Cited by 1,274Mobile Health and mHealth ApplicationsHealth, Technology, Consumer BehaviorDigital Mental Health Interventions

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Health App Use Among US Mobile Phone Owners: A National Survey

Open access

Krebs, Paul, Duncan, Dustin T·2015·JMIR mHealth and uHealth

DOI 10.2196/mhealth.4924PMC PMC4704953

Open this paper in CoChat — free account Open PDF

Abstract

BACKGROUND: Mobile phone health apps may now seem to be ubiquitous, yet much remains unknown with regard to their usage. Information is limited with regard to important metrics, including the percentage of the population that uses health apps, reasons for adoption/nonadoption, and reasons for noncontinuance of use. OBJECTIVE: The purpose of this study was to examine health app use among mobile phone owners in the United States. METHODS: We conducted a cross-sectional survey of 1604 mobile phone users throughout the United States. The 36-item survey assessed sociodemographic characteristics, history of and reasons for health app use/nonuse, perceived effectiveness of health apps, reasons for stopping use, and general health status. RESULTS: A little over half (934/1604, 58.23%) of mobile phone users had downloaded a health-related mobile app. Fitness and nutrition were the most common categories of health apps used, with most respondents using them at least daily. Common reasons for not having downloaded apps were lack of interest, cost, and concern about apps collecting their data. Individuals more likely to use health apps tended to be younger, have higher incomes, be more educated, be Latino/Hispanic, and have a body mass index (BMI) in the obese range (all P<.05). Cost was a significant concern among respondents, with a large proportion indicating that they would not pay anything for a health app. Interestingly, among those who had downloaded health apps, trust in their accuracy and data safety was quite high, and most felt that the apps had improved their health. About half of the respondents (427/934, 45.7%) had stopped using some health apps, primarily due to high data entry burden, loss of interest, and hidden costs. CONCLUSIONS: These findings suggest that while many individuals use health apps, a substantial proportion of the population does not, and that even among those who use health apps, many stop using them. These data suggest that app developers need to better address consumer concerns, such as cost and high data entry burden, and that clinical trials are necessary to test the efficacy of health apps to broaden their appeal and adoption.

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

Crossref · 10.2196/mhealth.4924

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Figure 1 US distribution of sample by ZIP code.CC BY · PubMed Central

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

A General Inductive Approach for Analyzing Qualitative Evaluation Data
Thomas, D. R. (2006) · American Journal of Evaluation · doi:10.1177/1098214005283748 · Cited by 10,858
Mobile medical and health apps: state of the art, concerns, regulatory control and certification
Kamel Boulos, M. N., Brewer, A. C., Karimkhani, C., Buller, D. B., & Dellavalle, R. P. (2014) · Online Journal of Public Health Informatics · doi:10.5210/ojphi.v5i3.4814 · Cited by 659
Mobile Applications for Diabetics: A Systematic Review and Expert-Based Usability Evaluation Considering the Special Requirements of Diabetes Patients Age 50 Years or Older
Arnhold, M., Quade, M., & Kirch, W. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.2968 · Cited by 446
A lot of action, but not in the right direction: systematic review and content analysis of smartphone applications for the prevention, detection, and management of cancer
Bender, J. L., Yue, R. Y. K., To, M. J., Deacken, L., & Jadad, A. R. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2661 · Cited by 346
Wearable technologies
Chuah, S. H., Rauschnabel, P. A., Krey, N., Nguyen, B., Ramayah, T., & Lade, S. (2016) · Rowan Digitals Works (Rowan University) · Cited by 213
Public Health Guidelines for Physical Activity: Is There an App for That? A Review of Android and Apple App Stores
Knight, E., Stuckey, M. I., Prapavessis, H., & Petrella, R. J. (2015) · JMIR mHealth and uHealth · doi:10.2196/mhealth.4003 · Cited by 112
Smartphone Medical Applications for Women’s Health: What Is the Evidence-Base and Feedback?
Derbyshire, E. J., & Dancey, D. (2013) · International Journal of Telemedicine and Applications · doi:10.1155/2013/782074 · Cited by 99
Smartphones and Pediatric Apps to Mobilize the Medical Home
Singh, A., Wilkinson, S., & Braganza, S. F. (2014) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2014.05.037 · Cited by 61
Using MapMyFitness to Place Physical Activity into Neighborhood Context
Hirsch, J. A., James, P., Robinson, J. R. M., Eastman, K. M., Conley, K. D., Evenson, K. R., & Laden, F. (2014) · Frontiers in Public Health · doi:10.3389/fpubh.2014.00019 · Cited by 39
High Quantity But Limited Quality in Healthcare Applications Intended for HIV-Infected Patients
Cortés, M. D. L. A. R., Cuenca, M. R. C., Verdugo, R. M., & Cidoncha, E. C. (2014) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2013.0262 · Cited by 26

