0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Original Investigation |

Evaluation of Immediate and 12-Week Effects of a Smartphone Sun-Safety Mobile Application A Randomized Clinical Trial FREE

David B. Buller, PhD1; Marianne Berwick, PhD2,3; Kathy Lantz, PhD4; Mary Klein Buller, MA1; James Shane, BA1; Ilima Kane, MA5; Xia Liu, MA1
[+] Author Affiliations
1Klein Buendel Inc, Golden, Colorado
2Department of Internal Medicine, University of New Mexico, Albuquerque
3Department of Dermatology, University of New Mexico, Albuquerque
4Global Monitoring Division, Earth System Research Laboratory, Cooperative Institute for Research in Environmental Studies, National Oceanic and Atmospheric Administration, University of Colorado, Boulder
5Colorado Foundation for Public Health and Environment, Denver
JAMA Dermatol. 2015;151(5):505-512. doi:10.1001/jamadermatol.2014.3894.
Text Size: A A A
Published online

Importance  Mobile applications on smartphones can communicate a large amount of personalized, real-time health information, including advice on skin cancer prevention, but their effectiveness may be affected by whether recipients can be convinced to use them.

Objective  To evaluate a smartphone mobile application (Solar Cell) delivering real-time advice about sun protection for a second time in a randomized clinical trial.

Design, Setting, and Participants  A previous trial conducted in 2012 used a randomized pretest-posttest design. For the present trial, we collected data from a volunteer sample of 202 adults 18 years or older who owned a smartphone. Participants were recruited nationwide through online promotions. Screening procedures and a 3-week run-in period were added to increase the use of the mobile application. We conducted follow-ups at 3 and 8 weeks after randomization to examine the immediate and the longer-term effects of the intervention.

Interventions  Use of the mobile application. The application gave feedback on sun protection (ie, sun-safety practices and the risk for sunburn) and alerted users to apply or to reapply sunscreen and to get out of the sun. The application also displayed the hourly UV Index and vitamin D production based on the forecast UV Index, time, and location.

Main Outcomes and Measures  Percentage of days with the use of sun protection, time spent outdoors in the midday sun (days and hours), and the number of sunburns in the last 3 months.

Results  Participants in the intervention group used wide-brimmed hats more at 7 weeks than control participants (23.8% vs 17.4%; F = 4.07; P = .045). Women who used the mobile application reported using all sun protection combined more than men (46.4% vs 43.3%; F = 1.49; P = .04), whereas men and older individuals reported less use of sunscreen (32.7% vs 35.5%; F = 5.36; P = .02) and hats (15.6% vs 17.9%; F = 4.72; P = .03).

Conclusions and Relevance  The mobile application initially appeared to confer weak improvement of sun protection. Use of the mobile application was greater than in a previous trial and was associated with greater sun protection, especially among women. Strategies to increase the use of the mobile application are needed if the application is to be deployed effectively to the general adult population.

Figures in this Article

Mobile computing devices, specifically smartphones and tablet computers, are everywhere. More such devices are sold in the United States than desktop and laptop computers. Their computing power, ability to access the mobile Web, and rich audiovisual displays have revolutionized the communication experience of many users in the United States in a number of ways15 that could be harnessed to improve health communication (ie, mHealth interventions).3 Specifically, mobile applications running on smartphones and/or tablet computers can deliver health information to users anywhere and anytime.2,6,7 This advice can be delivered proactively, unobtrusively, confidentially, and repeatedly, which should catch the user’s attention1,2,8,9 and create an urgency to respond,10 thus elevating the level of user engagement with health information.2,11,12 Also, the ecological validity of the health information can be increased by tailoring it to each user “in the moment,” that is, when and where it is most needed.2,8,11,13 Such communication may enhance social support for healthy behaviors by increasing adults’ accountability, conveying emotional support,8 and promoting a sense of volition, choice, and control.14 Especially important for sun protection, mobile devices can detect time and location and access remote databases7,15 to obtain real-time information on UV levels that change throughout the day and by season, latitude, and elevation. In addition, they can deliver reminders to take precautions appropriate for the temporal and geospatial context.

At present, no single theory explains how health interventions on mobile computing devices (mHealth) affect health behaviors,2,11,16 but the preceding characteristics of the mobile devices should improve self-efficacy and response efficacy17 and provide cues to action18 to motivate risk-reduction behaviors. Recently, a mobile application running on Android smartphones that provided sun safety advice showed promise in improving adults’ sun protection practices in a first randomized clinical trial,19 especially among participants who used it. Approximately 2 million nonmelanoma skin cancers (ie, basal and squamous cell carcinomas) and 76 100 cutaneous malignant melanoma (43 890 in men and 32 210 in women) will be diagnosed in 2014, nearly all caused by exposure to UV radiation from solar and nonsolar sources, making sun protection interventions a priority.20 However, many participants assigned to the intervention group in that initial trial did not download and use the mobile application, potentially undermining the experimental comparison. A second randomized clinical trial evaluating the mobile application is reported herein, again testing the hypothesis that participants in the group assigned to use the application would report greater sun protection and fewer sunburns than control participants, a change mediated by increased sun protection norms, self-efficacy, outcome expectations, and intentions.21 To ensure that more participants used the mobile application, the trial contained more extensive screening, a run-in period, and routine reminders to use the application. In addition, an iPhone (Apple, Inc) version of the Solar Cell mobile application was deployed and an interim follow-up assessment was included to detect immediate effects of the intervention.

