Healthcare via Cell Phones: A Systematic Review

Abstract

Regular care and informational support are helpful in improving disease-related health outcomes. Communication technologies can help in providing such care and support. The purpose of this study was to evaluate the empirical evidence related to the role of cell phones and text messaging interventions in improving health outcomes and processes of care. Scientific literature was searched to identify controlled studies evaluating cell phone voice and text message interventions to provide care and disease management support. Searches identified 25 studies that evaluated cell phone voice and text messaging interventions, with 20 randomized controlled trials and 5 controlled studies. Nineteen studies assessed outcomes of care and six assessed processes of care. Selected studies included 38,060 participants with 10,374 adults and 27,686 children. They covered 12 clinical areas and took place in 13 countries. Frequency of message delivery ranged from 5 times per day for diabetes and smoking cessation support to once a week for advice on how to overcome barriers and maintain regular physical activity. Significant improvements were noted in compliance with medicine taking, asthma symptoms, HbA1C, stress levels, smoking quit rates, and self-efficacy. Process improvements were reported in lower failed appointments, quicker diagnosis and treatment, and improved teaching and training. Cost per text message was provided by two studies. The findings that enhancing standard care with reminders, disease monitoring and management, and education through cell phone voice and short message service can help improve health outcomes and care processes have implications for both patients and providers.

Full text

DOI: 10.1089/tmj.2008.0099 © MARY ANN LIEBERT, INC. • VOL. 15 NO. 3 • APRIL 2009 TELEMEDICINE and e-HEALTH 231
Santosh Krishna, Ph.D., Ed.S.,1 Suzanne Austin Boren, Ph.D.,
M.H.A.,2–4 and E. Andrew Balas, M.D., Ph.D.5
1School of Public Health, Saint Louis University, St. Louis, Missouri.
2
Health Services Research and Development Program, Harry S.
Truman Memorial Veterans’ Hospital, Columbia, Missouri.
3
Health Management & Informatics, School of Medicine, University
of Missouri, Columbia, Missouri.
4
Center for Health Care Quality, School of Medicine, University of
Missouri, Columbia, Missouri.
5
College of Health Sciences, Old Dominion University, Norfolk,
Virginia.
Abstract
Regular care and informational support are helpful in improving dis-
ease-related health outcomes. Communication technologies can help
in providing such care and support. The purpose of this study was to
evaluate the empirical evidence related to the role of cell phones and
text messaging interventions in improving health outcomes and pro-
cesses of care. Scientific literature was searched to identify controlled
studies evaluating cell phone voice and text message interventions
to provide care and disease management support. Searches identi-
fied 25 studies that evaluated cell phone voice and text messaging
interventions, with 20 randomized controlled trials and 5 controlled
studies. Nineteen studies assessed outcomes of care and six assessed
processes of care. Selected studies included 38,060 participants with
10,374 adults and 27,686 children. They covered 12 clinical areas
and took place in 13 countries. Frequency of message delivery ranged
from 5 times per day for diabetes and smoking cessation support to
once a week for advice on how to overcome barriers and maintain
regular physical activity. Significant improvements were noted in
compliance with medicine taking, asthma symptoms, HbA1C, stress
levels, smoking quit rates, and self-efficacy. Process improvements
were reported in lower failed appointments, quicker diagnosis and
treatment, and improved teaching and training. Cost per text message
was provided by two studies. The findings that enhancing standard
care with reminders, disease monitoring and management, and edu-
cation through cell phone voice and short message service can help
improve health outcomes and care processes have implications for
both patients and providers.
Key words: cellular phone, SMS, text messaging, wireless, outcomes
of care, process of care
Introduction
isease management and prevention have been known to
reduce morbidity, yet it is an ongoing challenge to find
effective ways of providing care and preventive man-
agement support that will lead to behavior change and
improved health outcomes. Phone-based interventions have brought
positive results among persons of low socioeconomic status and eth-
nic minority.1 Interventions involving automated message systems
have been shown to improve knowledge and health outcomes in a
variety of health areas.2,3 Technologies such as cell phones and text
messaging that are already a part of people’s daily lives have great
potential for improving people’s health by assisting them with behav-
ior modification and disease self-management.
According to Cellular Telecommunications and Internet Association,
there are approximately 262 million cell phone subscribers in the
United States.4 Almost every household in the United States has one
cell phone. Not only is the use of cell phones for voice communication
increasing, but also its use for text messaging and Internet access is
on the rise.5–8 Text messaging has increased manyfold since the 35%
of Pew survey respondents said in 2006 that they had used text mes-
saging and an additional 13% wanted to add this feature to their cell
phones. The use of cell phones and text messaging has been found
to be even higher among teens and young adults compared to older
adults all over the world.5–10 Parents provide cell phones to teenagers
as a harm minimization strategy through increased communication.11
ORIGINAL RESEARCH
Healthcare via Cell Phones:
A Systematic Review
D

232 TELEMEDICINE and e-HEALTH APRIL 2009
KRISHNA ET AL.
