RESEARCH PAPER
“Mapping the current landscape of the evaluation of
mobile applications focused on a healthy life style”
Name: Marijke Fieten
Student number: S3514307
Date: 17-06-2018
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INTRODUCTION
“How many kilometers have you walked today? Wait, I am just checking it at my mobile phone.” Mobile phones have become an important part of today’s society and are also increasingly used for health purposes. Search for ‘health app’ in the app store and apps about calories, running and step monitoring appear. In 2017, 325.000 health applications were available in the app stores (Research2Guidance, 2017). This phenomenon is also called m-Health.
Varshney (2009) defined m-Health as “healthcare to anyone, anytime, and anywhere by removing locational and temporal constraints while increasing both the coverage and the quality of healthcare” (p. 50). This definition is very broad and still a bit vague. Free et al. (2010) were more
concrete and referred to “the use of mobile computing and communication in healthcare” (p. 1). A good addition to this, is the definition of the World Health Organization. The definition of the
World Health Organization (2011) is even more specific; they defined m-Health as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices” (p. 6). This paper focused on healthcare that is supported by mobile phones (i.e. applications) and is used by citizens. As explained in the methodology section, the perspective from the work field is excluded.
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In addition, Varshney (2009) also denoted the increasing quality of the healthcare through the use of m-Health. Lindrud (2014) questioned this cause effect relationship. In her paper, Doyle-Lindrud (2014) indicated her concerns like misdiagnosis, the lack of scientific evidence, and the lack of follow-up with healthcare providers. “Consumers will not know whether the app has scientifically valid information or if it is periodically updated with the latest evidence-based information” (Doyle-Lindrud, 2014, p. 635). According to Boudreaux et al. (2014), are healthcare providers in a quandary. “Patients are using the countless existing health apps, but providers and organizations are hesitant to act because these apps are shrouded in quality and validity concerns, and they do not know which ones to recommend” (Boudreaux et al., 2014, p. 364).
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METHODOLOGY
To map the current landscape of the evaluation of mobile applications focused on a healthy lifestyle, I conducted a systematic literature review. “A systematic review is a type of literature review that employs detailed, rigorous and explicit methods” (Green, Johnson and Adams, 2006, p. 104). The purpose of a systematic review is to obtain all origin research studies published on the topic under study by searching in multiple databases and to criticize the findings derived from this systematic method (Green, Johnson and Adams, 2006). A narrative literature review, rather unsystematic approach, pulls many pieces of information together and is helpful in presenting a broad, readable view on a topic (Green, Johnson and Adams, 2006). This approach is “usually subjective, lacks explicit criteria for inclusion and leads to biased review” (Green, Johnson and Adams, 2006, p. 104). To provide a comprehensive and more objective literature overview of the topic under study, a systematic approach was most appropriate in this paper. A systematic review can be qualitative or quantative of nature. Because this paper has not statistically combined the results of all of the included papers, it is more qualitative of nature (Green, Johnson and Adams, 2006).
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The conducted search started with the key search terms: mhealth OR m-Health OR “mobile health”
AND “mobile applications” OR “mobile apps” OR “smartphone applications” OR “smartphone apps” NOT design OR implementation NOT diabetes OR heart OR chronic OR cancer OR suicide OR mental OR psychiatry. The first part of these search terms focused the search on m-Health.
Because this paper is related to the outcomes of m-Health, design and implementation were excluded. In the first few searches, I found out that many papers emphasized a certain disease. At that moment, there were two options: 1) focusing more on a healthy lifestyle in the search terms or 2) demarcating on certain diseases in the search terms. Since healthy lifestyle is a broad area and it is difficult to demarcate and at the same time giving a comprehensive overview of the literature, I chose to demarcate the search by excluding the most common diseases.
Subsequently, the search strategy continued, consisting of a number of steps. Because not all diseases were excluded, I first scanned all titles. If the title referred to a certain disease, the paper was excluded. However, a title can sometimes give misleading information about the paper itself. In case of doubt, the paper was taken to the next phase of assessment. In this phase, the paper was assessed based on the inclusion and exclusion criteria. The abstract of the paper was consulted for this. If the abstract was not sufficient, I looked for additional information in the paper.
