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Research on digital technology to change health behavior has increased enormously in recent decades. Due to the interdisciplinary nature of this topic, knowledge and technologies from different research areas are required. Up to now, it is not clear how the knowledge from those fields is combined in actual applications. A comprehensive analysis that systematically maps and explores the use of knowledge within this emerging interdisciplinary field is required.
This study aims to provide an overview of the research area around the design and development of digital technologies for health behavior change and to explore trends and patterns.
A bibliometric analysis is used to provide an overview of the field, and a scoping review is presented to identify the trends and possible gaps. The study is based on the publications related to persuasive technologies and health behavior change in the last 18 years, as indexed by the Web of Science and Scopus (317 and 314 articles, respectively). In the first part, regional and time-based publishing trends; research fields and keyword co-occurrence networks; influential journals; and collaboration network between influential authors, countries, and institutions are examined. In the second part, the behavioral domains, technological means and theoretical foundations are investigated via a scoping review.
The literature reviewed shows a clear and emerging trend after 2001 in technology-based behavior change, which grew exponentially after the introduction of the smartphone around 2009. Authors from the United States, Europe, and Australia have the highest number of publications in the field. The three most active research areas are computer science, public and occupational health, and psychology. The keyword “mhealth” was the dominant term and predominantly used together with the term “physical activity” and “ehealth”. A total of three strong clusters of coauthors have been found. Nearly half of the total reported papers were published in three journals. The United States, the United Kingdom, and the Netherlands have the highest degree of author collaboration and a strong institutional network. Mobile phones were most often used as a technology platform, regardless of the targeted behavioral domain. Physical activity and healthy eating were the most frequently targeted behavioral domains. Most articles did not report about the behavior change techniques that were applied. Among the reported behavior change techniques, goal setting and self-management were the most frequently reported.
Closer cooperation and interaction between behavioral sciences and technological areas is needed, so that theoretical knowledge and new technological advancements are better connected in actual applications. Eventually, this could result in a larger societal impact, an increase of the effectiveness of digital technologies for health behavioral change, and more insight in the relationship between behavioral change strategies and persuasive technologies' effectiveness.
In the past two decades, researchers have spent a lot of effort to understand how digital technology can help people to make a positive change in their behavior. This research is often motivated by the increasing cost of our health care systems and the increasing demand for health care professionals. The field of influencing or changing human behavior through digital technologies started with the term
Often, behavior change support systems are used to support individuals in making lifestyle changes that will lead to better health. When a sufficiently large portion of the population starts making positive changes to health-related behaviors, this will lead to lower utilization of health care and, eventually, to a significant reduction of health care expenditure. Although the potential and societal impact of digital health interventions is highly attractive, so far, its impact on health care utilization and expenditure has been minimal. This is partly because of the limited deployment and implementation, in terms of potential beneficiary users, of effective digital technology for behavior change.
The development of BCSS for health requires an interdisciplinary approach. For example, social psychology provides us a useful theory about the role of people’s personalities, cognitions, and social environment, which system designers could use to better design and develop an effective system. The social science theories provide an accumulated understanding of what human behavior is and the contexts in which they occur, what are the mechanisms of action for change, and what are the ingredients required for change [
In behavioral sciences, 92-item taxonomy of behavior change techniques (BCTs) has been developed to better report about behavior change interventions [
To our knowledge, no bibliometric analysis of scientific literature on digital technologies for health behavior change has been published. However, a lot of research has been conducted on different aspects of PT and health domains, for example, the literature about the persuasive design principle in different technology domains [
Our study is related to the papers mentioned above, but in our study, the main focus is the application domains and the theoretical basis (eg, behavioral theories and BCTs) of actual BCSS. A better understanding of the main application domains, the usage of theories, and the gaps between them will help identify which areas of PT have sufficient evidence that implementation on a larger scale can be justified and which areas still require additional research.
In this study, we first conducted a bibliometric analysis of the literature to answer the questions about the quantitative trends in the literature and the geographical distribution of the researchers. With the help of a co-occurrence network of keywords and research fields, we tried to uncover meaningful insights based on the strength of links between the nodes in the network. We also studied the collaboration between scholars in the field and the collaboration between developers of digital interventions and health behavior change researchers. This is done at the author, institution, and country level.
Second, we have presented a scoping review that aims to critically evaluate the content of the published literature on digital health behavior change systems and answer the following questions: (1)
Finally, we concluded with a discussion on the identified pitfalls and possible future directions for research.
The information provided by this review will help analyze who are the relevant stakeholders that have contributed to the growing knowledge of digital health interventions and will help designers make more informed decisions regarding the development and design of PT for healthy behavior change.
