Kline et al , 2016 The effect of changes in cardiorespiratory fitness and weight on obstructive sleep apnea severity in overweight adults with type 2 diabetes

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SLEEP, Vol. 39, No. 2, 2016 317 Fitness, Weight, and OSA—Kline et al.
SLEEP DISORDERED BREATHING
The Effect of Changes in Cardiorespiratory Fitness and Weight on Obstructive 
Sleep Apnea Severity in Overweight Adults with Type 2 Diabetes
Christopher E. Kline, PhD1; David M. Reboussin, PhD2; Gary D. Foster, PhD3,4; Thomas B. Rice, MD, MS1; Elsa S. Strotmeyer, PhD, MPH1; 
John M. Jakicic, PhD1; Richard P. Millman, MD5; F. Xavier Pi-Sunyer, MD6; Anne B. Newman, MD, MPH1; Thomas A. Wadden, PhD7; Gary Zammit, PhD8; 
Samuel T. Kuna, MD7,9; Sleep AHEAD Research Group of the Look AHEAD Research Group
1University of Pittsburgh, Pittsburgh, PA; 2Wake Forest University, Winston-Salem, NC; 3Temple University, Philadelphia, PA; 4Weight Watchers International, New York, NY; 
5Brown University, Providence, RI; 6Columbia University, New York, NY; 7University of Pennsylvania, Philadelphia PA; 8Clinilabs, New York, NY; 9Philadelphia Veterans Affairs 
Medical Center, Philadelphia, PA
Study Objectives: To examine the effect of changes in cardiorespiratory fitness on obstructive sleep apnea (OSA) severity prior to and following adjustment 
for changes in weight over the course of a 4-y weight loss intervention.
Methods: As secondary analyses of a randomized controlled trial, 263 overweight/obese adults with type 2 diabetes and OSA participated in an intensive 
lifestyle intervention or education control condition. Measures of OSA severity, cardiorespiratory fitness, and body weight were obtained at baseline, year 1, 
and year 4. Change in the apnea-hypopnea index (AHI) served as the primary outcome. The percentage change in fitness (submaximal metabolic equivalents 
[METs]) and change in weight (kg) were the primary independent variables. Primary analyses collapsed intervention conditions with statistical adjustment for 
treatment group and baseline METs, weight, and AHI among other relevant covariates.
Results: At baseline, greater METs were associated with lower AHI (B [SE] = −1.48 [0.71], P = 0.038), but this relationship no longer existed (B [SE] = −0.24 
[0.73], P = 0.75) after adjustment for weight (B [SE] = 0.31 [0.07], P < 0.0001). Fitness significantly increased at year 1 (+16.53 ± 28.71% relative to 
baseline), but returned to near-baseline levels by year 4 (+1.81 ± 24.48%). In mixed-model analyses of AHI change over time without consideration of weight 
change, increased fitness at year 1 (B [SE] = −0.15 [0.04], P < 0.0001), but not at year 4 (B [SE] = 0.04 [0.05], P = 0.48), was associated with AHI reduction. 
However, with weight change in the model, greater weight loss was associated with AHI reduction at years 1 and 4 (B [SE] = 0.81 [0.16] and 0.60 [0.16], both 
P < 0.0001), rendering the association between fitness and AHI change at year 1 nonsignificant (B [SE] = −0.04 [0.04], P = 0.31).
Conclusions: Among overweight/obese adults with type 2 diabetes, fitness change did not influence OSA severity change when weight change was taken 
into account.
Clinical Trial Registration: ClinicalTrials.gov identification number NCT00194259
Keywords: apnea-hypopnea index, cardiorespiratory fitness, obstructive sleep apnea, physical activity, weight loss
Citation: Kline CE, Reboussin DM, Foster GD, Rice TB, Strotmeyer ES, Jakicic JM, Millman RP, Pi-Sunyer X, Newman AB, Wadden TA, Zammit G, Kuna ST, 
Sleep AHEAD Research Group. The effect of changes in cardiorespiratory fitness and weight on obstructive sleep apnea severity in overweight adults with 
type 2 diabetes. SLEEP 2016;39(2):317–325.
INTRODUCTION
Obstructive sleep apnea (OSA) is a sleep disorder with signifi-
cant public health implications. The prevalence of at least mod-
erate-severity OSA (i.e., apnea-hypopnea index [AHI] ≥ 15) 
is approximately 10% among adults, reflecting a dramatic 
increase over the past 20 y.1 Moreover, a wide range of ad-
verse health consequences have been linked to OSA, including 
cognitive impairment,2 metabolic dysfunction,3 cardiovascular 
disease,4 and early mortality.5 As such, it is important to iden-
tify potential modifiable factors associated with OSA severity.
OSA is commonly associated with excess weight.6 Epidemio-
logic research has shown that weight change is a strong predictor 
of OSA severity change,7,8 and experimental data demonstrate 
that weight loss significantly reduces OSA severity.9,10 In partic-
ular, lifestyle interventions have demonstrated that even modest 
weight loss can substantially reduce OSA severity through di-
etary modification and increased physical activity.11–15
The relationship between OSA and cardiorespiratory fit-
ness is less defined. As a physiological measure of the body’s 
ability to deliver and utilize oxygen during sustained activity,16 
pii: sp-00338-15 http://dx.doi.org/10.5665/sleep.5436
Significance
Behavioral weight loss interventions that promote dietary modification and increased physical activity have been shown to successfully reduce 
obstructive sleep apnea (OSA) severity, but the importance of change in cardiorespiratory fitness relative to weight change was unclear. In these 
analyses, we found that weight change was a stronger predictor of OSA severity reduction than change in fitness, suggesting that weight loss should 
be emphasized in the behavioral treatment of OSA regardless of change in fitness. Because physical activity is only modestly associated with 
cardiorespiratory fitness, future work should examine the influence of physical activity behavior on OSA severity relative to weight loss.