Show all 11 references

Sorted by citations

Items 301–400 of 1,000

Patient focused oral hygiene apps: an assessment of quality (using MARS) and knowledge content
Sharif, M. O., & Alkadhimi, A. F. (2019) · BDJ · doi:10.1038/s41415-019-0665-0 · Cited by 33
Generating value with mental health apps
Powell, A. C., Torous, J. B., Firth, J., & Kaufman, K. R. (2020) · BJPsych Open · doi:10.1192/bjo.2019.98 · Cited by 34
Mobile App for Mental Health Monitoring and Clinical Outreach in Veterans: Mixed Methods Feasibility and Acceptability Study
Betthauser, L. M., Stearns‐Yoder, K. A., McGarity, S., Smith, V., Place, S. S., & Brenner, L. A. (2020) · Journal of Medical Internet Research · doi:10.2196/15506 · Cited by 34
The effect of symptom‐tracking apps on symptom reporting
MacKrill, K., Groom, K. M., & Petrie, K. J. (2020) · British Journal of Health Psychology · doi:10.1111/bjhp.12459 · Cited by 33
Sleep pattern in the US and 16 European countries during the COVID-19 outbreak using crowdsourced smartphone data
Lee, P. H., Marek, J., & Nálevka, P. (2020) · European Journal of Public Health · doi:10.1093/eurpub/ckaa208 · Cited by 31
Influences on the Uptake of Health and Well-being Apps and Curated App Portals: Think-Aloud and Interview Study
Szinay, D., Perski, O., Jones, A., Chadborn, T., Brown, J., & Naughton, F. (2021) · JMIR mhealth and uhealth · doi:10.2196/27173 · Cited by 33
Missing the mark for patient engagement: mHealth literacy strategies and behavior change processes in smoking cessation apps
Paige, S. R., Alber, J. M., Stellefson, M. L., & Krieger, J. L. (2018) · Patient Education and Counseling · doi:10.1016/j.pec.2017.11.006 · Cited by 25
Mobile Apps for Dietary and Food Timing Assessment: Evaluation for Use in Clinical Research
Gioia, S., Vlasac, I., Babazadeh, D., Fryou, N. L., Do, E., Love, J. J., Robbins, R., Dashti, H. S., & Lane, J. M. (2023) · JMIR Formative Research · doi:10.2196/35858 · Cited by 44
How Do You Sleep? The Impact of Sleep Apps on Generation Z's Well-Being
Attié, E. A., & Meyer‐Waarden, L. (2023) · Journal of Interactive Marketing · doi:10.1177/10949968221142806 · Cited by 32
Performance of a Blood Pressure Smartphone App in Pregnant Women
Raichle, C. J., Eckstein, J., Lapaire, O., Leonardi, L., Brasier, N., Vischer, A. S., & Burkard, T. (2018) · Hypertension · doi:10.1161/hypertensionaha.117.10647 · Cited by 32
Exchanging personal health data with electronic health records: A standardized information model for patient generated health data and observations of daily living
Plastiras, P., & O’Sullivan, D. M. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.10.006 · Cited by 40
mHealth App recommendation based on the prediction of suitable behavior change techniques
Mao, X., Zhao, X., & Liu, Y. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113248 · Cited by 32
Desire to Be Underweight: Exploratory Study on a Weight Loss App Community and User Perceptions of the Impact on Disordered Eating Behaviors
Eikey, E. V., Reddy, M. C., Booth, K. M., Kvasny, L., Blair, J., Li, V., & Poole, E. S. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6683 · Cited by 32
Enhancing User Experience Through User Study: Design of an mHealth Tool for Self-Management and Care Engagement of Cardiovascular Disease Patients
Baek, H., Suh, J., Kang, S., Kang, S., Lim, T. H., Hwang, H. Y., & Yoo, S. (2018) · JMIR Cardio · doi:10.2196/cardio.9000 · Cited by 34
Changing Behavior Using the Transtheoretical Model
DiClemente, C. C., & Graydon, M. M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.010 · Cited by 35
Motivating consumers for health and fitness: The role of app features
Stancu, V., Frank, D., Lähteenmäki, L., & Grunert, K. G. (2022) · Journal of Consumer Behaviour · doi:10.1002/cb.2108 · Cited by 37
