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REVIEW

Predicting exercise adherence in cancer patients and survivors:

a systematic review and meta-analysis of motivational and

behavioural factors

Anne M Lunde Husebø, Sindre M Dyrstad, Jon A Søreide and Edvin Bru

Aims and objectives. To examine research findings regarding predictors of adherence to exercise programmes in cancer

populations.

Background. Cancer patients are advised to participate in daily exercise. Whether they comply with the recommendations

for physical activity or not remains unclear.

Design. A systematic review and meta-analysis.

Methods. Empirical articles published in English between 1995 and 2011 were searched in electronic databases and

in reference lists, using the search terms ‘adherence’, ‘predictors’, ‘exercise’, and ‘cancer’ in varying combinations.

Twelve of 541 screened abstracts met the inclusion criteria. The included studies’ eligibility considering predictors of

exercise adherence were reviewed. A quality assessment process evaluating the studies methodological quality was per-

formed. Eight of the reviewed studies were considered eligible for a meta-analysis involving Pearson’s r correlations.

Results. Exercise stage of change, derived from the transtheoretical model of behaviour change (TTM) was found to be sta-

tistically significant and a strong predictor of exercise adherence. In addition, the theory of planned behaviour (TPB) con-

struct; intention to engage in a health-changing behaviour and perceived behavioural control, demonstrated significant

correlations with exercise adherence.

Conclusions. The review identified that both the TPB and the TTM frameworks include aspects that predicts exercise adher-

ence in cancer patients, and thus contributes to the understanding of motivational factors of change in exercise behaviour in

cancer populations. However, the strengths of predictions were relatively weak. More research is needed to identify predic-

tors of greater importance.

Relevance to clinical practice. Surveying the patients’ readiness and intention to initiate and maintain exercise levels, as well

as tailoring exercise programmes to individual needs may be important for nurses in order to help patients meet exercise

guidelines and stay active.

Key words: adherence, cancer, exercise, meta-analysis, physical activity, review

Accepted for publication: 23 June 2012

Authors: Anne M Lunde Husebø, MSc, RN, PhD Candidate,

Department of Health Studies, University of Stavanger, Stavanger

and Research Department, Stavanger University Hospital, Stavan-

ger; Sindre M Dyrstad, PhD, Associate Professor, Department of

Education and Sports Science, University of Stavanger, Stavanger;

Jon A Søreide, PhD, MD, Attending Surgeon and Professor,

Department of Surgery/Division of Gastroenterological Surgery,

Stavanger University Hospital, Stavanger and Department of

Surgery, University of Bergen, Bergen; Edvin Bru, PhD, Professor,

Department of Educational Psychology, University of Stavanger,

Stavanger, Norway

Correspondence: Anne Marie Lunde Husebø, PhD Candidate,

Department of Health Studies, Kjell Arholms Hus, University of

Stavanger, 4036 Stavanger, Norway. Telephone: +4799262805.

E-mail: [email protected]

Introduction

Exercise, defined as regular moderate to vigorous physical

activity (MVPA), is likely to have many positive physiologi-

cal and psychological effects in cancer patients. Knols et al.

(2005) reviewed 34 trials on the effects of exercise in can-

cer patients, and concluded that exercise may be effective

and that specific beneficial effects could be explained by

individual variations related to stage of disease, type of

malignancy, differences in treatment and patients’ current

lifestyle. Their findings suggest that exercise can be an

important adjunct to various cancer treatments.

A review study of cancer patients’ adherence to exercise

interventions reported that only about a half of the patients

offered an exercise programme completed the programme.

Reasons for withdrawal involved their medical condition or

personal and social problems (Maddocks et al. 2009).

Exercise interventions are used for intervention pro-

grammes aiming to increase participation in MVPA but

also a general increase in PA. Intervention studies seldom

report the predictors of exercise adherence, and there is

insufficient knowledge of factors that motivate and form

barriers to exercise in cancer patients and survivors. Identi-

fying barriers and motivational factors for adherence to

treatment can prompt attention of health professionals to

patients at risk of poor adherence. Knowledge of predictors

of exercise adherence is also crucial to enhance participa-

tion and decrease dropout rates in exercise interventions in

cancer populations.

Adherence

Adherence is defined as: ‘the extent to which a person’s

behaviour (…) corresponds with agreed recommendations

from a health care provider’ (WHO 2003, p. 3). Adherence

is a complex phenomenon that embraces interrelating fac-

tors related to each patient’s health, condition, treatment

and environment, as well as psychological factors, all of

which influence patient adherence to the prescribed pro-

gramme. Adherence is an important modifiable factor that

can affect treatment outcome and is recognised as a chal-

lenge in exercise research. Treatment outcomes, costs

and effectiveness may be influenced by poor adherence

(Jack et al. 2010).

Theoretical frameworks in behavioural and motivational research

Several theoretical frameworks in intervention research

have been applied to better understand individual attitudes

and motivations for health behaviour change. The theory

of planned behaviour (TPB) by Ajzen (1991) has been the

theoretical basis for trials investigating motivation for

exercise as a health behaviour in general (Armitage &

Conner 2001), and particularly in cancer populations

(Andrykowski et al. 2006). The individual’s intention to

engage in a health-changing behaviour is central and

forecasts willingness and effort to change behaviour

(Ajzen 1991). Intention is facilitated by attitude toward

the specific action, subjective norms and perceived

behavioural control (PBC). It is hypothesised that PBC is

mainly influenced by perceived situational control and

self-efficacy (SE). SE, a key construct in social cognitive

theory (SCT; Bandura 1997), refers to the belief that the

individual has in his/her ability to perform the behaviour.

In a revised version of the TPB, Maddux et al. (1997)

claim that, in addition to integrating the major compo-

nents of the model and stages of change, a comprehensive

theory of behaviour change should include the individuals’

habits, like smoking behaviour, drinking behaviour and

previous exercise levels.

The transtheoretical model of behaviour change (TTM)

analyses the processes undergone by the individual when

trying to change a behaviour, which include the stages of

precontemplation, contemplation, preparation, action and

maintenance (Taylor 2003). The individual balances the

benefits of the behaviour change against its disadvantages.

An increased decisional balance score indicates a higher

motivational readiness to make a behaviour change

(Bogg 2008).

Purpose

The objective of this review is to provide an overview of

findings on how behavioural and motivational variables

such as the TPB constructs, self-efficacy, exercise stage of

change, decisional balance, pretrial exercise level, and

smoking and drinking habits predict adherence to exercise

interventions in cancer populations.

Methods

We used a systematic review method guided by Cochrane

Handbook for Systematic Review of Interventions to per-

form a survey of primary empirical research that examined

motivational and behavioural predictors of exercise adher-

ence in cancer populations (Higgins & Green 2011). Corre-

lations between exercise adherence and its predictors were

reviewed narratively and by meta-analysis (Schünemann

et al. 2011).

