Culturally Appropriate Behavioral Change in Maternal
Health: Role of Mobile and Online Technologies Over Time
Yuri Quintana, Ph.D.1 and Jennifer McWhirter, Ph.D.2
1Assistant Professor, Harvard Medical School, Harvard University, Cambridge, MA, USA
2 Assistant Professor, Department of Population Medicine, University of Guelph, Guelph,
Abstract: A major challenge in global health is improving newborn and maternal health. A
variety of economic, geopolitical, socio-cultural, and technical factors are involved. Online and
mobile technologies hold the potential to improve maternal and newborn health by supporting
access to education, improving coordinated care, and facilitating patient support. These online
and mobile systems have been designed to address a variety of maternal and newborn health
outcomes, including: increasing antenatal care attendance; cessation of unhealthy behaviors, such
as smoking and drinking alcohol; and increasing vaccination rates of newborns. The success of
these systems, however, is very much dependent on how – and whether – they can effect
behavioral changes in culturally appropriate ways. This chapter reviews the design of these
systems in both developed and developing countries, the technologies and behavioral
frameworks used, and the evaluation outcomes. The acceptance of these new patient support
networks requires the trust of patients and healthcare providers. Hence, this chapter also reviews
the evolution of these systems, and the potential benefits of, and challenges for, their sustained
Keywords: mHealth, mobile health, maternal health, behavior change, health behavior
Yuri Quintana, Ph.D.
Division of Clinical Informatics
Beth Israel Deaconess Medical Center
1330 Beacon Street, Suite 400
Brookline, MA, USA, 02446
Improving maternal and child health is an urgent issue and global priority. In 2013, 4.6 million
children died (74% of all under-5 deaths) within the first year of life . An estimated 270,000
newborns die during the first 28 days of life every year . Many of these deaths could be
prevented if there was improved prenatal care and early detection of high-risk conditions [3,4].
Preterm deliveries are one such high-risk condition. Let us consider it more closely and how
mobile technologies might help to address the issue. Evidence suggests that improving prenatal
care may lead to decreased preterm deliveries . Programs that provide face-to-face social
support programs, such as CenteringParenting® , have shown better outcomes when
compared to traditional prenatal care. Scaling such a program to a larger number of women is a
challenge because of the human resource limitations of facilitators and the need to travel to a
central location to receive these prenatal services.
Mobile technologies may provide a way to expand the delivery of prenatal care and, thus,
improve maternal and newborn health outcomes. For example, mobile apps can be used for the
collection of patient-generated data, such as weight and blood pressure, with devices connected
to Smartphones, sending reminders by age (e.g., immunization), and care guidance for
previously identified neonatal conditions. There are over 40,000 apps labeled as health Apps, but
few are used on a sustained basis . Evaluating the effectiveness of these apps is important,
and their success can hinge on the extent to which an understanding of health behavior change
has been built in and whether their approach is culturally appropriate. To achieve and maintain
behavior change in their target audiences, these Apps ought to be designed with an
understanding of behavioral models – frameworks we use to understand how and why we make
the behavioral decisions we do. Further, understanding cultural and socio-economic issues is
vital to understanding the local adoption of mobile applications and the sustainability of
Understanding the use of mobile technologies for health services can also be an effective way to
reach lower income populations that may have economic restrictions to reach large specialty
centers but may have mobile phone access. A recent study from the Pew Research Center 
showed that 64% of Americans have a smartphone, 85% of Americans aged 19-29 years are
smartphone owners, and 62% of smartphone users have used a smartphone to search for health
information. It also showed that 15% of Americans have limited options for online access other
than a cell phone. Of Americans with a household income under $30K, 24% have few data
access options other than a smartphone, 19% have no broadband at home, and 13% are totally
smartphone dependent. The smartphone is increasingly becoming a primary communication
medium for younger Americans. For some lower-income Americans, it is their only connection
to online services.
Globally, 3.2 billion people are using the Internet, of which 2 billion live in developing countries
. Between 2000 and 2015, people with Internet access has increased almost seven-fold from
6.5 percent to 43 percent of the global population. As of May 2015, there are more than 7 billion
mobile subscriptions worldwide, up from 738 million in 2000. Mobile broadband penetration
worldwide has reached 47 percent in 2015, a 12-fold increase since 2007. In 2015, 69 percent of
the global population had by 3G mobile broadband, up from 45 percent in 2011. The
International Telecommunication Union (ITU ) figures also indicate that 4 billion people in the
developing world remain offline. Of the nearly 1 billion people living in the Least Developed
Countries (LDCs), 851 million do not use the Internet.