Use of Fitness and Nutrition Apps: Associations With Body Mass Index, Snacking, and Drinking Habits in Adolescents
Cock, N. D., Vangeel, J., Lachat, C. K., Beullens, K., Vervoort, L., Goossens, L., Maes, L., Deforche, B. I., Henauw, S. D., Braet, C., et al. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6005 · Cited by 32
What Do You Say Before You Relapse? How Language Use in a Peer-to-peer Online Discussion Forum Predicts Risky Drinking among Those in Recovery
Kornfield, R., Toma, C. L., Shah, D. V., Moon, T. J., & Gustafson, D. H. (2017) · Health Communication · doi:10.1080/10410236.2017.1350906 · Cited by 33
On the body of the consumer: performance‐seeking with wearables and health and fitness apps
Hardey, M. (2019) · Sociology of Health & Illness · doi:10.1111/1467-9566.12879 · Cited by 31
Quality of life and its association with comorbidities and adverse events from antiepileptic medications: Online survey of patients with epilepsy in Australia
Welton, J. M., Walker, C. F., Riney, K., Ng, A., Todd, L., & D'Souza, W. J. (2020) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2019.106856 · Cited by 33
Crowdsourced smartphone data reveal altered sleep/wake pattern in quarantined Chinese during the COVID-19 outbreak
Lee, P. H., Marek, J., & Nálevka, P. (2020) · Chronobiology International · doi:10.1080/07420528.2020.1792486 · Cited by 31
Determining sample size and length of follow-up for smartphone-based digital phenotyping studies
Barnett, I., Torous, J. B., Reeder, H. T., Baker, J. T., & Onnela, J.-P. “. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa201 · Cited by 31
Prevalence and Predictors of Health-Related Internet and Digital Device Use in a Sample of South Asian Adults in Edmonton, Alberta, Canada: Results From a 2014 Community-Based Survey
Makowsky, M. J., Jones, C., & Davachi, S. (2020) · JMIR Public Health and Surveillance · doi:10.2196/20671 · Cited by 32
Smartphone Users’ Persuasion Knowledge in the Context of Consumer mHealth Apps: Qualitative Study
Joo, E., Kononova, A. G., Kanthawala, S., Peng, W., & Cotten, S. R. (2021) · JMIR mhealth and uhealth · doi:10.2196/16518 · Cited by 32
Effectiveness and Usability of Digital Tools to Support Dietary Self-Management of Gestational Diabetes Mellitus: A Systematic Review
Adesina, N., Doğan, H., Green, S. M., & Tsofliou, F. (2021) · Nutrients · doi:10.3390/nu14010010 · Cited by 33
Systematic Literature Review of Food-Intake Monitoring in an Aging Population
Moguel, E., Berrocal, J., & García-Alonso, J. M. (2019) · Sensors · doi:10.3390/s19153265 · Cited by 30
Using Smartphone Apps to Promote Psychiatric Rehabilitation in a Peer-Led Community Support Program: Pilot Study
Mueller, N. E., Panch, T., Macias, C., Cohen, B. M., Ongur, D., & Baker, J. T. (2018) · JMIR Mental Health · doi:10.2196/10092 · Cited by 26
Exploring user learnability and learning performance in an app for depression: usability study
Stiles-Shields, C., Montague, E., Lattie, E. G., Schueller, S. M., Kwasny, M. J., & Mohr, D. C. (2017) · JMIR Human Factors · doi:10.2196/humanfactors.7951 · Cited by 29
Folk theorizing the quality and credibility of health apps
Kanthawala, S., Joo, E., Kononova, A. G., Peng, W., & Cotten, S. R. (2018) · Mobile Media & Communication · doi:10.1177/2050157918796859 · Cited by 32
Availability of orthodontic smartphone apps
Siddiqui, N. R., Hodges, S. J., & Sharif, M. O. (2019) · Journal of Orthodontics · doi:10.1177/1465312519851183 · Cited by 30
Empowering Diabetes Self-Management Through Technology and Nurse Health Coaching
Miyamoto, S., Henderson, S., Fazio, S., Saconi, B., Thiede, E., Greenwood, D. A., & Young, H. M. (2019) · The Science of Diabetes Self-Management and Care · doi:10.1177/0145721719879421 · Cited by 32