Review Exercise adherence in cancer patients: a review

A computerised search for empirical articles about the

predictors of exercise adherence in cancer populations was

conducted using CINAHL, MEDLINE, PsychInfo, Aca-

demic Search Elite, SPORTDiscus and SocINDEX with Full

Text databases. In addition to e-database searches, search-

ing the reference lists of eligible articles identified relevant

studies. The databases were constantly monitored for new

articles during analysis and writing.

The search keywords ‘adherence’, ‘predictors’, ‘exercise’

and ‘cancer’ were used in varying combinations. ‘Adher-

ence’ was supplemented with the associated terms ‘compli-

ance’ and ‘attendance’, ‘predictors’ was supplemented with

‘determinants’ and ‘correlates’, and ‘exercise’ was supple-

mented with ‘physical activity’. The search was limited to

empirical articles in English published between January

1995 and January 2011. No study performed before 2000

was found and the cut-off date was therefore set to 2000.

Review procedure

The search combining ‘adherence’ and ‘exercise’ with other

supplementary terms identified 541 publications. The search

was refined by adding ‘cancer’, revealing twenty publica-

tions. Two reviewers examined the eligibility of the identi-

fied articles using the inclusion and exclusion criteria

presented in Table 1.

Finally, the review procedure identified 13 potential arti-

cles for inclusion. Upon further analysis, three of the identi-

fied articles failed to meet the inclusion criteria of focus on

predictors of exercise adherence. Two additional articles

were identified by screening reference lists of eligible arti-

cles, giving a total of 12 articles for further analysis. All of

the 12 included articles were published between 2001 and

2012, and most were published between 2006 and 2012.

The purpose stated in each of the included studies was to

identify key predictors of exercise adherence in cancer pop-

ulations. The samples in the studies represent different can-

cer populations. However, Maddocks et al. (2009) found

that sample characteristics like disease status and current or

previous treatment in a cancer population of 7224 patients

did not influence the ability to take up and complete an

exercise programme. This finding suggests that it is relevant

to conduct a meta-analysis across the selected studies. Eight

of the 12 studies provided Pearson’s correlation coefficients

for the prediction of adherence and were included in a

meta-analysis (Courneya et al. 2001, 2002, 2004a,b, 2008,

2010, Peddle et al. 2009, McNeely et al. 2012). These eight

studies comprised a total of 510 patients.

The meta-analysis procedure was conducted using the

software programme Comprehensive Meta Analysis Ver-

sion 2 analysis (Borenstein et al. 2005). A meta-analysis is

beneficial, allows for a more objective assessment of the

review evidence and increases understanding of inconsis-

tent results encountered in the reviewed studies (Egger

et al. 1997). In the meta-analysis, correlation coefficients

were weighted according to the number of cases in the

studies. The authors of studies giving insufficient statistical

information for meta-analysis were contacted to collect the

information needed; however, we were not able to retrieve

the necessary information for six correlations. In cases

of missing information, the correlation coefficient was

set to 0�00. Courneya et al. (2010) reported correlations for two dif-

ferent exercise groups, which were combined in the meta-

analysis. In two studies, the predictor ‘TPB-attitude’ was

reported in both ‘instrumental attitude’ and ‘affective atti-

tude’. An estimated mean coefficient of correlations was

included in the meta-analyses.

Statistically significant heterogeneity was detected for

associations of PBC and pretrial exercise behaviour with

adherence. Heterogeneity was tested by using the I2 estima-

tion (Ioannidis 2008). In the meta-analysis random-effect

model was implemented to compensate for between-studies

heterogeneity (Deeks et al. 2008). Moreover, a complemen-

tary fixed-effect model was computed. According to Alder-

son and Green (2002), it is unlikely that there is important

statistical heterogeneity if fixed-effect and random-effect

meta-analysis give identical results. When comparing the

results from the two models, substantial differences in coef-

ficients and meta-correlation were not found (Table 6).

There were therefore no indications of heterogeneity having

a critical influence on the meta-analysis performed.

Table 1 Inclusion and exclusion criteria

Inclusion criteria Exclusion criteria

Empirical articles in English

Publication years from 1995

to 2011

Primary Studies reporting

findings from exercise

intervention trials in

cancer patients

Focus on predictors of exercise

adherence in cancer patients

or cancer survivors

Analysis of correlates between

exercise and adherence

presented in adequate

statistical values

Studies scoring � 50% on the quality assessment

Observational studies, descriptive

studies, or only abstracts

Studies not reporting analysis

of adherence predictors

Studies reporting

postprogramme exercise

adherence

Studies reporting adherence

to exercise recommendation

and risk of cancer

AM Lunde Husebø et al.

O’Connor et al. (2011) warn against excluding studies

from systematic reviews just because they provide no ‘usable

data’. We therefore found it relevant to also include findings

concerning predictors of exercise adherence in studies not

providing data suitable for the meta-analysis, but otherwise

satisfied the quality criteria applied in the review. The assess-

ment was based on the statistical significance of findings.

Quality assessment

The quality assessment was carried out by two researchers.

Quality assessment criteria in accord with Cochrane Hand-

book for Systematic Reviews of Interventions (Higgins &

Green 2011) were used in so far as they were relevant for

prospective studies.

The quality assessment tool was adopted from Jack et al.

(2010). We modified this tool by implementing a criterion

for sample size calculation or power calculation as described

by Ingram et al. (2006), and specified the outcome measures

describing adherence scores and data regarding predictors of

adherence. In this review, predictors of exercise adherence

in exercise groups were the exclusive focus, and the ‘sample

size’ criterion was altered from � 300 to � 50 subjects according to the rule-of-thumb for number of cases in analy-

ses of correlation and regression given by Green (1991).

Finally, the assessment tool consisted of 12 criteria, as pre-

sented in Table 2. Criterion A (selection bias) concerns

issues such as percentage of selected subjects included and

likelihood of representing the target population (Ingram

et al. 2006). Sample size information gives N in the exercise

groups only (criterion D). Randomisation procedure in ran-

domized controlled trials (RCT) is not likely to affect the

prediction of exercise adherence; therefore non-randomised

design was not set as a quality criterion. Only two of the

included studies applied single-group designs (Courneya

et al. 2001, Peddle et al. 2009).

The methodological qualities of the studies were evalu-

ated using score points; those meeting the standard were

given a score of one, while those not meeting the standard

were given a score of zero. Studies that met � 50% of the criteria (scores � 6) were rated as ‘acceptable quality’, while studies that met <50% of the criteria (scores < 6)

were rated as ‘low quality’ (Jack et al. 2010). Disagreement

on scoring the studies was solved by consensus.

