study by Frederix et al. (22) did not provide p-values
of the effect on PA. Eight studies showed a significant
positive effect in favour of using feedback from a
wearable monitor in the intervention group (p < 0.05)
(23–25, 27, 29, 30, 32, 34).
Intervention strategies
H. E. M. Braakhuis et al.
154
Fig. 2. Risk of bias assessment of included studies (n = 14).
pulmonary disease (COPD), stroke, various cardio-
vascular diseases, Parkinson’s disease, and geriatric
patients. The duration of interventions varied between
20 days (28) and 2 years (34). The duration of 2 in-
terventions was dependent on the length of inpatient
rehabilitation (28, 35). In 12 studies, all participants
received usual care (UC), and the intervention group
received an objective feedback PA intervention in
addition to UC (Table I). In the 2 other studies the
control group received no care or wait list control (25,
29). Five interventions were performed in an inpatient
setting (22, 27, 28, 31, 35) and the other studies were
outpatient- or home-based.
Outcome measures used to calculate the significance
of the effect on PA were steps per day, walking time
per day, energy expenditure (in kJ or kcal per day or
per week), accelerometer counts per day, and time in
moderate intensity PA per week. These outcomes were
measured using a pedometer or accelerometer (Table
I). Steps/day was the most frequently used outcome
measure. The significance of the effect on PA was
calculated by the authors in 3 different ways: p-value
of (i) difference in mean change between intervention
and control group; (ii) difference between intervention
and control group at follow-up; and (iii) difference
between baseline and follow-up of the intervention
and control group calculated separately (Table I). The
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Intervention strategies used in each study are shown in
Table I. Table II shows the frequency of intervention
strategies used in the included studies. Five studies
used a pedometer for feedback (24–26, 29, 30) and
the others studies used accelerometers. The most
frequently used feedback parameter is steps per day
(Table III). Furthermore, frequency of feedback varied
between daily and monthly. In 4 studies, patients could
choose when to view their PA level (23, 25, 32, 34).
In 8 studies, subjects could see their real-time PA on
a display (24–26, 29–32, 35). Four studies (22, 25,
30, 34) used no verbal interaction with a coach or
therapist in real-life consultations or by telephone to
provide feedback.
The following BCT components mentioned in the
studies were identified: education (E), goal-setting
(GS), barrier identification (BI) and/or problem-
solving (PS), action planning (AP) and social support
(SS) (Table I). BCT components were used in a wide
variety of combinations. Table II shows the frequency
of BCT components present in all included studies.
Five studies used 3 or more BCT components as con-
current intervention strategies (23, 25, 29, 32, 34). GS
was the most-often used BCT component (Table II).
GS and E were frequently combined with BI and/or
PS. Only 1 study used social support (25).
Effect estimates
Authors were contacted when data on PA to calculate
SMD post-intervention were missing (22, 24, 26, 29,
34, 35). SMDs of 11 studies were calculated based on
original data, data sent by authors, or a combination of
both. In 3 studies, the SD of the outcome measure at
follow-up was estimated (29, 31, 33). One of the inter-
vention arms of McMurdo et al. (29) and Shoemaker
et al. (33) was excluded from meta-analysis based on
inclusion criteria. SMD of Frederix et al. (22) and Peel
et al. (27) (respectively SMD = 4.64 and 4.73) was more
than 3 times as large as SMD of other studies (SMD
between –0.09 and 1.17), as shown in Fig. 3. Leave-one-
out sensitivity analysis showed that after removing the
study of Frederix et al. (and Peel et al.), the overall ef-
fect changed to SMD with a smaller confidence interval
(SMD = 0.34 with 95% CI 0.23–0.44, z = 6.27, p < 0.01)
and considerable less heterogeneity (I 2 = 49%) (Fig. 3)
compared with the overall effect size when they were