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Physical Inactivity and Short-term All-Cause Mortality in Adults With Chronic Disease
Brian C. Martinson, PhD;
Patrick J. O'Connor, MD, MPH;
Nicolaas P. Pronk, PhD
Arch Intern Med. 2001;161:1173-1180.
ABSTRACT
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Objective To ascertain the relationship of physical inactivity and short-term
all-cause mortality in a prospective cohort of randomly selected managed care
organization members aged 40 years and older who have multiple chronic diseases.
Methods Clinical databases were used to identify all health plan members aged
40 years and older with 2 or more chronic health conditions (hypertension,
coronary heart disease, diabetes mellitus, or dyslipemia) in 1994. A random
sample of 2336 members was surveyed by mail and telephone interview regarding
their health-related behaviors. Survey data were linked to mortality data
from the 1995 to 1997 Minnesota Death Index. Cox proportional hazards regression
was used to ascertain the association between physical inactivity and subsequent
all-cause mortality, adjusting for potential confounders.
Results Members who reported less than 30 minutes a week of physical activity
at baseline had a subsequent mortality risk ratio of 2.82 (P<.001) vs those with 30 or more minutes of physical activity a
week. Increased mortality risk persisted (mortality risk ratio, 2.15; P<.001) after adjustments for age, sex, current smoking,
functional impairment, and comorbidity score.
Conclusions In adults with chronic diseases, the physically inactive had higher
observed mortality within a 42-month period. If physical inactivity reflects
an independent mortality risk, efforts to maintain physical activity in such
patients may yield significant clinical benefits within a short period. By
contrast, if inactivity is primarily a proxy for other factors that elevate
mortality risks, a simple physician inquiry regarding inactivity may help
to identify patients at risk of death.
INTRODUCTION
THE RELATIONSHIP between physical inactivity and adverse health outcomes
has been well established.1-2
A graded, inverse relationship has been demonstrated between measures of total
physical activity and all-cause mortality.3-4
Studies5-8
on changes in physical activity and fitness indicate that maintenance or improvement
of physical activity or fitness levels reduces the risk of all-cause mortality.
Hence, the adoption and maintenance of a physically active way of life appears
to improve health and delay death.9
Physical inactivity is a predictor of subsequent disability in midlife
and older populations.10 Individuals with fewer
health risks tend to live longer than those with more health risks, and have
fewer years of disability, with delay in onset of disability and compression
of disability into fewer years at the end of life.11-12
From a managed care perspective, it is of substantial interest to consider
the short-term impact of physical inactivity on mortality and morbidity. Short-term
impact may allow health benefits to accrue before the members disenroll from
the health plan, so that health investments made by the managed care organization
provide substantial return.13 Moreover, short-term
impact may provide compelling arguments for investment in health improvement
efforts by payers and health plans.
Managed care settings provide a unique environment to improve health
in defined populations.14-15 If
physical inactivity is directly related to short-term all-cause mortality
in a midlife and an older population, independent of chronic disease morbidity,
health plans and payers may want to invest resources in programs designed
to promote physically active lifestyles. This study examines the relationship
between physical inactivity and short-term all-cause mortality in a prospective
cohort of adults aged 40 years and older diagnosed as having a chronic disease.
SUBJECTS AND METHODS
STUDY SUBJECTS
The study was conducted at HealthPartners, a Minnesota health plan with
750 000 members in owned or contracted clinics. All members aged 40 years
and older enrolled as of December 15, 1994, were potential subjects for the
study. These individuals were classified using the International
Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)16 and pharmacotherapy
databases as having or not having each of 4 chronic conditions. A diagnosis
of diabetes was assigned if the member had 2 or more ICD-9-CM codes 250.xx or a filled prescription for a diabetes-specific drug,
such as insulin, a sulfonylurea, or a biguanide, in 1994. Heart disease was
assigned if the member had 1 or more ICD-9-CM codes
412, 413.9, 429.2, or 428.0 in 1994. Hypertension was assigned if the member
had 1 or more ICD-9-CM codes 401, 401.1, or 401.9
in 1994. Dyslipemia was assigned if the member had an ICD-9-CM code of 272.4 in 1994. A more detailed description of the identification
of members with specific conditions and the sensitivity, specificity, and
positive predictive value of this method has been previously published.17 From 7571 members aged 40 years and older who had
2 or more of the chronic conditions, a random sample of 2500 (33%) was selected.