cardiorespiratory fitness is a strong independent predictor of 
morbidity and mortality, especially cardiovascular outcomes.17,18 
Notably, recent research has found that cardiorespiratory fitness 
attenuates the health risks associated with obesity.19 Adults with 
OSA have been found to have lower cardiorespiratory fitness in 
multiple cross-sectional investigations,20–24 though others have 
failed to observe lower fitness when OSA patients were care-
fully matched to non-OSA controls on body mass and age.25–28
Whether change in cardiorespiratory fitness contributes to 
change in OSA severity is unclear. Physical activity is the main 
modifiable determinant of cardiorespiratory fitness,29 and a re-
cent meta-analysis found that 3–6 mo of structured physical ac-
tivity increases cardiorespiratory fitness by approximately 18% 
and reduces AHI by 32% in adults with OSA.30 However, only 
one study has specifically examined whether fitness was related 
to OSA severity change. In their trial, Kline and colleagues25 
found that change in cardiorespiratory fitness was not associ-
ated with AHI change following a 12-w exercise intervention.
Given the paucity of existing data, the relative importance 
of cardiorespiratory fitness versus weight loss in reducing OSA 
SLEEP, Vol. 39, No. 2, 2016 318 Fitness, Weight, and OSA—Kline et al.
severity remains uncertain. The current study utilized data 
from the Sleep AHEAD (Action for Health in Diabetes) study, 
a longitudinal trial investigating the effect of an intensive life-
style intervention (ILI) on OSA severity in overweight/obese 
adults with type 2 diabetes, to examine this question. As previ-
ously reported in the Sleep AHEAD cohort, the ILI produced 
a significant reduction in OSA severity despite weight regain 
at the 4-y follow-up. Moreover, the ILI effect on OSA severity 
remained significant even after accounting for weight change.15 
These findings suggest that factors in the ILI other than weight 
loss—for example, cardiorespiratory fitness—may have con-
tributed to the improvement in OSA severity.
Therefore, the current study investigated the cross-sectional 
relationship between cardiorespiratory fitness and OSA se-
verity and change in fitness and OSA severity over the 4-y 
follow-up in the Sleep AHEAD cohort. Analyses examined 
this relationship prior to and following adjustment for weight 
and weight change. We hypothesized that higher levels of car-
diorespiratory fitness would be significantly associatedwith 
lower OSA severity at baseline and that changes in cardiore-
spiratory fitness would be associated with changes in OSA se-
verity at 1 and 4 y. We also hypothesized that accounting for 
weight and its change over time would attenuate, though not 
eliminate, the cross-sectional and longitudinal relationships 
between fitness and OSA severity.
METHODS
The Sleep AHEAD study is an ancillary study of the Look 
AHEAD trial, a prospective randomized study investigating 
the long-term health effect of an ILI in 5,145 overweight/obese 
adults with type 2 diabetes. Details of the Look AHEAD and 
Sleep AHEAD study designs, baseline participant characteris-
tics, and interventions are provided elsewhere.31–34
Participants
Primary inclusion criteria for Look AHEAD were age 45–76 y, 
body mass index (BMI) ≥ 25 kg/m2 (or > 27 kg/m2 if taking 
insulin), physician-verified type 2 diabetes, hemoglobin A1c 
(HbA1c) < 11 %, and blood pressure < 160/100 mm Hg.31 An 
additional exclusion criterion for Sleep AHEAD included 
previous surgical or current medical treatment for OSA. In-
dividuals with previously diagnosed but untreated OSA were 
eligible to participate.
Prospective Sleep AHEAD participants were recruited at 4 
of the 16 Look AHEAD sites; a total of 306 Sleep AHEAD 
study participants were enrolled. Sleep AHEAD participants 
were representative of Look AHEAD participants at the four 
sites except they were slightly older (61.3 ± 6.5 y vs. 58.7 ± 6.9 
y) and had lower HbA1c values (7.2 ± 1.1 % vs. 7.4 ± 1.2 %).34 
For the purposes of these analyses, participants were ex-
cluded due to lack of OSA at baseline (apnea-hypopnea index 
[AHI] < 5; n = 41), diagnosis of central sleep apnea at baseline 
(n = 1) or lack of baseline cardiorespiratory fitness data (n = 1), 
leaving 263 participants for analysis.
The protocol was approved by each site’s Institutional 
Review Board and participants provided written informed 
consent. The study was registered at www.clinicaltrials.gov 
(NCT00194259).
Interventions
As part of the Look AHEAD study, participants were randomly 
assigned to an ILI or Diabetes Support and Education (DSE) 
condition, with randomization stratified by clinical site.
Intensive Lifestyle Intervention
Details of the ILI intervention have been previously re-
ported.31,33 Participants allocated to ILI received a behavioral 
weight loss program developed for obese patients with type 2 
diabetes. Efforts to modify dietary behavior included prescrip-
tion of caloric intake goals and a structured dietary program 
consisting of meal replacements and self-selected meals. Par-
ticipants were also prescribed 175 min/w of moderate-intensity 
physical activity (e.g., brisk walking), relying primarily on 
home-based activity. During months 1–6, the ILI consisted 
of both group (three times/month) and individual (one time/
month) sessions; during months 7–12, participants had one in-
dividual and two group sessions per month. In years 2 through 
4, the ILI intervention was provided mainly on an individual 
basis and included at least one on-site visit per month and a 
second contact by telephone, mail or Email. At each session, 
participants were weighed, self-monitoring records were re-
viewed, and a new lesson was presented.
Diabetes Support and Education
Details of the DSE intervention have previously been described 
in detail.31 Briefly, participants allocated to DSE attended three 
group sessions each year for 4 y; sessions focused on diet, phys-
ical activity, and social support as they related to effective dia-
betes management. Information was not provided on behavioral 
strategies, and participants were not weighed at the sessions.