Changing Behavior Using the Health Belief Model and Protection Motivation Theory
Orbell, S., Zahid, H., & Henderson, C. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.004 · Cited by 35
Remote and Long-Term Self-Monitoring of Electroencephalographic and Noninvasive Measurable Variables at Home in Patients With Epilepsy (EEG@HOME): Protocol for an Observational Study
Biondi, A., Laiou, P., Bruno, E., Viana, P. F., Schreuder, M., Hart, W. L., Nurse, E. S., Pal, D. K., & Richardson, M. P. (2020) · JMIR Research Protocols · doi:10.2196/25309 · Cited by 30
Comparing Two Commercially Available Diabetes Apps to Explore Challenges in User Engagement: Randomized Controlled Feasibility Study
Maharaj, A., Lim, D. H., Murphy, R., & Serlachius, A. S. (2021) · JMIR Formative Research · doi:10.2196/25151 · Cited by 29
Attitudes Toward Mobile Apps for Pandemic Research Among Smartphone Users in Germany: National Survey
Buhr, L., Schicktanz, S., & Nordmeyer, E. F. (2022) · JMIR mhealth and uhealth · doi:10.2196/31857 · Cited by 32
A survey of mobile technology usage and desires by caregivers of children with cancer
Mueller, E. L., Cochrane, A. R., Bennett, W. E., & Carroll, A. E. (2018) · Pediatric Blood & Cancer · doi:10.1002/pbc.27359 · Cited by 30
Body dissatisfaction, neuroticism, and female sex as predictors of calorie-tracking app use amongst college students
Embacher, K., McGloin, R., & Atkin, D. J. (2018) · Journal of American College Health · doi:10.1080/07448481.2018.1431905 · Cited by 29
Patient interest in mental health mobile app interventions: Demographic and symptom-level differences
Lipschitz, J. M., Connolly, S. L., Miller, C. J., Hogan, T. P., Simon, S. R., & Burdick, K. E. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.11.083 · Cited by 28
Enhancement of Neurocognitive Assessments Using Smartphone Capabilities: Systematic Review
Templeton, J. M., Poellabauer, C., & Schneider, S. L. (2020) · JMIR mhealth and uhealth · doi:10.2196/15517 · Cited by 29
Mobile health (mHealth) application loyalty in young consumers
Soni, M., Jain, K., & Jajodia, I. (2021) · Young Consumers Insight and Ideas for Responsible Marketers · doi:10.1108/yc-10-2020-1236 · Cited by 32
Validation of a Korean version of mobile app rating scale (MARS) for apps targeting disease management
Ko, K. K. H., Kim, S. K., Lee, Y., Lee, J. Y., & Stoyanov, S. R. (2022) · Health Informatics Journal · doi:10.1177/14604582221091975 · Cited by 30
What Can Big Data Offer the Pharmacovigilance of Orphan Drugs?
Price, J. (2016) · Clinical Therapeutics · doi:10.1016/j.clinthera.2016.11.009 · Cited by 31
Digital technology to support lifestyle and health behaviour changes in surgical patients: systematic review
Robinson, A., Husband, A., Slight, R. D., & Slight, S. P. (2020) · BJS Open · doi:10.1093/bjsopen/zraa009 · Cited by 29
Developing a Transparent, Participant-Navigated Electronic Informed Consent for Mobile-Mediated ResearchPreprint
Doerr, M. J., Suver, C., & WILBANKS, J. T. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2769129 · Cited by 29
Increasing mobile health application usage among Generation Z members: evidence from the UTAUT model
Aydın, G. (2023) · International Journal of Pharmaceutical and Healthcare Marketing · doi:10.1108/ijphm-02-2021-0030 · Cited by 40
Smartphone Ownership Among US Adult Cigarette Smokers: 2014 Health Information National Trends Survey (HINTS) Data
Heffner, J. L., & Mull, K. E. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7953 · Cited by 28
Systematic Review of Commercially Available Mobile Phone Applications for Prostate Cancer Education
Owens, O. L., Beer, J. M., Reyes, L. I., & Thomas, T. L. (2018) · American Journal of Men s Health · doi:10.1177/1557988318816912 · Cited by 28