Contrary to the commonly held belief that persons of low socioeco-
nomic status do not have access to technology, ownership and use of
cell phones is as prevalent among those from a lower socioeconomic
status as among those from the general population.12,13
In spite of such widespread ownership of cell phones, use of voice
or text-messaging in disease management and self-care is still in
its infancy. No systematic review of cell phone–based interventions
exists to our knowledge in published scientific literature that analyzes
evidence on whether the use of cell phones and text messaging inter-
ventions improves health outcomes or processes of care, whether it is
acceptable to users, and whether it is a cost-effective option. The goal
of this study was to gather scientific evidence on the effective uses
of cell phones with voice or text messaging for health information
interventions, disease management, or for improving process of care.
We systematically reviewed published studies to evaluate the contri-
bution of cell phones and text messaging in improving the process
and outcomes of care.
Methods
DATA SOURCES
We searched MEDLINE (1950–May 2008) for relevant studies using
combinations of the following search terms: (1) telephone (MeSH),
cellular phone (MeSH), handheld computers (MeSH), cell phone$
(truncated textword), mobile phone$ (truncated textword), text mes-
sag$ (truncated textword), short message service (SMS) (textword), or
personal digital assistant (PDA) (textword); and (2) patient education
as topic (MeSH), health education (MeSH), patient educat$ (truncated
textword), or health educat$ (truncated textword). We also systemati-
cally searched the reference lists of included studies.
INCLUSION AND EXCLUSION CRITERIA
Our inclusion criteria were randomized controlled trials or con-
trolled studies that evaluated delivery of health information or educa-
tional intervention using cell phone or text messaging and measured
change in the process of care and/or health outcomes. Studies that
used wired Internet to provide information through e-mail or the
Web in addition to wireless communication were included. Studies
published in a language other than English with a complete English
abstract were included if they met the specified inclusion criteria. We
excluded studies that did not use a control group.
STUDY SELECTION AND DATA EXTRACTION
The investigators reviewed the titles and abstracts of the identified
citations and applied inclusion and exclusion criteria described above.
The investigators collected data from each eligible article including
descriptions of the patient sample, technology used, duration, deliv-
ery frequency, intervention, process and outcome measures, and sta-
tistical significance. Information on study design, clinical areas, and
country were also abstracted from the full text of all eligible studies.
For the purposes of this review, a trial was successful if there was a
significant outcome (p < 0.05) for the intervention group compared
with the control group at follow-up. The investigators analyzed the
publications to assess which interventions led to significant or non-
significant results.
Results
Comprehensive literature searches in MEDLINE using the terms
“cellular phone” or “mobile phone” or “text messaging” or “SMS”
identified 2,735 citations. Limiting the identified citations to ran-
domized controlled trials or controlled studies produced 97 citations.
To identify studies of health improvement information or education
interventions, titles and abstracts of 97 articles were screened to
determine relevance. Those articles discussing the harmful health
effects of cellular phones such as damage to health from the electro-
magnetic fields were excluded. After reading the abstracts or full text
of articles, 25 articles meeting the eligibility criteria (i.e., publications
that reported the use of cell phones for educational or informational
interventions in improving the health outcomes or process of care)
were selected (Table 1).14–38
The final set of 25 studies included 20 randomized controlled tri-
als and 5 controlled trials, with 38,060 participants, including 10,374
adults and 27,686 children. The duration in these studies ranged