Results

Characteristics of reviewed studies

Common characteristics of the reviewed trials are presented

in Table 3. Five of the studies are authored by the same

research group, but represent distinct studies. Sample size

ranged between 19 and 160 individuals, with a median of 47

subjects calculated from number of participants randomised

to the exercise groups. Five studies included both genders

(Courneya et al. 2002, 2004a, 2010, Peddle et al. 2009,

McNeely et al. 2012), one study enrolled males only (Cour-

neya et al. 2004b) and six studies included females only

(Courneya et al. 2001, 2008, Daley et al. 2007, Latka et al.

2009, Pinto et al. 2009, Swenson et al. 2010). Breast cancer

was the most common type of cancer, addressed in seven tri-

als. Four studies enrolled cancer patients who were offered

adjuvant treatment (Courneya et al. 2004a,b, Swenson et al.

2010, McNeely et al. 2012) and in two studies the partici-

pants were pending treatment (Peddle et al. 2009, Courneya

et al. 2008). Three studies enrolled both cancer patients cur-

rently undergoing treatment and cancer survivors who had

completed therapy (Courneya et al. 2001, 2002, 2010).

The results of the quality assessment are presented in

Table 4. The studies were categorised into levels with

respect to their scores, with higher scores associated with

higher quality. All studies were considered acceptable qual-

ity, with a median quality score of 9�0 (range, 7–11). The quality assessment process still revealed some limitations.

Overall, the studies exhibited limitations regarding selection

bias (criterion A), sample size (criterion D), information on

drop-outs (criterion E) and power analysis (L). Accrual was

the most reported problem concerning criterion A. Five

studies reported accrual data; of these, four accrued <40%

of eligible subjects (Courneya et al. 2004a,b, 2008, Latka

et al. 2009). McNeely et al. (2012) reported accrual rates

between 33% and 57%, divided between two recruiting

sites. One other study scored zero points on selection bias,

Table 2 Quality assessment criteria (adapted from Jack

et al. 2010 and Ingram et al. 2006)

Criteria

Study population: selection bias (criterion A) and description of

inclusion and exclusion criteria (criterion B)

Study design: prospective design (criterion C) and sample size � 50 (criterion D)

Drop-outs: percentages of drop-outs/withdrawals (criterion E)

Outcome measures: defining adherence (criterion F), data

presenting adherence score (criterion G), data presenting

predictors of adherence (criterion H), and use of standardised or

valid measurements (criterion I)

Analysis and data presentation: appropriate univariate crude

estimates (criterion J), appropriate multivariate analysis techniques

(criterion K), and sample size calculation or power calculation

(criterion L)

Review Exercise adherence in cancer patients: a review

T a b le

3 O v er v ie w

o f th e re v ie w ed

st u d ie s

S tu d y

T h eo re ti ca l

fr a m ew

o rk

S u b je ct s

D es ig n

In te rv en ti o n

B eh a v io u ra l a n d

m o ti v a ti o n a l p re d ic to rs

o f ex er ci se

a d h er en ce

R es u lt s

C o u rn ey a

et a l.

(2 0 0 1 )

T h eo ry

o f

p la n n ed

b eh a v io u r

(T P B )

n = 2 4

W o m en

M ea n a g e = 5 1 y ea rs

B re a st

ca n ce r su rv iv o rs

st a g e I a n d II

P ro sp ec ti v e

co n v en ie n ce

sa m p le

S u p er v is ed

A d h er en ce : to ta l n u m b er

o f cl a ss es

a tt en d ed

a ss es se d b y o b je ct iv e

a tt en d a n ce

re co rd s

T P B co n st ru ct s

A cc es si b le

b el ie fs

B a se li n e p h y si ca l a ct iv it y

b eh a v io u r

In te n ti o n to

a tt en d w a s th e

so le

d et er m in a n t o f

ex er ci se

a d h er en ce

a n d

su b je ct iv e n o rm

, a n

im p o rt a n t d et er m in a n t o f

in te n ti o n . P a st

ex er ci se

d id

n o t ex p la in

ex er ci se

a d h er en ce

C o u rn ey a

et a l.

(2 0 0 2 )

T P B a n d fi v e

fa ct o r m o d el

n = 5 1 (e x er ci se

g ro u p )

B o th

se x es

M ea n a g e = 5 2 y ea rs

4 4 %

w it h b re a st

ca n ce r

d ia g n o si s

5 0 %

st a g e I a n d II

4 4 %

cu rr en tl y in

tr ea tm

en t

R a n d o m is ed

co n tr o ll ed

tr ia l (R

C T )

H o m e- b a se d

A d h er en ce : to ta l a m o u n t

o f ex er ci se

p er fo rm

ed , a n d

p er ce n ta g e o f th e a m o u n t

o f p re sc ri b ed

ex er ci se ,

se lf -r ep o rt ed

T P B co n st ru ct s

A cc es si b le

b el ie fs

S ig n ifi ca n t co rr el a ti o n s

b et w ee n o v er a ll R C T

ex er ci se

a n d p a st

ex er ci se ,

in te n ti o n , a tt it u d e,

p er ce iv ed

b eh a v io u ra l

co n tr o l, a n d co n tr o l

b el ie fs . In

th e ex er ci se

g ro u p , 4 v a ri a b le s

p re d ic te d ex er ci se

a d h er en ce

a n d ex p la in ed

4 1 %

o f th e v a ri a n ce : se x ,

ex tr o v er si o n , n o rm

a ti v e

b el ie fs , a n d P B C

C o u rn ey a

et a l.

2 0 0 4 a )

T P B a n d fi v e

fa ct o r m o d el

n = 6 9 (e x er ci se

g ro u p )

B o th

se x es

M ea n a g e = 6 0 y ea rs

C o lo n ca n ce r st a g e 0 – IV

C u rr en tl y re ce iv in g

tr ea tm

en t

R C T

H o m e- b a se d

A d h er en ce : a v er a g e

p er ce n ta g e o f se ss io n s

a tt en d ed

p er

p er so n ,

se lf -r ep o rt ed .

B eh a v io u ra l te ch n iq u es :

p er so n a li se d ex er ci se

p re sc ri p ti o n a n d te le p h o n e

ca ll s

T P B co n st ru ct s

T ra n s- th eo re ti ca l M o d el

ex er ci se

st a g e o f ch a n g e

B a se li n e p h y si ca l a ct iv it y

b eh a v io u r

S ig n ifi ca n t co rr el a ti o n s

b et w ee n ex er ci se

a d h er en ce

a n d ex er ci se

st a g e a n d P B C

AM Lunde Husebø et al.

T a b le

3 (C

o n ti n u ed )

S tu d y

T h eo re ti ca l

fr a m ew

o rk

S u b je ct s

D es ig n

In te rv en ti o n

B eh a v io u ra l a n d

m o ti v a ti o n a l p re d ic to rs

o f ex er ci se

a d h er en ce

R es u lt s

C o u rn ey a

et a l.

