The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Can Mobile Phone Apps Influence People’s Health Behavior Change? An Evidence Review
Open accessAbstract
BACKGROUND: Globally, mobile phones have achieved wide reach at an unprecedented rate, and mobile phone apps have become increasingly prevalent among users. The number of health-related apps that were published on the two leading platforms (iOS and Android) reached more than 100,000 in 2014. However, there is a lack of synthesized evidence regarding the effectiveness of mobile phone apps in changing people's health-related behaviors. OBJECTIVE: The aim was to examine the effectiveness of mobile phone apps in achieving health-related behavior change in a broader range of interventions and the quality of the reported studies. METHODS: We conducted a comprehensive bibliographic search of articles on health behavior change using mobile phone apps in peer-reviewed journals published between January 1, 2010 and June 1, 2015. Databases searched included Medline, PreMedline, PsycINFO, Embase, Health Technology Assessment, Education Resource Information Center (ERIC), and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Articles published in the Journal of Medical Internet Research during that same period were hand-searched on the journal's website. Behavior change mechanisms were coded and analyzed. The quality of each included study was assessed by the Cochrane Risk of Bias Assessment Tool. RESULTS: A total of 23 articles met the inclusion criteria, arranged under 11 themes according to their target behaviors. All studies were conducted in high-income countries. Of these, 17 studies reported statistically significant effects in the direction of targeted behavior change; 19 studies included in this analysis had a 65% or greater retention rate in the intervention group (range 60%-100%); 6 studies reported using behavior change theories with the theory of planned behavior being the most commonly used (in 3 studies). Self-monitoring was the most common behavior change technique applied (in 12 studies). The studies suggest that some features improve the effectiveness of apps, such as less time consumption, user-friendly design, real-time feedback, individualized elements, detailed information, and health professional involvement. All studies were assessed as having some risk of bias. CONCLUSIONS: Our results provide a snapshot of the current evidence of effectiveness for a range of health-related apps. Large sample, high-quality, adequately powered, randomized controlled trials are required. In light of the bias evident in the included studies, better reporting of health-related app interventions is also required. The widespread adoption of mobile phones highlights a significant opportunity to impact health behaviors globally, particularly in low- and middle-income countries.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Systematic reviews that focused on one specific behavior or a certain type of health or well-being app suggest that the effectiveness of evidence-based smartphone apps can be improved by targeting the design and engagement features, such as user-friendly design, individualized and culturally tailored content, or health professional support [17-19].”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In line with the findings of previous reviews, help with initial engagement could be achieved by improving the users’ app literacy skills and by providing knowledge [14,17].”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Behavior change techniques, widely reported by others previously [14,17-19], were also identified as important factors to sustain engagement, including self-monitoring, feedback, goal setting, reminders, rewards, and social support.”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Complementary delivery routes that include mobile health (mHealth) may help satisfy this need as they can continue to be used autonomously by participants after an intervention period [84–86].”
From Barriers and facilitators to diet, physical activity and lifestyle behavior intervention adherence: a qualitative systematic review of the literature (2023) · cites this paper as [85]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“In line with the emerging evidence from the human-computer interaction (HCI) literature, we found that an app coupled with human support [14,17] was likely to be more effective by increasing the intervention effectiveness and engagement [78,79].”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [17]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cochrane Handbook for Systematic Reviews of Interventions: Cochrane Book Series
(2008) · doi:10.1002/9780470712184 · Cited by 25,048 · cited 9×
“All 23 studies had some kind of risk of bias according to the Cochrane Risk of Bias Assessment Tool.”
BackgroundMethodInfluentialHealth Behavior and Health Education: Theory, Research, and Practice
(1992) · Annals of Internal Medicine · doi:10.7326/0003-4819-116-4-350_1 · Cited by 13,232
Des Jarlais, D. C., Lyles, C., Crepaz, N., & the TREND Group (2004) · American Journal of Public Health · doi:10.2105/ajph.94.3.361 · Cited by 2,817 · cited 1×
“Additionally, the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) statement could assist to improve the reporting quality of nonrandomized evaluations of public health interventions [56].”
BackgroundA taxonomy of behavior change techniques used in interventions
Abraham, C., & Michie, S. (2008) · Health Psychology · doi:10.1037/0278-6133.27.3.379 · Cited by 2,525 · cited 1×
“Behavior change techniques used in the interventions were coded according to Abraham and Michie’s taxonomy of behavior change techniques (BCTs) [25].”
MethodWebb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010) · Journal of Medical Internet Research · doi:10.2196/jmir.1376 · Cited by 2,517 · cited 9×
“With respect to the mechanisms of behavior change, it is important to use theory to inform intervention design as well as specifying BCTs [48,49].”