Measures
Polysomnography
OSA severity was assessed using a home-based unattended 
polysomnogram (PSG) during baseline and years 1, 2, and 4 
(baseline, years 1 and 2: P-Series PS2 [Compumedics, Abbots-
ville, Australia]; year 4: Safiro [Compumedics]).15 The tech-
niques and protocol developed for the Sleep Heart Health Study 
were used for the home sleep studies,35 and all PSG recordings 
were scored at a centralized reading laboratory. Sleep stage 
scoring was performed according to standard procedures.36 
An apnea was defined as the cessation of airflow for ≥ 10 sec, 
either with (i.e., obstructive) or without (i.e., central) respira-
tory effort. A hypopnea was defined as ≥ 30 % reduction in 
airflow or thoracoabdominal movement for ≥ 10 sec with ≥ 4 % 
oxygen desaturation from baseline. The AHI was calculated as 
the average number of apneas and hypopneas per hour of sleep 
and served as our primary measure of OSA severity.
After each visit, participants and their primary care pro-
viders were informed about the PSG results. If OSA was 
present (i.e., AHI ≥ 5), they were also notified of the severity 
of OSA. Prior to subsequent visits, participants were asked if 
they had received treatment for OSA since the last PSG; those 
who indicated current positive airway pressure treatment were 
asked to refrain from using the treatment for 3 nights prior to 
the subsequent PSG.
SLEEP, Vol. 39, No. 2, 2016 319 Fitness, Weight, and OSA—Kline et al.
Cardiorespiratory Fitness
A graded exercise treadmill test assessed cardiorespiratory fit-
ness at baseline and years 1 and 4.37 The speed of the treadmill 
for the baseline test was set at 1.5, 2.0, 2.5, 3.0, 3.5, or 4.0 mph 
based upon the participant’s preferred walking speed and heart 
rate response during the first minute of the test; this speed re-
mained constant throughout the test. The grade of the treadmill 
was initially set at 0 % and increased by 1 % at 1-min inter-
vals throughout the test. Heart rate was assessed at rest, during 
the last 10 sec of every 1-min interval, and at the point of test 
termination using a 12-lead electrocardiogram. Rating of per-
ceived exertion (RPE) was assessed using the Borg scale (range: 
6–20)38 during the last 15 sec of each 1-min interval and at the 
point of test termination. Blood pressure was assessed using a 
manual sphygmomanometer and stethoscope during the last 45 
sec of each even minute interval (e.g., 2 min, 4 min, etc.).
The exercise test at baseline was maximal. The treadmill test 
was terminated at the point of volitional exhaustion or at the 
point where American College of Sports Medicine test termi-
nation criteria39 were achieved. A baseline test was considered 
valid if the maximal heart rate was ≥ 85 % of age-predicted 
maximal heart rate (i.e., 220-age) for participants not taking 
beta-blocker medication. For those taking a beta-blocker, the 
baseline test was considered valid if RPE was ≥ 18 at the point 
of termination. To meet Look AHEAD eligibility, all partici-
pants needed to achieve ≥ 4.0 metabolic equivalents (METs) on 
the baseline test, calculated from the treadmill workload (i.e., 
speed and grade) at the point of test termination.39
The exercise tests at years 1 and 4 were submaximal. The 
tests were performed at the same walking speed as the baseline 
test and were terminated when the participant reached ≥ 80 % 
of age-predicted maximal heart rate if the participant was not 
taking beta-blocker medication at baseline or years 1 or 4. If 
the participant was taking a beta-blocker at the time of the ex-
ercise test at years 1 or 4 or had previously taken a beta-blocker 
at the time of a previous exercise test, the submaximal test was 
terminated at the point when the participant first reported an 
RPE ≥ 16 (i.e., 80 % of a maximal RPE of 20).
Cardiorespiratory fitness was defined as the estimated MET 
level based upon the treadmill workload using the aforemen-
tioned criterion of attaining 80% of maximal heart rate for 
participants not taking a beta-blocker medication or the crite-
rion of achieving an RPE of 16 for those taking a beta-blocker. 
Change in cardiorespiratory fitness was defined as the differ-
ence in estimated submaximal METs attained at years 1 and 4 
and the submaximal METs attained at baseline using the same 
terminationcriteria of attaining either 80% of age-predicted 
maximal heart rate or RPE ≥ 16.
Body Weight
Height and weight were assessed using standard methods 
within 1 w of each PSG recording.32
Statistical Analyses
All analyses examined the association between cardiorespira-
tory fitness and AHI without and with adjustment for weight. 
Analyses explored this relationship at baseline and the associa-
tion between change in METs and change in AHI across years 
1 and 4. The baseline relationship between fitness and AHI 
was assessed using multiple linear regression with baseline 
AHI as the dependent variable, baseline METs as the inde-
pendent variable, and adjustment for age, sex, race/ethnicity, 
clinical site, and use of beta-blocker medication at the time of 
the exercise test. Baseline weight was then added to the model 
as a second independent variable. The primary analyses uti-
lized two mixed-effect regression models of AHI change from 
baseline to years 1 and 4; the independent variable in Model 
1 was percent change in METs, with statistical adjustment for 
intervention group, age, sex, race/ethnicity, clinical site, OSA 
treatment (time-varying), beta-blocker medication use during 
exercise testing (time-varying), and baseline values of AHI 
and fitness. In Model 2, change in weight (kg) was added as 
a second independent variable and baseline weight was added 
as a covariate. An unstructured covariance pattern was used 
to model correlations over time for these analyses. Further-
more, using the same independent variables and covariates as 
listed previously, additional analyses investigated AHI change 
at years 1 and 4 in separate models. Finally, we also examined 
the influence of fitness and weight change on AHI change over 
time separately by ILI and DSE groups.
