Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach

Jennifer Caroll, Anne Moorhead, Raymond Bond, William LeBlanc, Robert Petrella, Kevin Fiscella

Research output: Contribution to journalArticle

89 Citations (Scopus)

Abstract

Background: Mobile phone use and the adoption of healthy lifestyle software apps (“health apps”) are rapidly proliferating.There is limited information on the users of health apps in terms of their social demographic and health characteristics, intentionsto change, and actual health behaviors.Objective: The objectives of our study were to (1) to describe the sociodemographic characteristics associated with health appuse in a recent US nationally representative sample; (2) to assess the attitudinal and behavioral predictors of the use of healthapps for health promotion; and (3) to examine the association between the use of health-related apps and meeting the recommendedguidelines for fruit and vegetable intake and physical activity.Methods: Data on users of mobile devices and health apps were analyzed from the National Cancer Institute’s 2015 HealthInformation National Trends Survey (HINTS), which was designed to provide nationally representative estimates for healthinformation in the United States and is publicly available on the Internet. We used multivariable logistic regression models toassess sociodemographic predictors of mobile device and health app use and examine the associations between app use, intentionsto change behavior, and actual behavioral change for fruit and vegetable consumption, physical activity, and weight loss.Results: From the 3677 total HINTS respondents, older individuals (45-64 years, odds ratio, OR 0.56, 95% CI 0.47-68; 65+years, OR 0.19, 95% CI 0.14-0.24), males (OR 0.80, 95% CI 0.66-0.94), and having degree (OR 2.83, 95% CI 2.18-3.70) or lessthan high school education (OR 0.43, 95% CI 0.24-0.72) were all significantly associated with a reduced likelihood of havingadopted health apps. Similarly, both age and education were significant variables for predicting whether a person had adopted amobile device, especially if that person was a college graduate (OR 3.30). Individuals with apps were significantly more likelyto report intentions to improve fruit (63.8% with apps vs 58.5% without apps, P=.01) and vegetable (74.9% vs 64.3%, P
LanguageEnglish
Pagese125
JournalJMIR
Volume19
Issue number4
Early online date19 Apr 2017
DOIs
Publication statusE-pub ahead of print - 19 Apr 2017

Fingerprint

Cell Phones
Telemedicine
Vegetables
Health
Fruit
Equipment and Supplies
Logistic Models
Education
National Cancer Institute (U.S.)
Health Behavior
Health Promotion
Internet
Weight Loss
Software
Odds Ratio
Demography
Surveys and Questionnaires

Keywords

  • smartphone
  • cell phone
  • Internet
  • mobile applications
  • health promotion
  • health behavior