2 0 0 4 b )

T P B

n = 8 2 (E x er ci se

g ro u p )

M en

M ea n a g e = 6 8 y ea rs

P ro st a te

ca n ce r

C u rr en tl y re ce iv in g

tr ea tm

en t

R C T

S u p er v is ed

A d h er en ce : a v er a g e

p er ce n ta g e o f se ss io n s

a tt en d ed

p er

p er so n

a ss es se d b y o b je ct iv e

a tt en d a n ce

re co rd s

B eh a v io u ra l te ch n iq u es :

in fo rm

a l in fo rm

a ti o n

T P B co n st ru ct s

T ra n s- th eo re ti ca l M o d el

ex er ci se

st a g e o f ch a n g e

B a se li n e p h y si ca l a ct iv it y

b eh a v io u r

S m o k in g h a b it

D ri n k in g h a b it

In d ep en d en t p re d ic to rs

th e fi n a l eq u a ti o n w er e

p re p ro g ra m m e o v er a ll

ex er ci se

st a g e a n d

in te n ti o n

D a le y

et a l.

(2 0 0 7 )

N o t re p o rt ed

n = 7 0 (E x er ci se

g ro u p )

W o m en

M ea n a g e u n re p o rt ed

B re a st

ca n ce r, ea rl y st a g e

C o m p le te d tr ea tm

en t

R C T

S u p er v is ed

A d h er en ce : th e le v el

o f

se ss io n p a rt ic ip a ti o n

a ch ie v ed

b y p a rt ic ip a n ts ,

re co rd ed

o b je ct iv el y in

a ct iv it y lo g s

E x er ci se

st a g e o f ch a n g e

B a se li n e p h y si ca l a ct iv it y

b eh a v io u r

S m o k in g h a b it

N o si g n ifi ca n t a ss o ci a ti o n s

b et w ee n h ea lt h b eh a v io u rs

a n d se ss io n a d h er en ce

th e in te rv en ti o n re co rd ed

C o u rn ey a

et a l.

(2 0 0 8 )

T P B

n = 1 6 0 (E x er ci se

g ro u p s)

W o m en

M ea n a g e = 4 9 y ea rs

B re a st

ca n ce r st a g e I– II IA

6 1 %

h a d st a g e II In it ia ti n g

tr ea tm

en t

R C T

S u p er v is ed

A d h er en ce : th e n u m b er

o f

se ss io n s a tt en d ed

d iv id ed

b y th e n u m b er

o f se ss io n s

ex p ec te d , b a se d o n th e

le n g th

o f th e ch em

o -

th er a p y p ro to co l, a ss es se d

b y o b je ct iv e a tt en d a n ce

re co rd s

T P B co n st ru ct s

B a se li n e p h y si ca l a ct iv it y

b eh a v io u r

S m o k in g h a b it

S m o k in g st a tu s sh o w ed

b o rd er li n e si g n ifi ca n t

co rr el a ti o n w it h ex er ci se

a d h er en ce

in th e ex er ci se

g ro u p s. E x er ci se

b eh a v io u r

a t b a se li n e d id

n o t p re d ic t

ex er ci se

p ro g ra m m e

a d h er en ce . M o ti v a ti o n a l

v a ri a b le s d id

n o t p re d ic t

ex er ci se

a d h er en ce

P ed d le

et a l.

(2 0 0 9 )

T P B

n = 1 9

B o th

se x es

M ea n a g e = 6 4 y ea rs

N o n sm

a ll -c el l lu n g ca n ce r,

st a g e I– II IA

A w a it in g su rg ic a l re se ct io n o f

m a li g n a n t lu n g le si o n

P il o t st u d y ,

si n g le -g ro u p ,

p ro sp ec ti v e

S u p er v is ed

A d h er en ce : th e n u m b er

o f se ss io n s a tt en d ed

d iv id ed

b y th e n u m b er

o f se ss io n s ex p ec te d b y

ea ch

p a rt ic ip a n t,

a ss es se d b y o b je ct iv e

a tt en d a n ce

re co rd s

T P B co n st ru ct s

S m o k in g h a b it

P B C

w a s st ro n g ly

co rr el a te d w it h ex er ci se

a d h er en ce , fo ll o w ed

b y

su b je ct iv e n o rm

B o rd er li n e p re d ic to rs

o f

a d h er en ce

w er e in te n ti o n ,

se lf -e ffi ca cy , a n d a tt it u d es .

S m o k in g st a tu s d id

n o t

p re d ic t a d h er en ce

ex er ci se

Review Exercise adherence in cancer patients: a review

T a b le

3 (C

o n ti n u ed )

S tu d y

T h eo re ti ca l

fr a m ew

o rk

S u b je ct s

D es ig n

In te rv en ti o n

B eh a v io u ra l a n d

m o ti v a ti o n a l p re d ic to rs

o f ex er ci se

a d h er en ce

R es u lt s

L a tk a

et a l.

(2 0 0 9 )

N o t re p o rt ed

n = 3 7

W o m en

M ea n a g e = 5 5 y ea rs

B re a st

ca n ce r st a g e 0 – II IA

C o m p le te d tr ea tm

en t

R C T

C o m b in ed

su p er v is ed

a n d

h o m e- b a se d

A d h er en ce : A v er a g e m in u te s/

w ee k o f m o d er a te -i n te n si ty

a er o b ic

ex er ci se

p er fo rm

fr o m

b a se li n e to

6 m o n th s,

se lf -r ep o rt ed .

B eh a v io u ra l te ch n iq u es :

p h y si ca l a ct iv it y lo g

T ra n s- th eo re ti ca l M o d el

ex er ci se

st a g e o f ch a n g e

B a se li n e p h y si ca l a ct iv it y

b eh a v io u r

A ct iv it y p ri o r to

b a se li n e

a n d ex er ci se

st a g e o f

ch a n g e p re d ic te d ex er ci se

a d h er en ce

P in to

et a l.

(2 0 0 9 )

T ra n s- th eo re ti ca l

M o d el

S o ci a l- C o g n it iv e

T h eo ry

(S C T )

n = 4 3

W o m en

M ea n a g e = 5 3 y ea rs

B re a st

ca n ce r st a g e 0 – II

C o m p le te d tr ea tm

en t

R C T

H o m e- b a se d

A d h er en ce : m in u te s o f

ex er ci se

a n d p ed o m et er

st ep s a n d p er ce n ta g e o f

p a rt ic ip a n ts

w h o m et

th ei r

ex er ci se

g o a l ea ch

w ee k ,

se lf -r ep o rt ed

T ra n s- th eo re ti ca l M o d el

ex er ci se

st a g e o f ch a n g e

T ra n s- th eo re ti ca l M o d el

d ec is io n a l b a la n ce

S o ci a l- C o g n it iv e T h eo ry

B a se li n e p h y si ca l a ct iv it y

b eh a v io u r

B a se li n e se lf -e ffi ca cy

fo r

ex er ci se

w a s a si g n ifi ca n t

p o si ti v e p re d ic to r o f

a d h er en ce

m ea su re

a n d

B a se li n e P A

p re d ic te d

ex er ci se

st ep s

C o u rn ey a

et a l.