This section describes the procedures that have been used in the different phases of our study. We discuss the query selection, the database selection, methodologies, eligibility criteria, data items, and the choice of the tools used for different types of analysis.
We chose to use the Web of Science (WoS) core collection and the Scopus database as basis for the study. The search query aimed to retrieve actually implemented behavior change systems with a technical component. Therefore, the following terms were used: (“persuas*” OR “ehealth” OR “mhealth”) AND (“ICT” OR “prototype” OR “techno*” OR “system”) AND “behavio* change.” The search was limited to the articles publicated during January 2000 and December 2018. The process of merging the collected data is discussed below. The query search resulted in a set of 317 and 325 articles from WoS and Scopus, respectively. Among the 325 items from Scopus, 11 appeared empty or incomplete at further inspection, so 314 were left.
For evaluating the existing scientific output through citations count, keywords, geographical data, authors collaborations, and discipline-wise interactions, the method of bibliometric analysis was used. The basic statistics included yearly publication output, publishing countries, the field of study, citation count, keyword co-occurrences, coauthorships, and collaboration networks between countries and institutions.
Several software packages are available to support a bibliometric analysis, each with different capabilities and limitations. Some of the most popular tools include HistCite [
In addition to tools for bibliometric analysis, we also used network visualization and analysis tools for the co-occurrence networks of keywords and research fields and the collaboration networks between countries and institutions. We used Gephi and VOSviewer that use a 3-dimensional render engine to render illustrations of large networks [
A scoping review is a useful methodology to determine the coverage of a body of literature on a given topic and identify and analyze knowledge gaps [
Owing to the broad scope of the review, any conventional systematic review or meta-analysis framework was not followed strictly. However, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews guidelines (PRISMA-Scr) were applied [
To be considered for review, the following inclusion criteria were formulated: (1) publication in a peer-reviewed academic journal; (2) publication in the English language; (3) the research should have the primary purpose of changing behavior, either increasing or decreasing the behavior or stopping the behavior altogether; and (4) the article should discuss a technological solution that is used for behavior change.
In this section, the key categories used in this study to classify the different interventions described in our dataset have been defined. The first category,
Classification and coding scheme.
Category | Possible values |
Technology | Web, mobile app, computer applications, mobile game, SMS, pedometers, virtual agent, and interactive voice response |
Target domain | Physical activity, healthy eating, smoking cessation, carbon emission, and energy consumption |
Theories/model employed for behavior change | Transtheoretical model, motivational interviewing, health belief model, and social cognitive theory |
Development frameworks/model | Persuasive system design, behavior intervention technology, intervention mapping, and behavior change wheels |
Behavior change techniques | Self-monitoring, motivation, goal setting, reward, punishment, and knowledge |
We applied our search query on 2 major literature databases, that is, WoS and Scopus. The WoS and Scopus databases returned 317 and 314 articles, respectively. There was an overlap of 179 articles between both datasets; thus, we identified 452 unique articles.
To perform our
Owing to its quality and completeness of data, we decided to base our bibliometric analyses on data extracted from the WoS database [
Our
Flowchart of study selection (n=the actual number of publications). WoS: Web of Science.
When looking at the number of publications in our selection, we can observe that it started in 2001, with 1 or 2 publications yearly, but rapidly increased since 2009 to 2010, as can be seen in
Publication trend from 2000 to 2018.
Persuasive technology for behavior change, scholarly papers by region.
Country | Publications, n (%) |
United States | 183 (46) |
England | 57 (14) |
The Netherlands | 33 (8) |
Australia | 30 (7) |
Canada | 23 (6) |
New Zealand | 19 (5) |
Finland | 17 (4) |
Italy | 16 (4) |
Belgium | 13 (3) |
Switzerland | 11 (2) |
From the
Disciplines involved in persuasive technology and health behavior change.
Top 5 of the globally most citied articles.
Title | Reference | Global citation count |
A behavior change model for internet interventions | [ |
235 |
New directions in electronic health communication: opportunities and challenges | [ |
205 |
Behavior change techniques implemented in electronic lifestyle activity monitors: a systematic content analysis | [ |
162 |
Virtual self-modeling: the effects of vicarious reinforcement and identification on exercise behaviors | [ |
151 |
Online interventions for social marketing health behavior change campaigns: meta-analysis of psychological architectures and adherence factors | [ |
139 |
Top 5 of the most citied articles within the network (locally).