The Role of Continuous Glucose Monitoring, Diabetes Smartphone Applications, and Self-Care Behavior in Glycemic Control: Results of a Multi-National Online Survey
Kebede, M. M., Schuett, C., & Pischke, C. R. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8010109 · Cited by 28
Ethical Considerations in HIV eHealth Intervention Research: Implications for Informational Risk in Recruitment, Data Maintenance, and Consent Procedures
Fisher, C. B., Bragard, E., & Bloom, R. F. (2020) · Current HIV/AIDS Reports · doi:10.1007/s11904-020-00489-z · Cited by 28
Changing Behavior by Changing Environments
Marteau, T. M., Fletcher, P. C., Hollands, G. J., & Munafo, M. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.014 · Cited by 32
Smartphone and App Usage in Orthopedics and Trauma Surgery: Survey Study of Physicians Regarding Acceptance, Risks, and Future Prospects in Germany
Dittrich, F., Back, D. A., Harren, A. K., Landgraeber, S., Reinecke, F., Serong, S., & Beck, S. (2020) · JMIR Formative Research · doi:10.2196/14787 · Cited by 26
Future Mobile Device Usage, Requirements, and Expectations of Physicians in German University Hospitals: Web-Based Survey
Maaßen, O., Fritsch, S. J., Palm, J., Deffge, S., Kunze, J., Marx, G., & Bickenbach, J. (2020) · Journal of Medical Internet Research · doi:10.2196/23955 · Cited by 28
Designing Effective Mobile Health Apps: Does Combining Behavior Change Techniques Really Create Synergies?
Spohrer, K., Fallon, M., Hoehle, H., & Heinzl, A. (2021) · Journal of Management Information Systems · doi:10.1080/07421222.2021.1912936 · Cited by 27
The Types and Pattern of Use of Mobile Health Applications Among the General Population: A Cross-Sectional Study from Selangor, Malaysia
Bhuvan, K. C., Alrasheedy, A. A., Goh, B. H., Blebil, A. Q., Bangash, N. S. A., Ibrahim, M. I. M., & Rehman, I. U. (2021) · Patient Preference and Adherence · doi:10.2147/ppa.s325851 · Cited by 28
Electronic Health Record–Based Recruitment and Retention and Mobile Health App Usage: Multisite Cohort Study
Coughlin, J. W., Martin, L., Zhao, D., Goheer, A. A., Woolf, T. B., Holzhauer, K., Lehmann, H. P., Lent, M. R., McTigue, K. M., Clark, J. M., et al. (2022) · Journal of Medical Internet Research · doi:10.2196/34191 · Cited by 30
A comparative study on HIPAA technical safeguards assessment of android mHealth applications
Mia, M. R., Shahriar, H., Valero, M., Sakib, N., Saha, B., Barek, M. A., Faruk, M. J. H., Goodman, B., Khan, R. A., & Ahamed, S. I. (2022) · Smart Health · doi:10.1016/j.smhl.2022.100349 · Cited by 28
Racial differences in user experiences and perceived value of electronic symptom monitoring in a cohort of black and white bladder and prostate cancer patients
Samuel, C. A., Smith, A. B., Elkins, W., Richmond, J., Mahbooba, Z., Basch, E., Bennett, A. V., Chung, A. E., Jonsson, M., Chen, R. C., et al. (2021) · Quality of Life Research · doi:10.1007/s11136-020-02442-4 · Cited by 27
Implementation of Systematic Financial Screening in an Outpatient Breast Oncology Setting
Beauchemin, M., DeStephano, D. M., Raghunathan, R. R., Harden, E., Accordino, M. K., Hillyer, G. C., Kahn, J. M., May, B. L., Mei, B., Rosenblat, T. L., et al. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00172 · Cited by 27
Situating Meditation Apps Within the Ecosystem of Meditation Practice: Population-Based Survey Study
Lam, S. U., Xie, Q., & Goldberg, S. B. (2023) · JMIR Mental Health · doi:10.2196/43565 · Cited by 25
Momentary Stressor Logging and Reflective Visualizations: Implications for Stress Management with Wearables
Neupane, S., Saha, M., Ali, N., Hnat, T. W., Samiei, S. A., Nandugudi, A., Almeida, D. M., & Kumar, S. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642662 · Cited by 39
Monster Appetite
