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Several reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions.
The primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume.
We conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy.
Included articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines.
Smartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions.
Physical inactivity has been identified as a
At the same time, the world is facing another life-threatening pandemic caused by SARS‑CoV‑2 or COVID‑19 [
Although confinement measures have been introduced to reduce the spread of the virus, with some success in flattening the curve, these interventions to contain the COVID-19 outbreak have unsurprisingly resulted in an increased use of digital communication technologies, such as in mobile health (mHealth) and telehealth approaches in the domains of PA and medicine [
A major decline in PA levels occurs during the transition from childhood to adolescence [
Face-to-face interventions are resource intensive and limited because of their attachment to their specific environment and multicomponent nature [
Despite their strong potential, the evidence concerning smartphone-based interventions to improve PA and decrease sedentary behaviors (SBs) is only emerging, and the literature is poorly systematized, which results in methodological inconsistencies and significant gaps in our understanding of the developments in the field of mHealth PA interventions.
There are four recent scoping reviews, which attempted to address these gaps [
Although these reviews are informative and have their strengths in different areas, they still fail to provide answers to several questions. First, behavior change components of mHealth interventions are often conceptualized as behavior change techniques (BCTs), which are described systematically in various BCT taxonomies [
This scoping review addresses these issues. It includes studies describing or evaluating smartphone apps alone or in combination with wearables as a primary intervention component to enhance PA levels, focusing on studies with healthy individuals without chronic or acute conditions (excluding cardiovascular diseases and obesity), and targeting studies with PA as a primary outcome. Although we included all age groups to provide a comprehensive review, we focused on one part of the analysis on studies involving adolescents, as the biggest impact on future generations’ health is to be expected from changing their behavior. The primary objective of this scoping review is to
This review is guided by the following research questions: (1) What kind of literature is available in the field, and how can the existing literature be categorized? (2) Which theories and techniques are implemented in smartphone-based PA interventions to support behavior changes, and how are these theories and techniques systematized? (3) Which practices and methods are used to systematically develop and evaluate smartphone-based PA interventions? and (4) Which devices and primary outcomes are used for data collection and analysis in smartphone-based PA interventions?
Methodological guidelines for scoping reviews developed by Arksey and O'Malley [
The literature search was conducted from September 2017 to August 2018 in three databases: MEDLINE/PubMed, ScienceDirect, and ResearchGate. The search was limited to studies published in 2008 and later, as Apple App Store and Google Play (formerly known as the Android Market) started in July and October of that year. Only publications in English were considered. Full papers, study protocols, conference proceedings, dissertations, and books were considered eligible. Reference lists of germane articles and review studies were manually searched to identify potentially relevant articles. The articles were initially screened by the first author (AD). As per best review practice, an assistant reviewer independently reviewed the eligibility of articles for inclusion in the review. Inconsistencies were resolved by discussion and consensus between the 2 reviewers.
Search strategies for MEDLINE/PubMed were developed using a combination of thesaurus and free terms based on Boolean logic (
Search builder for MEDLINE/PubMed.