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During the search process, some specific exclusion criteria emerged. The papers in which a sample with a particular disease or treatment (e.g. anorexia, chronic or bariatric surgery) was central, were excluded. Note that obesity is an exception. In this paper, obesity was treated the same as overweight. Finally, the papers that dealt with the perspective of the work field like dieticians, were also excluded. In the last step of the search process, a few papers did have this perspective. However, during the search process there was too little emphasis on this perspective to be able to state that this paper can give a complete overview from the work field perspective. Although this could be interesting, for this reason it is excluded. Table 1 presents a brief summary of the inclusion and exclusion criteria.
Table 1. Inclusion and exclusion criteria
Inclusion criteria Exclusion criteria
Involvement of a mobile application Sample with a particular disease or treatment Focus on a healthy lifestyle Emphasis on the work field perspective
Emphasis on evaluation Empirical nature Peer reviewed English written
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RESULTS
Figure 1 describes the search strategy and the results during the search process. Of the 681 potential
papers obtained from the databases, 19 papers were excluded because they were not written in English. As described in the methodology section, the remaining papers were first assessed by title. Of a total of 250 titles, it was clear that the paper was about a subject other than a healthy lifestyle. The 412 remaining papers, were assessed based on the inclusion and exclusion criteria. Duplicates were also excluded. Finally, there were 46 relevant papers included in this paper. The appendix of this paper contains a scheme with the included papers.
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Healthy lifestyle is a broad concept and consists of various aspects. To clarify this, Figure 2 divides the 46 papers into different aspects. Figure 2 makes the distinction between a general focus (e.g. multiple aspects like smoking, weight loss, nutrition, physical activity and alcohol abuse) and a more specific focus on one aspect of a healthy lifestyle. The boundary between these aspects might be unclear. Physical activity and nutrition for example, are means to achieve weight loss. Therefore, the purpose of the mobile application (e.g. weight loss, more physical activity, smoke cessation) central in the paper, was leading for determining the focus
Figure 2. Main focus of the paper
The development of the number of papers per year is shown in Figure 3. It is remarkable that this dataset contained no papers from before 2013. According to Research2Guidance (2017), the number of downloaded mobile health applications has increased enormously since 2013. This might be an explanation why from then on, it became a negotiable subject in the literature.
Figure 3. Development of the number of papers per year
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Boudreaux et al. (2014) have written a paper to provide healthcare providers an overview of the different strategies for evaluating mobile health applications. In total, they have established 7 evaluation strategies. Table 2 presents a categorization of the papers in the evaluation strategies of Boudreaux et al. (2014).
Table 2. Categorization by Boudreaux et al. (2014) evaluation strategies
Evaluation strategy References
Pilot the apps Lyles, Amresh, Huberty, Todd & Lee, 2017; Al Ayubi, Parmanto, Branch & Ding, 2014; Hull et al., 2017; Jimoh et al., 2018; Choo et al., 2016;
O’Malley, Dowdall, Burls, Perry & Curran, 2014; Pumper et al., 2015; Finkelstein & Cha, 2016; Simons et al., 2018; Youm & Park, 2014; Gordon et al., 2017a; Zeng, Heffner, Copeland, Mull &
Bricker, 2016; Gordon et al., 2017b; Zeng, Vilardaga, Heffner, Mull & Bricker, 2015; de la Torre Diez, Garcia-Zapirain, López-Coronado, Rodrigues & Del Pozo Vegas, 2017; Barrio, Ortega, López & Gual, 2017 (n = 16)
Review the scientific literature Mummah, Mathur, King, Gardner & Sutton, 2016; Mattila et al., 2013; Partridge et al., 2016; van Drongelen et al., 2014; Safran Naimark, Madar & Shahar, 2015; Martin et al., 2015; Hebden et al., 2014; Hurkmans et al., 2018; Heffner, Vilardaga, Mercer, Kientz & Bricker, 2015; Elbert, Dijkstra & Oenema, 2016; Kato-Lin, Padman, Downs & Abhishek, 2015; Glass et al., 2017 (n = 12) Search app stores and review
app descriptions, user ratings and reviews
Davis et al., 2016; Bondaronek, Alkhaldi, Slee, Hamilton & Murray, 2018; Chen, Cade & Allman-Farinelli, 2015; Bardus, van Beurden, Smith & Abraham, 2016; Jeon, Park, Min & Kim, 2014; Schoeppe et al., 2017; Schoffman, Turner-McGrievy, Jones & Wilcox, 2013; Rivera et al., 2013; Crane, Garnett, Brown, West, & Michie, 2015; Weaver, Horyniak, Jenkinson, Dietze & Lim, 2013 (n = 10)
Conduct a social media query within professional or patient networks
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Of the 46 papers, 16 papers conducted a pilot study to test the application. In addition, 12 papers reviewed the scientific literature which refers to systematic reviews and randomized controlled trials (Boudreaux et al., 2014). Due to the excluding of systematic reviews, these 12 papers only concerned randomized controlled trials. Whereas Boudreaux et al. (2014) made a distinction between the evaluation strategies ‘search app stores’ and ‘review app descriptions, user ratings and reviews’, in this paper these two evaluation strategies are taken together. In total, 10 papers searched for mobile applications in app stores and afterwards evaluated the descriptions, user ratings and reviews of these mobile applications.