Sample

All procedures and forms were approved by the Western Institutional Review Board. Adults were recruited through online advertisements placed through the Google Adwords service and posted to Facebook and Craigslist throughout the United States beginning on May 1, 2013. Adults interested in participating completed a brief contact form, a consent form, and screening questions online and then were contacted by telephone by the project staff, who asked additional screening questions and confirmed the type of smartphone used. Of 536 adults who submitted the contact form, 433 (80.8%) completed screening and met eligibility criteria (ie, they were 18 years or older, owned an iPhone or an Android smartphone, and were a US resident). Eligible individuals were required to complete and return a participation agreement indicating that they would follow study instructions to download and use the mobile application should they be assigned to the intervention. Recruitment was discontinued once 220 agreements had been returned; 214 of these individuals (97.3%) completed the baseline survey. No incentive was provided for the baseline survey. Enrollment ended on June 5, 2013. Participants were invited to complete a brief interim survey 4 weeks after enrolling. The 202 participants who completed it (94.4%) were fully enrolled and randomized to 1 of 2 study conditions.

Procedure

The trial involved a randomized pretest-posttest design, with a baseline survey, 2 brief interim surveys at 4 and 7 weeks after enrollment, and a posttest survey at 12 weeks after enrollment. The period from the baseline survey to the 4-week interim survey served as a run-in period before randomization, at the end of which individuals assigned to the intervention group received instructions to download, install, and use the mobile application for the remainder of the trial. Participants assigned to the control group did not receive the mobile application. The intervention period lasted approximately 8 weeks from the 4-week interim survey to the posttest survey. Eleven text messages were sent to participants in the intervention group reminding them to use the mobile application (1 message per week with additional messages on Saturdays). The 7-week interim survey and the posttest survey provided measures of the outcomes after randomization. The posttest period closed in September. Participants received as much as $50 in compensation (via Amazon gift cards) for completing the 2 interim surveys ($10 each) and the posttest survey ($30).

Mobile Application

The Android and iPhone versions of the mobile application provided identical advice that is described in detail elsewhere.22 The application provided users with an estimate of their risk for sunburn (time until sunburn and the level of risk [low, moderate, or extreme]), the time until they needed to reapply sunscreen, advice about sun protection practices (ie, sunglasses, sunscreen, hats, protective clothing, shade, and going indoors), the current forecast UV Index, and an estimate of the amount of vitamin D produced in their skin. This advice was generated by algorithms from the published literature22 that combined the 5-day hour-by-hour UV Index forecast of the National Oceanic and Atmospheric Administration for each 0.5° latitude-longitude grid in the United States (published daily online), the time and the location of the smartphone, and user-input information, including skin phenotype, clothing coverage, use of sunscreen and its sun protection factor, height, weight, and the use of medications that could increase sun sensitivity. Sunburn risk was tailored to the individual’s skin phenotype and adjusted in real time for the use of sunscreen and shade, being indoors, and hourly changes in the UV Index. Extreme sunburn risk, the need to reapply sunscreen, and achievement of the recommended daily dose of vitamin D were indicated using a visual and an auditory alert. We noted slight differences in the appearance and the function of the Android and iPhone versions of the application owing to differences in how their operating systems performed.

Measures

The baseline and the posttest surveys measured sun protection practices, time spent outdoors in the midday sun, and sunburn prevalence as the a priori primary outcomes. In addition, effect moderators and theoretical mediators used in the first trial19 were measured.

Sun Exposure and Sun Protection Practices

Recently validated open-ended measures assessed sun exposure and sun protection practices during the last 3 months.23 Participants reported the number of days and hours spent outdoors in the midday sun (from 10 am through 4 pm; solar noon ±3 hours) and the number of days on which they practiced the following 7 sun protection behaviors: wearing sunscreen with a sun protection factor of 15 or greater, using sunscreen lip balm with a sun protection factor of 15 or greater, using clothing that protected the skin from the sun, using a hat with a wide brim, using sunglasses, the number of days the participant kept time in the sun to the minimum, and the number of days the participant stayed in the shade when outdoors. The sun protection practice measures were converted to the percentage of days in the last 3 months. Also, sunburn prevalence was measured by asking participants whether they had ever been sunburned (defined as red and/or painful from exposure to the sun24) and, if so, how many times.

Moderators and Mediators

Effect moderators included a 3-item skin phenotype assessment,25 a 3-item tanning image scale (at baseline and the posttest survey, Cronbach α = 0.89),26 and personal history of skin cancer,27 assessed with measures obtained from the published literature. Participants who have more sun-sensitive skin and a history of skin cancer might be influenced more by the mobile application, whereas participants who desire a suntan might be less responsive to it. Theoretical mediators derived from social cognitive theory were measured with items created by us (D.B.B., M.K.B., and I.K.). The following 2 individual items assessed descriptive norms: “Out of 100 people like you, how many do you think will (a) get sunburned while outdoors this summer and (b) protect their skin from the sun this summer?” Two-item scales measured injunctive norms for suntanning (at baseline, Cronbach α = 0.63; at the posttest survey, Cronbach α = 0.75) and sun protection (at baseline, Cronbach α = 0.60; at the posttest survey, Cronbach α = 0.53) on a 5-point Likert scale with 1 indicating strongly disagrees and 5, strongly agrees. The following 2 individual items assessed self-efficacy expectations: I am confident I can (a) avoid getting sunburned while outdoors in the summer sun (on a 5-point Likert scale) and (b) practice sun safety, that is, wear sunscreen, protective clothing, a hat, and sunglasses the next time I go out in the sun (1, not at all confident; 4, very confident). Three items measured outcome expectations (a single item on complexity and a 3-item scale on the fit and ease of sun protection [at baseline, Cronbach α = 0.58; at the posttest survey, Cronbach α = 0.61]) using 5-point Likert scales. An individual item measured intention to spend time in the sun to get a tan (yes/no), and a 2-item scale measured intentions to practice sun protection (at baseline, Cronbach α = 0.45; at the posttest survey, Cronbach α = 0.57), with 1 indicating very unwilling and 5, very willing. The multi-item scales were identical to those scales used in the first trial19 to allow for comparison, despite some reduced internal consistency.