Search lines | Search terms | Filtered by |
Line 1 | mobile phone OR cell phone OR smartphone OR smart phone OR smart-phone OR mobile device OR iphone OR mobile technology OR mhealth OR android | Title or abstract |
2. AND | app OR apps OR application OR intervention OR trial OR behavior OR behaviour | Title or abstract |
3. AND | physical activity OR exercise OR fitness | Title or abstract |
4. NOT | heart attack OR heart failure OR cancer OR diabetes OR diabetic OR injury OR injuries OR alcohol OR sexual OR e-learning OR home OR HIV OR pain OR sleep OR smoke OR smoking OR epileptic OR rehabilitation OR asthma | Title |
The use of this search builder was not possible for ResearchGate and ScienceDirect. Consequently, various combinations of the following search terms were used:
To select articles that were related to mHealth interventions with the primary outcome in PA, the following terms were used to manually filter out articles from the initial search results:
Although all age groups were included, an additional subanalysis for adolescents’ target groups was conducted (specifically accounting for BCTs effective for this target population). This was also done to contrast the differences in BCTs used in adolescents and other target populations. Studies were included if (1) the primary component of the intervention involved a mobile app targeting PA and SB and (2) the study used smartphones with available embedded sensors alone (stand-alone intervention) or in conjunction with other external components, for example, accelerometers, pedometers, and websites accessed through desktop computers (multicomponent interventions). Studies were excluded from the review if (1) the intervention was limited to using text messages only, (2) the app was used for data collection only (eg, phone-based questionnaires), (3) the intervention included any mobile device other than smartphone or PA tracker, for example, PDAs, (4) the intervention targeted other preventive health issues, such as alcohol abuse, smoking, and sport injuries, and (5) they focused on patients with chronic conditions other than cardiovascular diseases and obesity, for example, diabetes mellitus. This review includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA. Two additional groups of studies were included: reviews or content analyses of PA trackers and meta-analyses exploring BCTs and their efficacy. This approach was used to obtain additional evidence from the domains, which are closely related to smartphone-based mHealth PA promotions, to provide theoretical evidence related to the field and to present the latest developments in the domain. Instead of considering studies using combined interventions designed to reduce body weight (ie, PA promotion and dietary interventions), we aimed to include studies promoting PA and reducing sedentary time, as it is difficult to disentangle the effects of specific intervention BCTs on particular behaviors in studies targeting several health behaviors. For example, a BCT such as
A data extraction form was developed specifically for this review and served as a basis for Tables S1-S10 presented in
For all groups of publications, data were extracted for author, year, target group, and targeted behavior. Depending on the group, data were further extracted for several additional categories, as follows:
For commercial smartphone app content analyses, data were extracted for evaluation framework or taxonomy used for coding, number of apps included, app market name and category, and findings related to the theoretical background.
For smartphone-based intervention review studies, data were extracted for taxonomy used for coding, information on BCTs, identified psychological theories, number of studies included, objective, industry-recognized reporting guidelines.
For activity tracker content analyses, data were extracted for evaluation criteria or taxonomy used for coding, number of trackers included, number of BCTs included (mean value), BCTs present in all included devices, and BCTs present in none of the included devices.
For activity tracker reviews, data were extracted for evaluation criteria or taxonomy used for coding, number of studies included, industry-recognized reporting guidelines.
For meta-analyses, data were extracted for taxonomy used for coding, BCTs associated with more effective interventions, BCTs associated with less effective interventions, and industry-recognized reporting guidelines.
For smartphone-based intervention studies, data were extracted for pilot, protocol, sample size, theoretical background, study design, study duration, stand-alone or multicomponent intervention, principal outcome measures, industry-recognized reporting guidelines, and PA recommendations.
For qualitative formative studies and qualitative follow-up studies, data were extracted for sample size, theoretical background, and method of data collection.
For app development descriptive studies, data were extracted for sample size, theoretical background, commonly reported systematic theory or evidence-based practices, and methods for development, evaluation, and reporting.
For all other related articles, data were extracted for keyword, title, type of study or methodology, and objective and narratively described further.
A total of 1531 articles were identified during the initial database search. The searches of the MEDLINE and PubMed and ScienceDirect databases yielded 785 and 546 results, respectively. ResearchGate database search results were restricted to 200 because the database search engine generated an unlimited number of search results. After the removal of duplicates, 1003 articles were screened for their titles and abstracts, resulting in 176 full-text articles. Of these, 94 full-text articles were excluded for the following reasons. The resulting 82 articles were hand-searched for references to relevant articles, leading to the identification of an additional 66 articles. As a result, 148 articles were included in the review (
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.
To categorize the included studies, we used the stepwise approach developed by Whittaker et al [
To improve further categorization attempts, we refined the outcome of our analysis, which resulted in the table presented below (
Map of search results by number of studies. PA: physical activity.
Possible categorization approach for smartphone-based interventions for physical activity promotion.
Steps in the development and evaluation process [ |
Common reported systematic theory or evidence-based practices and methods for development, evaluation, and reporting | Purpose | |||||
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Commercial smartphone app content analyses |
BCTc taxonomies Scales to assess app quality PA recommendations |
To critically evaluate the material that has already been published To provide an overview of the current state of knowledge |
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Smartphone-based intervention review studies |
Industry-recognized reporting guidelines Behavior change theories or models BCT taxonomies |
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Activity tracker review studies |
Industry-recognized reporting guidelines |
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Activity tracker content analyses |
BCT taxonomies |
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Meta-analyses of PA intervention studies |
BCT taxonomies Industry-recognized reporting guidelines |
To assess the strength of evidence present through establishing statistical significance |
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Qualitative formative studies (assessing general topic perception by target users) |
Mixed methods data collection (surveys, questionnaires, interviews, and focus groups discussions) |
To inform the development of the intervention |
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App development descriptive studies |
BCT taxonomies Behavior change theories or models PA recommendations Scales to assess app quality |
To describe the intervention development process and intervention features To control acceptability, engagement, and experiences of proposed intervention to target audience To improve and refine intervention on the basis of qualitative feedback |
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Pilot trials |
Behavior change theories or models PA recommendations Industry-recognized reporting guidelines Trial designs (RCTsd, experimental trials, and rapid design trials) |
To examine content of intervention To examine feasibility of a trial approach, trial processes (eg, recruitment, registration, data collection), methods |
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Study protocols |
Behavior change theories or models PA recommendations Industry-recognized reporting guidelines Trial designs (RCTs, experimental trials, and rapid design trials) |
To describe processes of trials (eg, recruitment, registration, data collection) |
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Clinical trials |
Behavior change theories or models PA recommendations Industry-recognized reporting guidelines Trial designs (RCTs, experimental trials, and rapid design trials) |
To examine the effect of the intervention as a whole package or the effect of one of its components |
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Qualitative follow-up studies (assessing the developed intervention by target users) |
Mixed methods data collection (surveys, questionnaires, interviews, and focus groups discussions) Behavior change theories or models |
To control acceptability, engagement, and experiences of proposed intervention to target audience To control implementation issues To control the effect of the intervention after dissemination |
amHealth: mobile health.
bPA: physical activity.
cBCT: behavior change technique.
dRCT: randomized controlled trial.
The characteristics of the included studies are presented in Tables S1-S10 of
Articles were allocated to this group if the objective of the study was to analyze the content of commercial apps presented on digital distribution platforms (ie, App Store, Google Play, and Microsoft Store). The included studies (n=11) were published from 2012 to 2018, and most of them targeted the general population (n=7) and adults (n=2), whereas only 2 targeted children and adolescents. More than half of the content analyses targeted PA behavior (n=7); the other reported lifestyle-related health behaviors, outcomes and aims were SB, diet, health and fitness, and obesity prevention. Sample sizes ranged between 25 and 3336 (mobile) apps, and the most common digital distribution platform was App Store (n=11). A total of 6 studies used different variations of the BCT taxonomy (26, 40, and 93 BCTs) as an evaluation or coding framework. The average number of the BCTs in those studies ranged from fewer than 4 to 8.1, and the most common BCTs for adults included
This group included intervention studies aimed at reviewing smartphone-based intervention publications. The included reports (n=13) were published between 2013 and 2017 and targeted the general population (n=6), adults (n=4), and children and adolescents (n=3). More than half of the reviews targeted PA behavior exclusively (n=7), whereas the other reported lifestyle-related health behaviors and outcomes and aims were SB, diet, weight reduction, obesity combatting, healthy nutrition, and overweight prevention. The number of articles included in these reviews ranged from 7 to 52. Only two studies used the taxonomy of BCTs (26 and 93 BCTs) to code the included interventions [
Articles were included in this group if the objective of the content analysis was to analyze the theoretical components included in the activity trackers. The included studies (n=3) were published between 2014 and 2017 and targeted the general population [
Review studies in this group aimed to provide evidence on the effectiveness, efficacy, feasibility, validity, or reliability of activity trackers. The included studies (n=6) were published between 2012 and 2018 and targeted adults (n=5) and children and adolescents (n=1) [
Articles were included in this group if the objective of the meta-analysis was to analyze PA intervention studies and to define the BCTs that were associated with more or less effective interventions. It is important to note that all identified meta-analyses (n=6) reviewed only
Articles included in this group were published between 2009 and 2017 and mainly targeted adults with one exception, where the targeted group included children and adolescents [
The smartphone-based intervention study group included 38 articles representing 32 research studies published between 2008 and 2018. The majority of these studies targeted adults (n=20), whereas 12 targeted adolescents, and the sample size ranged from 8 to 700 participants, and the duration of interventions ranged from 2-32 weeks (most common duration: 8 weeks). The participants’ ages ranged from 8-81 years. A total of 14 studies exclusively targeted PA behavior; the other reported lifestyle-related health behaviors, aims, concepts, outcomes, and conditions included weight loss, SB, cardiorespiratory fitness, diet, sleep, fitness, and obesity. The most common study design was a two-arm randomized controlled trial (RCT; n=10); for other study designs, the number of intervention groups ranged between 1 and 4. There was a preponderance in the number of multicomponent interventions (n=19) over stand-alone interventions (n=13). The interventions mainly used newly designed smartphone apps (n=29) rather than commercially available apps (n=3), the theoretical background of which was unknown. The most common outcome measures were minutes spent with moderate-to-vigorous PA (MVPA) and a daily step count. In total, 14 studies did not report a theoretical background. For adults, the most frequently used theoretical framework was Social Cognitive Theory (n=11), followed by Self-Regulatory Theory (n=3) and the Fogg Behavior Model (n=2). Of the 12 studies including adolescents, several (n=4) did not report any theoretical background, and among those who did, Self-Determination Theory (n=6) was the most frequently used. The other reported theoretical frameworks and models include the Theory of Meaning Behavior, the Five Factor Model of Personality, the Health Belief Model, the Technology Acceptance Model, the Theory of Motivation in Videogames, the Transtheoretical Model of Health Behavior Change, the Functional Triad, the Transcontextual Model of Motivation, the Synergy Hypothesis, Learning Theory, Basic Psychological Needs Theory, the COM-B (Capability, Opportunity, Motivation, Behaviour) model, and the Behavior Change Wheel. A total of 10 studies used CONSORT (Consolidated Standards of Reporting Trials) reporting guidelines, 1 study used SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) reporting guidelines, and 2 studies used both CONSORT and SPIRIT reporting guidelines.
Articles in this group used a qualitative approach to examine users’ views of and preferences for app features in terms of usability and attractiveness, among others, that can inform the development of future mHealth PA interventions. The identified studies (n=6) were published between 2011 and 2016 and included adults (n=5) and adolescents (n=1) target populations [
Social networking, that is, exposing one’s health behavior through integration of the PA app in social networks (eg, Facebook), was generally perceived negatively. Context sensing or personalization, self-monitoring, and goal setting were perceived as valued features in smartphone apps. The design of the app appeared to be a crucial feature, in that users preferred a simple and structured layout, which was easy to use, playful, and fun. Apps were not used or uninstalled if they contained unnecessary features, required excessive data entry for sign up, had complicated operating procedures, and required instructions that were time-consuming or burdensome.
Articles were included in this group if the objective of the study was to describe the intervention development process and intervention features. The identified studies (n=7) were published between 2012 and 2018 and included adults (n=5), adolescents (n=1), and general populations (n=1) [
This group of studies aimed at assessing the acceptability, engagement, and experiences of the target audience with the intervention and the effect of the intervention after dissemination. The identified studies (n=7) were published between 2012 and 2017 and included adults (n=4) and adolescents (n=3) as target populations [
The related articles group included 51 articles published between 2008 and 2018. These articles were mainly identified through manual reference searches, and although they were relevant to the topic of this review, they did not fit into the other groups presented above. The study types included methodological, theoretical, conceptual studies; reports; recommendations from workshops; other literature reviews (reviews of methodological, theoretical, and conceptual studies); and reviews and trials on related topics (eg, gamification) that represented theoretical and methodological findings and recommendations that were grouped into several topical subgroups: activity tracking, automation, BCT, behavior change theory, GPS, just-in-time adaptive interventions, mHealth apps, PA, profiling, and RCT alternatives for mHealth. Relevant information from these articles was analyzed and presented narratively in the
The science of behavior change has advanced significantly in recent years. Nevertheless, many challenges remain concerning the standardization of the development and reporting of methods of behavior change interventions. As presented in the tables of
To accomplish a more standardized methodological approach, several frameworks have been developed by Michie et al [
In addition, Michie et al [
To overcome the unsystematic intervention development and reporting, it is also important to understand how BCTs can be linked to theoretical mechanisms of action, which is currently being investigated [
Several important tendencies were identified in all the included groups of studies. First, studies aiming to promote PA via smartphone-based interventions in adolescents are underrepresented in comparison with those targeting adults. While analyzing the studies including adolescents, Schoeppe et al [
Second, the tables in
Third, the tables in
Reviews of the commercial app market (Table S1 in
As previously outlined, the included meta-analyses did not analyze smartphone-based interventions. We decided to include them here, based on the rationale of Brannon and Cushing [
There is a clear need to conduct meta-analyses on mHealth studies. Until now, such a meta-analysis has been conducted once by Direito et al [
In general, it is also important to consider the mechanisms of action and the parameters of effectiveness of coded BCTs. Although the current approach applied for coding, using the taxonomy of Michie et al [
When selecting a theoretical model, many researchers seem to assume that the basic motivation of the user is to become more physically active, which is not always the case [
As is evident from the results in this review and the tables presented in
These are evidence-based recommendations of the WHO that “address the links between the frequency, duration, intensity, type and total amount of PA needed for the prevention of NCDs” [
The MARS scale has been developed quite recently in many of the most recent mHealth research studies [
The following industry-recognized reporting guidelines have been illustrated:
PRISMA: this is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses [
PRISMA-P: this is a set of items aimed at facilitating the development and reporting of systematic review protocols [
CONSORT statement: this internationally acknowledged tool can be used to assess the quality of RCT studies and to design or report an RCT of the highest quality and standard [
SPIRIT: a guideline for minimum content of a clinical trial protocol [
Although RCT study designs are widely considered a
Continuous Evaluation of Evolving Behavioral Intervention Technologies
Sequential Multiple Assignment Randomized Trial
The Multiphase Optimization Strategy
Microrandomized trial (MRT)
Step-wedge design (ie, cluster randomized design)
n-of-1 trials
Practice-Based-Evidence methodology
Trial of Intervention Principles framework
Collaborative Adaptive Interactive Technology framework
However, these designs have rarely been implemented. According to the most recent review of PA apps, only 2 of 111 included studies used rapid research designs [
Although there is no one recommended methodology, the most commonly reported methods in identified studies include surveys, questionnaires, interviews, and focus group discussions. On the basis of this review, we cannot recommend any specific method, yet there is a clear need for more systematic reporting of results. Nevertheless, the studies summarized in Tables S7-S9 in
Design simplicity: Ease of use and navigation through the app, absence of unnecessary features, unambiguous information, and a structured layout were all listed as features that positively affected participants’ engagement. Apps with excessive data entry for sign up, presenting features that required instructions, and complicated operating procedures were negatively perceived by users [
Personal approach for each user- tailored coaching, goals, feedback, and notifications: Users perceive a personalized approach as an important factor for motivation and engagement. Therefore, it is important to consider sociodemographic user differences [
Reward: A transparent reward system was positively recognized by users [
Self-monitoring and goal setting: These app features were the key features enjoyed or rated positively by app users [
Gamification: This feature can positively affect user engagement by bringing more enjoyment to exercise or activity [
Social networking: This feature was perceived differently in various apps:
These findings demonstrate that a chosen method of social support can significantly affect the acceptability and usefulness of the app among users. Overall, it is important to underline the necessity of pretesting the app with a specific target audience to optimally refine the app’s features and components.
Smartphone-based interventions can be divided into stand-alone interventions, where only the app is used and multicomponent interventions, where the app is one of several intervention components. The choice of intervention components affects the intervention outcomes, and, if a multicomponent approach is chosen, may lead to the inclusion of various devices as additional components of the intervention.