Analyses were performed using SAS version 9.3 (SAS Insti-
tute, Cary, NC). All tests were two-tailed using an alpha level 
of 0.05.
RESULTS
Baseline Participant Characteristics
Baseline characteristics of the 263 participants are summa-
rized in Table 1. Mean (± standard deviation [SD]) age was 
61.3 ± 6.5 y, approximately 59% of the sample were female, 
Table 1—Baseline study participant characteristics (n = 263).
Sociodemographics
Age, y 61.3 (6.5)
Sex, female, n (%) 155 (58.9)
Race/ethnicity, n (%)
African-American 48 (18.3)
Caucasian 193 (73.4)
Other 22 (8.4)
Diabetes severity and duration
Hemoglobin A1c, % 7.2 (1.0)
Self-reported duration of diabetes, y 7.3 (7.0)
OSA severity
AHI, events/h 23.2 (16.5)
Body composition
Weight, kg 102.2 (18.2)
BMI, kg/m2 36.5 (5.7)
Cardiorespiratory fitness
Submaximal METs 4.9 (1.6)
Beta-blocker medication use, n (%) 73 (27.8)
Data are presented as mean (standard deviation) or n (%), as appropriate. 
AHI, apnea-hypopnea index; BMI, body mass index; METs, metabolic 
equivalents; OSA, obstructive sleep apnea.
SLEEP, Vol. 39, No. 2, 2016 320 Fitness, Weight, and OSA—Kline et al.
and 73.4% were Caucasian. AHI was 23.2 ± 16.5 events/h, BMI 
was 36.5 ± 5.7 kg/m2, and submaximal METs was 4.9 ± 1.6.
Baseline Association between Fitness and AHI
After basic covariate adjustment, greater baseline METs were 
significantly associated with lower baseline AHI (β = −0.14, 
P = 0.038). After further adjustment for baseline weight, METs 
were no longer associated with AHI (β = −0.02, P = 0.75); 
weight was the only significant predictor of AHI (β = 0.34, 
P < 0.0001), with greater weight being associated with greater 
AHI.
Changes in Fitness, Weight, and AHI from Baseline
Of the 263 participants with baseline data, 205 participants 
had valid METs and AHI data at years 1 and/or 4. At year 1, 
data were available for 188 participants; 30 participants were 
missing METs data only, 27 participants were missing AHI 
data only, and 18 participants were missing both METs and 
AHI data. At year 4, data were available for 131 participants; 
33 participants were missing METs data only, 66 participants 
were missing AHI data only, and 33 participants were missing 
both METs and AHI data.
Figure 1 shows the mean changes (adjusted least-squares 
means ± standard error) in fitness, body weight, and AHI from 
baseline by intervention group. The ILI group had signifi-
cantly greater improvement in fitness than the DSE group at 
years 1 and 4, with between-group MET change differences 
of 23.49 ± 3.44% and 11.62 ± 3.70% at years 1 and 4, respec-
tively (P < 0.01 each). Fitness significantly decreased from year 
1 to 4 in the DSE (P < 0.001) and ILI (P < 0.0001) groups. 
The ILI group also had significantly greater weight loss than 
the DSE group at years 1 and 4. The difference in weight loss 
between the two groups was 10.52 ± 0.92 kg and 5.01 ± 1.28 
kg at years 1 and 4, respectively (P < 0.001 each). From year 
1 to 4, the ILI group experienced significant weight regain 
(P < 0.0001), whereas weight change relative to baseline re-
mained stable from year 1 to 4 in the DSE group (P = 0.72). 
Across both intervention groups, changes in fitness and weight 
were significantly correlated at years 1 and 4 (r = −0.54 and 
r = −0.37, respectively, both P < 0.001). Finally, the ILI group 
had significantly greater AHI reduction than the DSE group 
at years 1 and 4. The difference in AHI reduction between the 
two groups was 9.63 ± 2.04 events/h and 7.10 ± 2.24 events/h 
at years 1 and 4, respectively (P < 0.01 each). The change in 
AHI from year 1 to 4 did not significantly differ in the DSE 
(P = 0.80) or ILI (P = 0.26) groups.
Twelve participants reported OSA treatment at year 1 (DSE: 
n = 7; ILI: n = 5), and 20 participants reported OSA treatment 
at year 4 (DSE: n = 15; ILI: n = 5). Sixty-three participants re-
ported beta-blocker medication use at year 1 (DSE: n = 35; ILI: 
n = 28), and 60 participants reported beta-blocker use at year 4 
(DSE: n = 32; ILI: n = 28).
Effect of Fitness Change on AHI Change over Time
Results of analyses focused on the effect of fitness change on 
AHI change over time are summarized in Table 2 (Model 1). In 
the primary model accounting for change in METs and change 
in AHI across years 1 and 4, intervention group was the only 
significant individual predictor of AHI change (P = 0.001); nei-
ther baseline METs (P = 0.13) nor the percentage change in 
METs from baseline (P = 0.13) were significantly associated 
with AHI change. However, the interaction term for visit year 
and percentage change in METs was significant (P = 0.047), 
indicating that the effect of fitness change on AHI differed 
across years 1 and 4. When focusing on AHI change at year 
1, the intervention group, baseline METs and the percentage 
change in METs from baseline to year 1 were significant pre-
dictors of AHI change (each P ≤ 0.01). For AHI change at year 
4, intervention group was the only significant predictor of AHI 
change (P = 0.02). Baseline METs and the percentage change 
in METs from baseline were not associated with AHI change 
at y 4 (P = 0.60 and P = 0.48, respectively). Results were un-
changed when restricting analyses to only those who provided 
year 4 data (n = 131; data not shown).
