Content uploaded by John Weinman
All content in this area was uploaded by John Weinman on Feb 19, 2015
Content may be subject to copyright.
British Journal of Health Psychology (2011)
2011 The British Psychological Society
A text message programme designed to modify
patients’ illness and treatment beliefs improves
self-reported adherence to asthma preventer
Keith J. Petrie
, Kate Perry
, Elizabeth Broadbent
Department of Psychological Medicine, Faculty of Medical and Health Sciences,
University of Auckland, New Zealand
Atlantis HealthCare, Auckland, New Zealand
Department of Psychological Medicine, Institute of Psychiatry, King’s College
Objective. While effective preventative medication is readily available for asthma,
adherence is a major problem due to patients’ beliefs about their illness and medication.
We investigated whether a t e x t message programme targeted at changing patients’
illness and medication beliefs would improve adherence in young adult asthma patients.
Methods. Two hundred and sixteen patients aged between 16 and 45 on asthma
preventer medication were recruited from pamphlets dispensed with medication and
e-mails sent to members of a targeted marketing website. Participants were randomized
to receive individually tailored text messages based on their illness and medication beliefs
over 18 weeks or no text messages. Illness and medication beliefs were assessed at
baseline and at 18 weeks. Adherence rates were assessed by phone calls to participants
at 6, 12, and 18 weeks and at 6 and 9 months.
Results. At 18 weeks, the intervention group had increased their perceived necessity
of preventer medication, increased their belief in the long-term nature of their asthma,
and their perceived control over their asthma relative to control group (all p’s < .05).
The intervention group also signiﬁcantly improved adherence over the follow-up period
compared to the control group with a relative average increase in adherence over the
follow-up period of 10% (p < .001). The percentage taking over 80% of prescribed
inhaler doses was 23.9% in the control group compared to 37.7% in the intervention
group (p < .05).
Correspondence should be addressed to Professor Keith J. Petrie, Department of Psychological Medicine, Faculty of
Medical and Health Sciences, University of Auckland, 85 Park Road, Private Bag 92019, Auckland, New Zealand (e-mail:
2 Keith J. Petrie et al.
preventer inhaler and may be useful for other illnesses where adherence is a major
Asthma is a common medical condition caused by chronic inﬂammation of the airways.
Characteristic symptoms of the illness include attacks of shortness of breath, wheezing,
tightness in the chest, and cough. Asthma is commonly treated by inhaled corticosteroids,
which help to suppress inﬂammation of the airways and reduce the frequency of severe
symptoms and attacks. This medication in the form of inhalers is known as preventer or
controller medication and many patients also take short-acting bronchodilators to control
acute symptoms (reliever medication). In order to provide therapeutic beneﬁt, preventer
medication needs to be taken regularly on a daily basis. However, non-adherence to
preventer medication is a common problem in patients diagnosed with asthma and
this results in the overuse of reliever medication, increased asthma symptoms, more
frequent asthma attacks, and hospital admissions (Stern et al., 2006). Optimal adherence
to inhaled corticosteroids requires patients to take their preventer medication on 80% or
more occasions, as this is associated with greatest asthma control (Lasmar et al.,2009).
Age is a factor that has been associated with non-adherence in a number of studies.
Younger patients in the 14–25 years - age range have been found to report using preventer
inhalers less than older patients (Diette et al.,1999;Legorretaet al.,1998).Ingeneral,
adherence rates to preventer medication improve with increasing age (Jessop & Rutter,
2003; Tettersell, 1993). This makes young people an important group to target for
improving adherence and reducing their underuse of preventer-inhaled corticosteroids.
A number of studies have also highlighted the low rates of adherence to preventer
inhalers being due to patients’ beliefs about the nature of the illness (Kaptein, Klok, Moss-
Morris, & Brand, 2010). A common pattern with asthma is to experience relatively normal
symptomless periods interspersed with intermittent periods of shortness of breath,
wheezing, and more serious attacks. This can reinforce the belief that asthma is only
present when symptoms are also apparent or a ‘no symptoms = no asthma’ perception
(Halm, Mora, & Leventhal, 2006; Ulrik et al., 2006). This perception is often strengthened
by the fact that the effectiveness of preventer medication is poor when used by patients
to reduce the acute symptoms of asthma. These two aspects will often lead patients
to erroneously rely more on reliever medication and less on preventer medication and
other long-term management strategies. Unfortunately, this pattern of medication usage
tends to be associated with worse patient outcomes, including lower quality of life,
worse asthma control, and greater symptom severity than those patients who use more
preventer medication (Schatz et al., 2006). Patients under-using preventer medication
are also more likely to have been hospitalized or attend emergency departments for their
asthma (Tan et al.,2009).