BackgroundInfluentialGlanz, K., & Bishop, D. B. (2010) · Annual Review of Public Health · doi:10.1146/annurev.publhealth.012809.103604 · Cited by 2,336
Effective techniques in healthy eating and physical activity interventions: A meta-regression.
Michie, S., Abraham, C., Whittington, C., McAteer, J., & Gupta, S. (2009) · Health Psychology · doi:10.1037/a0016136 · Cited by 2,229
Eysenbach, G., & CONSORT-EHEALTH Group (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1923 · Cited by 1,863 · cited 4×
“It might be possible to blind outcome assessors, those doing data analysis, or those administering co-interventions, which is one of the 22 essential items recommended in the CONSORT checklist [54].”
BackgroundMethodInfluentialHilpert, M., Brockmeier, K., Dordel, S., Koch, B., Weiß, V., Ferrari, N., Tokarski, W., & Gräf, C. (2017) · Obesity Facts · doi:10.1159/000464105 · Cited by 1,185
(2008) · Journal of Clinical Oncology · doi:10.1200/jco.2008.16.4368 · Cited by 986
Flagged first, then by citations
Research works printed but not linked: 2
- Ventola, CL, 2014, Pharm Ther, 39, 356
- Quanbeck, A, 2014, Alcohol Res, 36, 111
Cited by
Sorted by citations
Schoeppe, S., Alley, S., Van Lippevelde, W., Bray, N. A., Williams, S. L., Duncan, M. J., & Vandelanotte, C. (2016) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-016-0454-y · Cited by 949
Mobile phone text messaging and app-based interventions for smoking cessation
Whittaker, R., McRobbie, H., Bullen, C., Rodgers, A., Gu, Y., & Dobson, R. (2019) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd006611.pub5 · Cited by 452
“Zhao 2016 Zhao J, Freeman B, Li M.”
ResultMilne-Ives, M., Lam, C., De Cock, C., Van Velthoven, M. H., & Meinert, E. (2020) · JMIR mhealth and uhealth · doi:10.2196/17046 · Cited by 393
“In addition, data for these systemic reviews were collected in 2014 [15], 2015 [13], and 2017 [12].”
BackgroundSzinay, D., Jones, A., Chadborn, T., Brown, J., & Naughton, F. (2020) · Journal of Medical Internet Research · doi:10.2196/17572 · Cited by 289
“Systematic reviews that focused on one specific behavior or a certain type of health or well-being app suggest that the effectiveness of evidence-based smartphone apps can be improved by targeting the design and engagement features, such as user-friendly design, individualized and culturally tailored content, or healt…”
BackgroundResultInfluentialFleming, G. A., Petrie, J. R., Bergenstal, R. M., Holl, R. W., Peters, A. L., & Heinemann, L. (2020) · Diabetes Care · doi:10.2337/dci19-0062 · Cited by 306
Location based services: ongoing evolution and research agenda
Huang, H., Gärtner, G., Krisp, J. M., Raubal, M., & de Weghe, N. V. (2018) · Journal of Location Based Services · doi:10.1080/17489725.2018.1508763 · Cited by 320
“Researchers have also investigated how LBS in fl uence consumers ’ choice behaviour (Gazley, Hunt, and Lachlan 2015), and the changes to healthier mobility behaviour (Jing, Freeman, and Mu 2016).”
BackgroundVillinger, K., Wahl, D. R., Boeing, H., Schupp, H. T., & Renner, B. (2019) · Obesity Reviews · doi:10.1111/obr.12903 · Cited by 310
Roberts, A., Fisher, A., Smith, L., Heinrich, M., & POTTS, H. W. W. (2017) · Journal of Cancer Survivorship · doi:10.1007/s11764-017-0632-1 · Cited by 270
Han, M., & Lee, E. (2018) · Healthcare Informatics Research · doi:10.4258/hir.2018.24.3.207 · Cited by 247
“This result is similar to other previous evidence reviews [3].”
BackgroundResultInfluentialDeslippe, A. L., Soanes, A., Bouchaud, C. C., Beckenstein, H., Slim, M., Plourde, H., & Cohen, T. R. (2023) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-023-01424-2 · Cited by 241
Mobile App-Based Health Promotion Programs: A Systematic Review of the Literature
Lee, M., Lee, H., Kim, Y., Kim, J.-H., Cho, M., Jang, J., & Jang, H. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15122838 · Cited by 238
“…app-based health promotion programs (hereinafter abbreviated as “mobile app programs”) are said to be an ideal platform for efficient interventions [3–5] because such mobile apps provide an easy way to access the target group [6] and are cost-effective compared to phone-based [7] and clinic-based…”
BackgroundMobile Apps for Increasing Treatment Adherence: Systematic Review
Pérez-Jover, V., Sala-González, M., Guilabert, M., & Mira, J. J. (2019) · Journal of Medical Internet Research · doi:10.2196/12505 · Cited by 204
“Although these are just as effective, they involve high costs [20,26].”
InfluentialRohani, D. A., Faurholt-Jepsen, M., Kessing, L. V., & Bardram, J. E. (2017) · doi:10.2196/preprints.9691 · Cited by 195
“Several mobile solutions have been proposed to utilize a self-monitoring and intervention-based treatment of depression [2-5].”
BackgroundThe history and future of digital health in the field of behavioral medicine
Arigo, D., Jake‐Schoffman, D. E., Wolin, K. Y., Beckjord, E. B., Hekler, E. B., & Pagoto, S. (2019) · Journal of Behavioral Medicine · doi:10.1007/s10865-018-9966-z · Cited by 207
Iribarren, S., Akande, T. O., Kamp, K., Barry, D., Kader, Y. G., & Suelzer, E. M. (2020) · JMIR mhealth and uhealth · doi:10.2196/21563 · Cited by 216
“Other related systematic reviews focused on methodologic reporting of any mHealth-based intervention (not app specific) [7], reported on specific diseases or health outcomes [12,15,18-21], explored features (not a systematic review) [34], or did not extract data for a meta-analysis [35,36].”
BackgroundMüller, A. M., Maher, C. A., Vandelanotte, C., Hingle, M., Middelweerd, A., Lopez, M. L., DeSmet, A., Short, C. E., Nathan, N., Hutchesson, M. J., et al. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.8954 · Cited by 180
“This is promising because resulting findings can be used to address these health behaviors at scale [41,71,73].”
Lentferink, A., Oldenhuis, H. K. E., de Groot, M., Polstra, L., Velthuijsen, H., & van Gemert‐Pijnen, J. E. W. C. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7288 · Cited by 166
“Similar to this scoping review’s results, other recent reviews on eHealth also observed the contribution to effectiveness of reminders [68,69], personalization [70], and integration of self-tracking and persuasive eCoaching [68,71].”
Biological effects of non-ionizing electromagnetic fields: Two sides of a coin
Saliev, T. M., Бегимбетова, Д., Masoud, A., & Matkarimov, B. T. (2018) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2018.07.009 · Cited by 161
Mobile Health Applications in Weight Management: A Systematic Literature Review
Dounavi, K., & Tsoumani, O. (2019) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2018.12.005 · Cited by 154
Xu, H., & Long, H. (2020) · JMIR mhealth and uhealth · doi:10.2196/21759 · Cited by 161
“Recent evidence demonstrates the feasibility, acceptability, and success of digital interventions in changing the behavior of people with chronic conditions [10-17].”
BackgroundLeung, L., & Chen, C. (2019) · Telecommunications Policy · doi:10.1016/j.telpol.2019.01.005 · Cited by 151
Chapman, E., Haby, M. M., Toma, T., de Bortoli, M. C., Illanes, E., Oliveros, M., & Barreto, J. O. M. (2020) · Implementation Science · doi:10.1186/s13012-020-0974-3 · Cited by 102
Mahmood, A., Kedia, S., Wyant, D. K., Ahn, S., & Bhuyan, S. S. (2019) · Digital Health · doi:10.1177/2055207619882181 · Cited by 144
“suggest that some features improve the use and effectiveness of apps including user-friendly design, less time consumption, individualized elements, real-time feedback, detailed information, and health professional involvement.(17) Among patients with CCs, the ease of use of apps is a key factor motivating users to ma…”
BackgroundSchmuck, D. (2020) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2019.0578 · Cited by 146
Mobile Applications to Improve Emotional Intelligence in Autism – A Review
Papoutsi, C., Drigas, A. S., & Skianis, C. A. (2018) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v12i6.9073 · Cited by 145
“They provide the opportunity and have promised to help people change social behavior [38-40], enrich education [41-43] and promote well-being [44-46].”
BackgroundFleming, G. A., Petrie, J. R., Bergenstal, R. M., Holl, R. W., Peters, A. L., & Heinemann, L. (2020) · Diabetologia · doi:10.1007/s00125-019-05034-1 · Cited by 125
Thies, K., Anderson, D., & Cramer, B. (2017) · JMIR Human Factors · doi:10.2196/humanfactors.7709 · Cited by 118
“Despite reporting improved outcomes, many studies of mHealth apps are not adequately powered, have poor rates of retention, or have a high risk of bias in their methods [4].”
BackgroundScott, I. A., Scuffham, P., Gupta, D., Harch, T. M., Borchi, J., & Richards, B. (2018) · Australian Health Review · doi:10.1071/ah18064 · Cited by 129
Taki, S., Lymer, S., Russell, C. G., Campbell, K., Laws, R., Ong, K.-L., Elliott, R., & Denney-Wilson, E. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.7236 · Cited by 114
“This was further supported by the findings in a review which explored the effectiveness of mobile phone apps targeting health behaviors [9].”
ResultDiffusion of the Digital Health Self-Tracking Movement in Canada: Results of a National Survey
Paré, G., Leaver, C. A., & Bourget, C. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9388 · Cited by 113
“Future research on this topic may consider other variables such as information quality and personalization of content that have been recognized as facilitators for adherence (eg, [27]).”
BackgroundGilbey, D., Morgan, H., Lin, A., & Perry, Y. (2020) · Journal of Medical Internet Research · doi:10.2196/20158 · Cited by 112
“However, digital health interventions are also commonly reported to have very high rates of attrition and disengagement [37,38], with up to 60% to 80% of users discontinuing their use [39-41], and the quality of evidence supporting the effectiveness of digital health interventions is also frequently lacking [42-45].”
BackgroundImpact of Pokémon Go on Physical Activity: A Systematic Review and Meta-Analysis
Khamzina, M., Parab, K. V., An, R., Bullard, T., & Grigsby‐Toussaint, D. S. (2019) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2019.09.005 · Cited by 110
Samoggia, A., & Riedel, B. (2019) · Food Research International · doi:10.1016/j.foodres.2019.108766 · Cited by 116
Svendsen, M. J., Wood, K., Kyle, J., Cooper, K., Rasmussen, C. D. N., Sandal, L. F., Stochkendahl, M. J., Mair, F. S., & Nicholl, B. I. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-038800 · Cited by 99
“Qualitative studies that examine engagement, barriers and/or facilitators to patient uptake and utilisation of digital interventions for the self-management of LBP were included; inclusion and exclusion criteria are outlined in table 1.”
Wienert, J., Jahnel, T., & Maaß, L. (2022) · Journal of Medical Internet Research · doi:10.2196/31921 · Cited by 97
“Their goal is to promote health and a healthy lifestyle [20,21].”
BackgroundTheme Trends and Knowledge Structure on Mobile Health Apps: Bibliometric Analysis
Peng, C., He, M., Cutrona, S. L., Kiefe, C. I., Liu, F., & Wang, Z. (2020) · JMIR mhealth and uhealth · doi:10.2196/18212 · Cited by 93
“Worldwide, the use of mobile phones has reached widespread popularity at an unprecedented rate [1].”
BackgroundBadawy, S. M., Shah, R., Beg, U., & Heneghan, M. B. (2020) · Journal of Medical Internet Research · doi:10.2196/17883 · Cited by 86
“Access to personal and mobile technology is ubiquitous [23-25], and there has been strong evidence to support the efficacy of digital or mobile health (mHealth) behavioral interventions, in particular text messaging and apps as tools to improve medication adherence [26-34].”
BackgroundComputerized Training for Neuroplasticity and Cognitive Improvement
Pappas, M. A., & Drigas, A. S. (2019) · International Journal of Engineering Pedagogy (iJEP) · doi:10.3991/ijep.v9i4.10285 · Cited by 84
“Mobile apps are considered as effective, low-cost tools for behavioral improvement [34].”
BackgroundPetersen, J. M., Prichard, I. J., & Kemps, E. (2019) · Journal of Medical Internet Research · doi:10.2196/12687 · Cited by 84
“Despite the ever-increasing ubiquity of physical activity mobile apps, previous reviews have only demonstrated modest evidence from such apps in terms of the magnitude of their effectiveness to positively influence physical activity behavior [8-11].”
BackgroundWeber, S., Lorenz, C., & Hemmings, N. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02745 · Cited by 74
ACCU3RATE: A mobile health application rating scale based on user reviews
Biswas, M., Tania, M. H., Kaiser, M. S., Kabir, R., Mahmud, M., & Kemal, A. A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258050 · Cited by 82
An overview on the emerging area of identification, characterization, and assessment of health apps
Paglialonga, A., Lugo, A., & Santoro, E. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.05.017 · Cited by 77
Xie, Z., Nacioglu, A., & Or, C. K. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9002 · Cited by 83
“Perceived Impacts of Health App Use on Health Knowledge and Health Condition Management Research has shown that health apps have the potential to promote healthy behaviors, facilitate health management, and improve health outcomes [10-13].”
BackgroundBidargaddi, N., Pituch, T., Maaieh, H., Short, C., & Strecher, V. (2018) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2018.07.004 · Cited by 77
“From a public health perspective, apps have improved access to self-monitoring resources, as evident by an unprecedented number of monitoring apps now available on app stores (Hingle and Patrick, 2016; Zhao et al., 2016).”
BackgroundSchultchen, D., Terhorst, Y., Holderied, T., Stach, M., Meßner, E.-M., Baumeister, H., & Sander, L. (2020) · International Journal of Behavioral Medicine · doi:10.1007/s12529-020-09944-y · Cited by 79
Key Elements of mHealth Interventions to Successfully Increase Physical Activity: Meta-Regression
Eckerstorfer, L. V., Tanzer, N. K., Vogrincic-Haselbacher, C., Kedia, G., Brohmer, H., Dinslaken, I., & Corcoran, K. (2018) · JMIR mhealth and uhealth · doi:10.2196/10076 · Cited by 78
“intervention success in increasing physical activity [13-19].”
Zhang, R., Ringland, K. E., Paan, M., Mohr, D. C., & Reddy, M. C. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445771 · Cited by 81
“Smartphones, with their widespread use and availability, have opened a new frontier in mental health support [112].”
BackgroundHe, M., Blye, C.-J., & Halpenny, E. A. (2022) · Journal of Sustainable Tourism · doi:10.1080/09669582.2022.2095392 · Cited by 74
“mobile apps, SMS, social media) may offer a solution because users can access these services any time and any place (Bert et al., 2014; Vaghefi & Tulu, 2019; Zhao et al., 2016).”
Irfan, B., Kuoppamäki, S., Hosseini, A., & Skantze, G. (2025) · Autonomous Robots · doi:10.1007/s10514-025-10190-y · Cited by 92
Mobile Apps for Older Adults: Systematic Search and Evaluation Within Online Stores
Portenhauser, A. A., Terhorst, Y., Schultchen, D., Sander, L., Denkinger, M. D., Stach, M., Waldherr, N., Dallmeier, D., Baumeister, H., & Meßner, E.-M. (2021) · JMIR Aging · doi:10.2196/23313 · Cited by 77
“…engagement, such as entertainment, individual adaptability, interactivity, and target group specificity, are cited as essential principles for the development of mobile apps for older adults and have been associated with the effectiveness of health interventions in several studies [64,74-77].”
BackgroundSzinay, D., Forbes, C. C., Busse, H., DeSmet, A., Smit, E. S., & König, L. M. (2023) · Obesity Reviews · doi:10.1111/obr.13542 · Cited by 76
“As such, mHealth interventions carry the added potential of a higher retention rate at population scale compared to non-mHealth interventions.(1,2,4) The commonly used techniques of self-monitoring and feedback in mHealth tools have shown high levels of user satisfaction,(5,6) long-term engagement with the tool,(7) an…”
BackgroundResultHIV Stigma Reduction for Health Facility Staff: Development of a Blended- Learning Intervention
Nyblade, L. C., Srinivasan, K., Mazur, A., Raj, T. D. S., Patil, D. S., Devadass, D., Radhakrishna, K., & Ekstrand, M. L. (2018) · Frontiers in Public Health · doi:10.3389/fpubh.2018.00165 · Cited by 71
Clinical state tracking in serious mental illness through computational analysis of speech
Arevian, A. C., Bone, D., Malandrakis, N., Martínez, V. R., Wells, K. B., Miklowitz, D. J., & Narayanan, S. S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0225695 · Cited by 70
“While mobile assessment approaches hold promise for improving these kinds of longitudinal assessments, a key to feasibility in realworld clinical settings is sustained engagement which continues to be a challenge [34].”
BackgroundBender, M. S., Cooper, B. A., Park, L. G., Padash, S., & Arai, S. R. (2017) · JMIR Diabetes · doi:10.2196/diabetes.8156 · Cited by 70
Yang, X., Ma, L., Zhao, X., & Kankanhalli, A. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.104039 · Cited by 69
Mork, P. J., & Bach, K. (2018) · JMIR Research Protocols · doi:10.2196/resprot.9379 · Cited by 69
“Thus, mobile technologies have been suggested as a promising approach to improve self-management of various health conditions [16,17].”
Song, T., Deng, N., Cui, T., Qian, S., Liu, F., Guan, Y., & Yu, P. (2021) · Journal of Medical Internet Research · doi:10.2196/26670 · Cited by 73
“As one of the most used mHealth services, mHealth apps are widely used to record and evaluate patients’ vital signs and self-management behaviors such as medication, exercise, and diet; access health information; remind self-management behaviors; and communicate with health care providers [1,2,5].”
The effects of mobile apps on stress, anxiety, and depression: overview of systematic reviews
Khademian, F., Aslani, A., & Bastani, P. (2021) · International Journal of Technology Assessment in Health Care · doi:10.1017/s0266462320002093 · Cited by 62
Alyami, M. M., Giri, B., Alyami, H., & Sundram, F. (2017) · Evidence-Based Mental Health · doi:10.1136/eb-2017-102664 · Cited by 69
The relevance of health literacy to mHealth
Kreps, G. L. (2017) · Information Services and Use · doi:10.3233/isu-170828 · Cited by 62
A Systematic Review of Nutrition Recommendation Systems: With Focus on Technical Aspects
Abhari, S. (2019) · Journal of Biomedical Physics and Engineering · doi:10.31661/jbpe.v0i0.1248 · Cited by 65
“However, there are many mobile applications in the nutrition field useing “recommendation system” technology which have not been mentioned in the design methodology [57].”
BackgroundMethodCarrasco-Hernandez, L., Jódar-Sánchez, F., Núñez-Benjumea, F., Moreno Conde, J., Mesa González, M., Civit-Balcells, A., Hors-Fraile, S., Parra-Calderón, C. L., Bamidis, P. D., & Ortega-Ruiz, F. (2020) · JMIR mHealth and uHealth · doi:10.2196/17530 · Cited by 65
“Mobile and web-based interventions have been used to facilitate effective behavioral changes in different health domains [6-9], including smoking cessation.”
BackgroundBol, N., Høie, N. M., Nguyen, M. H., & Smit, E. S. (2019) · Digital Health · doi:10.1177/2055207619888074 · Cited by 68
“Most mobile health technologies are not evidenceor theory-based, and currently only have a small to moderate impact on health behaviour change.(16,17) This calls for more systematic knowledge to enhance our understanding of what makes mobile health apps effective and for whom.”
BackgroundMethodCovolo, L., Ceretti, E., Moneda, M., Castaldi, S., & Gelatti, U. (2017) · Patient Education and Counseling · doi:10.1016/j.pec.2017.07.032 · Cited by 70
“, [13] (seven RCTs) both focused on synthesizing the evidence on the effectiveness of mobile apps in changing health behaviours including disease management.”
BackgroundFadda, M., Galimberti, E., Fiordelli, M., Romanò, L., Zanetti, A. R., & Schulz, P. J. (2017) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2017.1360456 · Cited by 63
“Mobile apps can provide tremendous opportunities to influence people’s health behavior thanks to a combination of unique characteristics.(2,3) They are at the same time personal, connected, easy to use, customizable, empowering, increasingly technological and always at hand.”
BackgroundDorje, T., Zhao, G., Scheer, A., Tsokey, L., Wang, J., Chen, Y., Tso, K., Tan, B.-K., Ge, J., & Maiorana, A. (2018) · BMJ Open · doi:10.1136/bmjopen-2018-021908 · Cited by 68
Self-Management Education Through mHealth: Review of Strategies and Structures
Bashi, N. G. G., Fatehi, F., Fallah, M., Walters, D., & Karunanithi, M. (2018) · JMIR mhealth and uhealth · doi:10.2196/10771 · Cited by 63
“A large number of health-related software apps have been designed and are available for both health care professionals and patients [15].”
Nelson, K. M., Perry, N. S., Horvath, K. J., & Smith, L. R. (2020) · Translational Behavioral Medicine · doi:10.1093/tbm/ibaa007 · Cited by 61
SurvivorCHESS to increase physical activity in colon cancer survivors: can we get them moving?
Mayer, D. K., Landucci, G., Awoyinka, I., Atwood, A. K., Carmack, C. L., Demark‐Wahnefried, W., Mctavish, F. M., & Gustafson, D. H. (2017) · Journal of Cancer Survivorship · doi:10.1007/s11764-017-0647-7 · Cited by 59
“In a review of mobile phone apps’ ability to change health behaviors, self-monitoring was the most common change noted [15].”
BackgroundMethodInfluentialSandal, L. F., Stochkendahl, M. J., Svendsen, M. J., Wood, K., Øverås, C. K., Nordstoga, A. L., Villumsen, M., Rasmussen, C. D. N., Nicholl, B., Cooper, K., et al. (2019) · JMIR Research Protocols · doi:10.2196/14720 · Cited by 59
“Digital solutions such as mobile apps can be used as platforms for supporting self-management [17,18] and may solve some of the problems outlined above.”
Griauzde, D., Kullgren, J. T., Liestenfeltz, B., Ansari, T., Johnson, E. H., Fedewa, A., Saslow, L. R., Richardson, C., & Heisler, M. (2019) · JMIR mHealth and uHealth · doi:10.2196/11267 · Cited by 58
“Mobile smartphone apps and other mHealth technologies are increasingly used as tools to promote lifestyle changes [32], and technology-assisted translations of the DPP have been used to improve program reach [33].”
A Survey on Trend, Opportunities and Challenges of mHealth Apps
Jusoh, S. (2017) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v11i6.7265 · Cited by 57
Ahmad, N. A., Ludin, A. F. M., Shahar, S., Noah, S. A. M., & Tohit, N. M. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-054561 · Cited by 60
“41 While usage of mobile applications is increasing among the older adult population, they continue to lag behind their younger counterparts when it comes to technology adoption.”
BackgroundTouyz, L. M., Wakefield, C. E., Grech, A. M., Quinn, V. F., Costa, D. S. J., Zhang, F. F., Cohn, R. J., Sajeev, M. F., & Cohen, J. E. (2017) · Nutrition Reviews · doi:10.1093/nutrit/nux066 · Cited by 53
Adverse Childhood Experiences and Building Resilience With the JoyPop App: Evaluation Study
MacIsaac, A., Mushquash, A. R., Mohammed, S., Grassia, E., Smith, S., & Wekerle, C. (2020) · JMIR mhealth and uhealth · doi:10.2196/25087 · Cited by 60
“Specifically, recent evidence supports the use of various digital health interventions targeting coping, stress reduction, self-management skills, and symptom reporting in improving medication and treatment adherence, health knowledge, and anxiety in those with chronic health conditions [35-37].”
BackgroundThe Implementation of Behavior Change Techniques in mHealth Apps for Sleep: Systematic Review
Arroyo, A. C., & Zawadzki, M. J. (2022) · JMIR mhealth and uhealth · doi:10.2196/33527 · Cited by 54
“The search strategy and study selection methods were adopted from prior research reviewing the effectiveness of mobile phone apps in achieving behavior change for a broad range of health behaviors [34].”
MethodSmartphone Applications for Period Tracking: Rating and Behavioral Change among Women Users
Karasneh, R. A., Al-Azzam, S. I., Alzoubi, K. H., Muflih, S., & Hawamdeh, S. S. (2020) · Obstetrics and Gynecology International · doi:10.1155/2020/2192387 · Cited by 55
“On the other hand, factors contributing to the success of mHealth apps include ease of use and navigation, individualization, and a user-friendly design [5, 35].”
BackgroundMUBS: A Personalized Recommender System for Behavioral Activation in Mental Health
Rohani, D. A., Lopategui, A. Q., Tuxen, N., Faurholt‐Jepsen, M., Kessing, L. V., & Bardram, J. E. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376879 · Cited by 60
“Activity tracking and registration were adapted to the personal needs of each patient, a functionality that is known to improve retention rate and overcome patient barriers to system usage [34,56].”
BackgroundSmyth, J. M., Jones, D. R., Wen, C. K. F., Materia, F. T., Schneider, S., & Stone, A. A. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-049154 · Cited by 50
Li, A., Olmo, M. G. D., Fong, M., Sim, K. A., Lymer, S. J., Cunich, M. M., & Caterson, I. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-047041 · Cited by 52
“These interventions may be particularly effective for those with obesity, taking medications for type 2 diabetes and for people taking a relatively smaller number of medications.”
BackgroundZhang, C., Lakens, D., & IJsselsteijn, W. A. (2021) · Journal of Medical Internet Research · doi:10.2196/17127 · Cited by 60
“At the level of reflection-based adaptation, providing behavioral feedback to users to support self-monitoring is the most commonly used BCT in digital systems [19,150-153].”
Smartphone applications for physical activity promotion from physical education
Gil‐Espinosa, F. J., Rodríguez, A. N., Romance, R., & Burgueño, R. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11108-2 · Cited by 62
The role of personal air pollution sensors and smartphone technology in changing travel behaviour
Haddad, H., & de Nazelle, A. J. (2018) · Journal of Transport & Health · doi:10.1016/j.jth.2018.08.001 · Cited by 49
Delmas, M. A., & Kohli, A. (2019) · Journal of Business Ethics · doi:10.1007/s10551-019-04215-7 · Cited by 49
Drapkina, O. M., Karamnova, N. S., Васильевна, К. А., Gornyi, B., Дадаева, В. А., Drozdova, L. Y., Yeganyan, R. A., Олеговна, Е. С., Izmailova, O. V., Lavrenova, Е. A., et al. (2021) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2021-2952 · Cited by 62
Wiecek, E., Torres-Robles, A., Cutler, R. L., Benrimoj, S. I., & García‐Cárdenas, V. (2020) · Journal of Medical Internet Research · doi:10.2196/17834 · Cited by 53
“Success has already been demonstrated with using mobile phone apps to support health behavior changes, ranging from constructing a healthy diet to managing chronic pain or improving physical activity [26-28].”
BackgroundGabbiadini, A., & Greitemeyer, T. (2019) · The Journal of Sports Medicine and Physical Fitness · doi:10.23736/s0022-4707.18.08260-9 · Cited by 47
Park, L. G., Elnaggar, A., Lee, S. J., Merek, S., Hoffmann, T. J., von Oppenfeld, J., Ignacio, N., & Whooley, M. A. (2021) · JMIR Formative Research · doi:10.2196/20468 · Cited by 49
“Health apps can be effective adjuncts for the management of chronic conditions [12] and have a positive impact on long-term behavior change [13].”
Use of artificial intelligence in precision nutrition and fitness
Lopes, M. H. B. D. M., Ferreira, D. D., Ferreira, A. C. B. H., da Silva, G. R., Caetano, A. S., & Braz, V. N. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817133-2.00020-3 · Cited by 52
Litterbach, E., Russell, C. G., Taki, S., Denney‐Wilson, E., Campbell, K. J., & Laws, R. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.8515 · Cited by 48
“This suggests that factors influencing user engagement can have a direct bearing on how effective mHealth interventions will be [26].”
InfluentialEffectiveness of an mHealth Intervention for Infant Sleep Disturbances
Leichman, E. S., Gould, R. A., Williamson, A. A., Walters, R. M., & Mindell, J. A. A. (2020) · Behavior Therapy · doi:10.1016/j.beth.2019.12.011 · Cited by 46
Edwards, E. A., Caton, H., Lumsden, J., Rivas, C. A., Steed, L., Pirunsarn, Y., Jumbe, S. E., Newby, C., Shenvi, A., Mazumdar, S., et al. (2018) · JMIR Serious Games · doi:10.2196/10252 · Cited by 52
“To date, there have been few rigorous evaluations of the effectiveness of health apps [2,11-14] although pilot studies and small trials have shown promising results [9,11,13,15,16].”
BackgroundPrediction Models for Early Childhood Obesity: Applicability and Existing Issues
Butler, É. M., Derraik, J. G. B., Taylor, R. W., & Cutfield, W. S. (2018) · Hormone Research in Paediatrics · doi:10.1159/000496563 · Cited by 44
MacPherson, M., Merry, K. J., Locke, S., & Jung, M. E. (2019) · JMIR mhealth and uhealth · doi:10.2196/12956 · Cited by 46
“Despite the rapidly growing number of mHealth apps on the market and the advantages they may afford to consumers and researchers alike, there is a profound lack of theory-driven, evidence-based mHealth apps [5-8].”
Mobile health interventions: exploring the use of common relationship factors
Grekin, E. R., Beatty, J. R., & Ondersma, S. J. (2019) · JMIR mhealth and uhealth · doi:10.2196/11245 · Cited by 42
“There are also more than 100,000 iPhone and Android apps specifically designed to target health-related behaviors [2].”
BackgroundSun, Y., Zhou, J., Ji, M., Pei, L., & Wang, Z. (2022) · Journal of Medical Internet Research · doi:10.2196/38184 · Cited by 42
“It has been shown that mobile apps have potential effects on health behavior change [78] and chronic disease management.”
Evaluating Patient-Centered Mobile Health Technologies: Definitions, Methodologies, and Outcomes
Bruce, C. R., Harrison, P., Giammattei, C., Desai, S.-N., Sol, J. R., Jones, S. L., & Schwartz, R. L. (2020) · JMIR mhealth and uhealth · doi:10.2196/17577 · Cited by 42
“There are numerous quantitative measures available to evaluate patient satisfaction, although the validity and reliability of the instruments have been a point of debate [69-80].”
BackgroundSaliasi, I., Martinon, P., Darlington, E., Smentek, C., Tardivo, D., Bourgeois, D. M., Dussart, C., Carrouel, F., & Fraticelli, L. (2021) · JMIR mhealth and uhealth · doi:10.2196/30480 · Cited by 40
“mHealth apps are currently becoming handheld devices that can disseminate a variety of health-promoting knowledge and promote healthy behaviors relating, for example, to dietary habits [3], weight control [4], physical activity [5], addictive behaviors (ie, smoking), and mental health (ie, managing stress and depressi…”
BackgroundRughani, G., Nilsen, T. I. L., Wood, K., Mair, F. S., Hartvigsen, J., Mork, P. J., & Nicholl, B. I. (2023) · European Journal of Pain · doi:10.1002/ejp.2080 · Cited by 39
“Digital interventions (websites, mobile applications (apps), wearable technology) are seen as engaging and scalable ways to deliver this information (Dennison et al., 2013; Zhao et al., 2016).”
BackgroundPetersen, F., Jacobs, M., & Pather, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-45002-1_6 · Cited by 43
Metadata
- Authors
- Zhao, J., Freeman, B., & Li, M.
- Affiliations
- The University of Sydney (AU)
- Corresponding author
- Zhao, Jing
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2016-11-02
- Volume
- 18
- Issue
- 11
- Pages
- e287
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5295827
- OpenAlex
- W2546565370
- Cited by
- 618
- References
- 60
- Citations by year
- 2026: 24 · 2025: 52 · 2024: 31 · 2023: 51 · 2022: 74 · 2021: 104 · 2020: 111 · 2019: 71 · 2018: 68 · 2017: 28 · 2016: 2
- FWCI
- 10.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
- Keywords
- mobile health apps, health behavior change, mobile health intervention, behaviour change techniques, self-monitoring, theory of planned behavior, app retention, real-time feedback, app design features, health professional involvement, risk of bias, application effectiveness
- MeSH terms
- Behavior Therapy, Humans, Health Behavior, Cell Phone, Mobile Applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