The remaining 8 papers were not included in this table because they did not fit directly well in a category. Within this group, some papers analysed the data of a mobile application to measure the effectiveness of the mobile application. Other papers questioned a group of people that have used a mobile application in the past. Data analysis and surveys or questionnaires are also part of most pilot studies. However, in a pilot study, there is a preconceived research to let people test a certain mobile application. Because this is not the case with the remaining category, the 8 papers were not included in the evaluation strategy ‘pilot the apps’.
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‘Elicit feedback from patients’ means that the provider and patient together review the mobile health application on its usefulness (Boudreaux et al., 2014). ‘Clearinghouses’ organize, review and certify mobile health applications to help consumers and healthcare providers with mobile health application selection (Boudreaux et al., 2014). The result of this evaluation strategy is a guideline or library on a website.
The aim of the paper of Boudreaux et al. (2014), is to provide healthcare providers an overview of the different evaluation strategies. Some evaluation strategies are very suitable for a scientific paper (e.g. pilot study, randomized controlled trial or content analysis). This is less the case with the remaining 3 strategies, which might explain why they do not appear in the dataset. In addition, as already mentioned in the introduction, healthcare providers are in a quandary. They are in the phase that they have to discover which applications they want to recommend and which not (Boudreaux et al., 2014). Therefore, it might be too early for using the evaluation strategy ‘elicit feedback from patients’.
Table 3 presents the main findings and limitations of the papers within each evaluation strategy of
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Table 3. The main findings and limitations categorized by evaluation strategy
Evaluation strategy Main findings Main limitations
Pilot the apps (n = 16)
High usability* of the application (n = 11)
A positive health behavior change (e.g. more physical activity, healthier food, weight loss or less smoking) (n = 7) Willingness to use the application in the future (n = 6)
Small sample size (n = 10) Less diversed sample (n = 4) Short duration of the study (n = 5) Self-reported data (n = 1)
No comparison with other applications (n = 1)
Review the scientific literature
(n = 12)
A positive health behavior change (e.g. more physical
activity, healthier food, weight loss or less smoking) (n = 10) High usability* of the application (n = 3)
Desire for a more personalized intervention (n = 2)
Self-reported data (n = 9) Small sample size (n = 7) Less diversed sample (n = 7) Short duration of the study (n = 6) High attrition rate (n = 4)
Technical problems application (n = 1) Search app stores
and review app descriptions, user ratings and reviews (n = 10)
Low quality due to limited evidence-base information (n = 7) Limited presence of behavior change techniques (n = 4) High usability* and user ratings (n = 2)
Lack of safety in applications (n = 2) Lack of involvement of experts (n = 2)
Encourage instead of disencourage (alcohol abuse) (n = 2)
Limited analysis due to choice
platform or missing information (n = 6) Rapid evolving technologies that make
the study out of date (n = 3) Presence of fake reviews (n = 2) Only popular apps included (n = 2) Assesment bias of researcher (n = 1) Small sample (n = 1)
Self-reported data (n = 1) Remaining category
(n = 8)
A positive health behavior change (e.g. more physical activity, healthier food, weight loss or less smoking) (n = 7) High usability* and likeability (n = 1)
Less diversed sample (n = 4) Self-reported data (n = 3)
Short duration of the study (n = 2)
Besides the main findings presented in Table 3, some papers also evaluated which factors contributed to the attitude towards the use of mobile applications and thereby the adoption of mobile applications and which factors were associated with success of mobile applications. Success in this case is defined as a healthier lifestyle (e.g. smoking cessation, weight loss, healthier nutrition, more physical activity and less alcohol). Success factors can be seen as a kind of condition under which behavior change takes place. The findings of this analysis are presented in
Figure 4.