Previous work on patients’ illness and treatment beliefs shows they cluster along
speciﬁc dimensions (Horne, Weinman, & Hankins, 1999; Petrie & Weinman, 2006).
Patients’ illness perceptions are comprised of beliefs about: (1) the symptoms that
patients associate with their illness label; (2) why they developed the illness; (3) the
implications of the illness for their life; (4) how long the illness will last; and (5) how the
illness is cured or controlled by what the patient can do themselves or by the medication
itself. Research has shown that adoption of a chronic rather than an acute illness belief
model is associated with better adherence to preventer medication in patients with
asthma (Byer & Myers, 2000; Jessop & Rutter, 2003).
A text message programme designed to modify 3
Just as patients develop perceptions about their illness, they also develop ideas about
the medication they are prescribed to control their illness (Horne et al., 1999). Two
particular perceptions seem to be associated with adherence to preventer medication in
asthma – the ﬁrst is the patient’s beliefs about the necessity of the medication and the
second is the patient’s concerns about taking the medication. Research has consistently
conﬁrmed the relationship between believing a medication is necessary and using it as
prescribed (Byer & Myers, 2000; Hand, 1998; Tettersell, 1993). Asthma patients often cite
fear of unwanted negative effects of medication as a primary reason for non-adherence,
and this belief is strongly associated with low adherence (Horne & Weinman, 2002).
Worries about the long-term safety of taking steroids and dependence are common
concerns among patients who are non-adherent with preventer medication (Apter et al.,
In the current study, we tested whether text messages could be used to improve
adherence in young adults with asthma. Text messages have recently begun to be used
as a tool for behaviour change in a variety of health settings with mostly positive results
(see Cole-Lewis & Kershaw, 2010). Text message interventions have been developed to
deliver or supplement different health-promotion interventions including encouraging
diabetes management in young people (Franklin, Waller, Pagliari, & Greene, 2006;
Rami, Popow, Horn, Waldhoer, & Schober, 2006), as well as supporting weight loss
in overweight adults (Patrick et al.,2009)andasamethodofprovidingassistancewith
smoking cessation (Rodgers et al.,2005).
In this study, we investigated whether targeted text messaging based on an assessment
of patients’ illness and medication beliefs can improve adherence to asthma preventer
inhalers. Patients had their illness and medication beliefs assessed at baseline and were
either randomized to normal care or to receive tailored text messages for 18 weeks.
We hypothesized that the text message group would show changes in their illness and
medication beliefs as well as improved adherence to their preventer inhaler at follow-ups
over a 9-month period.
Two hundred and sixteen individuals were recruited from ﬂyers dispensed with asthma
preventer medication and e-mails sent to members of a targeted marketing website
(www.smilecity.co.nz). This website invites members to participate in online shopping,
surveys, and read e-mails in return for rewards. Participants were offered to go into the
draw to receive an Apple ipod. To be included, participants had to be between 16 and
45 years of age, diagnosed with asthma, be not currently adhering to their preventer
medication as prescribed, and own a mobile phone capable of receiving text messages.
Non-English speakers and individuals with a diagnosis of chronic obstructive pulmonary
disease were excluded from the study.
Instruments and procedure
People interested in participating called a phone number or e-mailed their contact details
to register for the study. These potential participants were called back, provided with
more information about the study, asked to return a consent form and questionnaire by
mail, and completed a baseline phone interview. The interview screened participants
for eligibility, and asked about the number of inhaler preventer doses prescribed each
4 Keith J. Petrie et al.
week by the participant’s doctor and the number of doses currently taken. Two hundred
and sixteen people responded to the advertisement and were screened, and of these
147 (100 females and 47 males) were eligible and sent in the consent form and baseline
questionnaire assessing illness perceptions.