The study protocol was approved in advance by the HealthPartners Institutional
Review Board.
In August 1995, study subjects were surveyed by mail, with postcard
reminders sent 1 week after the initial survey mailing, and a second survey
mailing sent to nonrespondents 3 weeks later. Nonrespondents received telephone
follow-up. Of the original 2500 subjects, 164 were unable to complete the
survey because of death (n = 66) or disenrollment, mailing address problems,
language problems, or other problems (n = 98). These subjects were considered
ineligible. Another 276 nonrespondents, and 159 proxy responses, were omitted
from all analyses. Thus, a total of 1901 respondents (representing 81.4% of
the total eligible sample [1901/2336]) are included in the present report.
Characteristics of survey respondents and nonrespondents are listed in Table 1.
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Table 1. Comparison of Respondents and Nonrespondents
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DATA DEFINITIONS
The 60-question survey instrument included items on demographics, health
status, use of preventive services, modifiable health risks, and readiness
to change modifiable health risks. The core of the survey was adapted from
the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance
System, which has reliability coefficients for behavioral risk factors above
0.70.18
The primary outcome for this study was time to death during the follow-up
period of August 15, 1995, through February 1, 1999. Mortal events and dates
of death were gathered from the Minnesota Death Index for the years 1995 to
1997. This index provides surveillance of deaths occurring in state residents
(within and outside of the state) as reported on death certificates. We supplemented
these data with information on deaths in 1998 and the first month of 1999
identified in HealthPartners' administrative records system. Because of incomplete
recording of death information in this system, deaths occurring after 1997
may be undercounted, but this is not expected to bias our estimates of the
association between physical activity and mortality.
Important independent variables needed for analyses included age, sex,
and chronic disease status. Age and sex were obtained from health plan administrative
databases. Age was calculated in years from date of birth to the date of the
initial survey and centered on its mean. Chronic disease status for heart
disease, diabetes mellitus, hypertension, and dyslipemia was determined based
on 1994 data, as previously described.
Physical activity was assessed using 2 measures. First, the Godin Leisure
Time Exercise Questionnaire19 measured how
many times in a 7-day period respondents reported engaging in strenuous, moderate,
or mild exercise for more than 15 minutes during their free time. Specific
examples of each category of activity are included in the standard Godin items.
The number of times reported for each of these categories was then multiplied
by 9, 5, or 3 metabolic equivalents, respectively. The total weekly leisure
activity score was calculated in arbitrary units by summing these products.
For analysis, a total Godin score of 0 to 12 was coded as "low"; 13 to 25,
"medium"; and 26 to 280, "high." Since approximately 12% of the sample had
incomplete information for computing the Godin score, our analyses also include
an indicator variable for incomplete data on this measure.
Second, respondents reported how many days in the past week they "have
gotten a total of 30 minutes or more" of physical activity. Respondents were
not instructed in how to define physical activity, but the context for responding
was set for them by having the Godin items appear in the survey immediately
before the single-item measure of physical activity. For analysis, we categorize
this measure as follows: no physical activity (0 days in the past week), low
physical activity (1-3 days in the past week), or high physical activity ( 4
days in the past week). Our analyses also include an indicator of missing
data on this measure.