Exploratory analyses indicated that baseline OSA severity 
influenced the association between fitness change and AHI 
Figure 1—Mean changes (adjusted least-squares means ± standard error) in fitness, body weight, and apnea-hypopnea index (AHI) from baseline at years 
1 and 4 according to intervention group. The dashed line (open circles) indicates the Diabetes Support and Education (DSE) group; the solid line (closed 
squares) indicates the Intensive Lifestyle Intervention (ILI) group. The asterisk indicates a significant difference between the DSE and ILI groups (P < 0.05).
SLEEP, Vol. 39, No. 2, 2016 321 Fitness, Weight, and OSA—Kline et al.
change across years 1 and 4 (P = 0.01), such that the effect of 
fitness change on AHI change was greatest among those with 
severe OSA at baseline (AHI ≥ 30) compared to those with 
baseline mild or moderate OSA (AHI 5 to < 15 and 15 to < 30, 
respectively). Whenrestricting to AHI change at year 1 or year 
4, the influence of baseline OSA severity was significant at 
year 1 (P < 0.001) but not year 4 (P = 0.60).
Effect of Fitness and Weight Changes on AHI Change over Time
Analyses investigating the independent effects of fitness change 
and weight change on AHI change over time are summarized 
in Table 2 (Model 2). In the primary model examining change 
in AHI across years 1 and 4, weight change was the only signif-
icant predictor of AHI change (P < 0.0001); intervention group 
(P = 0.36), baseline METs (P = 0.41), the percentage change in 
METs from baseline (P = 0.22), baseline weight (P = 0.44) and 
the visit × METs change and visit × weight change interaction 
terms (P = 0.10 and P = 0.11) were not associated with change 
in AHI. When isolating analyses to AHI change at year 1 only, 
baseline METs and weight change were significant predictors 
of AHI change (P = 0.05 and P < 0.0001, respectively); inter-
vention group (P = 0.62) and the percentage change in METs 
(P = 0.31) were not significantly associated with AHI change. 
For AHI change at year 4 only, the percentage change in METs 
and weight change were significant predictors of AHI change 
(P = 0.03 and P < 0.0001, respectively). Intervention group 
(P = 0.07), baseline METs (P = 0.35), and baseline weight 
(P = 0.11) were not associated with AHI change. Results were 
unchanged when restricting analyses to only those who pro-
vided year 4 data (n = 131; data not shown).
Exploratory analyses indicated that baseline OSA severity 
influenced the association between weight change and AHI 
change across years 1 and 4 (P < 0.0001), but the association 
between fitness change and AHI change did not differ ac-
cording to baseline OSA severity when weight change was 
added to the model (P = 0.13). The effect of weight change 
on AHI change was greatest among those with severe OSA at 
baseline (AHI ≥ 30) compared to those with baseline mild or 
moderate OSA (AHI 5 to < 15 and 15 to < 30, respectively; both 
P < 0.0001). When analyses were restricted to AHI change at 
Table 2—Mixed-effects models and exploratory analyses examining change in apnea-hypopnea index over time relative to changes in fitness and 
weight.
Variable
Model 1 (weight not included)a Model 2 (weight included)a
F value P value Estimate SE F value P value Estimate SE
AHI change at years 1 and 4 (n = 205)
Baseline AHI (events/h) 41.99 < 0.0001 −0.33 0.05 38.35 < 0.0001 −0.31 0.05
Intervention group (ILI) 10.72 0.001 0.83 0.36
Visit year 1.41 0.24 2.72 0.10
Visit * Intervention group 0.31 0.58 1.70 0.19
Baseline METs 2.37 0.13 −0.99 0.65 0.68 0.41 −0.55 0.66
% METs change 2.36 0.13 1.49 0.22
Visit * % METs change 3.99 0.047 2.66 0.10
Baseline weight (kg) 0.61 0.44 0.05 0.06
Weight change (kg) 43.21 < 0.0001
Visit * weight change 2.57 0.11
AHI change at year 1 only (n = 188)
Baseline AHI (events/h) 24.29 < 0.0001 −0.30 0.06 18.50 < 0.0001 −0.25 0.06
Intervention group (ILI) 6.37 0.01 −5.51 2.18 0.25 0.62 1.19 2.38
Baseline METs 6.38 0.01 −1.93 0.76 3.75 0.05 −1.50 0.78
% METs change 12.62 < 0.001 −0.15 0.04 1.04 0.31 −0.04 0.04
Baseline weight (kg) 0.06 0.81 −0.02 0.07
Weight change (kg) 27.00 < 0.0001 0.81 0.16
AHI change at year 4 only (n = 131)
Baseline AHI (events/h) 28.80 < 0.0001 −0.43 0.08 35.96 < 0.0001 −0.47 0.08
Intervention group (ILI) 5.75 0.02 −6.20 2.59 3.37 0.07 −4.58 2.50
Baseline METs 0.28 0.60 0.54 1.02 0.88 0.35 0.97 1.04
% METs change 0.50 0.48 0.04 0.05 4.86 0.03 0.13 0.06
Baseline weight (kg) 2.65 0.11 0.15 0.09
Weight change (kg) 14.24 < 0.001 0.60 0.16
Model 1 examined the effect of the change in fitness (% METs change) on change in AHI without accounting for weight change; Model 2 examined the 
effect of fitness change on AHI while accounting for weight change (in kg). An unstructured covariance pattern was used to model correlations over time 
for the primary model. aModel also adjusted for clinical site, age, sex, race/ethnicity, current treatment for OSA, and beta-blocker medication use during 
exercise testing. AHI, apnea-hypopnea index; ILI, Intensive Lifestyle Intervention group; METs, metabolic equivalents; SE, standard error; Visit, assessment 
timepoint following randomization (0, 1, or 4 y).
SLEEP, Vol. 39, No. 2, 2016 322 Fitness, Weight, and OSA—Kline et al.
year 1 or year 4, an effect of baseline OSA severity on the asso-
ciation between weight change and AHI change was observed 
at both year 1 (P < 0.0001) and year 4 (P = 0.001). Baseline 
OSA severity did not alter the association of fitness change 
with AHI change at year 1 (P = 0.87) or year 4 (P = 0.35) when 
weight change was added to the model.
Effect of Fitness and Weight Changes on AHI Change 
According to Intervention Group
We explored the possibility that the effects of fitness and 
weight change on AHI change could differ according to in-
tervention group using interaction terms inserted into Model 
2 (i.e., weight change included in model). Neither the effect 
of fitness change on AHI nor the effect of weight change on 
AHI differed by intervention group (P = 0.14 and P = 0.38, 
respectively).
When restricting analyses to only the ILI group, the asso-
ciation between fitness change and AHI change differed by 
visit year (P = 0.01) prior to adjustment for weight change (i.e., 
Model 1). At year 1, greater baseline METs and the percentage 
change in METs from baseline were associated with greater 
AHI reduction (i.e., reductions of 2.48 events/h for every one-
unit increase in baseline METs [P = 0.02] and 0.17 events/h for 
every percentage increase in METs from baseline [P < 0.0001], 
respectively); at year 4, neither baseline METs nor METs 
change were associated with AHI change. Following adjust-
ment for weight change (Model 2), the relationship between 
fitness change and AHI change differed by visit year (P = 0.04). 
At year 1, weight change was the only significant predictor of 
AHI change (P < 0.001; reduction of 0.65 events/h for every kg 
of weight loss); at year 4, weight loss was significantly associ-
ated with AHI reduction (P = 0.005; reduction of 0.73 events/h 
for every kg of weight loss), whereas increased fitness from 
baseline was associated with increased AHI (P = 0.04; in-
crease of 0.14 events/h for every percentage increase in METs 
from baseline).
When considering the DSE group only, no relationship was 
found between fitness change and AHI change prior to ad-
justment for weight change. Following adjustment for weight 
change, the relationship between fitness change and AHI 
change differed by visit year (P = 0.03). At year 1, weight change 
was the only significant predictor of AHI change (P = 0.01; re-
duction of 0.88 events/h for every kg of weight loss); at year 4, 
neither fitness change (P = 0.09) nor weight change (P = 0.27) 
were significantly associated with weight change.
DISCUSSION
Lifestyle interventions have demonstrated that weight loss re-
sults in reduced OSA severity,11–15 but whether cardiorespira-
tory fitness exerts an independent effect on OSA severity was 
unknown. In our previous report that documented the long-
term efficacy of a lifestyle intervention on OSA severity,15 we 
hypothesized that increased cardiorespiratory fitness may have 
had an additive effect on AHI reduction because the ILI had a 
significant effect on AHI change over a 4-y period even after 
accounting for weight change, with sustained AHI reduction 
despite a 50% weight regain from years 1 to 4. In the current 
analyses, baseline and short-term changes in cardiorespiratory 
fitness had a significant influence on baseline and short-term 
AHI change, respectively, but these effects were eliminated 
when we accounted for the effect of baseline weight and 
weight change on AHI. Moreover, although accounting for 
fitness change and weight change resulted in the intervention 
group being a nonsignificant predictor of AHI change at year 4, 
weight change was the only significant predictor of long-term 
AHI change.In addition to our results, other observational studies and 
lifestyle intervention trials have documented a significant re-
lationship between weight change and change in OSA severity. 
Data from the Wisconsin Sleep Cohort and the Sleep Heart 
Health Study indicated that even small changes in weight over 
4–5 y were associated with clinically significant changes in 
AHI.7,8 Johansson and colleagues11 found that short-term reduc-
tions in AHI attained with a very low calorie diet could be 
sustained at 1 y using a standard weight loss program, with a 
dose-response relation observed between weight loss and AHI 
reduction at 1 y. Finally, Tuomilehto and colleagues demon-
strated that a 1-y lifestyle intervention significantly reduced 
AHI among adults with mild OSA,12 and that these effects were 
still observed 1 and 4 y after the intervention concluded.40,41 
Moreover, regardless of treatment group, weight change fol-
lowing the intervention and at 1- and 4-y follow-up visits was a 
significant predictor of AHI change.40,41 Obesity is implicated 
in the pathogenesis of OSA through several mechanisms (e.g., 
upper airway narrowing, increased pharyngeal collapsibility, 
greater mechanical loading of the chest wall),42 and short-term 
weight loss has been shown to reduce upper airway collaps-
ibility43 and increase upper airway size.44 Recent data also 
highlight the importance of weight loss to reduced cardiovas-
cular morbidity in those with OSA.45 Therefore, weight loss 
has clear clinical significance as a behavioral treatment option 
for OSA.46
Much less investigation has focused on the relationship be-
tween cardiorespiratory fitness and OSA, and nearly all of the 
available evidence has been cross-sectional in nature. Multiple 
studies have found lower cardiorespiratory fitness among pa-
tients with OSA,20–24 with some even demonstrating an inverse 
linear relationship between fitness and OSA severity.21,22,24 The 
intermittent hypoxia and recurring sympathetic surges that 
are characteristic of OSA have been speculated to cause lower 
cardiorespiratory fitness through impaired oxidative capacity22 
or decreased cardiac output due to ventricular dysfunction.47 
However, other studies have found similar levels of cardiore-
spiratory fitness between OSA patients and non-OSA controls, 
most commonly when controls were matched to OSA patients 
on indices such as BMI and age.25–28 We found fitness to be in-
versely related to AHI, but this association did not persist after 
we adjusted for body weight. Thus, in agreement with these 
latter studies,25–28 our data suggest that lower cardiorespiratory 
fitness among adults with OSA may be driven by excess weight.
Existing data on the potential relationship between longitu-
dinal changes in cardiorespiratory fitness and OSA are even 
more sparse, as only one study examined this association. In 
this trial, Kline and colleagues25 found that a 12-w exercise 
training intervention led to increased cardiorespiratory fitness 
and reduced AHI, but these changes were unrelated. In our 
SLEEP, Vol. 39, No. 2, 2016 323 Fitness, Weight, and OSA—Kline et al.
initial statistical model, we found that greater increases in fit-
ness predicted greater AHI reduction at year 1; however, this 
relationship was no longer present following adjustment for 
weight change. Because body weight has a strong influence 
on cardiorespiratory fitness,48 adjusting for weight change may 
underestimate the actual effect of cardiorespiratory fitness 
change on OSA. Nevertheless, findings of the current study do 
not support the hypothesis that increased cardiorespiratory fit-
ness has a significant influence on AHI reduction independent 
of weight loss.
The long-term results involving changes in fitness and AHI 
are more difficult to interpret. Our initial model suggested that 
fitness change at year 4 was not related to AHI change but, 
following adjustment for weight change, increased fitness at 
year 4 was related to increased AHI. The findings may be at 
least partially explained by a lack of sustained fitness improve-
ment among the sample. Fitness levels significantly declined 
following the initial robust increase at year 1, reverting to near-
baseline levels by year 4 in our sample. This pattern mirrors 
those seen in the parent Look AHEAD sample.37,49 Given that 
increased fitness was unable to be sustained in this sample, it 
is difficult to determine its long-term influence on AHI. It is 
also possible that the age-related increase in upper airway col-
lapsibility50 could have contributed to the lack of association 
between fitness and OSA severity reduction. In older adults, 
AHI reduction following exercise training (and, presumably, 
increased cardiorespiratory fitness) seems to be more modest 
than in similar interventions conducted in young- and middle-
aged adults.30,51 Thus, the protective effects of increased cardio-
respiratory fitness on OSA may be insufficient to overcome the 
increased airway collapsibility that occurs with increased age.
Although cardiorespiratory fitness is sometimes used as a 
proxy for physical activity, it is important to note important 
distinctions between cardiorespiratory fitness, a physiological 
measure, and physical activity, a behavioral measure.16 Al-
though physical activity is the strongest modifiable determi-
nant of cardiorespiratory fitness,29 other factors (e.g., genetics, 
age, sex) exert strong influences on this physiological measure. 
Moreover, the association between physical activity and car-
diorespiratory fitness is often modest,52 especially when the 
physical activity is self-directed and relatively low intensity as 
in Look AHEAD (i.e., r = 0.25 for 1-y changes in fitness and 
self-reported leisure-time physical activity in a Look AHEAD 
subsample),37 and significant heterogeneity is often observed 
in the magnitude of fitness change following a given dose 
of exercise training.53 Because Look AHEAD did not assess 
habitual physical activity in the entire sample,31 we are un-
able to determine the contribution of longitudinal changes in 
physical activity to long-term AHI change. However, it may 
be that physical activity—and not the extent to which cardio-
respiratory fitness is improved—contributes to AHI reduc-
tion. As noted previously, increased cardiorespiratory fitness 
did not correlate with lower AHI following a 12-w supervised 
exercise intervention,25 and multiple other small-scale experi-
mental studies have demonstrated that relatively modest doses 
of structured physical activity result in significant reduction in 
OSA severity despite minimal to no weight loss.30 In addition 
to mild weight loss, physical activity could affect numerous 
factors (e.g., reduced central adiposity, lower upper airway fat 
deposition, increased slow wave sleep) which influence OSA se-
verity independent of any change in cardiorespiratory fitness.54
The current results may seem to be inconsistent with ex-
isting evidence regarding the numerous health-related benefits 
of cardiorespiratory fitness (e.g., reduced cardiovascular mor-
tality and morbidity,17 increased cognitive function55). However, 
numerous studies have found high body mass to be related to 
adverse health outcomes,56 and high levels of cardiorespiratory 
fitness may attenuate but do not often reverse these obesity-re-
lated associations.57,58 Given that OSA is a condition predomi-
nantly driven by excess weight,6,42 it is understandable that the 
current study’s associations between cardiorespiratory fitness 
and OSA are not evident when weight change is taken into ac-
count. Moreover, the current findings should not be taken to 
suggest that physical activity, as the primary contributor to 
cardiorespiratory fitness, is unimportant for the management 
of OSA. Although physical activity has a negligible effect 
beyond caloric restriction on short-term weight loss,59 phys-
ical activity is an important determinant of long-term weight 
maintenance.60Moreover, exercise training has been shown to 
reduce cardiovascular disease risk and improve daytime func-
tioning (e.g., quality of life, mood) in adults with OSA inde-
pendent of weight loss or changes in OSA severity.25,61 Thus, 
a combined regimen of dietary modification and physical ac-
tivity, regardless of the effect of activity on cardiorespiratory 
fitness, remains recommended to optimize long-term weight 
loss and, subsequently, OSA severity reduction.
The sample size, the largest available to investigate the ef-
fect of lifestyle intervention on OSA severity, the objective 
assessment of OSA and cardiorespiratory fitness, and the 
long-term follow-up of data constitute considerable strengths 
of the current study. However, several limitations also exist. 