Cite this

Caroll, Jennifer ; Moorhead, Anne ; Bond, Raymond ; LeBlanc, William ; Petrella, Robert ; Fiscella, Kevin. / Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach. In: JMIR. 2017 ; Vol. 19, No. 4. pp. e125.
@article{040e0a715d784613b1644ec4f55e41ad,
title = "Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach",
abstract = "Background: Mobile phone use and the adoption of healthy lifestyle software apps (“health apps”) are rapidly proliferating.There is limited information on the users of health apps in terms of their social demographic and health characteristics, intentionsto change, and actual health behaviors.Objective: The objectives of our study were to (1) to describe the sociodemographic characteristics associated with health appuse in a recent US nationally representative sample; (2) to assess the attitudinal and behavioral predictors of the use of healthapps for health promotion; and (3) to examine the association between the use of health-related apps and meeting the recommendedguidelines for fruit and vegetable intake and physical activity.Methods: Data on users of mobile devices and health apps were analyzed from the National Cancer Institute’s 2015 HealthInformation National Trends Survey (HINTS), which was designed to provide nationally representative estimates for healthinformation in the United States and is publicly available on the Internet. We used multivariable logistic regression models toassess sociodemographic predictors of mobile device and health app use and examine the associations between app use, intentionsto change behavior, and actual behavioral change for fruit and vegetable consumption, physical activity, and weight loss.Results: From the 3677 total HINTS respondents, older individuals (45-64 years, odds ratio, OR 0.56, 95{\%} CI 0.47-68; 65+years, OR 0.19, 95{\%} CI 0.14-0.24), males (OR 0.80, 95{\%} CI 0.66-0.94), and having degree (OR 2.83, 95{\%} CI 2.18-3.70) or lessthan high school education (OR 0.43, 95{\%} CI 0.24-0.72) were all significantly associated with a reduced likelihood of havingadopted health apps. Similarly, both age and education were significant variables for predicting whether a person had adopted amobile device, especially if that person was a college graduate (OR 3.30). Individuals with apps were significantly more likelyto report intentions to improve fruit (63.8{\%} with apps vs 58.5{\%} without apps, P=.01) and vegetable (74.9{\%} vs 64.3{\%}, P",
keywords = "smartphone, cell phone, Internet, mobile applications, health promotion, health behavior",
author = "Jennifer Caroll and Anne Moorhead and Raymond Bond and William LeBlanc and Robert Petrella and Kevin Fiscella",
note = "Compliant in UIR - see 'Other files' Reference text: Ericsson. 2015. Ericsson Mobility Report: On the pulse of the networked society URL: http://www.ericsson.com/res/docs/ 2015/ericsson-mobility-report-june-2015.pdf [accessed 2016-01-22] [WebCite Cache ID 6ejSFiicz] 2. OfCom. 2015. Smartphone usage URL: http://media.ofcom.org.uk/facts/ [accessed 2016-01-22] [WebCite Cache ID 6ejSSqrN7] 3. OfCom. Belfast: OfCom; 2014. Telecommunications facts and figures URL: https://www.ofcom.org.uk/ [accessed 2017-02-27] [WebCite Cache ID 6ob5yNLrU] 4. Smith A. Pew Research Center. 2015. The Smartphone Difference URL: http://www.pewinternet.org/2015/04/01/ us-smartphone-use-in-2015/ [accessed 2017-02-28] [WebCite Cache ID 6ejS9bn6M] 5. Boudreaux ED, Waring ME, Hayes RB, Sadasivam RS, Mullen S, Pagoto S. Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations. Transl Behav Med 2014 Dec;4(4):363-371 [FREE Full text] [doi: 10.1007/s13142-014-0293-9] [Medline: 25584085] 6. Research2guidance. Research2guidance URL: http://research2guidance.com [accessed 2016-01-22] [WebCite Cache ID 6ejS4CO9X] 7. Fox S, Duggan M. Pew Research Center. Mobile Health 2012: Half of smartphone owners use their devices to get health informationone-fifth of smartphone owners have health apps URL: http://www.pewinternet.org/2012/11/08/ mobile-health-2012/ [accessed 2017-02-27] [WebCite Cache ID 6ob6C9mfG] 8. Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C. A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. J Med Internet Res 2013;15(4):e85 [FREE Full text] [doi: 10.2196/jmir.1933] [Medline: 23615206] 9. Zhu F, Bosch M, Woo I, Kim S, Boushey CJ, Ebert DS, et al. The use of mobile devices in aiding dietary assessment and evaluation. IEEE J Sel Top Signal Process 2010 Aug;4(4):756-766 [FREE Full text] [doi: 10.1109/JSTSP.2010.2051471] [Medline: 20862266] 10. Mosa AS, Yoo I, Sheets L. A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak 2012;12:67 [FREE Full text] [doi: 10.1186/1472-6947-12-67] [Medline: 22781312] 11. O'Malley G, Dowdall G, Burls A, Perry IJ, Curran N. Exploring the usability of a mobile app for adolescent obesity management. JMIR Mhealth Uhealth 2014;2(2):e29 [FREE Full text] [doi: 10.2196/mhealth.3262] [Medline: 25098237] 12. O'Malley G, Clarke M, Burls A, Murphy S, Murphy N, Perry IJ. A smartphone intervention for adolescent obesity: study protocol for a randomised controlled non-inferiority trial. Trials 2014;15:43 [FREE Full text] [doi: 10.1186/1745-6215-15-43] [Medline: 24485327] 13. Knight E, Stuckey MI, Prapavessis H, Petrella RJ. Public health guidelines for physical activity: is there an app for that? A review of android and apple app stores. JMIR Mhealth Uhealth 2015;3(2):e43 [FREE Full text] [doi: 10.2196/mhealth.4003] [Medline: 25998158] 14. Pagoto S, Schneider K, Jojic M, DeBiasse M, Mann D. Evidence-based strategies in weight-loss mobile apps. Am J Prev Med 2013 Nov;45(5):576-582. [doi: 10.1016/j.amepre.2013.04.025] [Medline: 24139770] 15. Breton ER, Fuemmeler BF, Abroms LC. Weight loss-there is an app for that! But does it adhere to evidence-informed practices? Transl Behav Med 2011 Dec;1(4):523-529 [FREE Full text] [doi: 10.1007/s13142-011-0076-5] [Medline: 24073074] 16. Abroms LC, Padmanabhan N, Thaweethai L, Phillips T. iPhone apps for smoking cessation: a content analysis. Am J Prev Med 2011 Mar;40(3):279-285 [FREE Full text] [doi: 10.1016/j.amepre.2010.10.032] [Medline: 21335258] 17. Eng DS, Lee JM. The promise and peril of mobile health applications for diabetes and endocrinology. Pediatr Diabetes 2013 Jun;14(4):231-238 [FREE Full text] [doi: 10.1111/pedi.12034] [Medline: 23627878] 18. Kontos E, Blake KD, Chou WS, Prestin A. Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012. J Med Internet Res 2014;16(7):e172 [FREE Full text] [doi: 10.2196/jmir.3117] [Medline: 25048379] 19. McCully SN, Don BP, Updegraff JA. Using the Internet to help with diet, weight, and physical activity: results from the Health Information National Trends Survey (HINTS). J Med Internet Res 2013;15(8):e148 [FREE Full text] [doi: 10.2196/jmir.2612] [Medline: 23906945] 20. HINTS. 2015. Health Information National Trends Survey URL: http://hints.cancer.gov/ [accessed 2016-01-22] [WebCite Cache ID 6ejRyxj9Y] 21. Finney RL, Hesse BW, Moser RP, Ortiz MA, Kornfeld J, Vanderpool RC, et al. Socioeconomic and geographic disparities in health information seeking and Internet use in Puerto Rico. J Med Internet Res 2012 Jul;14(4):e104 [FREE Full text] [doi: 10.2196/jmir.2007] [Medline: 22849971] http://www.jmir.org/2017/4/e125/ J Med Internet Res 2017 | vol. 19 | iss. 4 | e125 | p.8 22. Kontos EZ, Bennett GG, Viswanath K. Barriers and facilitators to home computer and internet use among urban novice computer users of low socioeconomic position. J Med Internet Res 2007;9(4):e31 [FREE Full text] [doi: 10.2196/jmir.9.4.e31] [Medline: 17951215]",
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Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach. / Caroll, Jennifer; Moorhead, Anne; Bond, Raymond; LeBlanc, William; Petrella, Robert; Fiscella, Kevin.