Mobile health applications are also widely developed and deployed on a global scale. The World
Health Organization has a Global Observatory for e-health and has noted a rapid growth of
mobile applications in all continents across a wide range of application areas [10,11]. Formal
evaluations of these programs are needed to understand which programs are effective and how
the local context affects their implementation and sustainability.
1.2 Design of Systems
Designers of mobile applications can use several frameworks to guide and inform the design of
the system. We outline a few such frameworks below and discuss their application to mobile
1.2.1 Design Thinking Techniques
Design thinking is a design process that encourages cognitive activities and multidisciplinary
collaborations in the process of design. The notion of design as a "way of thinking" is based on
several early cognitive theories proposed in Herbert A. Simon's 1969 book The Sciences of the
Artificial  design engineering methods in Robert McKim's 1973 book Experiences in Visual
Thinking , Peter Rowe's 1987 book Design Thinking . The approach was further
developed by Stanford professors Rolf Faste and David M. Kelley in the 1980s. Kelley later
founded a design company called IDEO in 1991. IDEO has developed a freely-accessible design
thinking toolkit that introduces educators to the process and methods of design .
The design thinking process follows the five steps (adapted from the IDEO Design Thinking
● Phase 1- Discovery: I have a challenge. How do I approach it?
○ Steps: 1) Understand challenge 2) Prepare research 3) Gather inspiration
● Phase 2 - Interpretation: I learned something. How do I interpret it?
○ Steps: 1) Tell stories 2) Search for meaning 3) Frame opportunities
● Phase 3- Ideation: I see an opportunity. What do I create?
○ Steps: 1) Generate ideas 2) Refine ideas
● Phase 4 - Experimentation: I have an idea How do I build it?
○ Steps: 1) Make prototypes 2) Get feedback
● Phase 5 - Evolution - I tried something new. How do I evolve it?
○ Steps: 1) Track Learning 2) Move Forward?
Each of these phases involves engaging with key stakeholders, users, designers, and expert
facilitators to generate ideas and discuss observations that provide a comprehensive
understanding of the problems being tackled. This is particularly useful in global health, where
designers from higher-income countries may not be aware of the environment or needs of users
in low- and middle-income countries. Maternal health delivery preferences also vary by country,
and local customs and traditions. Relatedly, mobile technologies are changing rapidly, and the
selected technologies need to be appropriate for the target country. Given the complexity of
implementing global mobile maternal health programs, a design thinking approach is the
particularly useful way to create regionally and context-specific solutions.
1.2.3 Cultural Issues
Maternal health care and delivery preferences vary by region and culture [16,17]. Among the key
issues are: dietary preferences and restrictions, preferences for labor and delivery and midwives,
bonding approaches with the baby, approaches to baby naming and postpartum care, and
perspectives on death and miscarriage.
In a study of expectant and new mothers in India , it was found that many women tried to
access medical assistance, but various factors delayed their access to appropriate care. Delayed
decisions to seek care were the result of the underestimation of the severity of complications by
family members, gender inequity, and perceptions of low-quality delivery services. Another
study of women in Ethiopia  showed that maternal health is affected by factors that include
transportation and women’s education besides availability of health infrastructure and skilled
health workers. Cultural beliefs, attitudes, and practices were also found to be critical in
determining mothers’ health. Interventions and educational materials also need to be adapted to
local customs and traditions. For example, in a project in Peru  the intervention was adapted
to make delivery services culturally appropriate by including features to enable a vertical
delivery position, including family and traditional birth attendants in the delivery process, and
the use of the local Quechua language.
