

to date earlier and have more problem school

behav-iors. None of these studies included information

about sexual activity; nor did they assess levels of

cognitive development. Our research suggests that

the effects of pubertal timing are similar for boys and

girls and that earlier onset of puberty and low

cog-nitive complexity are a particularly troublesome combination.

We are not able to determine how differences in

the timing of puberty in relationship to peers or in

cognitive complexity influences risk behaviors. We

suspect that it is related to multiple

factors-biolog-ical, social, and psychological.2 Causal factors might

indude a greater opportunity because of association

with older peers who are more involved in risk

ac-tivities,15 social pressure to conform,2”1’13 parental

awareness of early puberty with premature

relax-ation of rules, and expectations for advanced

age-appropriate behaviors.5”8’192 Additional study is

needed to clarify this relationship.

Limitations

There are limitations to our study. The sample was

constricted in terms of age and levels of cognitive

complexity; this may account for our failure to

dern-onstrate a relationship between these two variables.

We do not believe this threatens the validity of our

findings.

We relied on self-report to determine relative

tim-ing of puberty. In fact, most research examining the

relationships between pubertal development and

be-haviors in larger populations, has used self-report

measures of pubertal status, ie, subjects are asked to

compare their level of physical development to line

drawings or pictures. Several investigators have

shown that self-assessed Sexual Maturity Rating

is correlated with physician assessment.6 It has

been suggested that there may be a “tendency” for

younger adolescents to overestimate and older

ado-lescents to underestimate their pubertal status. Our

method for determining pubertal timing has adapted

several of Petersen’s questions asking about timing

(5)

of pubertal events27 and may avoid this potential for desirability bias.

Although there is some bias associated with

recall-ing more distant events, our earlier work did not

demonstrate significant differences in the test-retest

reliabilities for ages of onset of pubertal events for

younger and older adolescents and is consistent with

other published reports about recall of pubertal

events.3’ Because some research indicates that early

puberty is socially advantageous (desirable) only for

males,2’12’14’16 one would anticipate that girls tend to

overestimate and boys to underestimate the ages of

maturational events. We observed that pubertal

tim-ing operated in a similar fashion for both boys and

girls in each cohort. The consistency of findings in

our two different samples of adolescents and the

previously observed test-retest reliabilities of these

questions suggest that recall bias and

misrepresenta-tion of the age of pubertal events may not be in

important source of error.

IMPLICATIONS

We believe that our findings are relevant for

prac-ticing pediatricians. Adolescents who begin puberty

earlier than peers appear at greater risk for

partici-pating in a variety of health-endangering

activities-sexual activity, riding with drivers under the

influ-ence of drugs or alcohol, and use of alcohol or other

substances. Because pubertal development clearly

falls within the domain of the physician, and may be

associated with anxiety for the young adolescents

and parents,4’ its onset appears to be a logical and

acceptable42 marker to initiate education and

coun-seling about risk prevention/reduction.

Recommen-dations to initiate risk discussions based on

appear-ance of pubertal changes might be more appropriate

than those based solely on chronological age.

Our data also demonstrate that individuals at the

lowest levels of cognitive complexity are at greater

risk regardless of age, gender, or pubertal timing.

Greater complexity appears somewhat protective.

How might this influence pediatricians’ interactions

with their adolescent patients? Research has

demon-strated that individuals learn and perform best when

the learning environment and their level of cognitive

complexity are matched20’ (Phillips M,

unpub-lished). Less complex individuals learn best in more

structured situations in which material is presented

in a supportive, straightforward fashion, and with

limited numbers of alternatives; performance

deteri-orates in unstructured, exploratory settings where

many alternatives are presented and expectations are

unclear. The ideal learning environment becomes

less structured with increasing cognitive

develop-ment. However, only among those at the highest

level of complexity does performance deteriorate

sig-nificantly in highly structured settings. It is rare for

young adolescents (none in this study) to display the

highest level of cognitive complexity20’; many

adults do not demonstrate the highest level of

cog-nitive complexity.53

We believe that younger adolescents and those

older individuals who are egocentric and

concrete-thinking (less cognitively complex) may benefit most

from more structured, supportive counseling about

health risk in which accurate information is

pre-sented simply and concretely, and which requires

the adolescent to choose from a more limited range of alternatives. As the established physician-patient

relationship becomes stronger and in discussions

with more cognitively complex adolescents, less

structure and more encouragement toward

indepen-dent thinking should prove beneficial. Although

group interventions designed to increase

adoles-cent’s skills in resisting social pressures, often

involv-ing role-playinvolv-ing exercises, have been found to reduce

participation in selected health risk behaviors,47 they are not designed for office-based use, are

time-con-suming, and are beyond the scope of most general

physicians. Pediatricians should be knowledgeable

about community resources of this type. Research

about optimal office-based counseling, including the

relationships to the levels of cognitive complexity, is

needed.

ACKNOWLEDGMENT

During the preparation of this manuscript, D.P.O. was

sup-ported in part by grant MCJ 1895% from the Health Resources and

Services Administration, Maternal and Child Health Bureau.

REFERENCES

I. Centers for Disease Control. Sexual behavior among high school

stu-dents-United States, 1990. MMWR. 1991;40:885-888

2. Irwin C, Millstein C. Biospsychosocial correlates of risk-taking in

adolescence: can the physician intervene? I Adolesc Health Care. 1986;7: 825-968

3. Orr D, Beiter M, IngersoliC. Premature sexualactivity is an indicator of psychosocial risk. Pediatrics. 199187:141-147

4. Milstein S, Irwin C, Adler N, et al. Health-risk behaviors and health

concerns among young adolescents. Pediatrics. 199289:422-428 5. Jemmott L, Jemmott J. Family structure, parental strictness, and sexual

behavior among inner-city black male adolescents. Adolesc Rca. 1992;

7:192-207

6. Jessor R, Jessor SL Problem Behavior and Psychosocial Development: A Longitudinal Study of Youth. New York: Academic Press; 1977

7. Ingersoll C, On D. Behavioral and emotional risk in early adolescents. JEarly Adolesc. 19899:3%-408

8. Orr D, ingersoll C. Adolescent development: a biopsychosocial review. Curr Probi Pediatr. 19888:443.-487

9. Nottelman E, Susman E, Inoff-Cermain C, et aL Developmental pro-ceases in early adolescence: relationships between adolescent adjust-ment problems and chronologic age, pubertal stage, and puberty-related serum hormone levels. JPediatr. 1987;110:473-480

10. Susman E, Nottelman E, Inoff-Germain C, et a!. The relation of relative hormonal levels and physicaldevelopment and social-emotional behav-ior in young adolescents. IYouth Adolesc. 1985;14:245-264

Il. Smith E, Udry J, Morris N. Pubertal development and Mends: a bioso-cial explanation of adolescent sexual behavior. IHealth Soc Behav. 1985; 26:183-192

12. Blyth D, Simmons, R, Zakin D. Satisfaction with body image for early

adolescent females: the impact of pubertal timing within different

school environments. JYouth Adolesc. 1985;14:207-225

13. Magnusson D, Stattin H, Allen V. Biologic maturation and social development: a longitudinal study of some adjustment processes from mid-adolescence to adulthood. JYouth Adolese. 1985;14:167-184

14. Petersen A, Crockett L Pubertal timing and grade effects on adjust-ment. JYouth Adolesc. 1985;14:191-205

15. Silbereisen R, Petersen A, Albrecht H, Kracke B. Maturational timing

and the development of problem behavior: longitudinal studies in

adolescence. JEarly Adolesc. 19899:247-268

16. Simmons R, Blyth D, Van Cleave E, Bush D. Entry into early

adolescence: the impact of school structure, puberty, and early dating on self-esteem. Am Soc Rev. 1979;44948-%7

(6)

18. Steinberg L. Impact of puberty on family relations: effects of pubertal status and pubertal timing. Dev Psychol. 1987;23:451-460

19. Steinberg L, Silvergery S. The vicissitudes of autonomy in early adoles-cence. Child Dev. 198657:M1-851

20. Miller A. Conceptual matching models and interactional research in

education. Rev Educat Res. 198151:3-84

21. Offer D, Ostrov E, Howard K. The Offer Self-Image Questionnaire for Adolescents: A Manual. Special publication. Chicago, IL: Michael Reese

Hospital and Medical Center; 1977

22. Orr DP, Ingersoll GM, Golden MP. At Risk Health Behaviors Among Adolescents with IDDM. Presented to the International Study Group of Diabetes in Children and Adolescents, Bled, Yugoslavia; October 23-26,

1989

23. Beiter M, Ingersoll G, Ganser J, Orr D. Relationships of somatic symp-toms to behavioral and emotional risk in young adolescents. IPediatr.

1991;1 18:473-478

24. Hunt D, Butler L, Noy J, Rosser H. Assessing Conceptual Level by the

Paragraph Completion Method. Toronto: Ontario Institute for Studies and Education; 1978

25. Morris N, Udry J. Validation of a self-administered instrument to assess stage of adolescent development. I Youth Adolesc. 19809:271-280 26. Duke P, Litt I, Gross R. Adolescents’ self-assessment of sexual

matura-lion. Pediatrics. 1980;66:918-920

27. Petersen A, Crockett L, Richards M, Boxer A. A self-report measure of pubertal status: reliability, validity, and Initial norms. JYouth Adolesc.