In: JMIR, Vol. 19, No. 4, 19.04.2017, p. e125.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach

AU - Caroll, Jennifer

AU - Moorhead, Anne

AU - Bond, Raymond

AU - LeBlanc, William

AU - Petrella, Robert

AU - Fiscella, Kevin

N1 - Compliant in UIR - see 'Other files' Reference text: Ericsson. 2015. Ericsson Mobility Report: On the pulse of the networked society URL: http://www.ericsson.com/res/docs/ 2015/ericsson-mobility-report-june-2015.pdf [accessed 2016-01-22] [WebCite Cache ID 6ejSFiicz] 2. OfCom. 2015. Smartphone usage URL: http://media.ofcom.org.uk/facts/ [accessed 2016-01-22] [WebCite Cache ID 6ejSSqrN7] 3. OfCom. Belfast: OfCom; 2014. Telecommunications facts and figures URL: https://www.ofcom.org.uk/ [accessed 2017-02-27] [WebCite Cache ID 6ob5yNLrU] 4. Smith A. Pew Research Center. 2015. The Smartphone Difference URL: http://www.pewinternet.org/2015/04/01/ us-smartphone-use-in-2015/ [accessed 2017-02-28] [WebCite Cache ID 6ejS9bn6M] 5. Boudreaux ED, Waring ME, Hayes RB, Sadasivam RS, Mullen S, Pagoto S. Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations. Transl Behav Med 2014 Dec;4(4):363-371 [FREE Full text] [doi: 10.1007/s13142-014-0293-9] [Medline: 25584085] 6. Research2guidance. Research2guidance URL: http://research2guidance.com [accessed 2016-01-22] [WebCite Cache ID 6ejS4CO9X] 7. Fox S, Duggan M. Pew Research Center. Mobile Health 2012: Half of smartphone owners use their devices to get health informationone-fifth of smartphone owners have health apps URL: http://www.pewinternet.org/2012/11/08/ mobile-health-2012/ [accessed 2017-02-27] [WebCite Cache ID 6ob6C9mfG] 8. Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C. A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. J Med Internet Res 2013;15(4):e85 [FREE Full text] [doi: 10.2196/jmir.1933] [Medline: 23615206] 9. Zhu F, Bosch M, Woo I, Kim S, Boushey CJ, Ebert DS, et al. The use of mobile devices in aiding dietary assessment and evaluation. IEEE J Sel Top Signal Process 2010 Aug;4(4):756-766 [FREE Full text] [doi: 10.1109/JSTSP.2010.2051471] [Medline: 20862266] 10. Mosa AS, Yoo I, Sheets L. A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak 2012;12:67 [FREE Full text] [doi: 10.1186/1472-6947-12-67] [Medline: 22781312] 11. O'Malley G, Dowdall G, Burls A, Perry IJ, Curran N. Exploring the usability of a mobile app for adolescent obesity management. JMIR Mhealth Uhealth 2014;2(2):e29 [FREE Full text] [doi: 10.2196/mhealth.3262] [Medline: 25098237] 12. O'Malley G, Clarke M, Burls A, Murphy S, Murphy N, Perry IJ. A smartphone intervention for adolescent obesity: study protocol for a randomised controlled non-inferiority trial. Trials 2014;15:43 [FREE Full text] [doi: 10.1186/1745-6215-15-43] [Medline: 24485327] 13. Knight E, Stuckey MI, Prapavessis H, Petrella RJ. Public health guidelines for physical activity: is there an app for that? A review of android and apple app stores. JMIR Mhealth Uhealth 2015;3(2):e43 [FREE Full text] [doi: 10.2196/mhealth.4003] [Medline: 25998158] 14. Pagoto S, Schneider K, Jojic M, DeBiasse M, Mann D. Evidence-based strategies in weight-loss mobile apps. Am J Prev Med 2013 Nov;45(5):576-582. [doi: 10.1016/j.amepre.2013.04.025] [Medline: 24139770] 15. Breton ER, Fuemmeler BF, Abroms LC. Weight loss-there is an app for that! But does it adhere to evidence-informed practices? Transl Behav Med 2011 Dec;1(4):523-529 [FREE Full text] [doi: 10.1007/s13142-011-0076-5] [Medline: 24073074] 16. Abroms LC, Padmanabhan N, Thaweethai L, Phillips T. iPhone apps for smoking cessation: a content analysis. Am J Prev Med 2011 Mar;40(3):279-285 [FREE Full text] [doi: 10.1016/j.amepre.2010.10.032] [Medline: 21335258] 17. Eng DS, Lee JM. The promise and peril of mobile health applications for diabetes and endocrinology. Pediatr Diabetes 2013 Jun;14(4):231-238 [FREE Full text] [doi: 10.1111/pedi.12034] [Medline: 23627878] 18. Kontos E, Blake KD, Chou WS, Prestin A. Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012. J Med Internet Res 2014;16(7):e172 [FREE Full text] [doi: 10.2196/jmir.3117] [Medline: 25048379] 19. McCully SN, Don BP, Updegraff JA. Using the Internet to help with diet, weight, and physical activity: results from the Health Information National Trends Survey (HINTS). J Med Internet Res 2013;15(8):e148 [FREE Full text] [doi: 10.2196/jmir.2612] [Medline: 23906945] 20. HINTS. 2015. Health Information National Trends Survey URL: http://hints.cancer.gov/ [accessed 2016-01-22] [WebCite Cache ID 6ejRyxj9Y] 21. Finney RL, Hesse BW, Moser RP, Ortiz MA, Kornfeld J, Vanderpool RC, et al. Socioeconomic and geographic disparities in health information seeking and Internet use in Puerto Rico. J Med Internet Res 2012 Jul;14(4):e104 [FREE Full text] [doi: 10.2196/jmir.2007] [Medline: 22849971] http://www.jmir.org/2017/4/e125/ J Med Internet Res 2017 | vol. 19 | iss. 4 | e125 | p.8 22. Kontos EZ, Bennett GG, Viswanath K. Barriers and facilitators to home computer and internet use among urban novice computer users of low socioeconomic position. J Med Internet Res 2007;9(4):e31 [FREE Full text] [doi: 10.2196/jmir.9.4.e31] [Medline: 17951215]