Title | Reference | Local citation count |
Health behavior models in the age of mobile interventions: are our theories up to the task? | [ |
45 |
Behavior change interventions delivered by mobile telephone short message service | [ |
44 |
Text messaging as a tool for behavior change in disease prevention and management | [ |
43 |
The theory of planned behavior | [ |
41 |
Persuasive technology: using computers to change what we think and do | [ |
41 |
keywords are assumed to compose an adequate description of the content of a research article. The co-occurrence of keywords could provide an interesting structure of the research field, as it reveals the semantic relations in the scientific literature. The most frequently used keywords are “mhealth,” “physical activity,” “ehealth,” “persuasive technology,” “smart phone,” and “behavior change” (see
The co-occurrence network of author keywords.
A coauthorship network was extracted using the Sci2 tool. Each node represented an author, and a connection between 2 nodes represented a coauthorship. A total of 1777 authors were identified with 5583 connections. For better visualization, only authors who had at least two publications together were considered. Node sizes were based on the number of articles coauthored with other authors. There were 3 strong clusters among the network that were strongly intraconnected but not interconnected (see
Coauthor graph.
The articles under review were published in 147 different journals.
List of top journal distribution.
Journal title | Publications (n) |
|
45 |
|
34 |
|
17 |
|
11 |
|
10 |
|
9 |
To investigate the collaboration between countries and organizations/universities, the geographical distribution of the research was analyzed. Coauthorship networks between countries and organizations/universities were extracted.
Countries collaboration graph.
Organization/institution collaboration graph. Coll: college; Hosp: hospital; Inst: Institute; NYU: New York University; Technol: technology; UCL: University College London; and Univ: University.
The second objective of this study was a scoping review. The contents of the 118 articles that resulted from our selection process were thoroughly studied. This revealed some interesting insights and trends. The findings about the trends regarding technological choices and the theoretical basis of digital health interventions are presented in the following sections.
Frequency of different technological platform used.
Digital technology | Usage count, n (%) |
Mobile apps | 62 (52) |
SMS | 25 (21) |
Web | 23 (19) |
Wearable sensors | 19 (16) |
Others | 11 (9) |
Game | 8 (6) |
Desktop apps | 4 (3) |
Social media | 3 (2) |
The most often targeted behaviors are health-related behaviors. The top 8 is formed by physical activity, healthy eating, diabetes management, smoking cessation, weight control, AIDS/sexual behavior, cardiovascular diseases, and alcohol consumption. Physical activity makes up 28.8% (34/118) of all the reviewed studies, followed by healthy eating and diabetes with a total of 18.6% (22) and 11% (13), respectively (see
Different targeted behavioral domains.
Targeted behavior | Count, n (%) |
Physical activity | 34 (28) |
Healthy eating | 22 (18) |
Diabetes management | 13 (11) |
Smoking cessation | 10 (8) |
Weight control | 10 (8) |
AIDS or sexual behavior | 6 (5) |
Cardiovascular disease | 5 (4) |
Carbon dioxide emission | 5 (4) |
Energy saving | 4 (3) |
Alcohol consumption, medical adherence, lower back pain, mental illnesses | 3 (2) |
Overdose prediction, mammography adherence, asthma control, sedentary behavior, knee osteoarthritis, waste management, educational behavior | 2 (1) |
Psychotropic, multiple sclerosis, sleeping behavior, screen time | 1 (0.8) |
Most of the analyzed articles seem not to be based on proper theories. Only 33% (59/118) of articles reported at least one or more theories among the 21 theories identified during out review for designing the system. The social cognitive theory (SCT), transtheoretical model, self-determination theory, and motivational interviewing are most frequently reported theories.
Percentage of reported theories (N=59).
Theory | Number reported, n (%) |
Social cognitive theory | 17 (29) |
Transtheoretical model | 6 (10) |
Self-determination theory | 4 (7) |
Motivational interviewing | 4 (7) |
Theory of planned behavior | 3 (5) |
Another important finding is about the usage of development frameworks and models. Such frameworks provide guidance on the development of a persuasive system, by suggesting a
Usage of different development framework/models (N=47).
Framework/model | Usage percentage, n (%) |
Persuasive system design | 9 (20) |
Gamification | 8 (17) |
User-centered design | 4 (9) |
Intervention mapping | 4 (9) |
BJ Fogg persuasive principles and model | 4 (9) |
Theoretical domains framework | 2 (4) |
There are many different BCTs that can be used to induce behavior change. Goal setting is the most frequently employed strategy with a total of 42 studies, followed by self-monitoring and motivation, by 39 and 34 times, respectively. Feedback is used for 33 times (see
It needs to be mentioned that there are no standard guidelines for reporting active components of the interventions, and people used different synonyms to report similar techniques [
It is relevant to mention that there is some overlap between the psychological constructs and BCTs [
Frequency of different behavior change techniques adopted.