The questionnaire assessed participants’ illness perceptions using the Brief Illness
Perception Questionnaire (BIPQ) (Broadbent, Petrie, Main, & Weinman, 2006). This
measure comprises eight items designed to assess patients’ perceptions of their asthma
along the following dimensions: identity, consequences, timeline, personal control,
treatment control, concern, understanding, and emotional response to the illness.
Each item is scored on an 11-point scale (0–10) with higher scores representing a
stronger endorsement of that item. The ninth causal item of this questionnaire was not
used for this study. In addition, participants were asked to rate their belief about the
necessity of their inhaler on an 11-point scale ‘How much do you feel you need to
take your preventer inhaler?’ from (0) ‘I don’t need it at all’ to (10) ‘It is absolutely
essential for me’. Concerns about using their prescribed preventer inhaler were assessed
by asking participants to rate ‘How concerned are you about using your preventer
inhaler?’ on a similar 11-point scale from (0) ‘Not concerned at all’ to (10) ‘Extremely
After completing the baseline assessment, participants were randomized to either the
text message group (n = 73) or control usual care group (n = 74). The randomization
sequence was generated by computer program and allocation was concealed in
consecutively numbered sealed envelopes. Adherence rates were assessed by phone
calls to participants at 6, 12, and 18 weeks as well as at 6 and 9 months. We examined
the average self-reported adherence as well as the proportion of participants in each
group achieving optimal asthma control of 80% or above adherence levels. As well as
being assessed at baseline, participants’ perceptions of their asthma and medication
necessity and concerns beliefs were assessed again at 18 weeks using the same
Tex t m es s age p ro gramm e
Participants assigned to the text message group received tailored text messages for 18
weeks. Prior to the study, a bank of 166 text messages was generated with approximately
24 texts for each of the seven target beliefs. The particular beliefs targeted and example
texts from the bank of texts associated with that belief are shown in Table 1. Each of
the texts was designed to counteract the speciﬁc illness and medication beliefs that had
previously been found to be associated with non-adherence to preventer medication
(Halm et al.,2006;Horne&Weinman,2002).
Texts were sent at a frequency of two texts per day during weeks 1–6, one text per
day from weeks 7 to 12, and three texts per week from weeks 13 to 18. The type of texts
sent was determined by the participant’s baseline scores on the BIPQ and the level of
medication belief ratings. Participants scoring low or high on each of the target beliefs,
deﬁned as one standard deviation above or below the mean score on that item, were
sent text messages chosen at random from that category that were designed to push the
belief in a direction more consistent with higher adherence. If a patient did not score in
the target low or high categories, they were not sent any text messages for that belief.
Only two participants in the intervention group did not score high or low on at least one
target illness perception and so did not receive any text messages (one in the control
group also met these criteria).
A text message programme designed to modify 5
Table 1. Ta r ge t b el i ef s a nd s a mp l e t e xt s
Belief Examples of texts designed to change belief
Short timeline ‘Your asthma is always there even when you don’t
‘Your asthma symptoms may come and go but
your asthma is always there’
Low personal control ‘You can control your asthma by taking your
preventer every day’
‘Take your preventer everyday and control your
asthma before it controls you’
Low illness identity (low symptoms) ‘No asthma symptoms doesn’t mean no asthma’
‘Asthma doesn’t take a holiday. Even if you don’t
have symptoms your asthma is still there’
High illness identity (high symptoms) ‘A puff of your preventer each day keeps the
‘Reduce your risk of having an asthma attack by
taking your preventer every day’
Low coherence (poor understanding) ‘The medicine in your preventer doesn’t work
immediately but used regularly it will reduce
the inﬂammation that causes asthma’
‘Asthma is caused by swollen and inﬂamed
Low necessity ‘Taking your preventer every day protects you
from asthma symptoms’
‘Your preventer works best when taken every
High concerns ‘Your preventer medication is not addictive’
‘Your preventer controls your asthma by
reducing the inﬂammation that causes asthma’
On the basis of previous research ﬁndings, mean baseline adherence rate was estimated
at 50% (SD = 25%) and an increase of 15% was deemed to be clinically relevant. These
ﬁgures generate an effect size, Cohen’s d of 0.6. To detect an effect size of 0.6 at the 5%
level of signiﬁcance and with 80% power, 50 participants were needed in each arm of
the two-arm (intervention and control) trial. Allowing for an attrition rate of 50%, a total
of 200 participants were screened at baseline.