The interim surveys contained only the question assessing sun protection practices. These practices included time spent in the midday sun and sunburn prevalence (whether the participant had ever been sunburned) reported for the last 3 weeks (rather than the last 3 months as at baseline and the posttest survey).

Statistical Analysis

The effect of the mobile application was tested by comparing the percentage of days on which sun protection was practiced, days and hours spent outdoors in the midday sun, sunburn prevalence, and theoretical mediators between the intervention and the control groups, with significance at P < .05 (2 tailed). These outcomes were examined at the 7-week interim survey and the posttest (12-week) survey. Comparisons were performed using analysis of covariance models, adjusted for baseline values and demographics. The baseline values were controlled by entering the values at baseline and the 4-week interim surveys separately or by entering the mean of the combined baseline and 4-week interim survey values. No differences were observed between these 2 methods, so we presented results using the combined baseline and 4-week interim survey values. We retained only those demographic covariates that were statistically significantly associated with the outcome variables using stepwise elimination procedures. The analyses were performed only on participants who completed the posttest survey, because very few participants were lost to or unavailable for follow-up (4 participants at the 7-week interim survey and 6 participants at the posttest survey). Potential moderators of the effect of the mobile application were tested by including 2-way interactions between the moderators (with levels as appropriate) and the intervention group in the analysis of covariance models.

Profile of the Sample

Overall, 202 individuals completed the baseline survey and the 4-week interim survey and were randomized (96 participants to the intervention group and 106 participants to the control group) (Figure). A total of 198 randomized participants (98.0%) completed the 7-week interim survey, and 196 participants (97.0%) completed the posttest survey. In the end, 198 individuals had complete data through the 7-week interim survey (93 participants in the intervention group and 105 participants in the control group), and 193 individuals had complete data through the posttest survey (89 participants in the intervention group and 104 participants in the control group); 196 of the randomized participants completed the posttest survey (91 participants in the intervention group and 105 participants in the control group).

Place holder to copy figure label and caption
Figure.
CONSORT Diagram for the Trial
Graphic Jump Location

The sample had a diverse profile (Table 1), although it included individuals who were younger (age range, 19-72 [mean, 33.3] years) and had a higher education level and included more women compared with the US population. In addition, 21.4% of participants were nonwhite (14.8%) or Hispanic (6.6%); nearly one-quarter (24.3%) had high-risk skin phenotypes (ie, skin scores of 4 or 5 on the phenotypic index), and two-fifths (40.6%) had been diagnosed as having skin cancer. Participants resided in 35 of the 50 US states.

Table Graphic Jump LocationTable 1.  Demographic Characteristics of Study Sample

Analyses on loss to or unavailability for follow-up revealed that participants who completed the 4-week interim survey and were eligible for randomization were younger (mean [SD] age, 33.3 [9.8] years) than those who did not complete the survey (mean [SD] age, 43.8 [12.0] years; P < .01). No statistically significant differences were observed between individuals who completed follow-up successfully at 7 weeks and those who did not (P > .05 for all characteristics). Only 1 statistically significant difference emerged for the posttest survey: participants who completed follow-up had lower-risk skin phenotypes (1.5% had 5 [the highest risk score] on the skin score of the phenotypic index) compared with participants who did not complete follow-up (16.7% had 5 on the skin score of the phenotypic index; P = .007). Finally, randomization created equivalent groups, with no statistically significant differences observed in any of the outcomes (P > .05 for all).

Use of the Mobile Application

Of the 96 participants assigned to use the mobile application, 74 (77%) used it (ie, ran the application and received the feedback screen) at least once after installing it (downloading and use were detected by web servers). Of these 74 participants, 24 (32%) used the application 1 to 5 times; 17 (23%), 6 to 10 times; and 33 (45%), 11 or more times. These users created 189 user profiles and ran existing profiles 556 times.

Effect of the Mobile Application

The mobile application produced a few improvements in sun protection practices at the 7-week interim survey but not at the posttest survey (Table 2). At 7 weeks, individuals assigned to use the mobile application reported wearing wide-brimmed hats when outdoors in the midday sun on a greater percentage of days than controls. We found no evidence that being assigned to use the mobile application increased the time spent in the midday sun, with no statistically significant differences by condition on the number of days or hours spent outdoors in the past 3 months (eTable 1 in the Supplement). Overall, 31.7% of adults who were randomized reported being sunburned in the last 3 months. We detected no statistically significant effect of being assigned to the intervention on the prevalence of sunburn at the 7-week interim survey or the posttest survey (Table 2).