Hwang, M. L., & Mamykina, L. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3026052 · Cited by 27
Digital interventions to improve adherence to maintenance medication in asthma
Chan, A. H. Y., Simoni, A. D., Wileman, V., Holliday, L., Chisari, C., Newby, C., Taylor, S. J., Fleming, L., Griffiths, C., & Horne, R. (2018) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013030 · Cited by 29
Acceptability of Mobile Health Technology for Promoting Fluid Consumption in Patients With Nephrolithiasis
Streeper, N. M., Lehman, K. K., & Conroy, D. E. (2018) · Urology · doi:10.1016/j.urology.2018.08.020 · Cited by 27
Insights into mobile health application market via a content analysis of marketplace data with machine learning
Aydın, G., & Silahtaroğlu, G. (2021) · PLoS ONE · doi:10.1371/journal.pone.0244302 · Cited by 37
Mobile health in preventive cardiology: current status and future perspective
Kozik, M., Isakadze, N., & Martin, S. S. (2021) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000891 · Cited by 29
Mobile Health Apps for Breast Cancer: Content Analysis and Quality Assessment
Yang, S., Bui, C. N., & Park, K. (2023) · JMIR mhealth and uhealth · doi:10.2196/43522 · Cited by 31
Effectiveness of computer-aided learning in oral health among patients and caregivers: a systematic review
Malik, N. A., Zhang, J., Lam, O., Jin, L., & McGrath, C. P. J. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw045 · Cited by 27
Examining Use of Mobile Phones for Sleep Tracking Among a National Sample in the USA
Robbins, R., Krebs, P. M., Rapoport, D. M., Jean‐Louis, G., & Duncan, D. T. (2018) · Health Communication · doi:10.1080/10410236.2017.1422104 · Cited by 30
Self-citation
How Users Experience and Use an eHealth Intervention Based on Self-Regulation: Mixed-Methods Study
Poppe, L., der Mispel, C. V., Crombez, G., Bourdeaudhuij, I. D., Schroé, H., & Verloigne, M. (2018) · Journal of Medical Internet Research · doi:10.2196/10412 · Cited by 27
Mobile technology use and skills among individuals with fragile X syndrome: implications for healthcare decision making
Raspa, M., Fitzgerald, T. M., Furberg, R., Wylie, A. C., Moultrie, R., DeRamus, M., Wheeler, A., & Mccormack, L. A. (2018) · Journal of Intellectual Disability Research · doi:10.1111/jir.12537 · Cited by 26
Use of Weight-Management Mobile Phone Apps in Saudi Arabia: A Web-Based Survey
Aljuraiban, G. S. (2019) · JMIR mhealth and uhealth · doi:10.2196/12692 · Cited by 24
Patient awareness of orthodontic mobile phone apps
Sharif, M. O., Siddiqui, N. R., & Hodges, S. J. (2019) · Journal of Orthodontics · doi:10.1177/1465312518821361 · Cited by 27
How standards and user involvement can improve app quality: A lifecycle approach
van Velthoven, M. H., Wyatt, J. C., Meinert, E., Brindley, D. A., & Wells, G. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.08.003 · Cited by 27
Information and communication technology use in asthmatic patients: a cross-sectional study in Latin America
Calderón, J. C., Chérrez, A., Ramon, G. D., Jove, O. R. L., Baptist, A. P., Matos‐Benavides, E., Maciel, B. M., Calero, E., Sánchez‐Borges, M., Chérrez, S., et al. (2017) · ERJ Open Research · doi:10.1183/23120541.00005-2017 · Cited by 27
A Comprehensive Profile of Those Who Have Health-Related Apps
Chae, J. (2018) · Health Education & Behavior · doi:10.1177/1090198117752784 · Cited by 24
Understanding the use of digital technology to promote human papillomavirus vaccination – A RE-AIM framework approach
Stephens, A. B., Wynn, C. S., & Stockwell, M. S. (2019) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2019.1611158 · Cited by 27
Developing and pilot testing ASTHMAXcel, a mobile app for adults with asthma