from 3 weeks16 to 12 months,15,18,22,25,27,35 with an average duration
of 6 months, and one study taking place over only 2 days.33 Use
of cell phones and SMS was applied to 12 different clinical areas,
with nine articles on diabetes,15,22,24–27,32,35,38 four articles on smoking
cessation,17,18,34,36 two articles each on HIV/AIDS14,29 and general out-
patient clinics,20,21 and one article each on asthma,31 hypertension,27
physical activity,23 orthodontics,16 hepatitis vaccinations,37 stress
management,33 physical disabilities,30 and health promotion.19 Studies
took place in several countries. Four studies were conducted in
Australia,20–22,30 and three in the United Kingdom.23,29,35 Five reports of
three studies took place in Korea,24–27,38 two each in New Zealand,17,34
Spain,28,37 and the United States,14,36 and one study each in Austria,32
China,19 Croatia,31 Italy,33 France,15 Netherlands,16 and Norway.18
TECHNOLOGY AND FREQUENCY OF INTERVENTION
The technology used in all 25 studies was voice or the SMS fea-
ture of cell phones. Four studies14,30,33,36 used only the voice feature of
cell phones for the intervention. Whereas 8 of 25 studies used voice

© MARY ANN LIEBERT, INC. • VOL. 15 NO. 3 • APRIL 2009 TELEMEDICINE and e-HEALTH 233
HEALTHCARE VIA CELL PHONES
Table 1. Cell Phone Intervention Studies
AUTHOR/
YR SAMPLE
SIZE TECHNOLOGY DURATION
(MONTHS) DELIVERY
FREQUENCY CONTROL INTERVENTION MEASURES RESULTS
C VS. I
Andrade
200514
58 V 6 Medication
schedule
No
medication
reminder
DMAS/cell phone reminder
with medicine name and a
specific dose. Other instruc-
tions optional, such as
“Take one tablet on empty
stomach”
Adherence to
medicine taking
56% vs. 79%, p < 0.05
Benhamou
200715
30 SMS, V, PDA, I 12 Weekly No weekly
SMS
support
Weekly SMS diabetes
treatment advice from
their healthcare providers
based on weekly transfer
of SMBG and QOL survey
every 3 months
HbA1c
SMBG
QOL score
Satisfaction with Life
Hypo episodes
No. of BG tests/day
+0.12 vs. -0.14%, p < 0.10
+5 vs. -6 mg/dL, p = 0.06
0.0 vs. +5.6, p < 0.05
-0.01 vs. + 8.1, p < 0.05
79.1 vs. 69.1/patient, NS
-0.16 vs. -0.11/day, NS
Bos 200516 301 SMS 0.75 Once 24 h
prior
No
reminder
sent
SMS reminder the day
before
Failed attendance rate
Method preference:
Mail
Phone
SMS
Satisfaction
NS
Mail 56%
Phone 26%
SMS 18%
≥80%
Bramley
200517
1705 SMS 6.5 Daily No advice
or support
SMS smoking cessation
advice, support and dis-
traction
Quit rate non-Maori
vs. Maori % quitting
11% vs. 26%
Brendryen
200718
396 SMS, V, I, EM 12 Daily Self-help
booklet
Internet- and cell-phone-
based Happy Ending
intervention
Repeated point absti-
nence rate
NRT adherence rate
Self-efficacy level
13.1% vs. 22.3%, p < 0.05
93% vs. 87%, NS
5.10 vs. 4.38, p < 0.001
Chen 200819 1848 SMS 2 Once, 72 h
prior
No
reminder,
telephone
reminder
A phone call by an assis-
tant or a SMS reminder
sent, one time, 72 hours
prior to the appointment
Attendance rate:
Control vs. phone vs.
SMS
Cost – SMS vs. phone
80.5% vs. 87.5% vs.
88.3%, p < 0.05
0.31 vs. 0.48 Yuan/
message, p < 0.05
Downer
200520
2864 SMS 2 Once, 72 h
prior
No SMS
reminder
SMS reminder, 72 hours
prior
FTA rate 14% vs. 23%, p < 0.01
Downer
200621
22,658 SMS 3 Once, 72 h
prior
No SMS
reminder
SMS reminder, 72 h prior FTA rate-new patients
FTA rate-other patients
Cost per message
Costs saved
14.7% vs. 9.2%, p < 0.01
20.9% vs. 9.9%, p < 0.01
$0.25 per message
$12.20/appointment kept
Franklin
200622
92 SMS, V 12 Daily IT,
IT with ST
Daily text-messages with
personalized goal-specific
prompts and tailored to
patient’s age, gender, and
insulin regimen
HbA1c
Self-efficacy
Adherence
10.3 vs. 10.1 vs. 9.2%, p <
0.01 56.0 vs. 62.1, p < 0.01
70.4 vs. 77.2, p < 0.05
continued
➞

234 TELEMEDICINE and e-HEALTH APRIL 2009
KRISHNA ET AL.
Table 1. Cell Phone Intervention Studies
AUTHOR/
YR SAMPLE
SIZE TECHNOLOGY DURATION
(MONTHS) DELIVERY
FREQUENCY CONTROL INTERVENTION MEASURES RESULTS
C VS. I
Hurling
200723
77 SMS, V, I 4 Weekly Verbal
advice
during
clinic visit
Internet, e-mail and
mobile phone personal-
ized advice and moti-
vational tips on how to
overcome barriers and
maintain appropriate
level of physical activity,
utilizing various types of
activities. Chart display
of daily, weekly, and
overall activity levels
input by participant.