(2 0 1 0 )

T P B

n = 6 0

B o th

se x es

M ea n a g e = 5 3 y ea rs

1 0 0 %

ly m p h o m a , 8 2 %

n o n -H

o d g k in

ly m p h o m a

C u rr en tl y re ce iv in g

tr ea tm

en t

R C T

S u p er v is ed

A d h er en ce : n u m b er

o f

se ss io n s a tt en d ed

d iv id ed

b y th e n u m b er

o f se ss io n s

ex p ec te d , a ss es se d b y

o b je ct iv e a tt en d a n ce ,

d u ra ti o n a n d in te n si ty

re co rd s.

B eh a v io u ra l te ch n iq u es :

b o o k ed

ex er ci se

se ss io n s,

te le p h o n e fo ll o w -u p , a n d

p o si ti v e re in fo rc em

en t, p a id

p a rk in g

T P B co n st ru ct s

B a se li n e p h y si ca l a ct iv it y

b eh a v io u r

S m o k in g h a b it

P a st

ex er ci se

a n d sm

o k in g

w er e si g n ifi ca n t p re d ic to rs

o f ex er ci se

a d h er en ce .

M o ti v a ti o n a l v a ri a b le s d id

n o t p re d ic t a d h er en ce

AM Lunde Husebø et al.

T a b le

3 (C

o n ti n u ed )

S tu d y

T h eo re ti ca l

fr a m ew

o rk

S u b je ct s

D es ig n

In te rv en ti o n

B eh a v io u ra l a n d

m o ti v a ti o n a l p re d ic to rs

o f ex er ci se

a d h er en ce

R es u lt s

S w en so n

et a l.

(2 0 1 0 )

T P B , S C T , &

T h e P h y si ca l A ct iv it y

A d h er en ce

M o d el

n = 3 6

W o m en

M ea n a g e = 4 7 y ea rs

B re a st

ca n ce r st a g e I– II I

C u rr en tl y re ce iv in g

tr ea tm

en t

L o n g it u d in a l a n d

o b se rv a ti o n a l st u d y

o f p a rt ic ip a n ts

th e p h y si ca l a ct iv it y

g ro u p o f a n R C T

H o m e- b a se d

A d h er en ce : to ta l n u m b er

o f st ep s a n d m ea n

n u m b er

o f st ep s p er

d a y

o n d a y s w it h a n y st ep s

re co rd ed , se lf -r ep o rt ed

B eh a v io u ra l te ch n iq u es :

ex er ci se

lo g s, p ed o m et er s,

p u ls e m o n it o ri n g

B a se li n e p h y si ca l a ct iv it y

b eh a v io u r

P re d ic to rs

o f th e

a d h er en ce

m ea su re

‘m ea n

n u m b er s o f

st ep s p er

d a y ’

w er e h o u rs

p er

w ee k sp en t

sl ee p in g o r

re cl in in g a t

b a se li n e.

T h e

fe w er

st ep s th e

le ss

ex er ci se

M cN

ee ly

et a l.

(2 0 1 2 )

T P B

n = 5 2

B o th

se x es

M ea n a g e = 5 2 y ea rs

H ea d a n d n ec k ca n ce r,

st a g e I– IV

C u rr en tl y re ce iv in g

tr ea tm

en t

R C T

C o m b in ed

su p er v is ed

a n d

h o m e- b a se d

A d h er en ce : p er ce n ta g e o f

a tt en d ed

se ss io n s,

a ss es se d b y o b je ct iv e

a tt en d a n ce

re co rd s

T P B co n st ru ct s

B a se li n e p h y si ca l a ct iv it y

b eh a v io u r

S m o k in g h a b it

D ri n k in g h a b it

S ig n ifi ca n t o r b o rd er li n e

a ss o ci a ti o n s b et w ee n

ex er ci se

a d h er en ce

a n d

a lc o h o l co n su m p ti o n .

S u b je ct s n o t co n su m in g

a lc o h o l o n a d a il y b a si s

a cc o m p li sh ed

h ig h er

a d h er en ce . M o ti v a ti o n a l

v a ri a b le s a n d b a se li n e

ex er ci se

le v el s d id

n o t

p re d ic t a d h er en ce

Review Exercise adherence in cancer patients: a review

because it used a single-group design and convenient

recruiting from a special interest group, including 100% of

eligible subjects (Courneya et al. 2001).

Six studies contained at least 50 subjects in the exercise

group (Courneya et al. 2002, 2004a,b, 2008, 2010,

McNeely et al. 2012). Only three of these studies reported

to have conducted power analysis (Courneya et al. 2002,

2004a, 2010).

Five studies reported data on drop-outs or withdrawals

(Courneya et al. 2004a,b, Peddle et al. 2009, Pinto et al.

2009, Swenson et al. 2010), with completion rates between

79% and 91%. Adherence rates did not differ significantly

between studies with a high or low drop-out rate in the

exercise groups.

The quality criteria – prospective design (criterion C),

defining adherence (criterion F), data presenting adherence

score (criterion G), data presenting predictors of adherence

(criterion H) and standardised or valid measurements (crite-

rion I) —obtained the highest assessment scores. All of the

reviewed studies provided sufficient information about these

criteria. However, irregularities in defining and calculating

adherence, and inconsistencies in the analysis and presenta-

tion of data regarding correlations between exercise adher-

ence scores and predictors of adherence were observed.

Assessment of adherence

The study outcomes were specific to exercise interventions

in 10 out of 12 studies. In the studies of Pinto et al. (2009)

and Swenson et al. (2010), the outcomes were specific to

PA interventions. In all of the studies the intervention

employed strength or/and endurance training of moderate

to vigorous intensity. Reported adherence to intervention

programmes varied extensively, with rates from various

studies ranging between 42% and 91%.

One aspect of exercise adherence examined in all trials

was training volume. Training volume included the total

amount of exercise performed, average minutes or steps per-

formed per week, quantity of prescribed exercise achieved by

the intervention group and total amount of exercise divided

by the expected amount of exercise. Two studies (Latka

et al. 2009, Pinto et al. 2009) referred to adherence meaning

prescribed exercise goals. Seven studies assessed adherence

by using objective attendance records to determine adher-

ence to the exercise protocol (Courneya et al. 2001, 2004b,

Daley et al. 2007, Courneya et al. 2008, Peddle et al. 2009,

Courneya et al. 2010, McNeely et al. 2012), while in five

studies exercise adherence was assessed by self-reporting

methods, such as exercise logs, weekly surveys and weekly

phone calls to the participants (Courneya et al. 2002, 2004a,

Latka et al. 2009, Pinto et al. 2009, Swenson et al. 2010).

Slightly higher adherence was observed in studies employing

supervised exercise programmes than in those using home-

based programmes (70�5% vs. 67�5%, respectively).

Predictors of exercise adherence

All of the 12 reviewed articles provided data concerning

predictors of exercise adherence. The eight studies provid-

ing Pearson’s r correlations are presented in Table 5. The

Table 4 Rating of methodological quality

Study A B C D E F G H I J K L

Quality

score

Courneya et al. (2004a) 1 0 1 1 1 1 1 1 1 1 1 1 11

Courneya et al. (2004b) 1 1 1 1 1 1 1 1 1 1 1 0 11

Courneya et al. (2002) 0 1 1 1 0 1 1 1 1 1 1 1 10

Courneya et al. (2008) 1 1 1 1 0 1 1 1 1 1 1 0 10

McNeely et al. (2012) 1 1 1 1 0 1 1 1 1 1 1 0 10

Peddle et al. (2009) 1 1 1 0 1 1 1 1 1 0 1 0 9

Courneya et al. (2010) 0 0 1 1 0 1 1 1 1 1 1 1 9

Swenson et al. (2010) 0 1 1 0 1 1 1 1 1 1 1 0 9

Latka et al. (2009) 1 1 1 0 0 1 1 1 1 0 1 0 8

Pinto et al. (2009) 0 1 1 0 1 1 1 1 1 1 0 0 8

Courneya et al. (2001) 0 0 1 0 0 1 1 1 1 1 1 0 7

Daley et al. (2007) 0 0 1 0 0 1 1 1 1 1 0 1 7

A, selection bias; B, description of inclusion and exclusion criteria; C, prospective design; D, sample size � 50; E, percentages of drop-outs/ withdrawals; F, defining adherence; G, data presenting adherence score; H, data presenting predictors of adherence; I, using standardised or

valid measurements; J, appropriate univariate crude estimates; K, appropriate multivariate analysis techniques; L, sample size calculation or

power calculation.

AM Lunde Husebø et al.

results of the meta-analysis are presented in Table 6. Many

of the reviewed studies employed motivational and behavio-

ural theories to examine predictors of adherence, with the

TPB as the most used theory. In the reviewed studies TPB-

constructs and self-efficacy (SE) were all measured accord-

ing to standardised guidelines (Ajzen 2001). All studies in

the meta-analysis investigated TPB constructs as determi-

nants of adherence. The results of the meta-analysis indi-

cated that, overall, meta-correlations between the different

predictors and exercise adherence ranged from very low to

medium (r = �0�02 to 0�22, see Figures 1–9 for Forest Plots giving information about the confidence intervals of

the correlation coefficients). Intention and PBC demon-

strated moderate, but statistically significant meta-correla-

tions with exercise adherence (r = 0�22 and 0�17, respectively). In the meta-analysis attitude was found to be

non-significant. Subjective norm and SE were weak predic-

tors of adherence, meta r = 0�10 and 0�11, respectively, but only subjective norm was found significant. Pinto et al.

(2009) investigated the SCT concept of SE and reported its

significance as a predictor of home-based exercise among

cancer survivors. SCT had a notable effect on both mean

minutes of weekly exercise (p = 0�004) and mean pedo- meter steps per week (p = 0�005). Exercise stage of change drawn from TTM of behaviour

change was examined as an exercise adherence predictor in

five of the reviewed studies, two of which were included in

the meta-analysis. In both studies stage of change was

measured with the same scale. Courneya et al. (2004a,b)

established statistically significant correlations between

exercise adherence and exercise stage in cancer survivors,

and the meta-correlation was found to be statistically

significant and moderately strong. The two studies did not

report which stage of change corresponded to the

correlation with adherence. Latka et al. (2009) observed

significant univariate associations between a higher readi-

ness for change and adherence to the exercise prescription.

The final two studies observed non-significant correlations

between adherence and exercise stage of change (Daley

et al. 2007, Pinto et al. 2009). The TTM construct deci-

sional balance for exercise, associated with stage of exercise

adoption, was examined as the only motivational variable

in Pinto et al. (2009). It was not statistically significant in

relation to a home-based exercise programme.

The association between pretrial exercise behaviour and

adherence to programme exercise was investigated in eight

of the studies. Pretrial exercise yielded quite heterogeneous

results between studies included in the meta-analysis, result-

ing in a non-significant meta-correlation. Reviewed narra-

tively this predictor achieved a contradictory result. Pinto

et al. (2009) report baseline PA as a significant predictor of

weekly pedometer steps (p < 0�05) among breast cancer

Table 5 Pearson’s correlations of behavioural and motivational variables with adherence to physical exercise in studies

included in the meta-analysis

Study n Intention

Perceived

behavioural

control Attitude

Subjective

norm

Self-

efficacy

Exercise

stage of

change

Smoking

habit

Drinking

habit

Pretrial

exercise

behaviour

Courneya

et al. (2001)

24 0�59** 0�27 0�03 0�25 – – – – 0�32*

Courneya

et al. (2002)

51 0�27 0�26 n.s. n.s. – – – – 0�37**

Courneya

et al. (2004a)

62 0�22 0�26* n.s. n.s. – 0�43** – – n.s.

Courneya

et al. (2004b)

82 0�30** 0�22 �0�01 0�23* – 0�31** 0�11 0�11 n.s.

Courneya

et al. (2008)

160 0�09 0�13 �0�05 0�07 – – �0�14 – 0�10

Peddle

et al. (2009)

19 0�35 0�63** 0�30affe 0�51* 0�32 – 0�16 – –

Courneya

et al. (2010)

60 0�21 �0�13 0�14inst �0�01 0�01 – �0�22 – �0�32*

McNeely

et al. (2012)

52 0�11 0�03 0�05 0�15 0�15 – 0�01 �0�24** 0�15

Coefficients given are Pearson’s r, affective attitude (affe), instrumental attitude (inst).

n.s., reported not significant and set to 0�00; –, not included in the studies. *p < 0�05; **p < 0�01.

Review Exercise adherence in cancer patients: a review

patients. Swenson et al. (2010) observed baseline activity

levels to be inversely correlated with exercise adherence,

and sedentary subjects recorded significantly fewer steps per

day. Daley et al. (2007) and Latka et al. (2009) report no

significant correlations between pretrial exercise behaviour

and adherence to programme exercise.

Smoking behaviour was included as a behavioural variable

in six studies, of which five provided sufficient data for the

meta-analysis (Courneya et al. 2004b, 2008, 2010, Peddle

et al. 2009, McNeely et al. 2012). Smoking status exhibited

no significant associations with exercise behaviour in the

meta-analysis (r = �0�06). Drinking habits as a determinant of exercise adherence was examined in two studies

(Courneya et al. 2004b, McNeely et al. 2012). In these two

trials, alcohol consumption emerged as a weak but still

statistically significant determinant of exercise adherence.

McNeely et al. (2012) reported alcohol intake as the

strongest predictor of adherence, establishing significant

correlations between high exercise adherence and low

alcohol consumption. Courneya et al. (2004b) also report

alcohol consumption to be significantly and inversely associ-

ated with exercise adherence. Drinking habit, examined in

the meta-analysis, did not predict exercise adherence

(r = �0�06).