PY - 2017/4/19

Y1 - 2017/4/19

N2 - Background: Mobile phone use and the adoption of healthy lifestyle software apps (“health apps”) are rapidly proliferating.There is limited information on the users of health apps in terms of their social demographic and health characteristics, intentionsto change, and actual health behaviors.Objective: The objectives of our study were to (1) to describe the sociodemographic characteristics associated with health appuse in a recent US nationally representative sample; (2) to assess the attitudinal and behavioral predictors of the use of healthapps for health promotion; and (3) to examine the association between the use of health-related apps and meeting the recommendedguidelines for fruit and vegetable intake and physical activity.Methods: Data on users of mobile devices and health apps were analyzed from the National Cancer Institute’s 2015 HealthInformation National Trends Survey (HINTS), which was designed to provide nationally representative estimates for healthinformation in the United States and is publicly available on the Internet. We used multivariable logistic regression models toassess sociodemographic predictors of mobile device and health app use and examine the associations between app use, intentionsto change behavior, and actual behavioral change for fruit and vegetable consumption, physical activity, and weight loss.Results: From the 3677 total HINTS respondents, older individuals (45-64 years, odds ratio, OR 0.56, 95% CI 0.47-68; 65+years, OR 0.19, 95% CI 0.14-0.24), males (OR 0.80, 95% CI 0.66-0.94), and having degree (OR 2.83, 95% CI 2.18-3.70) or lessthan high school education (OR 0.43, 95% CI 0.24-0.72) were all significantly associated with a reduced likelihood of havingadopted health apps. Similarly, both age and education were significant variables for predicting whether a person had adopted amobile device, especially if that person was a college graduate (OR 3.30). Individuals with apps were significantly more likelyto report intentions to improve fruit (63.8% with apps vs 58.5% without apps, P=.01) and vegetable (74.9% vs 64.3%, P

AB - Background: Mobile phone use and the adoption of healthy lifestyle software apps (“health apps”) are rapidly proliferating.There is limited information on the users of health apps in terms of their social demographic and health characteristics, intentionsto change, and actual health behaviors.Objective: The objectives of our study were to (1) to describe the sociodemographic characteristics associated with health appuse in a recent US nationally representative sample; (2) to assess the attitudinal and behavioral predictors of the use of healthapps for health promotion; and (3) to examine the association between the use of health-related apps and meeting the recommendedguidelines for fruit and vegetable intake and physical activity.Methods: Data on users of mobile devices and health apps were analyzed from the National Cancer Institute’s 2015 HealthInformation National Trends Survey (HINTS), which was designed to provide nationally representative estimates for healthinformation in the United States and is publicly available on the Internet. We used multivariable logistic regression models toassess sociodemographic predictors of mobile device and health app use and examine the associations between app use, intentionsto change behavior, and actual behavioral change for fruit and vegetable consumption, physical activity, and weight loss.Results: From the 3677 total HINTS respondents, older individuals (45-64 years, odds ratio, OR 0.56, 95% CI 0.47-68; 65+years, OR 0.19, 95% CI 0.14-0.24), males (OR 0.80, 95% CI 0.66-0.94), and having degree (OR 2.83, 95% CI 2.18-3.70) or lessthan high school education (OR 0.43, 95% CI 0.24-0.72) were all significantly associated with a reduced likelihood of havingadopted health apps. Similarly, both age and education were significant variables for predicting whether a person had adopted amobile device, especially if that person was a college graduate (OR 3.30). Individuals with apps were significantly more likelyto report intentions to improve fruit (63.8% with apps vs 58.5% without apps, P=.01) and vegetable (74.9% vs 64.3%, P

KW - smartphone

KW - cell phone

KW - Internet

KW - mobile applications

KW - health promotion

KW - health behavior

U2 - 10.2196/jmir.5604

DO - 10.2196/jmir.5604

M3 - Article

VL - 19

SP - e125

JO - JMIR

T2 - JMIR

JF - JMIR

SN - 1439-4456

IS - 4

ER -