Figure 4. From adoption of a mobile application to a healthier lifestyle
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DISCUSSION
This paper has mapped the current landscape of the evaluation of mobile applications focused on a healthy lifestyle. The concept healthy lifestyle consists of multiple aspects, where the literature emphasized weight management. In recent years, especially from 2015, the number of papers focused on a healthy lifestyle has grown. Based on the framework of Boudreaux et al. (2014), the papers were divided into evaluation strategies. The papers in the dataset were focused on 1) piloting the apps, 2) reviewing the scientific literature, and 3) searching app stores and reviewing app descriptions, user ratings and reviews. In their paper, Boudreaux et al. (2014) discussed the strength and weaknesses of these evaluation strategies.
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The evaluation strategies ‘search app stores and reviewing app descriptions, user ratings and reviews’ are easy ways to obtain information. Though, these evaluation strategies have a lack of systematic evaluation. It is hard to consider and evaluate the evidence base, validity and accuracy of the reviews and ratings (Boudreaux et al., 2014). The reliability of reviews and ratings is not yet recognized by the papers within these evaluation strategies. Only two papers within this evaluation strategies indicated fake reviews as a limitation. According to Hill (2018), a remarkable finding. He stated that because of the many reviews, they are not always useful. While some reviews with one-star ratings are very short and contain less information, other reviews with five-star ratings are very extensive and almost unbelievable (Hill, 2018). The findings of these evaluation strategies were focused on the quality of mobile applications (i.e. the presence of evidence-based information and behavior change techniques). The question is with how much certainty we can accept these findings.
It can be stated that the evaluation strategies ‘pilot the apps’ and ‘review the scientific literature’ are relatively good ways to evaluate mobile applications. Whereas the former mainly dealt with the usability of a mobile application, dealt the latter more with behavior change. It is noteworthy that the main findings of these two evaluation strategies were mainly positive. Small-scale tests gave reasonable good results but as already stated, the generalizability is limited. The limitations in
Table 3 gave a good indication of the weaknesses of both evaluation strategies. There is a need for
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There are several suggestions for future research. The evaluation strategies ‘pilot the study’ and ‘review the scientific literature’ should work with large-scale, long-term studies with a diversified sample to create more generalizable conclusions. In addition, adding the perspective from the work field into future research might provide new insights in the evaluation of mobile applications. Eventually, when locational and temporal constraints are removed like Varshney (2009) said, it is the work field that must recommend certain mobile applications to replace their consult.
Another suggestion, less focused on research, but in line with this, is to create more involvement of healthcare providers in the development process of new mobile applications. More collaboration between developers and healthcare providers can increase the evidence-based content of mobile applications. Eventually, when healthcare providers have decided to recommend certain mobile applications, the evaluation strategy ‘elicit feedback from patients’ of Boudreaux et al. (2014) could be a follow-up to ensure continuous evaluation and improvement of mobile applications.
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Zeng, E. Y., Heffner, J. L., Copeland, W. K., Mull, K. E., & Bricker, J. B. (2016). Get With the Program: Adherence to a Smartphone App for Smoking Cessation. Addictive
Behaviors, 63120-124.
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E-Health: The Official Journal Of The American Telemedicine Association, 21(12),
27
Nr Title Authors Year Objective
1 A Mobile, Avatar-Based App for
Improving Body Perceptions Among Adolescents:A Pilot Test
Lyles, A. A., Amresh, A., Huberty, J., Todd, M., & Lee, R. E.
2017 The purpose of this pilot test was to develop and assess acceptability and usability of an avatar-based, theoretically derived mobile app entitled Monitor Your Avatar (MYA).
2 A Persuasive and Social mHealth Application for Physical Activity: A Usability and Feasibility Study
Al Ayubi, S. U., Parmanto, B., Branch, R., & Ding, D.
2014 The objectives of this project were to conduct a focused review on the fundamental characteristics of mHealth for physical activity promotion, to develop an mHealth
application that meets such characteristics, and to conduct a feasibility study to deploy the application in everyday life.
3 A Smartphone App for Families With Preschool-Aged Children in a Public Nutrition Program: Prototype Development and Beta-Testing
Hull, P., Emerson, J. S., Quirk, M. E., Canedo, J. R., Jones, J. L.,
Vylegzhanina, V., Schmidt, D. C., Mulvany, S. A., Beech, B. M., Briley, C., Harris, C., Husaini, B. A.
2017 This paper describes the development and beta-testing of the Children Eating well (CHEW) smartphone app. The objective of beta-testing was to test the CHEW app prototype with target users, focusing on usage, usability, and perceived barriers and benefits of the app.
4 Self-management and Shared Decision-Making in Alcohol Dependence via a Mobile App: a Pilot Study
Barrio, P., Ortega, L., López, H., & Gual, A.
2017 The aim of this study is to report the results of a pilot study testing the usability of and satisfaction with a mobile app (called SIDEAL) in AD patients.
5 Comparing Diet and Exercise
Monitoring Using Smartphone App and Paper Diary: A Two-Phase Intervention Study
Jimoh, F., Lund, E. K., Harvey, L. J., Frost, C., Lay, W. J., Roe, M. A., Berry, R., Finglas, P. M.
2018 The objective of this study was to investigate the use of a smartphone app (FoodWiz2) in supporting healthy lifestyle choices in adolescence.
6 Controlling Your “App”etite: How Diet and Nutrition-Related Mobile Apps Lead to Behavior Change
West, J. H., Belvedere, L. M.,
Andreasen, R., Frandsen, C., Hall, P. C., & Crookston, B. T.
2017 The purpose of this study was to identify which behavior change mechanisms are associated with the use of diet and nutrition-related health apps and whether the use of diet- and nutrition-related apps is associated with health behavior change.
7 Behavior Change Techniques in Popular Alcohol Reduction Apps: Content Analysis
Crane, D., Garnett, C., Brown, J., West, R., & Michie, S.
29 8 Development of a Weight Loss Mobile
App Linked With an Accelerometer for Use in the Clinic: Usability,
Acceptability, and Early Testing of its Impact on the Patient-Doctor
Relationship
Choo, S., Kim, J. Y., Jung, S. Y., Kim, S., Kim, J. E., Han, J. S., Kim, S., Kim, J. H., Kim, J., Kim, Y., Kim, D., Steinhubl, S.
2016 The objective of our study was to evaluate the usability and acceptability of a newly developed mobile app linked with an accelerometer and its early effects on patient-doctor relationships.
9 Effect of telephone calls and text messages on goal attainment in a Ehealth coaching service
Brivio, E., Gatti, F., Galimberti, C., Gambini, P., & Binello, M.
2015 The aim of this contribution therefore are to describe the results obtained by a first group of Yukendu’s clients and identify the main factors in a eHealth coaching service in attaining health-related goals (weight loss).
10 Exploring the Usability of a Mobile App for Adolescent Obesity
Management
O'Malley, G., Dowdall, G., Burls, A., Perry, I. J., & Curran, N.
2014 This study aimed to test the usability (technical effectiveness, efficiency, and user satisfaction) of the Reactivate mobile app in obese adolescents.
11 Health Behavior Theory in Popular Calorie Counting Apps: A Content Analysis
Davis, S. F., Ellsworth, M. A., Payne, H. E., Hall, S. M., West, J. H., &
Nordhagen, A. L.
2016 This study evaluates the presence of health behavior theory in calorie counting apps.
12 Mobile Exercise Apps and Increased Leisure Time Exercise Activity: A Moderated Mediation Analysis of the Role of Self-Efficacy and Barriers
Litman, L., Rosen, Z., Spierer, D., Weinberger-Litman, S., Goldschein, A., & Robinson, J.
2015 Our aim was to examine whether the use of exercise apps is associated with increased levels of exercise and improved health outcomes.