Table Graphic Jump LocationTable 2.  Results of ANCOVA Models Comparing Outcomes by Experimental Group at the 7-Week Interim and Posttest Surveys

Further, the effect of the mobile application on the use of sunglasses was moderated by race (white vs nonwhite) at the 7-week interim survey (75.1% vs 73.1%; F = 5.49; P = .02) and the posttest survey (70.2% vs 68.6%; F = 7.08; P = .01). In both assessments, nonwhite participants assigned to use the mobile application reported wearing sunglasses on a smaller percentage of days when in the sun than nonwhite participants in the control group (eTable 1 in the Supplement). White individuals showed little difference in the use of sunglasses between the intervention and the control groups.

We examined whether use of the mobile application (ie, running the application and receiving the feedback screen) by individuals in the intervention group predicted sun protection practices, sunburn prevalence, or time spent outdoors in the midday sun. No statistically significant differences associated with the use of the mobile application emerged in the sun protection practices or sunburn prevalence (Table 3). However, age and sex moderated the effect of using the mobile application on sun protection practices. Younger adults who used the mobile application reported wearing a hat with a wide brim in the 7-week interim survey more than nonusers (17.9% vs 2.0%; F = 4.72; P = .03; eTable 1 in the Supplement). By contrast, men who used the mobile application used less lip balm with sunscreen (32.7% vs 66.7%; F = 5.36; P = .02; eTable 1 in the Supplement) and fewer sun protection practices combined (43.3% vs 50.8%; F = 4.19; P = .04; eTable 1 in the Supplement) at the posttest survey than those not using the mobile application. Women using the mobile application reported using more of all sun protection practices combined than those not using the application. Older participants using the mobile application actually wore wide-brimmed hats less than those not using the mobile application. However, participants who used the mobile application reported spending fewer days outdoors in the midday sun at the 7-week interim survey than nonusers (Table 3), with this effect emerging among the most educated participants (25.20 vs 42.56; F = 3.55; P = .03; eTable 1 in the Supplement).

Table Graphic Jump LocationTable 3.  Results of ANCOVA Models Comparing Outcomes by Whether Participant Used the Mobile Application at the 7-Week Interim and the Posttest Surveys
Effect of the Mobile Application on Theoretical Mediators

The effect of the mobile application on theoretical mediators at the posttest survey—injunctive and descriptive norms, self-efficacy expectations, innovation attributes, and intentions related to sun exposure—was tested by comparing the intervention and control groups (eTable 2 in the Supplement). Individuals assigned to the mobile application reported statistically significantly lower intentions (P = .04) to spend time in the sun to get a suntan than those in the control condition. No other statistically significant differences emerged by condition.

Once again, the mobile application promoted improved sun protection but did not reduce the prevalence of sunburn. The application had a favorable effect on the use of hats at 7 weeks, similar to a recent text messaging intervention with adolescents.28 Individuals who used the mobile application reported spending less time in the sun at 7 weeks than the nonusers, suggesting that its effects were real but weak. In the first 3 weeks after randomization, more participants used the mobile application than in the last 5 weeks, which may explain why the mobile application’s benefits occurred early. Findings that the mobile application still did not reduce sunburns, despite adults’ professed desire to receive such feedback, were disappointing.22

Sex moderated the effect of using the mobile application at the posttest survey as in the first trial,19 possibly because women are predisposed to taking precautions against UV radiation.29 Younger adults have typically been less sun protective than the older ones,29 so we are encouraged that the mobile application convinced them to take precautions. Younger US residents may be more technologically knowledgeable than older US residents, so they may have received more benefit from the mobile application.

The results of this trial and the earlier one19 underscore that developers of health-related mobile applications must discover ways to convince adults to use them if the promise of these applications is to be realized. Implementation is an issue in most intervention research3032 and appears important for smartphone mobile applications, too. We implemented extensive screening and run-in procedures and continuously reminded participants to use the mobile application, which succeeded in elevating its use to the levels seen in other recent trials on mobile interventions.33,34 A similar screening protocol was successful at recruiting a sample motivated to use a smoking cessation mobile application in another trial.35 The run-in may have obtained a sample compliant with follow-up procedures. However, these procedures are impractical in a real-world dissemination, so other ways to motivate the use of mobile applications are needed.

Once again, providing advice on sunburn risk on a mobile application did not cause adults to spend more time outdoors and thus increase their UV radiation exposure, the principal cause of skin cancer.20 The advice delivered by the mobile application communicated real-time risk for sunburn but provided clear advice on what sun protection practices were needed and showed that these practices reduced personal risk. This advice enabled individuals to make informed decisions regarding sun exposure and sun protection, rather than merely using the mobile application to spend more time outdoors, an undesirable effect seen with sunscreen and personal UV monitors.36 Similar real-time feedback on UV radiation exposure provided online to Australian students and teachers motivated less sun exposure.37

Similar to the first trial, the national sample, equivalent groups created by randomization, and extremely high follow-up rates were strengths of this second trial of the mobile application. The 2 assessments conducted before randomization provided a more stable estimate of the baseline values of the outcomes,38 and the 7-week interim survey and the posttest survey permitted examination of immediate effects and those accrued over time. However, weaknesses included the use of self-report of the outcome measures, although self-report had been validated.23 The sample in the present trial was less diverse than the entire US population and the sample in the first trial.19 However, the sample in the present trial may be more representative of adults who would be interested in using a mobile application for sun protection. Individuals decide to download and use mobile applications based on personal interest and recommendations from others, and participants in this second trial likely had a personal interest in testing a mobile application addressing sun safety and thus would be attracted to the application in the marketplace. On the plus side, the present sample was more racially diverse than that of the first trial. Although skin cancer is much more common among white than nonwhite individuals,3941 the similarity of the results across all racial groups is encouraging.