Changes in illness perceptions over time were computed by subtracting baseline
scores from scores at 18 weeks. ANCOVA analyses were conducted to assess differences
in changes in illness perceptions between treatment groups controlling for baseline
scores. To analyse adherence over time and between groups, a mixed ANOVA was
conducted. Due to the high drop-out rate, only those participants who responded at
week 6 were retained in analysis and the mean replacement method was used for missing
data from these participants for further time points. In addition, the average adherence
for each person was calculated across all time points and an independent samples t-test
was conducted to compare overall mean adherence between groups. The number of
6 Keith J. Petrie et al.
people who had an average adherence rate ≥ 80% was also compared between groups
using Pearson’s chi square. We repeated the analyses using the carry last observation
forward approach for missing data, as well as by running multiple imputation procedures.
All tests were two-tailed and p < 0.05 was considered statistically signiﬁcant.
There was an expected attrition rate: 147 of the 216 people screened returned the
consent form (68%); by week 6, 124 of these original 147 participants completed the
follow-up questionnaire (84%), 58 in the intervention and 66 in the control group; and
93 of the 124 completed to last-follow-up point (75%), 41 in the intervention and 52 in
the control. Chi-squared tests showed the drop-out rates were not signiﬁcantly different
between groups. A sample size of 124 participants still allows the detection of an effect
size of .60 with power of 80%. A comparison of the baseline adherence scores using the
sample of 216 people screened, between those who remained in the study at 6 weeks
and those who did not, showed that those who dropped out were signiﬁcantly more
adherent at baseline than those who remained (mean 67.1% vs. mean 46.7%; t = 4.47, p
Illness and medication beliefs
There were no signiﬁcant differences in illness perceptions between groups at baseline
(p > .05). Changes in illness perceptions between groups are shown in Table 2. By
18 weeks, the intervention group had increased perceived duration of their asthma,
increased perceived control over their asthma, and increased perceived necessity of
preventer medication. This analysis shows the text message group did change their
beliefs in a direction consistent with greater adherence.
Across the entire sample of 216 participants, baseline mean adherence was 54%
(SD = 31.8%) in the control group, and 56.5% (SD = 35.3%) in the intervention group,
t(213) = -.53, p = .60. Figure 1 shows adherence over time in the 124 participants who
responded at week 6. A mixed ANOVA showed no overall time effect, but a signiﬁcant
group effect (F(1,122) = 9.35, p = .003), and a signiﬁcant group by time effect (F(5) =
2.27, p < .05).
Average self-reported adherence over all time points in the control group was 43.2%
(SD = 26) and the intervention group was 57.8% (SD = 27.1), t(122) = -3.06, p = .003).
The proportions with average adherence of 80% or above for the control group was 7 of
66 (10.6%) and for the intervention group 15 of 58 (25.9%). The difference between the
two groups was 15.3%, p = 0.034 (Fisher’s exact test). Repeating these analyses using
the carry last observation forward approach to missing data, or using multiple imputation
procedures, did not change the signiﬁcance of the results, Figures 1 and 2.