Hsia, B. C., Mowrey, W. B., Keskin, T., Wu, S., Aita, R., Kwak, L., Ferastraoarou, D., Rosenstreich, D. L., & Jariwala, S. P. (2020) · Journal of Asthma · doi:10.1080/02770903.2020.1728770 · Cited by 27
Computer-Assisted CBT and Mobile Apps for Depression: Assessment and Integration Into Clinical Care
Wright, J. H., & Mishkind, M. C. (2020) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.20190044 · Cited by 27
Design, Implementation, and Evaluation of Behavior Change Interventions: A Ten-Task Guide
Abraham, C., & Denford, S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.019 · Cited by 27
Changing Behavior Using the Reflective-Impulsive Model
Deutsch, R., & Strack, F. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.012 · Cited by 30
Determinants of Continuance Intention to Use Health Apps among Users over 60: A Test of Social Cognitive Model
Kim, E., & Han, S. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph181910367 · Cited by 33
Factors associated with using the internet for medical information based on the doctor-patient trust model: a cross-sectional study
Fu, Y., Tang, T., Long, J., Lin, B., Li, J., Quan, G., Yang, H., Zhao, C., Yin, M., & Shi, L. (2021) · BMC Health Services Research · doi:10.1186/s12913-021-07283-6 · Cited by 26
Patient and physician perspectives on the use and outcome measures of mHealth apps: Exploratory survey and focus group study
Jezrawi, R., Balakumar, S., Masud, R., Gabizon, I., Bhagirath, V. C., Varughese, J. K., Brown, M., Trottier, D., Schwalm, J., McGillion, M. H., et al. (2022) · Digital Health · doi:10.1177/20552076221102773 · Cited by 25
An overview of clinically and healthcare related apps in Google and Apple app stores: connecting patients, drugs, and clinicians
Gan, S. K., Koshy, C., Nguyen, P., & Haw, Y.-X. (2016) · Scientific Phone Apps and Mobile Devices · doi:10.1186/s41070-016-0012-7 · Cited by 26
Mobile app increases vegetable-based preparations by low-income household cooks: a randomized controlled trial
Clarke, P., Evans, S. H., & Neffa-Creech, D. (2018) · Public Health Nutrition · doi:10.1017/s1368980018003117 · Cited by 26
WaistonBelt X: A Belt-Type Wearable Device with Sensing and Intervention Toward Health Behavior Change
Nakamura, Y., Matsuda, Y., Arakawa, Y., & Yasumoto, K. (2019) · Sensors · doi:10.3390/s19204600 · Cited by 24
Seizure action plans in the pediatric population with epilepsy: Uptake, determinants, and parental interest in a mobile application
Chiu, M. Y., Peinhof, S., Guzman, C. D., Borhani, M., Siu, C., Kuzeljevic, B., Schrader, D. V., Huh, L. L., & Connolly, M. B. (2021) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2021.107860 · Cited by 25
Social Determinants of Digital Health Adoption: Pilot Cross-sectional Survey
Patel, S. P., Sun, E., Reinhardt, A., Geevarghese, S., He, S., & Gazmararian, J. A. (2022) · JMIR Formative Research · doi:10.2196/39647 · Cited by 25
Patient’s Intention to Use Mobile Health App
Yee, T. S., Lim, C. S., & Chin, W. S. (2019) · Journal of Management Research · doi:10.5296/jmr.v11i3.14776 · Cited by 26
Evaluation of a Mobile Phone App for Patients With Pollen-Related Allergic Rhinitis: Prospective Longitudinal Field Study
Glattacker, M., Boeker, M., Anger, R., Reichenbach, F., Tassoni, A., Bredenkamp, R., & Giesler, J. M. (2020) · JMIR mhealth and uhealth · doi:10.2196/15514 · Cited by 28
Health Gain, Cost Impacts, and Cost-Effectiveness of a Mass Media Campaign to Promote Smartphone Apps for Physical Activity: Modeling Study
Mizdrak, A., Telfer, K., Direito, A., Cobiac, L. J., Blakely, T. A., Cleghorn, C., & Wilson, N. (2020) · JMIR mhealth and uhealth · doi:10.2196/18014 · Cited by 25
Mobile Applications for Training Plan Using Android Devices: A Systematic Review and a Taxonomy Proposal