1.3 Behavior Models
Effective health behavior change requires the use of best practices and the informed guidance of
the most appropriate health behavior theory for the intervention or program at hand. Some of the
most common theories or models used to investigate health behaviors are Social Cognitive
Theory, Health Belief Model, Theory of Reasoned Action/Theory of Planned Behavior, and the
Transtheoretical Model/Stages of Change Theory . These health behavior models can and
have been used to inform the design of mobile health applications. We consider each
theory/model briefly below, tying them to mobile health in general, and then, further on, discuss
each in the context of maternal health more specifically. The models/theories can be classified
into two types: 1) individual health behavior models; and 2) interpersonal or group health
1.3.1 Individual Health Behavior Models/Theories
Health Belief Model
The Health Belief Model is a social psychological model that sets out to explain and predict
health behaviors by focusing on a person’s attitudes and beliefs . The key assumption of the
theory is that a person will perform a given health behavior if they perceive they are susceptible
to the disease or condition, if they expect that taking action will help them to avoid the negative
health outcome or condition, and if they believe they can successfully perform the action or
There are six constructs to the Health Belief Model which influence whether a person will
engage in a health behavior or not: perceived susceptibility (beliefs about chances of getting the
condition); perceived severity (beliefs about the seriousness of the condition or its
consequences); perceived benefits (beliefs about the effectiveness of taking action to reduce risk
or severity); perceived barriers (beliefs about the costs of taking action), cues to action (factors
that motivate readiness to change); and, finally, self-efficacy (confidence in one’s ability to take
This theory has been used fairly extensively to develop successful health interventions .
When constructs from the theory are used to guide messaging within health interventions, the
health behaviors of interest are changed more successfully [24,25]. In the context of mobile
health applications, the Health Belief Model constructs are most useful to inform the messages,
information, or educational content components of a behavioral intervention. There is one
exception to this: cues to action. Cues to action can refer to information that would facilitate a
person’s action towards a behavior, but cues to action have also been operationalized as
“prompts” to engage a person to act on health information. In the context of this latter definition,
a mobile app itself (and not just the content it delivers) could be considered a cue to action. We
will consider how this theory is applied in research on maternal health and mobile health
applications later in the chapter.
Theory of Reasoned Action and Theory of Planned Behavior
The Theory of Reasoned Action  and the Theory of Planned Behavior  are two very
closely related theories with constructs that focus on individual motivational factors that
determine how likely it is that a person will perform a particular behavior.
The Theory of Reasoned Action posits that the best predictor of a person’s behavior is their
intention to act. An individual’s behavior is determined directly by attitude (their beliefs about
the behavior and outcome associated with the behavior, including their evaluations of those
outcomes); and subjective norms (their perceptions of how people will view the behavior and, in
turn, their motivation to comply).
The Theory of Planned Behavior adds to this, indicating that in addition to attitude and
subjective norms, their intention to perform the behavior is also influenced by perceived control
(beliefs about their ability to perform the behavior or self-efficacy). Perceived control was added
to the model because engaging in a behavior is determined by both motivation (intention) and
ability (behavioral control).
Transtheoretical Model/Stages of Change Theory
The Transtheoretical Model was developed to explain different stages of change towards a
behavior based on the assumption that behavior change is a process that unfolds over time rather
than a one-time event . Within this model, the six stages of change are considered to be:
precontemplation (no intention of changing behavior); contemplation (thinking of changing
behavior in the future); preparation (planning to change behavior, intends to take action in the
near future, and has already taken steps towards this); action (behavior change is initiated);
maintenance (behavior change is sustained over time); and termination (no temptation to engage
in unhealthy behavior or relapse). The Stages of Change are one of four key sets of constructs
from the Transtheoretical Model. The other three constructs are: Processes of Change (e.g.,
finding and learning new information to support the healthy behavior change, making a firm
commitment to change, seeking social support for the change), Decisional Balance (the pros and
cons of changing), and Self-Efficacy (confidence to engage in healthy behavior, temptation to
engage in unhealthy behavior). Importantly, most often when this theory is discussed, the six
stages of change are the focus.
Self-efficacy has thus far been mentioned in the context of other healthy behavior theories;
however, it is on its own also considered a theory or model. Self-efficacy is the strength of one's
belief in one's own abilities and the ability to persist to succeed with a task or goal . The
level of belief that a person holds regarding his or her power to affect situations strongly
influences the power of that person to face challenges competently. Self-efficacy can have a
significant influence on people’s behaviors affecting health. Self-efficacy influences the choices
that people make that affect health, such as smoking, physical exercise, and dieting. For
example, if someone is going to quit smoking, that person has to believe they have the ability to
follow through and cease to smoke. A person’s self-efficacy beliefs are cognitive states that
determine whether health behavior change will be initiated, how much effort will be expended,
and how long it will be sustained.
1.3.2 Interpersonal and Group/Community Health Behavior Change Models
Social Cognitive Theory
Social Cognitive Theory states that when people observe someone performing a behavior, and
the consequences of that behavior, they remember it and use it to guide their own subsequent
behaviors . People do not learn new behaviors simply by trying the behavior and observing
the result but, rather, by the replication of the actions of others. Social Cognitive Theory focuses
on how personal factors, behavior, and the environment interact. These three factors constantly
interact by both influencing, and being influenced, by one another. This theory is relevant to
health because it explains how people start and maintain behaviors, and can be used as a
framework to design health behavior change interventions or programs.
Patient-provider interaction is an interpersonal health behavior model to explain different types
of interactions between provider and patients. Emanuel  presents four versions of this model:
the paternalistic model, the informative model, the interpretive model, and the deliberate model.
In the paternalistic model, the physician provides the patient with select information that will
encourage the patient to consent to the intervention that the physician considers best and, in
extreme versions of this model, the physician authoritatively informs the patient when the
intervention will be initiated. In the informative model, the physician provides the patient with
all relevant information for the patient to select the medical interventions he or she wants. The
physician would inform the patient of his or her disease state, the nature of possible diagnostic
and therapeutic interventions, the nature and probability of risks and benefits associated with the
interventions, and any uncertainties of knowledge. In the interpretive model, the physician helps
elucidate the patient's values and helps the patient select from the available medical interventions
that realize these values. In the deliberate model, the physician helps the patient determine the
best health-related values that can be realized in the clinical situation. The physician suggests
why certain health-related values are more worthy and should be aspired to.
The models used for teaching medical students about the patient–physician interaction have
remained relatively static [31,32]. More recent models  propose a revised paradigm that
models autonomy, health care-related values formation, and medical knowledge as varying from
patient to patient. More recently, patient-physician advocacy groups, such as the Society for
Participatory Medicine , have arisen with the mission to promote a movement in which
networked patients shift from being mere passengers to responsible drivers of their health, and in
which providers encourage and value them as full partners.
Diffusion of Innovations
Diffusion of Innovations is a theory proposed by Everett Rogers that seeks to explain how, why
new ideas and technology spread in society . There are four elements that influence the
spread of a new idea: the innovation, communication channels, time, and the social system.
There is also a point at which an innovation reaches critical mass and can become sustained. This
theory has been used to study the adoption of innovation in many sectors, including healthcare.
Berwick  discussed the rate of diffusion of innovations within healthcare organizations and
noted three major factors: 1) the perceptions of the innovation, 2) the characteristics of the
individuals who may adopt the change, 3) and contextual and managerial factors within the
organization. This theory has also been used to understand the promotion of healthy behaviors.
Rogers  defines homophily as "the degree to which pairs of individuals who interact are
similar in certain attributes, such as beliefs, education, social status, and the like". Individuals
usually interact with others similar to themselves and a result engage in more effective
communication because their similarities lead to greater knowledge gain as well as attitude or
behavior change. Thus, homophilous people tend to promote diffusion among each other .
1.4 Case Studies
1.4.1 Early Maternal and Newborn e-Health Systems
In the Baby CareLink project , an Internet-based system was developed to connect parents to
neonates in the intensive care unit. The goal of the project was to increase family engagement,
knowledge, communication, and collaboration with the healthcare team that was managing
critically ill children in a neonatal intensive care unit (NICU). In this system, parents could view
their baby in the intensive care system via remote video, access medical status of the baby via a
secure portal, and view parent-focused education on the care of the neonate. Baby CareLink
included communication tools that:
● Enabled the clinical care team to share healthcare information about the neonate.
● Enabled parents to receive daily updates from care providers and correspond with the
● Provided parents and a defined network of family members access to a shared care plan.
The evaluation demonstrated that use of Baby CareLink helped parents to gain a better
understanding of the complexities of the NICU and enhanced communication between parents
and their child’s care team . The long-term sustainability of the network was a major
challenge since at the time the cost to implement the system was high. The system could only be
used with an Internet browser, not mobile phones. However, the program demonstrated the
feasibility of delivering maternal care education to parents and provided key insights for future
remote maternal health applications.
MAMA, the Mobile Alliance for Maternal Action , was launched in 2011 as a three-year,
public-private partnership between USAID, Johnson & Johnson, the United Nations Foundation,
BabyCenter. The goal was to catalyze a global community to deliver vital health information to
new and expectant mothers and their families through mobile phones. With health content
provided by BabyCenter and verified by an external medical advisory board, MAMA and its
partners created a core set of messages that can be timed and targeted to where the woman is in
her pregnancy, or her baby is in his or her development. The program helped to facilitate
maternal mobile maternal health projects in several counties.
Currently, there are hundreds of mobile apps for maternal health care, but few have formal
evaluations, and even fewer have been designed with behavioral models. We review some recent
evaluations and the behavioral models used in these applications.
1.4.2 Mobile Health Applications and Text Message Services Explicitly Using Health
Text4baby  is an antenatal care mobile health program that is well-grounded in behavioral
theory. This program, launched in 2010, delivers text messages to otherwise underserved
pregnant women and new mothers with the goals of improving their health, health care beliefs,
practices, and behaviors towards more favorable clinical outcomes. The pilot evaluation of the
text4baby mobile health program was a randomized controlled trial involving 90 low-income
pregnant women. The intervention group received messages from text4baby on antenatal care
and health behaviors/beliefs in addition to regular healthcare.
The program was based on Social Cognitive Theory, Transtheoretical Model, and Health Belief
Model. Combining core principles from each of these theories, this program’s theory was that
belief-targeted messages would positively impact specific beliefs, which then will lead to
associated improvements in health related behaviors. Examining the results of the evaluation, we
see that this theory was upheld in one belief area. Text4baby was successful in changing
expectant mothers’ beliefs and attitudes regarding birth and pregnancy behaviors. For example,
mothers who used text4baby were three times more likely to believe that they were prepared to
be new mothers compared to those in the control group. Importantly, the results also indicated
that education influenced reaction to, and perhaps comprehension of, the content of the text
messages. Participants with higher levels of education were, in some cases, more likely to have
their beliefs influenced by the text messages than those with lower levels of education. It could
be that women with greater education, and thus greater literacy skills, can better understand the
content being shared in the text messages, which better facilitates belief and behavior change.
For example, those with higher education were more likely to hold the belief that alcohol during
pregnancy will harm the unborn baby. This highlights the importance of health literacy and
readability in mHealth interventions for maternal health. While it is important for a theory to
guide and inform mobile health interventions, the content of those interventions should be
written is such a way that it is clear, comprehensible, and actionable to ensure their effectiveness.
Quit4Baby  is a smoking cessation text messaging program designed specifically for
pregnant smokers in the United States. Quit4baby was designed as an add-on to Text4baby
(discussed previously), which is an existing national text message program providing perinatal
health information to pregnant women since 2010. In the pilot study for the Quit4baby mobile
app, participants -- in this case, 20 pregnant women who were current smokers or had quit
smoking very recently -- received one to five text messages per day over the course of the four-
week trial. The messages provided, as appropriate, content to motivate participants before
quitting, after quitting, and for those who did not quit. The messages also provided tips and
games, and contained stories of smokers who had successfully quit. The app also had a “quitpal”,
a peer female former smoker who had quit during her pregnancy, who offered app users
evidence-informed advice on quitting smoking.
The development of Quit4baby was informed by Social Cognitive Theory. The researchers used
this behavioral theory to inform their intervention by aiming to improve self-efficacy to quit
smoking (the app provided encouragement and motivational messages), describing outcome
expectations in connection to quitting smoking (to improve beliefs about the likelihood and value
of quitting smoking), facilitating observational learning (through the peer-modelling of the
“quitpal”), increasing capacity to quit (achieved through a quit plan and date, as well as
interactive support, all designed to make quitting easier), and regular calls to the quitline. The
results indicated that participants found the program helpful in quitting smoking. They rated the
program content, skills it taught them, and the encouragement and social support it provided as
favorable. Future research is still needed to determine if this tool is effective for actual smoking
cessation during pregnancy; however, the mobile app upon which Quit4baby was based,
Text2quit, has confirmed that it increased quit rates among adult smokers.
Odeny  tested whether interactive text messages improved attendance at clinics and infant
HIV testing in Kenya. In this study, 391 HIV-positive pregnant women who were enrolled in a
program to prevent mother-to-child transmission (PMTCT) of HIV, were randomized to receive
either text messages or usual care. The text messages were sent to mothers during pregnancy
and weekly for the first six weeks after the baby was born. The message content of the text
messages was informed by the constructs of the Health Belief Model, combined with results of
previous empirical research on factors that influence the study’s outcomes of interest. In fact, the
authors published a very helpful and detailed separate paper on how the messages were
developed . The researchers used focus groups to help them determine what the message
content ought to be, with the focus group discussion structured by the Health Belief Model. The
participants in the focus groups consisted of mothers and health workers from health clinics.
Each of the six constructs from the Health Belief Model formed a topic discussed by the focus
● Perceived Susceptibility explored perceptions of the risk that their child could be HIV
positive, and the risks related to not attending health clinic for early infant testing
● Perceived Severity was addressed through questions about mothers’ perceived
consequences of not attending a health clinic after delivery and of HIV testing for infants
● Perceived Benefits focused on beliefs about why mothers should return to the health
clinic and bring their infant for HIV testing, including the potential benefits of this
● Perceived Barriers explored mothers’ material costs of returning to the health clinic and
bringing their infant for HIV testing, as well as what might prevent them from doing this
and the help that would be needed to overcome this
● Cues to Action were determined by querying mothers about the types of text messages
that would help them understand the importance of postnatal health clinic visits and
infant HIV testing
● Self-Efficacy was investigated by asking the women what types of messages would be
encouraging and increase self-confidence in connection to attending postnatal clinic visits
and ensuring HIV testing for their infants.
The results of the research conducted by Odeny and colleagues indicate that text messaging
significantly improved maternal postpartum visit attendance. Further, those receiving text
messages had significantly higher HIV testing rates compared to those in the control group. The
implications of this work are that SMS can increase clinic visits and HIV testing of infants. The
success of their mobile health behavioral intervention was due, at least in part, to the careful
attention to the message content, ensuring it was developed based on previous research and
guided by theory.
Mauriello ) tested an iPad behavioral intervention for pregnant women, called Healthy
Pregnancy: Step by Step, to address smoking cessation, stress management, and fruit and
vegetable consumption. In their research, conducted in the U.S., 335 pregnant women were
randomized to either receive three interaction sessions with the iPad intervention or to receive
brochures in order to test the efficacy of the program. The researchers were interested in whether
using the iPad intervention during pregnancy influenced the number of behavior risks.
Citing the utility of Transtheoretical Model-based interventions in terms of effectiveness on
behavior change, Mauriello  selected this theory to guide their work. Thus, their program,
Healthy Pregnancy: Step by Step, was grounded in the Transtheoretical Model’s stages of change
(precontemplation, contemplation, preparation, action, maintenance) and other constructs from
the theory (decisional balance, self-efficacy, processes of change). By taking the steps of change
into account, they were able to develop a population-based, but individually tailored, program.
The women participating in the study received stage-matched and tailored guidance through the
iPad program during their regularly scheduled prenatal care appointments about the areas of
focus (smoking, stress, fruit and vegetable consumption) based on their responses to risk
assessment questions. The women were able to interact with and use the program while they
waited for their prenatal appointment. The results of the research indicate that women receiving
the iPad-delivered intervention reported significantly fewer health risks at one month and four
months postpartum than those receiving standard care (brochures), suggesting the mHealth
program successfully reduced health risks and led to sustained healthy lifestyle behaviors.
1.5.1 Design Challenges
There are several design challenges that arise in the development of mobile health that is
especially challenging in developing countries.
Understanding local environments – Low resource healthcare providers have unique
challenges such as limited computer facilities, unreliable Internet, lack of infrastructure to store
electronic data, limited personnel with technical skills in mobile technology management, and
limited budgets. Consequently, the design of the system and the cost to implement and manage
needs to consider these limitations. The local environment may also change during the project
due to changes in government, possible civil unrest, staff turnover, and environmental conditions
such floods or natural disasters.
Time – Countries with limited resources are among the most overloaded with patient loads. The
ratio of patients to providers is very high. Any solution needs to keep in mind that the time to
train and install a new system must be kept very low to succeed. Patients may also have limited
time to engage in behavioral changes if they are economically stressed and have multiple jobs
and families to take care of.
Diversity of Culture – Some regions have a large and diverse populations that may vary in
customs and traditions, including indigenous groups with different languages. So the design may
need to focus on a narrower set of the population if there are going to be cultural adaptations and
to optimize the incentives for behavior change.
Literacy – Low literacy levels may pose a challenge for the patient to understand written, text-
based educational materials. Designing mobile health interventions to take this into account
needs to be addressed, perhaps through the use of more visual educational content. Patient
consent for enrolling in scientific studies may be a challenge when patient have low literacy
levels or are illiterate. Patient advocates may be needed to help transmit educational messages
and do informed consent.
1.5.2 Limitations of applications of behavioral models
The challenges of applying some of these behavioral models to mobile health applications in the
context of maternal health, particularly in developing countries, is that poor maternal health
outcomes do not only occur because of some behavioral choice to either do or not do a healthy or
unhealthy behavior. For example, a pregnant woman may be aware that she is at increased risk of
dying from a complication during labor if she gives birth at home alone compared to at a health
clinic staffed with a midwife, nurse, or doctor, but there may be no material way for her to reach
a health clinic. Perhaps there is no money to take transportation, perhaps there is no
transportation even available, or perhaps she did arrive at the clinic only to be turned away
because there were too many women in labor there or because she did not have money to pay for
the health service. These are examples of the real and deeply concerning barriers that women can
face in developing countries with respect to childbirth, and it is important to remember that
factors in the larger system and world are vital to address, at the same time that individual
behaviors are addressed, perhaps as guided by the individual behavior models outlined in this
Interpersonal and group behavior change models may help to address some of the limitations of
the individual behavior models noted above. While many maternal mobile health apps are
targeted to individual behavior, it remains very true that technology, systems, and even apps, too,
can also be used to address the bigger picture, system issues related to maternal health. Consider,
for example, an online platform that might connect healthcare providers with limited training in
labor complications to health care providers with extensive training in labor complications in real
time to help share information and expertise to manage potentially dangerous situations. Or, an
app that could be used by patients themselves to notify a nearby clinic that they are on their way,
enabling the clinic to plan ahead for their arrival. Apps focused on individual’s health behaviors,
in combination with system-level approaches, are needed if mobile health interventions of
maternal and newborn health are to be effective and sustainable in the long term.
1.5.3 Implementation Challenges in Developing and Developed Countries
Implementation mobile health care systems in developing countries pose some particular
challenges focused around socio-economic and cultural issues. In some countries more than half
the population will not be able to afford mobile phones and the phone may be shared among
multiple family members. Sharing phones will reduce the availability of messages and the
privacy of those messages. In some cases, male partners may not be supportive of the spouse and
the services being offered by phone. Further, mobile service may be limited in some regions, and
may be particularly limited for data service and Internet access via phone. Payment for services
or reimbursement for services via mobile banking may also be limited.
1.5.4 Evaluation Challenges
Some new, formal evaluation frameworks are emerging for mobile health applications. However,
many of these lack specific evaluations on design methodologies or use of behavioral models.
A group of researchers met at the mHealth Evidence Workshop at NIH in 2011 and arrived a
consensus framework for evaluating mobile health applications. Some of the key challenges for
evaluation that were discussed at this workshop are listed below .
Comprehensive Data Sets – Mobile health allows for the collection of data from multiple
sensors, divergent physiologic, behavioral, environmental, biological, and self-reported factors
that can be simultaneously linked to other indicators of social and environmental context. These
rich data sets may enhance the validity and reliability of the inferences and improve the
statistical power of the assessment process. The vast amounts of data may be a challenge to
collect in a uniform format that can be analyzed, and ensuring patient confidentiality and
Reliability - Reliability refers to the consistency of a measure. A measure is said to have a high
reliability if it produces consistent results under consistent conditions. Sample methods include
Test–retest, Inter-method reliability. The challenge for mobile health is to capture and account
for variability in user behaviors during a usage session or between sessions.
Validity - Validity is considered to be the degree to which an assessment measures what it
claims to measure. Sample measures include concurrent validity, convergent validity, divergent
validity, predictive validity. The challenge for mobile health is that many mHealth assessments
have no gold standard (or “ground truth”) as a point of comparison.
Future mobile health applications will need to have more comprehensive data sets and
reproducible evaluations that yield both valid and reliable evaluation results.
While it is an exciting and expanding field, there are also challenges and areas for improvement
on mHealth and MNCH research and practice. For example, the impact of mHealth on MNCH-
related behaviors could be more effectively measured with: more research guided by health
behavior theories and frameworks; more research focusing not only on the technical aspects of
the app and its use, but at least as much on the development and testing of the content of the app
with relevant experts and stakeholders (e.g., patients, health education and communication
experts); and, as in many types of health and medical research, the use of larger sample sizes and
stronger experimental designs. While there are many challenges in the design and evaluation of
global maternal mobile health systems, the early success of the case studies presented show
promising opportunities for the future development of successful and sustainable systems that
achieve long-term behavioral outcomes.
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