Change in:
PA overall: MET min/week
PA Leisure time, MET
min/week
Hours sitting-overall
Hours sitting: week-
days
Hours sitting: weekends
BMI
Lost % body fat
BP, diastolic
BP, systolic
Perceived control
Intention to exercise
Internal control
External control
4.0 vs. 12, NS
-5.5 vs. 4.1, p < 0.05
0.17 vs. -2.18, p < 0.05
1.4 vs. -5.9, p < 0.05
-0.2 vs. -5.2, NS
208.7 vs. 218.5, p < 0.05
0.10 vs. -0.24, NS
-0.17% vs. -2.18%, p < 0.05
0.73 vs. 0.69, NS
0.41 vs. 0.13, NS
-0.37 vs. 0.57, p < 0.01
-0.01 vs. 0.45, p < 0.01
5.85 vs. 7.24, p < 0.001
5.33 vs. 6.38, p < 0.01
Kim HS
200724
51 SMS, I 3 Weekly No weekly
SMS
support.
Standard
care
during
clinic visit
Weekly recommenda-
tions by a nurse to adjust
medication or insulin
based on patient’s SMBG,
medications, insulin dose,
diet, and exercise level.
Patients received remind-
ers if they did not input
data at least once a week.
HbA1c
FPG levels mg/dL
2HPMG
7.7 vs. 7.0, p < 0.05
149.5 vs. 145.7, NS
218.0 vs. 192.6, p < 0.05
Kim 200725 51 SMS, I 3 Weekly No weekly
SMS
support.
Standard
care
during
clinic visit
Weekly recommenda-
tions by a nurse to adjust
medication or insulin
based on patient’s input
of SMBG, medications,
insulin dose, diet, and
exercise level. Patients
received reminders if they
did not input information
at least once a week.
Grp 1: <7%:
HbA1c
FPG levels mg/dL
2HPMG
Grp 2: ≥7%:
HbA1c
FPG levels mg/dL
2HPMG
0.53, NS vs. -0.21, p < 0.05
-5.8, NS vs. -13.4, p < 0.05
-3.1, NS vs. -56.0, p < 0.05
0.22, NS vs. -2.15, p < 0.05
14.5, NS vs. -3.3, NS
24.8, NS vs. -115.2, NS
Kim SI
200826
34 SMS, V, I 6 Weekly No weekly
SMS
support.
Usual care
during
clinic visit
Weekly recommenda-
tions by a nurse to adjust
medication or insulin
based on patient’s input
of SMBG, medications,
insulin dose, diet, and
exercise level. Patients
received reminders if
they did not input infor-
mation at least once a
week.
HbA1C (mg/dL)
FPG (mg/dL)
2-HPMG (mg/dL)
TC (mg/dL)
TG (mg/dL)
HDL (mg/dL)
7.66 vs. 7.07, p < 0.05
144.9 vs. 151.6, p < 0.05
227.9 vs. 213.7, p < 0.05
190.4 vs. 175.9, p < 0.05
213.2 vs. 178.2, p < 0.05
43.3 vs. 47.3, p < 0.05
continued
➞
continued

© MARY ANN LIEBERT, INC. • VOL. 15 NO. 3 • APRIL 2009 TELEMEDICINE and e-HEALTH 235
HEALTHCARE VIA CELL PHONES
Table 1. Cell Phone Intervention Studies
AUTHOR/
YR SAMPLE
SIZE TECHNOLOGY DURATION
(MONTHS) DELIVERY
FREQUENCY CONTROL INTERVENTION MEASURES RESULTS
C VS. I
Kim HS
200827
34 SMS, I 12 Weekly Usual care
during
clinic visit
Weekly SMS optimal
recommendations based
on clinical history, smok-
ing habits, BMI, blood
pressure, and lab data.
Continuous education
and reinforcement of
diet and exercise, medi-
cation adjustment, and
frequent self-monitoring
of blood glucose levels.
HbA1C (mg/dL)
FPG (mg/dL)
2-HPMG (mg/dL)
TC (mg/dL)
TG (mg/dL)
HDLC (mg/dL)
8.19 vs. 6.67, p < 0.05
175.8 vs. 149.6, p < 0.05
264.7 vs. 169.7, p < 0.05
p = NS
p = NS
p = NS
Marquez
Contreras
200428
104 SMS 4 Twice/Week Standard
treatment
SMS messages with rec-
ommendations to control
Blood Pressure
% of compliers
Rate of compliance
% of patients with BP
control
51.5% vs. 64.7%, p = NS
88.1% vs. 91.9%, p = NS
85.7% vs. 84.4%, p = NS
Menon-
Johansson
200629
47 SMS, V 6 Once, within
2 weeks of
lab results
Standard
results
notifica-
tion and
recall to
clinic
SMS notification of HIV/
AIDS lab test results and
recall to clinic within two
weeks of test results
No. of messages sent
% results sent
Staff time saved
Mean days to
diagnosis
Median time to
treatment
952 messages
33.9% results
46 hours/month saved
11.2 vs. 7.9 days, p < 0.01
15.0 vs. 8.5 days, p < 0.05
Nguyen
200630
10,
14-80
yrs,
mean
33 yrs
SMS, V .75 Daily Withdrawal
of
technology
Training in the use of cell
phone to persons with
Cerebral Palsy and MS
% who improved
performance
% improved
satisfaction
90%
90%
Ostojic
200531
16 SMS 4 Weekly Standard
care and
education,
diary
Standard care plus PEF
monitoring and therapy
adjustment by SMS
Asthma -cough
Night symptoms
Ave daily dose-ICS
Ave daily dose-LABA
1.85 vs. 1.42, p < 0.05
1.22 vs. 0.95, p < 0.05
81.25 vs. 77.63, p < 0.01
17.31 vs. 14.80, p < 0.01
Rami 200632 36 SMS, V 6 Daily Usual
support
and paper
diary
Cell phone and SMS
monitoring and support
by a diabetologist, with
an automated SMS mes-
sage or a personalized
message advising insulin
dose adjustment
Change in HbA1c 3
mos
Change in HbA1c 6
mos
+1.0 vs. -0.15, p < 0.05
+0.15 vs. -0.05, p < 0.05
Riva 200633 33 V, I 0.07 Once, over
two days
No
treatment
1. Multimedia narratives of
a trip to a desert tropical
beach 2. New Age music
video with a tropical beach
visual content
Anxiety level
Relax scale
STAI level
Chisq = 2.943, p < 0.01
Chisq = 2.0
Chisq = 20.749, p < 0.01
continued
➞
continued

236 TELEMEDICINE and e-HEALTH APRIL 2009
KRISHNA ET AL.
along with SMS,18,22,23,26,27,29,32,34 the other 8 studies18,23–27,35,38 combined
Internet and the SMS for delivering self-care education and informa-
tion. In two studies18,35 messages were sent via e-mail in addition to
utilizing other communication technologies. Five of the outcomes
studies15,17,28,31,27 and four process of care studies16,19–21 used only SMS.
Most studies used “Push” technology where participants received per-
sonalized text messages or automated voice mail messages delivered
to their cell phones tailored to their specific health needs and personal
preferences. Two studies22,30 used two-way communication encourag-
ing participants to use their cell phones to ask questions.
Frequency of message delivery ranged from daily to once a week
and varied by disease or behavior modification area. Messages were
sent daily in three of the diabetes management studies22,32,35 and three
smoking cessation studies.17,18,34 In the smoking cessation studies, 5
Table 1. Cell Phone Intervention Studies
AUTHOR/
YR SAMPLE
SIZE TECHNOLOGY DURATION
(MONTHS) DELIVERY
FREQUENCY CONTROL INTERVENTION MEASURES RESULTS
C VS. I
Rogers
200534
1705 SMS, V 6 Daily first
1.5 months,
3/week 4.5
months
No advice
or support
Personalized text mes-
sages for cessation,
advice, support and
distraction
Quit rate 6 weeks
Quit rate 12 weeks
Quit rate 26 weeks
% abstaining 26 weeks
13% vs. 28%, p < 0.01
26% vs. 41%, p < 0.01
45% vs. 56%, p < 0.01
5% vs. 8%, p < 0.05
Tasker
200735
37 SMS, EM, I 12 Daily Paper
diary
during
clinic visit
Daily text message
requesting response to
questions related to hypo
events
Frequency of hypos
Hypos response rate
(Paper diary, CBI, V)
Preference over diary
5.2/month
65% vs. 89% vs. 95%
CBI 54%, V 65%
Vidrine
200636
95 V 4 Twice/
month, 24-h
hotline,
phone call
every 2
months
Usual care
and
physician
advice to
quit
Usual care plus eight
weekly smoking cessa-
tion counseling sessions
tailored specifically to the
needs of persons with
HIV/AIDS, delivered by cell
phones, 24-h quit hotline
PP abstinence (24 h)
Sustained abstinence
(7 days)
Made quit attempt
Days of abstinence
10.3% vs. 36.8%, p < 0.01
7.7% vs. 21.1%, NS
74.4% vs. 97.4%, p < 0.01
12.3 vs. 30.8 days, p < 0.01
Vilella
200437
4043 SMS, I 4 Once, 72 h
prior
No SMS
reminder
SMS reminder for the
next Hep A+B vaccina-
tion
Compliance C1 vs. C2
vs. Intervention:
Hep A+B 2nd dose
Hep A+B 3rd dose
Hep A 2nd dose
77.2 vs. 80.7% vs. 88.4%,
p < 0.05
23.6% vs. 26.9% vs.
47.1%, p < 0.05
13.2 vs. 16.4% vs. 27.7%,
p < 0.05
Yoon
200838
51 SMS, I 12 Weekly Weekly optimal advice
from a nurse via SMS or
the computer Internet
based on patient input
of SMBG, medication
details, diet and exercise
9 months:
HbA1c
FPG levels mg/dL
2HPMG
12 months:
HbA1c
FPG levels mg/dL
2HPMG
Total cholesterol
Triglycerides
HDL
0.33 vs. -1.31, p < 0.05
12.2 vs. -10.5, NS
-17.4 vs. -66.8, p < 0.05
0.81 vs. -1.32, p < 0.05
27.7 vs. -10.7, NS
18.1 vs. -100, p < 0.05
NS
NS
NS
2HPMG, 2-h postmeal glucose; BMI, body mass index; BP, blood pressure; CBI, computer-based interview; DMAS, disease management assistance system; EM, e-mail; FPG,
fasting plasma glucose; FTA, failure to attend; HbA1c, hemoglobin A1c; HDL, high density lipoprotein; VAS, visual analog scale; Hypo, hypoglycemic; I, Internet; ICS, inhaled
corticosteroids; IT, insulin therapy; LABA, long-acting β-agonist; MET, metabolic equivalent; MS, multiple sclerosis; NRT, nicotine replacement therapy; NS, not significant; PA,
physical activity; PDA, personal digital assistant; PEF, peak expiratory flow; PP, point prevalence; QOL, quality of life; SMBG, self-monitored blood glucose; SMS, short message
service; ST, Sweet Talk; STAI, state-trait anxiety inventory; TC, total cholesterol; TG, triglycerides; V, voice.
continued

© MARY ANN LIEBERT, INC. • VOL. 15 NO. 3 • APRIL 2009 TELEMEDICINE and e-HEALTH 237
HEALTHCARE VIA CELL PHONES
messages per day were sent during the first 6 weeks, then reduced to
2–3 per week for the remaining weeks.17,18,34 In one study, text messag-
es were sent daily for the first 10 weeks.18 In another smoking cessation
study, participants received one counseling session per week over a cell
phone for 8 weeks and had access to a 24-hour hotline.36 A hyperten-
sion control study advising participants to take blood pressure control
medication sent messages twice per week.28 A third group of studies in
asthma,31 diabetes,24–27 and smoking cessation36 sent messages once per
week. Medication reminders were delivered according to the prescribed
medication schedule,14 whereas the general appointment remind-
ers were sent just once 1 to 3 days prior to the appointment.16,19–21
Vaccination appointment reminders were sent 2 to 3 days prior to the
scheduled appointment.37 Riva33 evaluated a stress-relieving multime-
dia intervention delivered to cell phones during commuting hours for
2 days. Two studies that did not report significant differences between
groups included one that sent medication compliance reminder text
messages twice per week28 and the other that sent a reminder 1 day
before the orthodontics clinic appointments.26 Twenty of 25 studies
(80%) reported significant differences between control and interven-
tion groups as a result of cell phone and text messaging interventions
regardless of the frequency of message delivery.
STUDIES WITH PROCESSES OF CARE MEASURED
The set of 25 studies was categorized by whether processes or out-
comes of care were measured. The process of care is defined as activi-
ties involved in the delivery of healthcare. Studies that focused on
improving the process of care were grouped into two areas. Menon-
Johansson and colleagues used text messaging for notification of
diagnoses and recall of patients with positive lab results to the clinic
for treatment consultation. They reported fewer days to diagnosis and
treatment among those who were notified of test results via text mes-
sages.29 Two studies that evaluated sending appointment reminder
text messages to cell phones found that failure-to-attend rates were
significantly lower among persons who were sent SMS reminders
than among those who were not sent a reminder about their upcom-
ing clinic appointment.19,21 In contrast, failure-to-attend rate did
not significantly differ between groups in two other appointment
reminder studies.16,20 Nguyen et al. used cell phones and their text
messaging capabilities to teach persons with disabilities to improve
communication. After 3 weeks of training, 90% of participants had
improved performance and 90% were satisfied with their learning.30
STUDIES WITH OUTCOMES OF CARE MEASURED
We defined outcomes of care to refer to change in disease-specific
health outcomes as specified as the outcomes under study. Sixteen
of 19 studies (84%) evaluated health outcomes and reported change
in health outcomes as a result of an intervention delivered through
cell phones using voice or SMS. These studies were grouped into
the following outcome categories: (1) behavior change: 10 stud-
ies,14,15,17,18,22,23,28,34,36,37 (2) clinical improvement: 13 studies,15,22–28,31–33,35,38
and (3) social functioning: 3 studies.15,18,22
Behavior change. We defined behavior change as an action taken
that has been documented to lead to better health outcomes. Smok-
ing cessation, compliance with medication taking, and getting timely
vaccinations were the behaviors that were compared among interven-
tion and control groups. Eight of 10 studies (80%) reported change
in behavior following an informational intervention delivered to cell
phones using voice or short text message service.14,17,18,22,23,34,36,37 Smok-
ing cessation studies reported significantly greater success in behavior
modification among the intervention group participants who received
a smoking cessation–related educational intervention delivered to their
cell phones.17,18,34,36 Bramley et al., who compared 355 Maori and 1,350
non-Maori young participants in evaluating the effectiveness of a cell
phone-based smoking cessation intervention consisting of personalized
advice and support in both English and Maori language, found that
Maori young men and women in the intervention group were two times
more likely to report quitting at 6 weeks than participants in the control
group.17 A randomized controlled trial demonstrated a positive health
outcome for participating young smokers who were sent personalized
text messages to their cell phones for 26 weeks.34 Smoking cessation
advice, support, and distraction messages were sent five times per day
1 week prior to an agreed-upon quit date and for 4 weeks following
the quit date, and three messages per week for the remaining 21 weeks
of the study duration. Authors found that continuous abstinence with
three or fewer lapses remained significantly higher among interven-
tion group participants at 26 weeks.34 There was a significantly greater
increase in compliance with medication taking among HIV-positive
patients with memory impairment compared to those without impair-
ment14 and with keeping hepatitis A and B dose vaccination schedules
among international travelers37 as a result of reminders sent to the cell
phones of study participants. There was also a significant improvement
in insulin adherence (p < 0.05) among persons with type 1 diabetes who
received tailored text messages with goal-specific prompts.22
Clinical improvement. Twelve of 13 studies (92%) measured and re-
ported significant changes in clinical outcomes, as a result of voice or
text messages sent to a cell phone.15,22–28,31–33,38 Nine studies assessed
the effectiveness of using cell phones on diabetes control and man-
agement,15,22,24–27,32,35,38 one on asthma,31 and one on hypertension.28

238 TELEMEDICINE and e-HEALTH APRIL 2009
KRISHNA ET AL.
Other clinical areas covered by clinical improvement studies included
stress management33 and physical activity.23
Of the nine studies that evaluated the effectiveness of diabetes
control and management information and education messages and
advice delivered via cell phones, four studies15,22,32,35 were among
patients with type 1 diabetes and five studies24,25–27,38 were among
patients with type 2 diabetes. All studies but one reported significant
improvement in diabetes-related health outcomes.35 Studies that used
weekly recommendations from a nurse to adjust insulin or medica-
tion based on information input via SMS by the patient showed
significant improvements in blood sugar levels following the inter-
vention (p < 0.05).24–27,38 Studies also found that diabetes education
and advice via cell phone and text messaging resulted in significant
reductions in HbA1c (p < 0.05).15,24–27,32,37 One study noted an overall
HbA1c difference of 1% between those receiving conventional insulin
therapy alone and those receiving intensive insulin therapy plus text
messaging support from a diabetes care health professional.22
Peak flow monitoring is a recommended asthma care guideline
that helps prevent an asthma exacerbation by regular monitoring of
asthma symptoms. In a randomized controlled trial by Ostojic and
others, patients with asthma who received standard care plus peak
expiratory flow monitoring and weekly treatment adjustment using
text messaging for 4 months showed significantly greater improve-
ments in asthma cough and night-time symptoms while lowering
daily doses of inhaled corticosteroids and long-acting β-agonist than
those who received only standard care.31
Data from automated blood pressure monitoring using cell phones
was used to send alerts and reminders twice per week for 4 months to
intervention group patients on how to control their blood pressure.28
Results indicated that participants in both groups had nearly equal per-
cent of patients with controlled blood pressure at follow-up. Although
rate of compliance with blood pressure control advice and percent of
compliers were slightly higher in the intervention patients, there was no
significant difference between groups in either of the two measures.
Cell phones were shown to help people relax in real-life situations
of stress.33 Multimedia messages narrating relaxation on a tropical
beach were sent to cell phones of commuters in the intervention group.
Follow-up outcome measures of the intervention group participants
showed significant decrease in anxiety score (State–Trait Anxiety
Inventory) (p < 0.01) compared to two control groups exposed to com-
mercial videos with New Age music, and to no intervention, respec-
tively.33 A study of mobile phone personalized advice and motivational
tips for physical activity observed a significant improvement (p < 0.05)
in percent body fat lost; however, body mass index (BMI), diastolic
blood pressure, and systolic blood pressure were unchanged.23
Social functioning. Three studies measured social functioning out-
comes. One study in the area of diabetes observed a significant
improvement in quality of life (p < 0.05) and satisfaction with life
(p < 0.05).15 Another diabetes study and a smoking cessation study
observed significant improvement in self-efficacy (p < 0.00118 and
p < 0.0122).
SUCCESSFUL PROCESSES AND OUTCOMES OF CARE
Our purpose in doing this review was to examine whether cell
phones and text messaging can be effectively used to improve pro-
cesses and outcomes of care. An intervention was considered effec-
tive if the measures were significantly improved (p < 0.05) among the
intervention group participants compared with the participants in the
control group. A total of 101 processes and outcomes were measured
in the 25 studies, with some evaluated in more than one study. There
were 61 (60%) successful process or outcome measures among those
receiving the cell phone–based intervention.
Discussion
As shown by the results of this review, information and education
interventions delivered through wireless mobile technology resulted
in both clinical and process improvements in the majority of studies
included in this review. Chronic diseases such as diabetes and asthma,
requiring regular management, as well as smoking cessation requir-
ing ongoing advice and support, benefited most from the cell phone
interventions. Use of cell phones and text messaging in improving
healthcare, although gaining interest, is still in its infancy. As the
ownership and use of cell phones increases, and more patients are
willing to incorporate them into their daily lives for regular disease
management such as for diabetes or asthma, more benefits will
be documented. The strength of this study is in the international
applicability of this technology. Studies were conducted on several
continents—America, Europe, and Asia—indicating that this technol-
ogy can be used all over the world.
STUDY LIMITATIONS
When interpreting the results of this study, some of the limitations
should be taken into consideration. One of the limitations of this
review is the small sample sizes, with two studies included in this
review having less than 20 participants. The findings of these studies
may not be generalizable to other populations. Second, this review
includes one study28 for which we only have a published English
abstract. Since full text of this study was not accessible, we may have
left out some information. We decided to include this study since
sufficient details were provided in the published English abstract

© MARY ANN LIEBERT, INC. • VOL. 15 NO. 3 • APRIL 2009 TELEMEDICINE and e-HEALTH 239
HEALTHCARE VIA CELL PHONES
and important evidence from this randomized controlled trial would
otherwise be missed. The cost of technology is always of interest to
adopters. When interventions lead to comparable outcomes, the more
feasible or less costly intervention should be selected. Unfortunately,
only two studies in this review provided cost information (Table
1).19,21 Also, the reviewed studies did not express any concerns over
the impediments to the use of cell phones such as lack of reimburse-
ments to health professionals receiving the call, time commitment, or
potential abuse of cell phone and SMS privilege.
Implications for Practice
In addition to improving healthcare outcomes, wireless mobile
technology has other implications for practice. It may help remove
disparities. This is the first technology where industry has documented
a trend toward a digital divide in the reverse.13,39 This increases the
likelihood of successfully delivering health improvement interventions
to traditionally hard-to-reach populations. Sending cell phone text
messages has been helpful for patients in reducing missed physician
appointments40 and for staying in touch with their physician for fol-
low-up questions or consults.41 Interactive multimedia capabilities and
portability of cell phones have proven to be beneficial, even life-saving
in some areas of healthcare.42 Since compared to computer technology,
the ownership and use of cell phones is more prevalent among persons
of low socioeconomic status, use of cell phones may reduce the impact
of digital divide inherent in Web-based health interventions.43,44 As
more and more patients own and are willing to use mobile technologies
for chronic disease management,45 initial costs of automated message
delivery may be offset by lower healthcare utilization costs.
In order to have a better understanding and greater insight into
the effectiveness of cell phone interventions in improving health
outcomes and processes of healthcare, more controlled studies with
larger sample sizes need to be conducted. Healthcare providers
should be willing to incorporate this common everyday technol-
ogy. Therefore, studies are also needed on the cost-effectiveness and
technical and financial feasibility of adoption in real clinical settings.
Cell phones, through combination of voice and text messaging, their
location-independence, and flexibility offer a great opportunity for
designing and developing health interventions for the populations.
Where traditional interventions have not been successful in reaching
out to all, theory-based mobile e-health behavioral interventions are
more likely to succeed46,47 and have the potential of lowering health-
care costs by lowering the use of healthcare resources. Cell phones, a
common everyday technology, are already in the hands of millions
of people.48 Harnessing this technology for improving the health of
populations would be a step in the right direction.49
Acknowledgment
The authors would like to thank Ms. Teira Gunlock for helping
with the library runs and photocopying.
Author Disclosure Statement
The views expressed in this article are those of the authors and do
not necessarily represent the views of the Department of Veterans
Affairs. No competing financial interests exist.
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Address reprint requests to:
Santosh Krishna, Ph.D., Ed.S.
School of Public Health
Saint Louis University
3545 Lafayette Avenue, Suite 300
St. Louis, MO 63104
E-mail: SantoshKrishna5@gmail.com
Received: July 26, 2008
Accepted: August 11, 2008