13 Mobile Technology for Vegetable Consumption: A Randomized
Controlled Pilot Study in Overweight Adults
Mummah, S. A., Mathur, M., King, A. C., Gardner, C. D., & Sutton, S.
2016 The purpose of this pilot study was to assess the initial efficacy and user acceptability of a theory-driven mobile app to increase vegetable consumption.
14 Personal Health Technologies in Employee Health Promotion: Usage Activity, Usefulness, and Health-Related Outcomes in a 1-Year Randomized Controlled Trial
Mattila, E., Orsama, A., Ahtinen, A., Hopsu, L., Leino, T., & Korhonen, I.
2013 This study investigated personal health technologies in supporting employee health promotion targeting multiple behavioral health risks. We studied the relations of usage activity to demographic and physiological characteristics, health-related outcomes (weight, aerobic fitness, blood pressure and cholesterol), and the perceived usefulness of technologies in wellness management.
15 Process evaluation of TXT2BFiT: a multi-component mHealth randomised controlled trial to prevent weight gain in young adults
Partridge, S., Allman-Farinelli, M., McGeechan, K., Balestracci, K., Wong, A., Hebden, L., Harris, M. F., Bauman, A., Phongsavan, P.
30 16 Quality of Publicly Available Physical
Activity Apps: Review and Content Analysis
Bondaronek, P., Alkhaldi, G., Slee, A., Hamilton, F. L., & Murray, E.
2018 The purpose of this review and content analysis was to evaluate the quality of the most popular PA apps on the market using health care quality indicators.
17 Treatment seeking as a mechanism of change in a randomized controlled trial of a mobile health intervention to support recovery from alcohol use disorders
Glass, J. E., McKay, J. R., Gustafson, D. H., Kornfield, R., Rathouz, P. J.,
McTavish, F. M., Atwood, A. K., Isham, A., Quanbeck, A. & Shah, D.
2017 We estimated the efficacy of the Addiction-Comprehensive Health Enhancement Support System (A-CHESS) in increasing the use of services for addiction and examined the extent to which this use of services mediated the effects of A-CHESS on risky drinking days and abstinence from drinking.
18 The Most Popular Smartphone Apps for Weight Loss: A Quality Assessment
Chen, J., Cade, J. E., & Allman-Farinelli, M.
2015 The purpose of this study was to evaluate the quality of the most popular dietary weight-loss smartphone apps on the commercial market using comprehensive quality
assessment criteria, and to quantify the behavior change techniques (BCTs) incorporated.
19 Using a facebook group as an adjunct to a pilot mHealth physical activity intervention: a mixed methods approach
Pumper, M. A., Mendoza, J. A.,
Arseniev-Koehler, A., Holm, M., Waite, A., & Moreno, M. A.
2015 The purpose of this study was to evaluate the use of a Facebook group as part of a mHealth physical activity intervention trial.
20 Using a Mobile App to Promote Smoking Cessation in Hospitalized Patients
Finkelstein, J., & Cha, E. M. 2016 This study was conducted to assess the feasibility of using a mobile app for the hazards of smoking education delivered via touch screen tablets to hospitalized smokers.
21 ‘‘Working out for likes’’: An empirical study on social influence in exercise gamification
Hamari, J., & Koivisto, J. 2015 In this study we investigate how social influence aids people in continuing and maintaining the beneficial behaviours promoted by the gamification technology. 22 “Let’s get Wasted!”and Other
Apps:Characteristics, Acceptability, and Use of Alcohol-Related
Smartphone Applications
Weaver, E. R., Horyniak, D. R., Jenkinson, R., Dietze, P., & Lim, M. S.
2013 The purpose of the current study is to review the most popular alcohol-related smartphone apps and to explore young people’s opinions of these apps.
23 Evaluation of an mHealth intervention aiming to improve health-related behavior and sleep and reduce fatigue among airline pilots
van Drongelen, A., Boot, C. R., Hlobil, H., Twisk, J. W., Smid, T., & van der Beek, A. J.
2014 The aim of this study was to evaluate the effects of an mHealth intervention (intervention using mobile technology) consisting of tailored advice regarding
31 24 A Smartphone App to Promote an
Active Lifestyle in Lower-Educated Working Young Adults: Development, Usability, Acceptability, and Feasibility Study
Simons, D., De Bourdeaudhuij, I., Clarys, P., De Cocker, K., Vandelanotte, C., & Deforche, B.
2018 The aim of this study was to describe the development, usability, acceptability, and feasibility of a new theory- and evidence-based smartphone app to promote an active lifestyle in lower-educated working young adults. 25 Development and Evaluation of a
Mobile Application for Personal Lifestyle Check-Up and Improvement
Youm, S., & Park, S. 2014 This study aimed (1) to help individuals analyze their own health status by checking their lifestyle, (2) to develop a user-friendly mobile application that offered prescriptions for lifestyle improvement, and (3) to examine whether the developed application had positive effects on users. 26 Discover Users’ Perception of Health
and Fitness Apps with the UTAUT2 Model
Yuan, S., Ma, W., Kanthawala, S., & Peng, W.
2015 This study adopted the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) Model to examine the predictors of the users’ intention to adopt health and fitness apps.
27 Characterizing user engagement with health app data: a data mining approach
Serrano, K. J., Coa, K. I., Yu, M., Wolff-Hughes, D. L., & Atienza, A. A.
2017 This exploratory study examined behavioral engagement with a weight loss app, Lose It! and characterized higher versus lower engaged groups.
28 The Impact of a Web-Based App (eBalance) in Promoting Healthy Lifestyles: Randomized Controlled Trial
Safran Naimark, J., Madar, Z., & Shahar, D. R.
2015 Our aim was to compare people receiving a new Web-based app with people who got an introductory lecture alone on healthy lifestyle, weight change, nutritional knowledge, and physical activity, and to identify predictors of success for maintaining a healthy lifestyle.
29 How Do Apps Work? An Analysis of Physical Activity App Users’
Perceptions of Behavior Change Mechanisms
Hoj, T. H., Covey, E. L., Jones, A. C., Haines, A. C., Hall, P. C., Crookston, B. T., & West, J. H.
2017 The purpose of this study was to identify the mechanisms by which the use of physical activity apps may influence the users’ physical activity behavior.
30 mActive: A Randomized Clinical Trial of an Automated mHealth Intervention for Physical Activity Promotion
Martin, S. S., Feldman, D. I.,
Blumenthal, R. S., Jones, S. R., Post, W. S., McKibben, R. A., Michos, E. D., Ndumele, C. E., Ratchford, E. V., Coresh, J., Blaha, M. J.
2015 We conducted a randomized, clinical trial (“mActive”) testing the hypothesis that a fully automated, fully mobile, and physician-designed mHealth intervention using new technologies to provide individual encouragement and foster feedback loops increases physical activity. 31 A mobile health intervention for weight
management among young adults: a pilot randomised controlled trial
Hebden, L., Cook, A., van der Ploeg, H. P., King, L., Bauman, A., & Allman-Farinelli, M.
32 32 Face-to-Face Versus Mobile Versus
Blended Weight Loss Program: Randomized Clinical Trial
Hurkmans, E., Matthys, C., Bogaerts, A., Scheys, L., Devloo, K., & Seghers, J.
2018 The aim of this study was to compare the effectiveness of different weight loss programs using a combination of conventional and mobile programs among adults who are overweight (body mass index [BMI]>29 kg/m²).
33 Mining Health App Data to Find More and Less Successful Weight Loss Subgroups
Serrano, K. J., Yu, M., Coa, K. I., Collins, L. M., & Atienza, A. A.
2016 The purposes of this study were to analyze data from a commercial health app (Lose It!) in order to identify successful weight loss subgroups via exploratory analyses and to verify the stability of the results.
34 A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management
Bardus, M., van Beurden, S., Smith, J., & Abraham, C.
2016 The aim of this study was to evaluate both the quality and content of popular weight management apps available from both iTunes and GP, to answer the following research questions: (1) What is the overall quality of these apps in terms of engagement, functionality, aesthetics, and information quality? (2) What type of change techniques are included in these apps? (3) What are the relationships between user ratings, app quality, other app features, and techniques included, specifically techniques previously found to be associated with weight loss?
35 Analysis of the Information Quality of Korean Obesity-Management
Smartphone Applications
Jeon, E., Park, H., Min, Y. H., & Kim, H.
2014 This study analyzed smartphone obesity-management applications developed in Korea and the quality of the information that they provide.
36 Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: a review of quality,
features and behaviour change techniques
Schoeppe, S., Alley, S., Rebar, A., Hayman, M., Bray, N., Van Lippevelde, W., Gnam, J., Bachert, P., Direito, A., Vandelanotte, C.
2017 This review systematically evaluated the content and
quality of apps to improve diet, physical activity and sedentary behaviour in children and adolescents, and examined relationships of app quality ratings with number of app features and behaviour change techniques (BCTs) used.
37 Mobile apps for pediatric obesity prevention and treatment, healthy eating, and physical activity promotion: just fun and games?
Schoffman, D., Turner-McGrievy, G., Jones, S., & Wilcox, S.
33 38 Mobile Apps for Weight Management:
A Scoping Review
Rivera, J., McPherson, A., Hamilton, J., Birken, C., Coons, M., Iyer, S.,
Agarwal, A., Lalloo, C., Stinson, J.
2016 The objective of the current study was to conduct an updated and comprehensive systematic review of weight management mobile apps across four major commercial app stores to describe the inclusion of evidence-based strategies for weight control, health care expert involvement, and scientific evaluation.
39 Development and evaluation of the See Me Smoke-Free multi-behavioral mHealth app for women smokers
Gordon, J. S., Armin, J., D Hingle, M., Giacobbi, P. J., Cunningham, J. K., Johnson, T., Abbate, K., Howe, C. L., Roe, D. J.
2017 The goals of this pilot study were to develop and test the feasibility and potential of the See Me Smoke-Free™ mHealth app to address smoking, diet, and physical activity among women smokers.
40 Feature-level analysis of a novel smartphone application for smoking cessation
Heffner, J. L., Vilardaga, R., Mercer, L. D., Kientz, J. A., & Bricker, J. B.
2015 Using data from a pilot trial of a novel smoking cessation app, we examined: (i) the 10 most-used app features, and (ii) prospective associations between feature usage and quitting.
41 Get with the program: Adherence to a smartphone app for smoking cessation
Zeng, E. Y., Heffner, J. L., Copeland, W. K., Mull, K. E., & Bricker, J. B.
2016 This study examines the extent to which adherence
measures based on the underlying behavioral change theory of an Acceptance and Commitment Therapy (ACT) app for smoking cessation predict smoking outcomes, and user characteristics associated with adherence.
42 Lessons learned in the development and evaluation of RxCoachTM, an mHealth app to increase tobacco cessation medication adherence
Gordon, J. S., Armin, J. S.,
Cunningham, J. K., Muramoto, M. L., Christiansen, S. M., & Jacobs, T. A.
2017 In this project we developed and evaluated a mobile health app to improve adherence to tobacco cessation medication.
43 Predictors of Utilization of a Novel Smoking Cessation Smartphone App
Zeng, E. Y., Vilardaga, R., Heffner, J. L., Mull, K. E., & Bricker, J. B.
2015 This study examines the degree to which baseline
34 44 A Mobile Phone App Intervention
Targeting Fruit and Vegetable
Consumption: The Efficacy of Textual and Auditory Tailored Health
Information Tested in a Randomized Controlled Trial
Elbert, S. P., Dijkstra, A., & Oenema, A. 2016 In a randomized controlled trial, we tested the efficacy of a 6-month intervention delivered via a mobile phone app that communicated either textual or auditory tailored health information aimed at stimulating fruit and vegetable intake. A control condition in which no health information was given was added. Perceived own health and health literacy were included as moderators to assess for which groups the interventions could possibly lead to health behavior change. 45 A New mHealth App for Monitoring
and Awareness of Healthy Eating: Development and User Evaluation by Spanish Users
de la Torre Díez, I., Garcia-Zapirain, B., López-Coronado, M., Rodrigues, J. C., & Del Pozo Vegas, C.
2017 The main objective of this work is to develop and subsequently assess a mobile app, named DietApp, that provides advice about obtaining a healthy diet according to age, clinical history and physical condition.
46 Evaluating Consumer m-Health
Services for Promoting Healthy Eating: A Randomized Field Experiment
Kato-Lin, Y., Padman, R., Downs, J., & Abhishek, V.