Several mobile applications purporting to provide advice to help individuals practice sun safety have appeared in the mobile application market during the last few years, but only a small fraction have been developed by reputable health organizations. The Solar Cell mobile application is one of the first such mobile applications to be subjected to careful evaluation. However, unless strategies for increasing the use of the mobile application are identified in future research, the positive effects witnessed in this trial and the earlier one19 will be difficult to replicate when taking the mobile application to wide-scale distribution. In this way, the public’s investment in the Solar Cell mobile application can benefit all US residents whose jobs, lifestyles, recreational preferences, interests in the natural environment, and residential locations take them outdoors into high-risk UV radiation environments.

Accepted for Publication: September 16, 2014.

Corresponding Author: David B. Buller, PhD, Klein Buendel Inc, 1667 Cole Blvd, Ste 225, Golden, CO 80401 (dbuller@kleinbuendel.com).

Published Online: January 28, 2015. doi:10.1001/jamadermatol.2014.3894.

Author Contributions: Dr Buller and Ms Liu had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: D. B. Buller, Berwick, Lantz, M. K. Buller, Kane, Liu.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: D. B. Buller, Kane, Liu.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: D. B. Buller, Liu.

Obtained funding: D. B. Buller, Berwick, Lantz, M. K. Buller, Shane.

Administrative, technical, or material support: All authors.

Study supervision: D. B. Buller, Kane.

Conflict of Interest Disclosures: Ms Buller is the owner of Klein Buendel Inc and Dr Buller’s spouse. Ms Buller and Dr Buller receive a salary from Klein Buendel Inc. No other disclosures were reported.

Funding/Support: This study was supported by contract HHSN261201100108C from the National Cancer Institute.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: Craig Long, MS, at the National Oceanic and Atmospheric Administration helped to obtain the daily UV Index forecast data. He was not compensated for his role.

Gerber  BS, Stolley  MR, Thompson  AL, Sharp  LK, Fitzgibbon  ML.  Mobile phone text messaging to promote healthy behaviors and weight loss maintenance: a feasibility study. Health Informatics J. 2009;15(1):17-25.
PubMed   |  Link to Article
Abroms  L, Padmanabhan  P, Evans  W. Mobile phones for health communication to promote behavior change. In: Noar  S, Harrington  N, eds. E-Health Applications: Promising Strategies for Behavior Change. New York, NY: Routledge; 2012:147-166.
Fox  S. Health topics. Pew Internet & American Life Project Web site. http://www.pewinternet.org/2011/02/01/health-topics-2/. Accessed June 20, 2014.
Zickuhr  K. Generations and their gadgets. Pew Internet & American Life Project Web site. http://www.pewinternet.org/files/old-media//Files/Reports/2011/PIP_Generations_and_Gadgets.pdf. Accessed May 8, 2014.
Fox  S. The social life of health information.2011. Pew Internet & American Life Project Web site. http://www.pewinternet.org/2009/06/11/the-social-life-of-health-information/. Accessed June 20, 2014.
Fjeldsoe  BS, Marshall  AL, Miller  YD.  Behavior change interventions delivered by mobile telephone short-message service. Am J Prev Med. 2009;36(2):165-173.
PubMed   |  Link to Article
Linsalata  D, Slawsby  A. Addressing Growing Handset Complexity With Software Solutions. Framingham, MA: IDC Analyze the Future; August 2005. Report IDCUS05WP002070.
Fjeldsoe  B, Miller  Y, Marshall  A. Text messaging interventions for chronic disease management and health promotion. In: Noar  S, Harrington  N, eds. E-Health Applications: Promising Strategies for Behavior Change. New York, NY: Routledge; 2012:167-186.
Suffoletto  B, Callaway  C, Kristan  J, Kraemer  K, Clark  DB.  Text-message–based drinking assessments and brief interventions for young adults discharged from the emergency department. Alcohol Clin Exp Res. 2012;36(3):552-560.
PubMed   |  Link to Article
Parrott  R. Motivation to attend to health messages: presentation of content and linguistic considerations. In: Maibach  E, Parrott  R, eds. Designing Health Messages: Approaches From Communication Theory and Public Health Practice. Thousand Oaks: Sage; 1995:7-23.
Riley  WT, Rivera  DE, Atienza  AA, Nilsen  W, Allison  SM, Mermelstein  R.  Health behavior models in the age of mobile interventions: are our theories up to the task? Transl Behav Med. 2011;1(1):53-71.
PubMed   |  Link to Article
Boulos  MN, Wheeler  S, Tavares  C, Jones  R.  How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomed Eng Online. 2011;10:24.
PubMed   |  Link to Article
Heron  KE, Smyth  JM.  Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol. 2010;15(pt 1):1-39.
PubMed   |  Link to Article
Westmaas  JL, Bontemps-Jones  J, Bauer  JE.  Social support in smoking cessation: reconciling theory and evidence. Nicotine Tob Res. 2010;12(7):695-707.
PubMed   |  Link to Article
Picchi  A, Daurat  C. Viacom, CBS, News Corp. to sell content on cellphones. The Denver Post. February 28, 2006:4C.
Coomes  CM, Lewis  MA, Uhrig  JD, Furberg  RD, Harris  JL, Bann  CM.  Beyond reminders: a conceptual framework for using short message service to promote prevention and improve healthcare quality and clinical outcomes for people living with HIV. AIDS Care. 2012;24(3):348-357.
PubMed   |  Link to Article
Krishna  S, Boren  SA, Balas  EA.  Healthcare via cell phones: a systematic review. Telemed J E Health. 2009;15(3):231-240.
PubMed   |  Link to Article
Janz  NK, Champion  VL, Strecher  VJ. The health belief model. In: Glanz  K, Rimer  BK, Viswanath  K, eds. Health Behavior and Health Education: Theory, Research and Practice.3rd ed. San Francisco, CA: Jossey-Bass; 2002:45-66.
Buller  DB, Berwick  M, Lantz  K,  et al.  Smartphone mobile application delivering real-time sun protection advice: a randomized clinical trial [published online January 28, 2014]. JAMA Dermatol. doi:10.1001/jamadermatol.2014.3889.
American Cancer Society. Cancer Facts & Figures 2014. Atlanta, GA: American Cancer Society; 2014.
Bandura  A. Social Foundations of Thought and Action: a Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall; 1986.
Buller  DB, Berwick  M, Shane  J, Kane  I, Lantz  K, Buller  MK.  User-centered development of a smart phone mobile application delivering personalized real-time advice on sun protection. Transl Behav Med. 2013;3(3):326-334.
PubMed   |  Link to Article
Hillhouse  J, Turrisi  R, Jaccard  J, Robinson  J.  Accuracy of self-reported sun exposure and sun protection behavior. Prev Sci. 2012;13(5):519-531.
PubMed   |  Link to Article
Shoveller  JA, Lovato  CY.  Measuring self-reported sunburn: challenges and recommendations. Chronic Dis Can. 2001;22(3-4):83-98.
PubMed
Kanetsky  PA, Rebbeck  TR, Hummer  AJ,  et al.  Population-based study of natural variation in the melanocortin-1 receptor gene and melanoma. Cancer Res. 2006;66(18):9330-9337.
PubMed   |  Link to Article
Banerjee  SC, Greene  K, Bagdasarov  Z, Campo  S.  “My friends love to tan”: examining sensation seeking and the mediating role of association with friends who use tanning beds on tanning bed use intentions. Health Educ Res. 2009;24(6):989-998.
PubMed   |  Link to Article
Bandura  A.  Health promotion by social cognitive means. Health Educ Behav. 2004;31(2):143-164.
PubMed   |  Link to Article
Hingle  MD, Snyder  AL, McKenzie  NE,  et al.  Effects of a short messaging service-based skin cancer prevention campaign in adolescents [published online July 19,2014]. Am J Prev Med. doi:10.1016/j.amepre.2014.06.014.
PubMed
Buller  D, Cokkinides  V, Hall  H,  et al.  Prevalence of sunburn, sun protection, and indoor tanning behaviors among Americans: systematic review from national surveys. J Am Acad Dermatol. 2011;65(5)(suppl 1):114-123.
PubMed   |  Link to Article
Fixsen  DL, Blase  KA, Naoom  SF, Wallace  F.  Core implementation components. Res Soc Work Pract. 2009;19(5):531-540.
Link to Article
Bellg  AJ, Borrelli  B, Resnick  B,  et al; Treatment Fidelity Workgroup of the NIH Behavior Change Consortium.  Enhancing treatment fidelity in health behavior change studies: best practices and recommendations from the NIH Behavior Change Consortium. Health Psychol. 2004;23(5):443-451.
PubMed   |  Link to Article
Rabin  BA, Glasgow  RE, Kerner  JF, Klump  MP, Brownson  RC.  Dissemination and implementation research on community-based cancer prevention: a systematic review. Am J Prev Med. 2010;38(4):443-456.
PubMed   |  Link to Article
Steinberg  DM, Levine  EL, Askew  S, Foley  P, Bennett  GG.  Daily text messaging for weight control among racial and ethnic minority women: randomized controlled pilot study. J Med Internet Res. 2013;15(11):e244. doi:10.2196/jmir.2844.
PubMed   |  Link to Article
Hashemian  TS, Kritz-Silverstein  D, Baker  R.  Text2Floss: the feasibility and acceptability of a text messaging intervention to improve oral health behavior and knowledge [published online August 4, 2014]. J Public Health Dent. doi:10.1111/jphd.12068.
PubMed
Buller  DB, Borland  R, Bettinghaus  EP, Shane  JH, Zimmerman  DE.  Randomized trial of a smartphone mobile application compared to text messaging to support smoking cessation. Telemed J E Health. 2014;20(3):206-214.
PubMed   |  Link to Article
Autier  P.  Sunscreen abuse for intentional sun exposure. Br J Dermatol. 2009;161(s3)(suppl 3):40-45.
PubMed   |  Link to Article
Kimlin  M, Parisi  A.  Usage of real-time ultraviolet radiation data to modify the daily erythemal exposure of primary schoolchildren. Photodermatol Photoimmunol Photomed. 2001;17(3):130-135.
PubMed   |  Link to Article
Shadish  WR, Cook  TD, Campbell  DT. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, MA: Houghton Mifflin Co; 2002.
Wu  XC, Eide  MJ, King  J,  et al.  Racial and ethnic variations in incidence and survival of cutaneous melanoma in the United States, 1999-2006. J Am Acad Dermatol. 2011;65(5)(suppl 1):S26-S37.
PubMed   |  Link to Article
Cockburn  MG, Zadnick  J, Deapen  D.  Developing epidemic of melanoma in the Hispanic population of California. Cancer. 2006;106(5):1162-1168.
PubMed   |  Link to Article
Gohara  MA.  Skin cancer in skins of color. J Drugs Dermatol. 2008;7(5):441-445.
PubMed

Figures

Place holder to copy figure label and caption
Figure.
CONSORT Diagram for the Trial
Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Demographic Characteristics of Study Sample
Table Graphic Jump LocationTable 2.  Results of ANCOVA Models Comparing Outcomes by Experimental Group at the 7-Week Interim and Posttest Surveys
Table Graphic Jump LocationTable 3.  Results of ANCOVA Models Comparing Outcomes by Whether Participant Used the Mobile Application at the 7-Week Interim and the Posttest Surveys

References

Gerber  BS, Stolley  MR, Thompson  AL, Sharp  LK, Fitzgibbon  ML.  Mobile phone text messaging to promote healthy behaviors and weight loss maintenance: a feasibility study. Health Informatics J. 2009;15(1):17-25.
PubMed   |  Link to Article
Abroms  L, Padmanabhan  P, Evans  W. Mobile phones for health communication to promote behavior change. In: Noar  S, Harrington  N, eds. E-Health Applications: Promising Strategies for Behavior Change. New York, NY: Routledge; 2012:147-166.
Fox  S. Health topics. Pew Internet & American Life Project Web site. http://www.pewinternet.org/2011/02/01/health-topics-2/. Accessed June 20, 2014.
Zickuhr  K. Generations and their gadgets. Pew Internet & American Life Project Web site. http://www.pewinternet.org/files/old-media//Files/Reports/2011/PIP_Generations_and_Gadgets.pdf. Accessed May 8, 2014.
Fox  S. The social life of health information.2011. Pew Internet & American Life Project Web site. http://www.pewinternet.org/2009/06/11/the-social-life-of-health-information/. Accessed June 20, 2014.
Fjeldsoe  BS, Marshall  AL, Miller  YD.  Behavior change interventions delivered by mobile telephone short-message service. Am J Prev Med. 2009;36(2):165-173.
PubMed   |  Link to Article
Linsalata  D, Slawsby  A. Addressing Growing Handset Complexity With Software Solutions. Framingham, MA: IDC Analyze the Future; August 2005. Report IDCUS05WP002070.
Fjeldsoe  B, Miller  Y, Marshall  A. Text messaging interventions for chronic disease management and health promotion. In: Noar  S, Harrington  N, eds. E-Health Applications: Promising Strategies for Behavior Change. New York, NY: Routledge; 2012:167-186.
Suffoletto  B, Callaway  C, Kristan  J, Kraemer  K, Clark  DB.  Text-message–based drinking assessments and brief interventions for young adults discharged from the emergency department. Alcohol Clin Exp Res. 2012;36(3):552-560.
PubMed   |  Link to Article
Parrott  R. Motivation to attend to health messages: presentation of content and linguistic considerations. In: Maibach  E, Parrott  R, eds. Designing Health Messages: Approaches From Communication Theory and Public Health Practice. Thousand Oaks: Sage; 1995:7-23.
Riley  WT, Rivera  DE, Atienza  AA, Nilsen  W, Allison  SM, Mermelstein  R.  Health behavior models in the age of mobile interventions: are our theories up to the task? Transl Behav Med. 2011;1(1):53-71.
PubMed   |  Link to Article
Boulos  MN, Wheeler  S, Tavares  C, Jones  R.  How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomed Eng Online. 2011;10:24.
PubMed   |  Link to Article
Heron  KE, Smyth  JM.  Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol. 2010;15(pt 1):1-39.
PubMed   |  Link to Article
Westmaas  JL, Bontemps-Jones  J, Bauer  JE.  Social support in smoking cessation: reconciling theory and evidence. Nicotine Tob Res. 2010;12(7):695-707.
PubMed   |  Link to Article
Picchi  A, Daurat  C. Viacom, CBS, News Corp. to sell content on cellphones. The Denver Post. February 28, 2006:4C.
Coomes  CM, Lewis  MA, Uhrig  JD, Furberg  RD, Harris  JL, Bann  CM.  Beyond reminders: a conceptual framework for using short message service to promote prevention and improve healthcare quality and clinical outcomes for people living with HIV. AIDS Care. 2012;24(3):348-357.
PubMed   |  Link to Article
Krishna  S, Boren  SA, Balas  EA.  Healthcare via cell phones: a systematic review. Telemed J E Health. 2009;15(3):231-240.
PubMed   |  Link to Article
Janz  NK, Champion  VL, Strecher  VJ. The health belief model. In: Glanz  K, Rimer  BK, Viswanath  K, eds. Health Behavior and Health Education: Theory, Research and Practice.3rd ed. San Francisco, CA: Jossey-Bass; 2002:45-66.
Buller  DB, Berwick  M, Lantz  K,  et al.  Smartphone mobile application delivering real-time sun protection advice: a randomized clinical trial [published online January 28, 2014]. JAMA Dermatol. doi:10.1001/jamadermatol.2014.3889.
American Cancer Society. Cancer Facts & Figures 2014. Atlanta, GA: American Cancer Society; 2014.
Bandura  A. Social Foundations of Thought and Action: a Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall; 1986.
Buller  DB, Berwick  M, Shane  J, Kane  I, Lantz  K, Buller  MK.  User-centered development of a smart phone mobile application delivering personalized real-time advice on sun protection. Transl Behav Med. 2013;3(3):326-334.
PubMed   |  Link to Article
Hillhouse  J, Turrisi  R, Jaccard  J, Robinson  J.  Accuracy of self-reported sun exposure and sun protection behavior. Prev Sci. 2012;13(5):519-531.
PubMed   |  Link to Article
Shoveller  JA, Lovato  CY.  Measuring self-reported sunburn: challenges and recommendations. Chronic Dis Can. 2001;22(3-4):83-98.
PubMed
Kanetsky  PA, Rebbeck  TR, Hummer  AJ,  et al.  Population-based study of natural variation in the melanocortin-1 receptor gene and melanoma. Cancer Res. 2006;66(18):9330-9337.
PubMed   |  Link to Article
Banerjee  SC, Greene  K, Bagdasarov  Z, Campo  S.  “My friends love to tan”: examining sensation seeking and the mediating role of association with friends who use tanning beds on tanning bed use intentions. Health Educ Res. 2009;24(6):989-998.
PubMed   |  Link to Article
Bandura  A.  Health promotion by social cognitive means. Health Educ Behav. 2004;31(2):143-164.
PubMed   |  Link to Article
Hingle  MD, Snyder  AL, McKenzie  NE,  et al.  Effects of a short messaging service-based skin cancer prevention campaign in adolescents [published online July 19,2014]. Am J Prev Med. doi:10.1016/j.amepre.2014.06.014.
PubMed
Buller  D, Cokkinides  V, Hall  H,  et al.  Prevalence of sunburn, sun protection, and indoor tanning behaviors among Americans: systematic review from national surveys. J Am Acad Dermatol. 2011;65(5)(suppl 1):114-123.
PubMed   |  Link to Article
Fixsen  DL, Blase  KA, Naoom  SF, Wallace  F.  Core implementation components. Res Soc Work Pract. 2009;19(5):531-540.
Link to Article
Bellg  AJ, Borrelli  B, Resnick  B,  et al; Treatment Fidelity Workgroup of the NIH Behavior Change Consortium.  Enhancing treatment fidelity in health behavior change studies: best practices and recommendations from the NIH Behavior Change Consortium. Health Psychol. 2004;23(5):443-451.
PubMed   |  Link to Article
Rabin  BA, Glasgow  RE, Kerner  JF, Klump  MP, Brownson  RC.  Dissemination and implementation research on community-based cancer prevention: a systematic review. Am J Prev Med. 2010;38(4):443-456.
PubMed   |  Link to Article
Steinberg  DM, Levine  EL, Askew  S, Foley  P, Bennett  GG.  Daily text messaging for weight control among racial and ethnic minority women: randomized controlled pilot study. J Med Internet Res. 2013;15(11):e244. doi:10.2196/jmir.2844.
PubMed   |  Link to Article
Hashemian  TS, Kritz-Silverstein  D, Baker  R.  Text2Floss: the feasibility and acceptability of a text messaging intervention to improve oral health behavior and knowledge [published online August 4, 2014]. J Public Health Dent. doi:10.1111/jphd.12068.
PubMed
Buller  DB, Borland  R, Bettinghaus  EP, Shane  JH, Zimmerman  DE.  Randomized trial of a smartphone mobile application compared to text messaging to support smoking cessation. Telemed J E Health. 2014;20(3):206-214.
PubMed   |  Link to Article
Autier  P.  Sunscreen abuse for intentional sun exposure. Br J Dermatol. 2009;161(s3)(suppl 3):40-45.
PubMed   |  Link to Article
Kimlin  M, Parisi  A.  Usage of real-time ultraviolet radiation data to modify the daily erythemal exposure of primary schoolchildren. Photodermatol Photoimmunol Photomed. 2001;17(3):130-135.
PubMed   |  Link to Article
Shadish  WR, Cook  TD, Campbell  DT. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, MA: Houghton Mifflin Co; 2002.
Wu  XC, Eide  MJ, King  J,  et al.  Racial and ethnic variations in incidence and survival of cutaneous melanoma in the United States, 1999-2006. J Am Acad Dermatol. 2011;65(5)(suppl 1):S26-S37.
PubMed   |  Link to Article
Cockburn  MG, Zadnick  J, Deapen  D.  Developing epidemic of melanoma in the Hispanic population of California. Cancer. 2006;106(5):1162-1168.
PubMed   |  Link to Article
Gohara  MA.  Skin cancer in skins of color. J Drugs Dermatol. 2008;7(5):441-445.
PubMed

Correspondence

CME
Also Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
Please click the checkbox indicating that you have read the full article in order to submit your answers.
Your answers have been saved for later.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.

Multimedia

Supplement.

eTable 1. Statistically Significant Moderators of Experimental Group and Effects of Mobile Application Use on Sun-Safety Outcomes at Posttest Survey

eTable 2. Results of Analysis of Covariance Models Comparing Theoretical Mediator Norms by Experimental Group at Posttest Survey

Supplemental Content

Some tools below are only available to our subscribers or users with an online account.

1,530 Views
4 Citations
×

Related Content

Customize your page view by dragging & repositioning the boxes below.

See Also...
Articles Related By Topic
Related Collections
Jobs