For the majority of researchers, the selection of smartphone-based intervention components depends on several factors, such as the accuracy of data collection, device compatibility with the user, and durability. As can be seen from Table S6 in
As presented in Table S6 of
The validation reviews presented in Table S4 of
Medical-grade activity trackers (hip, waist, or wrist worn), for example, ActiGraph devices, provide the highest measurement accuracy levels; however, they also have certain drawbacks. The hip and waist location can lead to low user compatibility levels and reduced exposure, whereas HR can only be measured with a wireless HR monitor [
Consequently, while developing PA interventions, researchers should consider these factors and choose the device according to the characteristics most suitable for their projects. It is important to note the findings of a recent review, which confirms that multicomponent interventions tend to be associated with higher intervention efficacy [
Researchers working in the smartphone-based mHealth field often face problems with participants’ engagement: the long-term retention levels are usually quite low at 18 months follow-up measurements [
As the articles listed in Table S10 of
On the basis of this review and in light of the widely used international reporting guidelines, several recommendations for future research can be inferred:
Support uniformity of reporting by describing interventions and procedures in an adequate and consistent manner, using industry-recognized reporting guidelines, such as PRISMA, CONSORT, and SPIRIT [
Develop and code interventions in a more systematic way, using recommended practices while taking into account new models that offer additional opportunities in behavior research [
Meta-analyses, including modern mHealth solutions (eg, smartphones) and excluding outdated devices or methods (intervention based solely on SMS, PDAs, etc), provided there is a sufficient number of studies meeting the inclusion criteria.
Profit from interdisciplinary collaboration while developing mHealth interventions. Various researchers and research groups working on the development of PA mHealth interventions have underlined the positive effect of collaboration between related stakeholders and experts in the domains of behavior change, software development, machine learning or data science, physiology, and public health [
Perform more studies designed for adolescents, accounting for differences in levels of motivation and lifestyle compared with adults.
Implement rapid study designs while evaluating the intervention (eg, MRT, Multiphase Optimization Strategy, Sequential Multiple Assignment Randomized Trial, etc) [
Implement wearable activity monitors with built-in sensors (eg, HR and GPS) will provide more opportunities for data collection. Both commercial and research-grade trackers are advantageous. However, the collaboration of two domains, for instance ActiGraph and Garmin, is yet to bring fruitful results [
Implement the latest findings of machine learning or data mining and artificial intelligence domains into behavior change interventions [
Improve engagement with smartphone-based mHealth interventions by testing and implementing meaningful gamification and social networking features [
Build the reward and engagement engine of the app in a way that users will become autonomously physically active over time and do not depend on an app, a tracker, or an intervention in perpetuity.
The strength of this scoping review is the comprehensive search strategy, which allows the majority of published related articles to be included. Therefore, the scope of the review is wider than the scope of systematic reviews on smartphone-based mHealth interventions for PA promotion. However, a scoping review does not consider the methodological quality assessment of the included studies. Consequently, several studies had moderate methodological quality, which calls for their findings into question. It is important to emphasize that the included interventions developed and evaluated apps and activity trackers that provide sensor-based feedback on PA. Smartphone-based interventions related to chronic diseases other than cardiovascular diseases and obesity (eg, diabetes mellitus), preventive health issues (eg, alcohol abuse, smoking, and sports injuries), weight loss, diet, and nutrition were not included in this review. Finally, yet most importantly, only smartphone-based mHealth interventions were included in this review.
Smartphone-based mHealth interventions aimed at PA promotion in adolescents and adults show promising results for effective behavior change. Although there is a plethora of published studies with adults, the number of studies and, consequently, the evidence base for adolescents is very limited. In the past few years, a growing number of researchers have developed multicomponent mHealth interventions that, in addition to the app, include commercial or research-grade activity trackers, which can provide additional insight into a participant’s lifestyle. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach to developing, reporting, and coding of the interventions. Specifically, researchers should aim to develop theory-based rather than theory-inspired interventions, which is currently challenging, as there is no consensus on development, evaluation, or coding practice. Finally, the current stage of behavior science advocates an interdisciplinary approach to the development of behavior change interventions, including innovative approaches such as machine learning and data mining.
Articles included in the scoping review.
behavior change technique
Coventry, Aberdeen & London–Refined
Capability, Opportunity, Motivation, Behaviour
Consolidated Standards of Reporting Trials
healthy eating
heart rate
Mobile App Rating Scale
mobile health
microrandomized trial
moderate-to-vigorous physical activity
physical activity
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols
randomized controlled trial
sedentary behavior
Standard Protocol Items: Recommendations for Interventional Trials
World Health Organization
The first author is supported by an Industrial Fellowship from the Luxembourg National Research Fund (reference number 12674722) and by Actimage Luxembourg SA. The authors are grateful for the support of the graduate psychology student Claudia Manuela Vila Verde Gonçalves for reviewing the literature.
None declared.