A current smoker was defined as a respondent
who reported ever having smoked at least 100 cigarettes and who indicated
smoking now. Subjects were defined as functionally impaired using an indicator variable coded 1 if they responded "yes" or "unsure"
to any of a set of 3 questions asking whether any impairments or health problems
limited any of their activities in any way, or caused them to need help with
personal care needs or routine household needs. A modified Charlson score
was calculated using ICD-9-CM diagnostic codes.20 Diagnoses were identified over a 12-month period
preceding the survey. Any members who had not received health care services
in the 12 months before the survey were assigned a missing value for a score,
and did not appear in subsequent analysis. Members with health services use,
but with none of the 19 chronic conditions within the Charlson index, were
assigned a score of 0. Since outpatient encounters may contain "rule-out"
coding, for a member to receive a weight in one of the Charlson conditions,
the member must have had 2 or more diagnoses within the condition. Primary
and secondary ICD-9-CM codes were included. The distribution
of the Charlson score is skewed, but previous work21
has demonstrated that scores of 3 or higher are strongly predictive of mortality.
Therefore, we operationalized the Charlson score as an indicator variable
(1 or 0) for a score of 3 or higher. Body mass index was calculated based
on self-reported body weight and height as kilograms divided by the square
of height in meters.
ANALYTIC MODEL
Cox proportional hazards regression was used as the primary analytic
method.
We tested the assumption of proportionality of covariate effects for
the primary independent variables of interest and found no substantial violations
of this assumption. Analyses were restricted to individuals with complete
responses on all analysis variables. A left truncation issue occurred in our
data, due to the roughly 8-month duration between identification of the study
cohort and first survey administration. This was addressed in standard fashion
by removing all individuals from the risk set between the point of study origin
(December 31, 1994) and the initial survey contact point (August 15, 1995).
To assess undue influence of any individual observation on particular coefficient
estimates, we obtained residuals of the approximate changes in estimates between
the full sample and the sample minus the individual observation.22
These residuals were then plotted against identification number. We identified
no observations that appeared to unduly influence any of the coefficient estimates.
RESULTS
Of the 1901 subjects who responded to the 1995 survey (81.4% response
rate), 1832 had complete data on all study variables, and are the basis of
this report. There were 197 observed deaths within the 42-month follow-up
period.
Table 2 lists the baseline
characteristics of study subjects with and without mortal events during the
follow-up period. There were significant differences in those with and without
events. Those with no event were significantly younger, had significantly
less chronic disease, had a higher body mass index, were less likely to be
impaired in their activities, and were more physically active.
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Table 2. Characteristics of Study Subjects Who Did and Did Not Die
During Follow-up*
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Table 3 summarizes the results
of 4 Cox proportional hazards regression models predicting rates of all-cause
mortality. The models differ only in which baseline measures of physical activity
were included as covariates. Models 1 and 2 include measures based on the
number of days in the past week that respondents reported getting at least
30 minutes of physical activity. Models 3 and 4 include measures of physical
activity based on the Godin Leisure Time Exercise questions. All models include
baseline smoking status and measures of important potential confounders, including
age, sex, comorbidity (Charlson score 3), and functional impairment status.
We assessed 2-way interaction terms between the physical activity measures
and smoking, and between physical activity and impairment, and none were found
to be significant. We report raw coefficient estimates from the models and
mortality risk ratios (MRRs) associated with these coefficients. Coefficient
estimates indicate the extent to which the baseline hazard rate (mortality
rate in this case) is shifted up or down in association with a given individual
characteristic. The MRR provides a more intuitive expression of the coefficient
estimate, describing the mortality risk associated with a characteristic relative
to the reference category.
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Table 3. Models of Probability of Mortality During the 42-Month Follow-up*
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The estimated effects of the confounding variables were nearly identical
across these 4 models, so we limit our discussion of these variables to the
estimates from model 1. Based on these results, we see that older age was
associated with higher mortality, with a 1-SD change in age (11.4 years) increasing
the likelihood of death more than 2-fold (P<.001).
Women were significantly less likely than men to have died during the follow-up
period (P<.001). Having a Charlson comorbidity
score of 3 or greater was associated with more than a 3-fold higher likelihood
of death during follow-up (P<.001). Being functionally
impaired in either routine care needs or usual activities was associated with
a mortality rate that was approximately 70% higher than that of the reference
category (P<.002). Current smokers were nearly
twice as likely to die during follow-up as were nonsmokers in the reference
category (P<.008).
In model 1, baseline physical activity was assessed in terms of an indicator
variable distinguishing those who reported getting less than 30 minutes of
physical activity in the past week from those who got 30 minutes or more of
physical activity. The risk of mortality during follow-up was higher among
those who were completely sedentary at baseline (P<.001),
relative to those with at least 30 minutes of physical activity in the past
week. By contrast, the unadjusted risk of mortality for those totally sedentary
at baseline was somewhat higher (MRR, 2.82; P<.001).
Model 2 compares those who reported getting 30 minutes of physical activity
on 1 to 3 days during the past week with those who reported getting physical
activity on 4 or more days in the past week, with the totally sedentary individuals
being the reference group. There appeared to be a protective effect of moderate
and higher amounts of physical activity, with the risk of mortality being
lower for the 1- to 3-day group (P<.001) and the
4-day or more group (P<.001), relative to those
who were sedentary.
In models 3 and 4, we modeled physical activity in terms of the Godin
scores, categorized into tertiles. Those with Godin scores ranging from 1
to 12 were assigned to the lowest tertile; 13 to 25, the middle tertile; and
26 to 280, the highest tertile. The results of these models were quite similar
to the results for models 1 and 2. Model 3 contrasts those in the lowest tertile
to those in the middle and highest tertiles, and indicates a roughly 2-fold
higher mortality risk associated with being in the lowest tertile of the Godin
score distribution (P<.001). Model 4 also indicates
a protective effect of physical activity, with follow-up mortality being approximately
50% lower among those in either the middle (P<.001)
or top (P<.003) tertile of the Godin score distribution.
The extent to which the association between inactivity and mortality
is modified by the inclusion of potential confounders in the model is not
observable in Table 3. Of most
interest are the modifying effects of including the Charlson measure and the
measure of impairment. In Table 4,
we present the results of model 1 (from Table 3) in a form that demonstrates the effect modification attributable
to these factors. Model 1.a adjusts for all factors except for Charlson score
and impairment. Models 1.b and 1.c show some slight attenuation of this association
after including the Charlson measure and the impairment measure, respectively.
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Table 4. Probability of Mortality During the 42-Month Follow-up Using
Model 1*
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We were concerned that lack of physical activity among those with chronic
illnesses might be an indicator of physical limitations resulting from illnesses
that independently increase their risk of mortality. We tested this possibility
in additional models similar to those reported in Table 3, excluding from the sample those with Charlson scores of
3 or higher. Although SEs increased somewhat because of reduced sample size,
the physical activity findings remained consistent. A third set of analyses,
including the subsample with high Charlson scores but excluding those reporting
functional impairment as previously defined, also yielded similar results.
COMMENT
Study subjects who reported baseline activity of less than 30 minutes
in the past week had a significantly increased risk of mortality over the
subsequent 3 years (MRR, 2.82; P<.001) vs those
with 30 minutes or more of physical activity a week. Increased risk persisted
(MRR, 2.15; P<.001) after adjustments for age,
sex, current smoking, functional impairment, and comorbidity scores. This
result was consistently noted using various measures of physical activity.
Other studies3-8,24
have reported that low levels of physical activity are predictive of subsequent
higher mortality rates. Some of these reports have also noted this association
following a relatively short follow-up period. For example, in the Longitudinal
Study on Aging, Rakowski and Mor24 reported
1098 deaths among a study sample of 6901 after 5 years of follow-up for men
and women aged 70 years and older. Several measures of physical activity were
inversely related to mortality. In one of the largest prospective studies
conducted in the area of physical activity and mortality, Kushi et al,4 in a 7-year follow-up of 40 417 postmenopausal
women aged 55 to 69 years, demonstrated a graded, inverse relationship between
physical activity and all-cause mortality. Their study provides evidence that
even infrequent moderate-intensity activity, ie, once or twice a week, is
associated with a significant reduction in death compared with a completely
sedentary lifestyle.
While it is not clear why our study failed to detect a graded, inverse
association, there are various possible explanations. Prior documentation
of a graded effect of physical activity may be due, in part, to a correlation
between the frequency of physical activity and its intensity. We found little
high-intensity activity in this sample of respondents with multiple chronic
diseases. Thus, our measures would fail to capture any part of the graded
effect that is due to effects of intensity, vs frequency, of physical activity.
Another possibility is that, unlike prior studies, ours was a study
of people with multiple chronic illnesses. It is possible that the benefit
"profile" of physical activity is simply different in healthy vs diseased
populations. Our study documents a clear benefit at the low end of the physical
activity profile. There may well be declining returns to increasing activity
beyond some minimum level among those with chronic disease.
Finally, in one of the largest prior studies,4
the graded effect of physical activity on mortality was not uniform across
the range of frequency of physical activity, but was more pronounced at lower
frequencies, with much less grading in effect between those reporting moderate
physical activity 2 to 4 times per week and those reporting physical activity
more than 4 times per week. Thus, our choice of category cut points might
obscure some effect of grading at lower frequencies of activity.
The Lipid Research Clinics Mortality Follow-up Study25
showed that in men with preexisting cardiovascular disease, the observed adjusted
relative risks of dying after 8 years was 2.9 for those who were unfit
as quantified via treadmill exercise test. At approximately 3 years of follow-up,
the relative risk of dying was 2.15 for those who reported completely sedentary
lifestyles, similar to the estimates derived in our study.
However, results presented herein extend our understanding of this topic
in 2 ways. First, we controlled for functional health status, chronic conditions,
and comorbidities in the analytic model. Most other reports controlled only
for self-reported baseline disease or for limited measures of lipid levels,
blood pressure, and glucose level. We were able to control in the analyses
for 19 chronic conditions as diagnosed by physicians and scored as a sum of
their weights.20 Second, the results indicate
that in some groups of patients the relationship between physical inactivity
and increased mortality operates over a short period—less than 3 years.
The fact that lower levels of physical activity were associated with
significantly higher mortality within only a 42-month period is important
from the perspective of health plans and from the point of view of public
health policy. Health plans often estimate return on investment, and discount
future benefits against present costs. Demonstrating a short-term relationship
between physical activity and mortality indicates the possibility of a positive
health plan return on investment for programs or interventions that promote
physical activity. The potential of a positive return on investment is also
influenced by rates of disenrollment. Health plan disenrollment rates among
older, sicker health plan members have been reported as low in Minnesota13 and high in southern Florida.26
Furthermore, researchers27-28
have shown that a substantial fraction of health care costs is attributable
to low levels of physical activity, obesity, and tobacco use. Some of these
costs may be averted if health plan–enrolled populations maintain favorable
profiles of modifiable health risks.
These findings are not without their limitations. Although the study
was well designed, population based, and prospective, it is still observational.
The data do not prove that increasing physical activity will reduce mortality—only
that physical inactivity is associated with double the risk of mortality after
adjustment for comorbidity, functional status, and other factors.
Thus, there are 2 competing interpretations of the associations found
between inactivity and mortality. The first is that older adults with multiple
chronic diseases may derive a survival benefit from at least minimal physical
activity (at least 30 minutes a day, once a week). The primary clinical implication
of this interpretation would be that there is need for wider recognition of
the potential value of maintaining at least minimal levels of physical activity.
In patients with multiple chronic conditions, there is a tendency for physicians
to emphasize pharmacotherapy, a strategy that has proved worth.29-31
Alternative pathways to reduce mortality, such as encouraging physical activity,
may not be emphasized.