This study tested whether sending text messages designed to encourage patients with
asthma to adopt beliefs about their illness and medication that are more compatible
with adherence would improve adherence with preventer inhaler medication. We
found targeted text messages changed timeline, personal control, and medication
A text message programme designed to modify 7
Table 2. Baseline, 18 weeks, and estimated marginal mean changes controlling for baseline values, in
illness perceptions and medication beliefs between groups
Control (N = 46) Intervention (N = 57)
Baseline 18-week Baseline 18-week
mean mean Adjusted mean mean Adjusted
Perceptions (SD)(SD)difference(SD)(SD)difference p
Asthma perceptions – (BIPQ)
Consequences 4.50 3.96 −.46 4.07 3.88 −.26 .51
(2.16) (2.04) (2.10) (2.11)
Identity 4.87 4.17 −.57 4.43 3.86 −.64 .88
(2.12) (2.31) (2.13) (2.03)
Timeline 8.17 7.83 −.43 8.46 9.09 .70 .006
(2.11) (2.73) (2.22) (1.81)
Concern 5.17 4.37 −.63 4.46 4.26 −.34 .48
(2.59) (2.62) (2.53) (2.20)
Personal 6.41 6.96 .38 6.79 8.02 1.36 .009
control (2.05) (2.21) (1.96) (1.56)
Coherence 6.70 7.35 .65 6.70 7.46 .71 .83
(2.27) (2.16) (2.30) (2.21)
Tre a tm e nt 7 .1 5 7 .2 6 . 13 7 .0 7 7 .8 4 . 75 . 08
control (2.14) (2.20) (2.59) (1.80)
Emotional 3.30 2.37 −.98 3.43 2.49 −.86 .77
representation (2.36) (2.20) (2.51) (2.31)
Preventer 3.37 2.52 −.77 3.12 1.75 −1.48 .10
concern (3.25) (2.76) (2.77) (1.91)
Preventer 5.83 5.11 −.80 6.18 6.52 .46 .01
necessity (2.71) (2.88) (3.16) (2.98)
necessity beliefs at 18 weeks in the intervention group. At follow-up, intervention group
participants held longer timeline or a more chronic view of their illness, which is more
consistent with regular adherence particularly in the absence of asthma symptoms (Halm
et al., 2006). Furthermore, the text message intervention also increased participants’
perceptions of how much they could control their illness and their personal necessity
for preventer medication. These beliefs are also compatible with increased adherence
to long-term medication (Horne & Weinman, 2002; Lavole et al., 2008). Data from the
study also show the intervention to increase adherence by around 10% in the intervention
group. The text message programme also resulted in a signiﬁcantly higher percentage
of the intervention group achieving the 80% or greater adherence level.
The results of the study are consistent with a number of recent trials designed to
change health behaviour that have shown improved disease monitoring and management
through the use of text messages (Krishna, Boren, & Balas, 2009). While several studies
have used text messaging purely as reminders (e.g., Charles et al.,2007;Downer,Meara,
DaCosta, & Sethuraman, 2006), more interventions are being developed to send more
personalized messages targeting speciﬁc behaviours (e.g., Franklin et al.,2006;Kim&
Kim, 2008). Text messaging has the advantage of being inexpensive, easy to access –
especially across different socio-economic groups – and texting programmes are readily
scalable to large populations. At present penetration into older populations may not be
8 Keith J. Petrie et al.
Figure 1. Adherence levels by group across the follow-up period.
Figure 2. Average percentage adherence over time, and the percentage of participants over 80%
adherent, in each group.
possible often due to a lack of familiarity with the technology but this is likely to change
as people who are using texting regularly move into an older age group.
The current study is limited by the large dropout in participation early in the trial,
which could have been due to a large number of individuals initially enrolling for the
study motivated to win a prize. It is interesting to note that these early dropouts were
A text message programme designed to modify 9
those with higher adherence at baseline and may have also felt less need to receive
adherence-focused messages and hence to stay in the study. However, after the 6-week
follow-up those still in the trial generally remained until the end of the study and the study
was still large enough to detect a difference in rates of adherence. It should also be noted
that the study was limited to participants aged 45 or under and it is not clear whether
the study results will generalize to an older group of patients with asthma. As adherence
problems with asthma medication are most evident in younger age groups, this may not
be such a problem when considering interventions with this group but may be important
when applying such a texting programme to other disease states. Future research may
wish to investigate the effects of the intervention on health outcomes resulting from
higher adherence, such as reduced health service use or reduced work absenteeism. The
study assessed self-reported adherence and future research could utilize more objective
measures of adherence.