1988;17:1 17-133

28. Schlossberger N, Turner R, Irwin C. Validity of self-report of pubertal maturation in early adolescents. IAdolesc Health. 1992;13:109-113

29. On D, Brack C, Ingersoll G. Pubertal maturation and cognitive maturity

in adolescents. IAdolesc Health Care. 1988;9:273-279

30. Brack C, Orr D, Ingersoll C. Pubertal maturation and adolescent

self-esteem. IAdolesc Health Care. 1988;9:280-285

31. Gilger 1 Geary D, Eisele L. Reliability and validity of retrospective self-reports of the age of pubertal onset using twin, sibling and college

student data. Adolescence. 1991;26:41-53

32. Duncan P. Ritter P. Dornbusch 5, et al. The effects of pubertal timing on body image, school behavior and deviance. I Youth Adolesc. 1985;14: 227-235

33. Kerlinger F, Pedhazur E. Multiple Regression in Behavioral Research. New York: Holt, Rinehart and Winston; 1973

34. Hockberg Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika. 1988;75:800-802

35. SAS Institute Inc. SAS Statistics for Personal Computers, Release 6.03 Edition. Cary, NC: SAS Institute Inc; 1988

36. Marshall W, Tanner J. Variations in the pattern of pubertal changes in

boys. Arch Dis Child. 197034:13-23

37. National Center for Health Statistics. Age at Menarche United States.

Vital and Health Statistics, PHS Pub. No. 133, Series 11, Public Health

Service, Washington: US Government Printing Office; 1973

38. Marshall W, TannerJ. Variations in pattern of pubertal changes in girls.

Arch Dis Child. 1969;44:291-303

39. Hunt D,Joyce B, CreenwoodJ, NoyJ, ReidJ Weil M. Student conceptual

level and models of teaching: theoretical and empirical coordination of

two models. Interchange. 19745:19-30

40. Heck E, Davis C. Differential expression of empathy in a counseling analogue. ICounsel Psychol. 1973;20:101-104

41. Cohen M, Adler N, Beck A, Irwin C. Parental reactions to the onset of

adolescence. JAdolesc Health Care. 1986;7:101-106

42. Fisher M. Parents’ views of adolescent health issues. Pediatr. 1992;90: 335-341

43. Arlin P. Cognitive development in adulthood: A fifth stage? Dev Psy-chol. 1975;11:602-606

44. Peterson A. Pubertal change and cognition. In Brooks-Gunn J,Petersen A. (Ed) Girls at Puberty, Biological and Psychosocial Perspectives. New

York: Plenum Press; 1983:179-198

45. Harvey 0, Hunt P. Schroder H. Conceptual Systems and Personality Organziation. New York: John Wiley & Sons; 1961

46. Bruch M, Heisler B, Conroy C. Effects of conceptual complexity on

assertive behavior. JCounsel Psychol. 1981;28:377-385

47. Botvin C, Baker E, Dusenbury L, Tortu 5, Botvin E. Preventing adoles-cent drug abuse through a multimodal cognitive-behavioral approach: Results of a 3-year study. IConsult Clin Psychol. 199058:437-446

48. ingersoll C, Orr D, Herrold A, Golden M. Cognitive maturity and self-management among adolescents with Insulin Dependent Diabetes. IPediatr. 1986;108:620-623

49. Ingersoll C, Orr D, Vance M, at al. Cognitive maturity, stressful events and metabolic control among diabetic adolescents. In: Susman E, Fagans L, Ray W, eds. Emotion, Cognition, Health, and Development in Children and Adolescents. Hilisdale, NJ: Earlbaum Publishers; 1992:121-132

50. Orr D, Langefeld C, Katz B, et al. Factors associated with condom use among sexually active female adolescents. I Pediatr. 1992;120:311-317

51. Orr D, Langefeld C. Factors associated with condom use in sexually active adolescent males at risk for sexually transmitted disease. Pediat-rics. 199391:873-879

52. Feldman 5, Wood D. Parents’ expectations for preadolescents sons’

behavioral autonomy: a longitudinal study of correlates and outcomes. IRes Adolescence. 1994;4:45-.70

53. Petersen A, Tobin-Richards M, Boxer A. Puberty. Its measurement and

its meaning. JEarly Adolesc. 19843:47-62

TWIN WHO SURVIVED SEPARATION SURGERY IS DEAD

. ..She died at 1 ii after her lung problems had worsened.

No relatives were at the hospital when she died.

. . .The bill for Angela’s medical care has exceeded $1 million at Children’s

Hospital alone.

New York Times, June 10, 1994.

Noted by J.F.L., MD

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1995;95;528

Pediatrics

Donald P. Orr and Gary M. Ingersoll

Behavioral Risk in Young Adolescents