Discussion

The review and meta-analysis revealed some significant,

although weak, correlations between adherence to exercise

protocols and motivational and behavioural factors. A rela-

tively strong predictor of adherence to exercise in the cancer

populations studied was exercise stage of change. The trans-

theoretical model of behaviour change (TTM) suggests that

a behaviour change intervention might be more successful if

it were individualised and aimed at the individual’s stage of

change (Taylor 2003). Rogers et al. (2005b) observed that

breast cancer patients who exercised regularly (i.e. action or

maintenance stage participants) reported exercise barriers

less frequently. For cancer patients and survivors to main-

tain recommended exercise levels, barriers to exercise should

be addressed when planning exercise interventions (Rogers

et al. 2005b). Pinto et al. (2005) report significant increases

in motivational readiness to meet exercise guidelines and

adhere to the intervention programme among breast cancer

patients who received tailored counselling.

Regular exercise over a long period of time is believed to

increase the chance that this behaviour will become habit-

ual (Taylor 2003). Additionally, the ability to overcome

barriers to exercise may be stronger in cancer patients who

exercise regularly before diagnosis. While most cancerT a b le

6 R es u lt s fr o m

m et a -a n a ly se s o f P ea rs o n ’s

co rr el a ti o n s o f b eh a v io u ra l a n d m o ti v a ti o n a l v a ri a b le s w it h a d h er en ce

to p h y si ca l ex er ci se

In te n ti o n

P er ce iv ed

b eh a v io u ra l

co n tr o l

A tt it u d e

S u b je ct iv e

n o rm

S el f- ef fi ca cy

E x er ci se

st a g e

o f ch a n g e

S m o k in g h a b it

D ri n k in g h a b it

P re tr ia l ex er ci se

b eh a v io u r

F ix ed -e ff ec ts

m o d el

0 �2 1 * *

0 �1 3 * *

�0 �0 2

0 �1 0 *

0 �1 1

0 �3 6 * *

�0 �0 9

�0 �0 3

0 �0 6

9 5 %

C I

0 �1 2 – 0 �2 9

0 �0 4 – 0 �2 1

�0 �1 1 to

0 �0 7

0 �0 2 – 0 �1 9

�0 �0 7 to

0 �2 8

0 �2 1 to

0 �5 0

�0 �1 8 to

0 �0 1

�0 �2 0 to

0 �1 5

�0 �0 3 to

0 �1 5

R a n d o m -e ff ec ts

m o d el

0 �2 2 * *

0 �1 7 *

�0 �0 2

0 �1 0 *

0 �1 1

0 �3 6 * *

�0 �0 6

0 �0 7

9 5 %

C I

0 �1 2 – 0 �3 2

0 �0 3 – 0 �3 1

�0 �1 1 to

0 �0 7

0 �0 2 – 0 �1 9

�0 �0 7 to

0 �2 8

0 �2 1 – 0 �5 0

�0 �2 0 to

0 �0 7

�0 �3 8 to

0 �2 8

�0 �0 9 to

0 �2 3

N o te

th a t th e m et a -a n a ly si s is b a se d o n d a ta

fr o m

ei g h t o f 1 2 re v ie w ed

st u d ie s p ro v id in g d a ta

o n P ea rs o n ’s co rr el a ti o n s.

C o ef fi ci en ts

g iv en

a re

P ea rs o n ’s r.

F ix ed -e ff ec t a n d ra n d o m -e ff ec t m o d el

w a s im

p le m en te d , a n d co ef fi ci en ts

w er e w ei g h te d a cc o rd in g to

th e sa m p le

si ze s o f th e d if fe re n t st u d ie s.

* p < 0 �0 5 ; * * p < 0 �0 1 .

AM Lunde Husebø et al.

Figure 1 Forest plots of the correlations

between intention and exercise adherence.

Figure 2 Forest plots of the correlations

between perceived behavioural control and

exercise adherence.

Figure 3 Forest plots of the correlations

between attitude and exercise adherence.

Figure 4 Forest plots of the correlations

between subjective norm and exercise

adherence.

Review Exercise adherence in cancer patients: a review

Figure 5 Forest plots of the correlations

between self efficacy and exercise adherence.

Figure 6 Forest plots of the correlations

between exercise stage of change and exercise

adherence.

Figure 7 Forest Plots of the correlations

between smoking habit and exercise

adherence.

Figure 8 Forest Plots of the correlations

between drinking habit and exercise

adherence.

Figure 9 Forest Plots of the correlations

between pretrial exercise behaviour and

exercise adherence.

AM Lunde Husebø et al.

patients decrease their physical activity levels after diagno-

sis, the importance of a positive attitude towards exercise

including an established exercise behaviour before the can-

cer diagnosis has been made, increase the adherence to

exercise intervention programmes (Midtgaard et al. 2009).

In a review of 38 studies including 1088 healthy adults,

Trost et al. (2002) found exercise habits to be a consistent

predictor of current exercise behaviour. Findings in this

review contrast somewhat with the above. Results for pre-

trial exercise levels varied considerably across studies

included in the meta-analysis, a non-significant meta-corre-

lation. This finding was also confirmed in the review of

studies explored narratively. Because of the contrast with

previous studies and also heterogeneity in findings between

the reviewed studies, no firm conclusions should be made.

The result may reflect that getting cancer might profoundly

influence people, leading to radical change in behavioural

patterns including exercise behaviour. Given this interpreta-

tion, this is a positive finding which indicates that a history

of sedentary behaviour does not hinder cancer patients

from becoming more physical active. However, more

research is needed to test this hypothesis.

The meta-analysis identified intention as the most

significant predictor among theory of planned behaviour

(TPB) constructs. Godin and Kok (1996) claim that a

combined effect of intention and PBC can explain one-third

of variations in health behaviour, with intention being the

key construct.

Current literature on health psychology emphasises self-

efficacy as an important predictor for exercise adherence,

and claims that people with high self-efficacy are more

likely to adhere to exercise programmes, and also have a

stronger belief in the benefits of regular exercise (Taylor

2003). This opinion is shared by authors who claim that

self-efficacy is a significant determinant of exercise behav-

iour in cancer patients (Rogers et al. 2005a). However, in

contrast to this view, self-efficacy showed only weak corre-

lations with exercise adherence in the present meta-analysis,

and obtained no firm conclusion concerning significance in

the complementary studies. Martin and Sinden (2001) pos-

tulate problems related to the choice of efficacy measure-

ment as reasons for non-correlation between adherences

and previous exercise behaviours.