The receipt of text messages meant that the intervention group received more contact
than the control group, and this may have induced demand characteristics. However,
this contact was minimal compared with many other psychological interventions, as it
did not involve personal face-to-face interactions, so relationships were not formed with
the researchers through the intervention. At 9 months (18 weeks after the intervention
had ﬁnished and text messages were no longer being sent), adherence continued to
remain higher in the intervention group, providing evidence for effects independent of
Overall, the results of the study are consistent with the results of previous inter-
ventions that have targeted illness perceptions as a way of changing health behaviour
(Broadbent, Ellis, Thomas, Gamble, & Petrie, 2009; Petrie, Cameron, Ellis, Buick, &
Weinman, 2002). The results of the study are encouraging for developing further text
messaging interventions in the adherence and disease management area as texting seems
to be becoming more acceptable to patients (Pinnock, Slack, Pagliari, Price, & Sheikh,
2006) and texting has advantages in terms of reaching patients who may ﬁnd face-to-face
interventions difﬁcult to access.
Apter, A. J., Boston, R. C., George, M., Norﬂeet, A. L., Tenhave, T., Coyne, J. C., . . . Feldman
H. I. (2003). Modiﬁable barriers to adherence to inhaled steroids among adults with asthma:
It’s not just black and white. Journal of Allergy and Clinical Immunology, 111, 1219–
Broadbent, E., Ellis, C. J., Thomas, J., Gamble, G., & Petrie, K. J. (2009). Further development of an
illness perception intervention for myocardial infarction patients: A randomized trial. Journal
of Psychosomatic Research, 67, 17–23. doi:10.1016/j.jpsychores.2008.11.006
Broadbent, E., Petrie, K. J., Main, J., & Weinman, J. (2006). The Brief Illness Perception Ques-
tionnaire (BIPQ). Journal of Psychosomatic Research, 60, 631–637. doi:10.1016/j.jpsychores.
Byer, B., & Myers, L. (2000). Psychological correlates of adherence to medication in asthma.
Psychology, Health & Medicine, 5, 389–393.
Charles, T., Quinn, D., Weatherall, M., Aldington, S., Beasley, R., & Holt, S. (2007). An audiovisual
reminder function improves adherence with inhaled corticosteroid therapy in asthma. Journal
of Allergy and Clinical Immunology, 119, 811–816.
Cole-Lewis, H., & Kershaw, T. (2010). Text messaging as a tool for behaviour change in disease
prevention and management. Epidemiologic Reviews, 100, 56–69.
10 Keith J. Petrie et al.
Diette, G. B., Wu, A. W., Skinner, E. A., Markson, L., Clark, R. D., McDonald, R. C, . . . Steinwachs,
D. M. (1999). Treatment patterns among adult patients with asthma: Factors associated with
overuse of inhaled beta-agonists and underuse of inhaled corticosteroids. Archives Internal
Medicine, 159, 2697–2704.
Downer, S. R., Meara, J. G., DaCosta, A. C., & Sethuraman, K. (2006). SMS text messaging improves
outpatient attendance. Australian Health Review, 30, 389–396.
Franklin, V. L., Waller, A., Pagliari, C., & Greene, S. A. (2006). A randomized controlled trial of Sweet
Talk, a text-messaging system to support young people with diabetes. Diabetic Medicine, 23,
Halm, E. A., Mora, P., & Leventhal, H. (2006). No symptoms, no asthma: The acute episodic disease
belief is associated with poor self-management among inner-city adults with persistent asthma.
Chest, 129, 573–580.
Hand, C. (1998). Adherence and asthma. In L. Myers & K. Midence (Eds.), Adherence to treatment
in medical conditions (pp. 383–422). Amsterdam: Harwood Academic Publishers.
Horne, R., & Weinman, J. (2002). Self-regulation and self-management in asthma: Exploring the
role of illness perceptions and treatment beliefs in explaining non-adherence to preventer
medication. Psychology and Health, 17, 17–32. doi:10.1080/ 08870440290001502
Horne, R., Weinman, J., & Hankins, M. (1999). The Beliefs about Medicines Questionnaire: The
development and evaluation of a new method for assessing the cognitive representation of
medication. Psychology and Health, 14, 1–24. doi:10.1080/08870449908407311
Jessop, D., & Rutter, D. (2003). Adherence to asthma medication: The role of illness representa-
tions. Psychology and Health, 18, 595–612. doi:10.1080/0887044031000097009
Kaptein, A. A., Klok, T., Moss-Morris, R., & Brand, P. L. P. (2010). Illness perceptions: impact on self-
management and control in asthma. Current Opinion in Allergy and Clinical Immunology,
Kim, S. I., & Kim, H. S. (2008). Effectiveness of mobile and internet intervention in patients with
obese type 2 diabetes. International Journal of Medical Informatics, 77, 399–404.