An alternative interpretation of our primary findings is that inactivity
among such patients is, to some extent, a proxy or marker for other factors
that elevate mortality risk (eg, degree of functional impairment, severity
of chronic diseases, or disease progression). While we have attempted to assess
this issue several ways in our analyses, we cannot completely rule out the
possibility that this explains some portion of the observed association. If
this second interpretation is correct, the primary clinical implication would
be that a simple inquiry regarding lack of activity in the past week may help
clinicians to identify which of their patients with multiple chronic diseases
are at increased risk of dying in the short-term.
While the data we present do not establish that increased levels of
physical activity will reduce mortality in the short-term, other reports32 suggest that such a benefit is plausible on physiologic
grounds. For example, regular physical activity has been shown to decrease
insulin resistance, lower blood pressure, and reduce serum fibrinogen levels,
plasminogen activator inhibitor 1 activity, and platelet adhesion.33-38
These short-term benefits of physical activity would be especially beneficial
to patients similar to those enrolled in this study—those with established
hypertension, lipid disorders, diabetes mellitus, or heart disease. Although
physical activity levels at baseline may reflect longer-duration physical
activity over decades,39 it is plausible from
the biological point of view that activity benefits are greater for more recent
physical activity than for more remote years of physical activity. Thus, efforts
to increase physical activity levels of patients who have long been sedentary
may still have a pronounced beneficial effect. In the present study, we did
not measure lifetime physical activity or change in physical activity level
during the study period. Thus, it could be argued that the observed short-term
relationship of activity to mortality may actually derive from the cumulative
effect of many previous years of activity or inactivity. However, physical
activity in patients with chronic disease may exert a beneficial effect within
a short time. Benefits of physical activity are likely mediated metabolically,
and most proposed metabolic effects of physical activity are rapid in onset
and relatively short.40
There is considerable interest in addressing the problem of physical
inactivity through public health policy. The data presented herein suggest
that, for some subjects—and possibly for many people—policy initiatives
that encourage at least low levels of physical activity may have the potential
to yield substantial clinical and public health benefits. Interventions that
might be considered include such practices as walking a dog daily, walking
in malls, or walking in the neighborhood. One logical group to target is those
who are completely inactive—approximately 25% of adults in most surveys,
including ours.
In a previous report,41 it was established
that patient readiness to change to better health-related behaviors is higher
in patients with chronic conditions than in those without such chronic conditions.
Furthermore, lower health care charges are associated with higher levels of
physical activity,27 and the net potential
savings appear to be greatest in those with highest charges—namely,
those who are oldest or sickest. Thus, the benefits of increased physical
activity may be greatest in the oldest, sickest patients. Contrary to the
fears of many elderly patients, and possibly their physicians, we observe
no increased mortality associated with increasing levels of physical activity.
Despite the limitations of these data, we believe the results are interesting
and important. The data establish that physical inactivity in patients with
chronic conditions is associated with twice the rate of subsequent mortality
over a short follow-up period than that of more active people, and suggest
that clinical and population-based interventions to increase physical activity
in such patients may have the potential to decrease mortality and costs of
care.
AUTHOR INFORMATION
Accepted for publication October 3, 2000.
This study was funded by HealthPartners Center for Health Promotion
and HealthPartners Research Foundation, Minneapolis, Minn.
We thank George Isham, MD, George Halvorson, and Ted Wise for their
help in the conceptualization of this study.
Corresponding author and reprints: Brian C. Martinson, PhD, HealthPartners
Research Foundation, 8100 34th Ave S, PO Box 1524, Minneapolis, MN 55440-1524
(e-mail: Brian.C.Martinson{at}HealthPartners.com).
From HealthPartners Research Foundation (Drs Martinson and O'Connor)
and HealthPartners Center for Health Promotion (Dr Pronk), Minneapolis, Minn.
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