The methods by which OSA and fitness were assessed could be 
viewed as limitations. Because of the high night-to-night vari-
ability in AHI,62 the effects of weight loss and fitness on AHI 
may have been masked by the error associated with the single-
night OSA assessments. Moreover, cardiorespiratory fitness 
was estimated based upon treadmill workload, and exercise 
testing at years 1 and 4 were submaximal; each of these factors 
may have introduced measurement error. Another limitation 
involves the significant loss of data over time relative to the 
baseline sample. The missing data were the combined influ-
ence of study dropout, PSG and/or exercise test refusal, and 
unusable exercise test data (e.g., test termination criteria not 
met, missing beta-blocker medication data). In our previous 
report,15 analyses suggested that the missing data were not 
related to AHI change; nevertheless, the missing data limited 
the robustness of our analyses. Finally, these findings are re-
stricted to overweight/obese older adults with type 2 diabetes. 
Whether these results generalize to other OSA populations or 
are representative of patients normally seen in sleep medicine 
clinics is unknown.
In summary, using data from the Sleep AHEAD study, we 
found that baseline and short-term changes in cardiorespiratory 
fitness had a significant effect on baseline and short-term AHI 
change, but these effects were eliminated when we accounted 
for the effect of weight change on AHI. It is possible that the 
SLEEP, Vol. 39, No. 2, 2016 324 Fitness, Weight, and OSA—Kline et al.
lack of sustained fitness improvement affected its potential in-
fluence on AHI, and that (unmeasured) physical activity be-
havior may be more important than fitness in contributing to 
OSA severity reduction. Nevertheless, these data suggest that 
change in body weight is a stronger predictor of AHI reduction 
than change in cardiorespiratory fitness.
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ACKNOWLEDGMENTS
Members of the Sleep AHEAD research group at each site consisted of the 
following individuals:
St.Luke’s-Roosevelt Hospital/Clinilabs: Jon Freeman, PPSGT, PhD, 
Jennifer Patricio; University of Pennsylvania: Brian McGuckin, 
Stephanie Krauthamer-Ewing, Allan Pack, MB, ChB, PhD, 
Richard Schwab, MD, Mary Jones-Parker, RPSGT, 
Matthew Anastasi, RPSGT, Beth Staley, RPSGT, Liz Roben; Brown 
University: Marie Kearns, Caitlin Egan; Temple University: Nida Cassim, 
Valerie Darcey, Sakhena Hin, Stephanie Vander Veur.
A detailed list of the Look AHEAD Research Group is provided in 
reference 32.
Members of the Observational Safety and Management Board were:
Kingman P. Strohl, MD (chair); Donald L. Bliwise, PhD; 
Helaine E. Resnick, PhD.
SUBMISSION & CORRESPONDENCE INFORMATION
Submitted for publication June, 2015
Submitted in final revised form September, 2015
Accepted for publication September, 2015
Address correspondence to: Christopher E. Kline, PhD, Department of 
Health and Physical Activity, University of Pittsburgh, 32 Oak Hill Court, 
Room 227, Pittsburgh, PA 15261; Tel: (412) 383-4027; Fax: (412) 383-4045; 
Email: chriskline@pitt.edu
DISCLOSURE STATEMENT
This was not an industry supported study. Financial support was provided 
by HL070301, DK60426, DK56992, DK057135, HL118318. Dr. Zammit 
is a consultant for Acorda, Actelion, Alexza, Arena, Aventis, Biovail, 
Boehringer-Ingelheim, Cephalon, Elan, Eli Lilly, Evotec, Forest, Glaxo 
Smith Kline, Jazz, King Pharmaceuticals, Ligand, McNeil, Merck, 
Neurocrine Biosciences, Organon, Pfizer, Purdue, Renovis, Sanofi-Aventis, 
Select Comfort, Sepracor, Shire, Somnus, Takeda Pharmaceuticals, Vela,and Wyeth; has grant support from Abbott, Abbvie, Actelion, Ancile, 
Apnex, Arena, Aptalis, Astra-Zeneca, Aventis, Banyu, Biomarin, BMS, 
Catalyst, Celgene, Cephalon, CHDI, Edgemont, Elan, Epix, Eisai, Elminda, 
Evotec, Forest, Fresca, Galderma, Genentech, Gilead, Glaxo Smith 
Kline, Gilead, H. Lundbeck A/S, Janssen, Jazz, Johnson & Johnson, King, 
Merck and Co., National Institutes of Health (NIH), Neurim, Neurocrine 
Biosciences, Naurex, Neurim, Neurogen, Novo Nordisk, Organon, Orphan 
Medical, Otsuka, Pfizer, Predix, Respironics, Roxane, Saladax, Sanofi-
Aventis, Sanofi-Synthelabo, Schering-Plough, Sepracor, Sunovion, Shire, 
Somaxon, Takeda, Takeda Pharmaceuticals North America, Targacept, 
Teva, Thymon, Transcept, UCB, USWorldmeds, Pharma, Ultragenyx, 
Predix, Vanda, and Wyeth-Ayerst Research; and has received honoraria 
from King Pharmaceuticals, McNeil, Merck, Neurocrine Biosciences, 
Sanofi-Aventis, Sanofi-Synthelabo, Sepracor, Takeda Pharmaceuticals, 
Vela Pharmaceuticals, and Wyeth-Ayerst Research. Dr. Wadden serves on 
Scientific Advisory Boards for Orexigen, Novo Nordisk, Nutrisystem, and 
Weight Watchers and has received grant support, on behalf of the University 
of Pennsylvania, from Orexigen, Novo Nordisk, Nutrisystem, and Weight 
Watchers. Dr. Jakicic is a member of Weight Watchers International 
Scientific Advisory Board and has served as the Principal Investigator on a 
research grant received from Jawbone that was awarded to the University 
of Pittsburgh. Dr. Foster is the Chief Scientific Officer for Weight Watchers 
International. The other authors have indicated no financial conflicts of 
interest. The work was performed at: Brown University, Providence, RI; 
St. Luke’s-Roosevelt Hospital/Clinilabs, New York, NY; Temple University, 
Philadelphia, PA; University of Pennsylvania, Philadelphia, PA; and 
University of Pittsburgh, Pittsburgh, PA.