Krishna, S., Boren, S. A., & Balas, E. A. (2009). Healthcare via cell phones: A systematic review.
Telemedicine and e-health, 15, 231–240.
Lasmar, L., Camargos, P., Champs, N. S., Fonseca, M. T., Fontes, M. J., Ibiapina, C, . . . Moura, J. A.
(2009). Adherence rate to inhaled corticosteroids and their impact on asthma control. Allergy,
Lavole, K. L., Bouchard, A., Joseph, M., Campbell, T. S., Favreau, H., & Bacon, S. L. (2008).
Association of asthma self-efﬁcacy to asthma control and quality of life. Annals of Behavioral
Medicine, 36, 100–106.
Legorreta, A. P., Christian-Herman, J., O’Connor, R. D., Hasan, M. M., Evans, R., & Leung, K. M.
(1998). Compliance with national asthma management guidelines and specialty care: A health
maintenance organization experience. Archives of Internal Medicine, 158, 457–464.
Patrick, K., Raab, F., & Adams, M. A., Dillon, L., Zabinski, M., Rock, C. L, . . . Norman, G. J. (2009).
A text message-based intervention for weight loss: randomized controlled trial. Journal of
Medical Internet Research, 11(1), e1. doi:10.2196/jmir.1100
Petrie, K. J., Cameron, L. D., Ellis, C. J., Buick, D., & Weinman, J. (2002). Changing illness
perceptions following myocardial infarction: an early intervention randomized controlled trial.
Psychosomatic Medicine, 64, 580–586.
Petrie, K. J., & Weinman, J. (2006). Why illness perceptions matter. Clinical Medicine, 6, 536–539.
Pinnock, H., Slack, R., Pagliari, C., Price, D., & Sheikh, A. (2006). Professional and patient attitudes
to using mobile phone technology to monitor asthma: Questionnaire survey. Primary Care
Respiratory Journal, 15, 237–245
Rami, B., Popow, C., Horn, W., Waldhoer, T., & Schober, E. (2006). Telemedical support to
improve glycemic control in adolescents with type 1 diabetes mellitus. European Journal of
Pediatrics, 165, 701–705.
A text message programme designed to modify 11
Rodgers, A., Corbett, T., Bramley, D., Riddell, T., Wills, M., Lin, R. B., & Jones, M. (2005). Do u
smoke after txt? Results of a randomised trial of smoking cessation using mobile phone text
messaging. Tobacco Control, 14, 255–261.
Schatz, M., Zeiger, R. S., Vollmer, W. M., Mosen, D., Mendoza, G., Apter, A. J, . . . Cook, E. F.
(2006). The controller-to-total asthma medication ratio is associated with patient-centered as
well as utilization outcomes. Chest, 130, 43–50.
Stern, L., Berman, J., Lumry, W., Katz, L., Wang, L., Rosenblatt, L., & Doyle, J. J. (2006). Medication
compliance and disease exacerbation in patients with asthma: A retrospective study of managed
care data. Annals of Allergy, Asthma, and Immunology, 97, 402–408.
Tan, H., Sarawate, C., Singer, J., Elward, K., Cohen, R. I., Smart, B. A., . . . Schatz, M. (2009). Impact
of asthma controller medication on clinical, economic, and patient-reported outcomes. Mayo
Clinic Proceedings, 84, 675–684.
Tettersell, M. J. (1993). Asthma patients’ knowledge in relation to compliance with drug therapy.
Journal of Advanced Nursing, 18, 103–113.
Ulrik, C., Backer, V., Soes-Petersen, U., Lange, P., Harving, H., & Plaschke, P. (2006). The patient’s
perspective: Adherence or non-adherence to asthma controller therapy? Journal of Asthma,
Received 2 September 2010; revised version received 3 May 2011