The meta-analysis provides partial support for the opinion

that TPB constructs are important motivational variables

related to exercise, both in general (Hagger et al. 2002) and

in cancer populations (Blanchard et al. 2002). This finding

might be partly explained by the suggestion that external

motivational factors are more important than internal

motivation with respect to cancer patients’ adherence to

exercise programmes. A critique of both the TPB and social-

cognitive theory is that they try to explain decision-making

processes when individuals adopt a new health behaviour,

without addressing how to maintain the behaviour (Martin

& Sinden 2001). Varying strengths of relationships between

TPB motivational variables and exercise adherence can also

be explained by ceiling effects caused by a biased sample of

highly motivated subjects and by little variability in the

variables at baseline.

The finding that smoking status is not a significant con-

tributor is somewhat surprising. Smoking is known to be

negatively associated with adherence to health behaviour

recommendations, and is significantly and inversely related

to exercise adherence (Martin & Sinden 2001).

As a complex phenomenon, exercise adherence is also

influenced by the exercise programme offered and by the

exercise environment (WHO 2003), a claim supported by

this review as it reveals large variations in adherence rates

among the reviewed studies. This finding may be particu-

larly important for assessing the quality of the studies and

for calculating and interpreting correlation variables. Fur-

ther research is needed to clarify the importance of external

elements as determinants of exercise adherence. Supervised

trials applying behavioural change techniques may counter

the effects of individual internal motivation and result in

increased exercise adherence (Courneya et al. 2008, 2010,

McNeely et al. 2012).

Methodological considerations

Also, this review has faced some limitations. Although a

precise and well-defined literature search was conducted

over an extended time period, relevant studies may have

escaped the electronic search. In addition, exclusion of arti-

cles not published in peer-reviewed journals, and those not

written in English may have reduced the number of avail-

able and relevant trials. Additional articles would have

contributed to the validity and generalisability of the

conclusions drawn from the present review.

The meta-analysis was based on relatively few trials. One

explanation is the inconsistent use of statistics for estimating

the prediction of adherence in the reviewed studies. This

made reviewing a challenge and complicated the conclusions

drawn from the review data, a problem pointed out with

respect to the methodology of meta-analysis (Egger et al.

1997, Martin & Sinden 2001). Due to this critical issue,

four out of 12 studies had to be excluded, and accordingly

were 150 subjects omitted from the meta-analysis. For two

of the variables correlation coefficients were available in

two studies only. Combining data on predictors from only

Review Exercise adherence in cancer patients: a review

two studies in a meta-analysis can be considered a weakness

in the methodology and raises the question of how many

studies are required for a meta-analysis (Deeks et al. 2008).

Valentine et al. (2010) claims that two studies are enough,

stating: ‘because all other synthesis techniques are less trans-

parent and/or are less likely to be valid’ (p. 245). The results

should nonetheless be interpreted with care.

A total of six correlations were reported as non-signifi-

cant in the primary studies, and the exact coefficients were

not given. In cases of missing information, the correlation

coefficient was set to 0�00. If both negative and positive correlations were expected, 0�00 would be the median non- significant coefficient. This could be considered as a conser-

vative estimate in this context, because positive correlations

are more likely than negative correlations. Therefore, a

replacement with 0�00 may have resulted in an under- estimation of meta-correlations (Blue 1995, Armitage &

Conner 2001, Hagger et al. 2002).

Inclusion of few studies in the meta-analysis may increase

the possibility for publication bias and heterogeneity among

studies. Heterogeneity may compromise conclusions regard-

ing patients’ ability to be physically active and potentially

influence the reliability of the data (Egger et al. 1997). Sig-

nificant heterogeneity between studies was detected for two

of the predictions for which meta-analysis were conducted.

However, random- and fixed-effect yielded only moderately

different coefficients of meta-correlation for these variables.

Thus, indicating that heterogeneity is a modest problem in

the present study. Nonetheless, heterogeneity might indicate

publication bias (Deeks et al. 2008), and conclusions

should be made with caution.

Studies reporting low accrual rates highlight the issue of

selection bias and generalisation. Low accrual rates may

reduce the representativeness of the sample and compro-

mise the strength of the research results, affecting general-

isation and external validity (Oldervoll et al. 2005).

In addition, the relatively small sample sizes and large

number of predictors in the studies considered might indi-

cate artificially strong correlations and provide less robust

data regarding the relationship between exercise adherence

and its determinants (Green 1991). Green (1991) recom-

mends that researchers estimate effect size based on the

characteristics of their study and employ the rules-of-thumb

that include effect size or conduct power analyses.

Few studies reported data regarding dropouts or with-

drawals, which made it difficult to draw conclusions about

the feasibility of multivariate analysis and its results, especially

when multiple variables and small samples were involved.

The studies varied in their use of adherence measure-

ments, which complicated comparison between the charac-

teristics of high-adherence studies to low-adherence studies.

Patients who report their exercise achievements in diaries

may compromise the validity of adherence data through

over-reporting or recall vagueness (WHO 2003). Objective

observation of the participants’ attendance at exercise clas-

ses might be a more accurate measure of adherence and

could affect patients’ motivation and attitude towards exer-

cise behaviour (Armitage & Conner 2001).

Conclusion

The present review contributes to the knowledge of factors

that motivate or form barriers to exercise in cancer popula-

tions. It identifies exercise stage of change, intention and

PBC as statistically significant, although moderately strong

predictors of adherence to exercise intervention pro-

grammes. Findings give some support to TPB and TTM as

relevant frameworks for the understanding of what moti-

vates cancer patients to engage in exercise behaviour.

Predictors of exercise adherence in cancer populations are

seldom reported on. Few studies and small sample sizes

increase risk of biased conclusions made in reviews and

meta-analysis. To meet this challenge more research is war-

ranted. Moreover, relatively weak predictions were identi-

fied, and, more research is needed to identify predictors

that could be of greater importance.

Relevance to clinical practice

The results of this review suggest that more attention should

be paid to what will improve cancer survivors’ adherence to

exercise and to how motivational and behavioural predictors

can play a substantial role when cancer survivors take part in

health promotion behaviour, which calls for research based

on motivational and behavioural theory. Our findings entail

the necessity of substantial basic work for a nursing practice

that focuses on what motivates survivors to take on exercise,

and what will enhance their belief in managing their exercise

programme. A change in lifestyle toward initiating physical

health behaviour can be a challenge to cancer survivors dur-

ing and after treatment. The time around diagnosis and treat-

ment has been hypothesised as a ‘teachable moment’, which

may increase the individuals’ motivation to change their life-

style. Using the findings of our review that exercise stage of

change represents important predictors of exercise adher-

ence, nurses should establish strategies for surveying patients’

motivation and readiness to engage in exercise programmes.

More attention should be given to the significance of external

motivational factors and how these can be applied clinically

in health behaviour change programmes.

AM Lunde Husebø et al.

Contributions

Study design: AMLH, EB; data collection and analysis:

AMLH, EB and manuscript preparation: AMLH, SMD,

JAS, EB.

Conflict of interest

The authors have no financial, personal, political, academic

or other relations that could lead to a conflict of interest.

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