After analyzing the usage of BCTs, theories, and technologies previously, we now analyze a combination of them. First, we compare the targeted behavior with the technological platforms that are used. We found that for increasing physical activity, almost all technological platforms were used, whereas for healthy eating, mobile apps were used in more than 80% of the cases (see
Bar graph representing the different targeted health domains using different technological platforms.
Change in physical activity was targeted by a number of different BCTs, mostly by goal setting, self-monitoring, and motivation. Healthy eating was mostly targeted by self-monitoring, goal setting, and feedback (see
Bar graph represents the different target behavior using different behavior change techniques.
Some techniques were more frequently applied in one technological platform than in another. In mobile apps, the most frequent strategies are goal setting (22 times), self-monitoring (20 times), and feedback (17 times; see
Frequency of different behavior change techniques per technological platform.
This study presents a comprehensive review of digital technologies and health behavior change. The review comprises 2 parts. First, a thorough bibliometric analysis has been conducted to present the scholarly networks and the global research trends. The bibliometric analysis identifies influential articles, authors, and collaboration networks among different stakeholders and shows where interdisciplinary collaboration is already strong and where further collaboration could strengthen the field. The bibliometric analysis is followed by a scoping review to map the collected literature and answer questions about the theoretical grounding of digital behavior change interventions and the use of technological platforms and targeted domains.
The field of PT is still quite young, and the literature regarding persuasive technologies for health and well-being started to appear in early 2000 [
Given the fact that technology for behavior change requires expertise from different scientific areas, we expected quite some collaborations between technological and behavioral scientists. This study shows that interdisciplinary collaboration was not as widespread as expected. As human-centered disciplines such as psychology and other behavior sciences are quite mature and can provide essential knowledge about human behavior, technological researches cannot develop effective digital behavior change interventions without their contribution. Similarly, behavioral scientists require knowledge and insights from technological areas to apply their knowledge with modern means.
The keyword network illustrates the most important knowledge structures and thematic evolution in the field of digital behavior change systems. The keyword “mhealth” was strongly connected with the word “physical activity” and “ehealth.” This finding was not so different from the findings of our scoping review. The author collaboration network can be useful for expanding the collaboration network. The network shows a very strong intracollaboration among 3 groups of authors, where one is specifically working on behavior change, another on mobile health interventions, and the last one on persuasive system or BCSS. There is an opportunity to increase the intergroup collaboration, which could add valuable knowledge to the field. For example, with the help of remote sensors and Internet of Things (IoT) devices, health practitioners can monitor and tune systems for better adherence for their target group.
The translation of theories and theoretical frameworks/models into practice is essential for the development of any intervention. Davis et al [
The creation of a taxonomy of BCTs has been an important development in behavioral science, which is visible in the topmost cited articles in this review. However, these BCTs are still underreported in publications that describe persuasive systems [
On the basis our study, we can formulate a number of suggestions for future directions of the research in this domain.
Our study found a large gap in the process of designing digital technologies for health behavior change. They are, usually, weak in their theoretical grounding, and the papers describing them do not clearly report the different components, for example, persuasive strategies, theories, and BCTs. The main reason for this is the lack of design guidelines for these components. For better utilization and reporting of behavioral theories, the development frameworks also need to be updated to the most recent technological advances, for example, the IoT, that is, technologies capable of collecting a large amount of data, such as sensors and mobiles. Furthermore, computational models based on different theories can be designed that could be used by digital intervention developers [
Moreover, the relation between BCTs and behavior change theories/mechanisms requires more elaboration. For example, both for the SCT and theory of planned behavior, the use of self-efficacy construct is important. Self-efficacy is the strongest predictor of intention [
A clear framework or mechanism for reporting the components of health behavior change systems will not only advance the evaluation and its research design (eg, assess engagement, acceptability, and effectiveness) but also could enhance the costly process of development.
There are 2 limitations to our study worth mentioning. First, owing to technical reasons, we only considered the WoS database for bibliometric analysis; 58 papers that were relevant according to Scopus were not included in our analysis. A second limitation is a possible subjectivity in our scoping review. The categorizing of the reviewed papers has been done in a thorough manner but might still be influenced by subjective interpretations. Unfortunately, this problem cannot be avoided in this type of studies.
Overview over behavior change theories, frameworks, models and techniques used in the included persuasive technology of health.
behavior change support system
behavior change technique
Institute for Scientific Information
Internet of Things
persuasive system design
persuasive technology
Science of Science
social cognitive theory
University College London
Web of Science
None declared.