Tavares, B. F., Pires, I. M., Marques, G., García, N. M., Zdravevski, E., Lameski, P., Trajkovik, V., & Jevremović, A. (2020) · Information · doi:10.3390/info11070343 · Cited by 27
Applying Mobile Technology to Sustain Physical Activity After Completion of Cardiac Rehabilitation: Acceptability Study
Elnaggar, A., von Oppenfeld, J., Whooley, M. A., Merek, S., & Park, L. G. (2021) · JMIR Human Factors · doi:10.2196/25356 · Cited by 25
Automated health chats for symptom management of head and neck cancer patients undergoing radiation therapy
Ma, D., Orner, D., Ghaly, M. M., Parashar, B., Ames, J. W., Chen, W. C.-Y., Potters, L., & Teckie, S. (2021) · Oral Oncology · doi:10.1016/j.oraloncology.2021.105551 · Cited by 24
Home, sweet home: How well-being shapes the adoption of artificial intelligence-powered apartments in smart cities
Meyer‐Waarden, L., Cloarec, J., Adams, C., Aliman, D. N., & Wirth, V. (2022) · Systèmes d information & management · doi:10.3917/sim.214.0055 · Cited by 26
Critical Criteria and Countermeasures for Mobile Health Developers to Ensure Mobile Health Privacy and Security: Mixed Methods Study
Rezaee, R., Khashayar, M., Saeedinezhad, S., Nasiri, M., & Zare, S. (2023) · JMIR mhealth and uhealth · doi:10.2196/39055 · Cited by 26
Defining Adherence to Dietary Self-Monitoring Using a Mobile App: A Narrative Review
Payne, J. E., Turk, M. T., Kalarchian, M. A., & Pellegrini, C. A. (2018) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2018.05.011 · Cited by 26
Impact of Digital Engagement on Weight Loss Outcomes in Obesity Management Among Individuals Using GLP-1 and Dual GLP-1/GIP Receptor Agonist Therapy: Retrospective Cohort Service Evaluation Study
Johnson, H., Huang, D., Liu, V., Ammouri, M. A., Jacobs, C., & El-Osta, A. (2025) · Journal of Medical Internet Research · doi:10.2196/69466 · Cited by 27
“The increasing pervasiveness of mobile health technologies facilitates continuous engagement and data collection [42] and presents an unprecedented opportunity for self-driven health care approaches to deliver interventions at scale and reach populations traditionally underserved or facing barriers…”
A Mobile App for Assisting Users to Make Informed Selections in Security Settings for Protecting Personal Health Data: Development and Feasibility Study
Zhou, L., Parmanto, B., Alfikri, Z. F., & Bao, J. (2018) · JMIR mhealth and uhealth · doi:10.2196/11210 · Cited by 26
Women’s Attitudes Toward Self-Monitoring of Their Pregnancy Using Noninvasive Electronic Devices: Cross-Sectional Multicenter Study
Schramm, K., Graßl, N., Nees, J., Hoffmann, J., Stepan, H., Bruckner, T. M., Haun, M. W., Maatouk, I., Haist, M., Schott, T. C., et al. (2019) · JMIR mhealth and uhealth · doi:10.2196/11458 · Cited by 25

Authors: Krebs, P., & Duncan, D. T.
Affiliations: VA NY Harbor Healthcare System (US)
New York University (US)
Corresponding author: Krebs, Paul
ORCID: Krebs, Paul
Duncan, Dustin T
Venue: JMIR mHealth and uHealth
Publisher: JMIR Publications Inc.
Published: 2015-11-04
Volume: 3
Issue: 4
Pages: e101
ISSN: 2291-5222 (ISSN-L)
DOI: 10.2196/mhealth.4924
PMC: PMC4704953
OpenAlex: W2126335261
Cited by: 1,274
References: 11
Citations by year: 2026: 47 · 2025: 98 · 2024: 101 · 2023: 128 · 2022: 127 · 2021: 156 · 2020: 211 · 2019: 140 · 2018: 151 · 2017: 85 · 2016: 28
FWCI: 160.52
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: JMIR mhealth and uhealth (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Verizon Foundation
Topics: Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Keywords: health apps, mobile health, app adoption, fitness apps, nutrition apps, data privacy concerns, app cost, obesity

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

Health App Use Among US Mobile Phone Owners: A National Survey

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks