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Original Investigation |

Smartphone Mobile Application Delivering Personalized, Real-Time Sun Protection Advice 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):497-504. doi:10.1001/jamadermatol.2014.3889.
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Published online

Importance  Mobile smartphones are rapidly emerging as an effective means of communicating with many Americans. Using mobile applications (apps), they can access remote databases, track time and location, and integrate user input to provide tailored health information.

Objective  A smartphone mobile app providing personalized, real-time sun protection advice was evaluated in a randomized clinical trial.

Design, Setting, and Participants  The trial was conducted in 2012 and had a randomized pretest-posttest controlled design with a 10-week follow-up. Data were collected from a nationwide population-based survey panel. A sample of 604 non-Hispanic and Hispanic adults from the Knowledge Panel 18 years or older who owned an Android smartphone were enrolled.

Interventions  The mobile app provided advice on sun protection (ie, protection practices and risk of sunburn) and alerts (to apply or reapply sunscreen and get out of the sun), hourly UV Index, and vitamin D production based on the forecast UV Index, the phone’s time and location, and user input.

Main Outcomes and Measures  Percentage of days using sun protection and time spent outdoors (days and minutes) in the midday sun and number of sunburns in the past 3 months were collected.

Results  Individuals in the treatment group reported more shade use (mean days staying in the shade, 41.0% vs 33.7%; P = .03) but less sunscreen use (mean days, 28.6% vs 34.5%; P = .048) than controls. There was no significant difference in number of sunburns in the past 3 months (mean, 0.60 in the treatment group vs 0.62 for controls; P = .87). Those who used the mobile app reported spending less time in the sun (mean days keeping time in the sun to a minimum, 60.4% for app users vs 49.3% for nonusers; P = .04) and using all protection behaviors combined more (mean days, 39.4% vs 33.8%; P = .04).

Conclusions and Relevance  The mobile app improved some sun protection. Use of the mobile app was lower than expected but associated with increased sun protection. Providing personalized advice when and where people are in the sun may help reduce sun exposure.

Figures in this Article

The rapid proliferation and enormous reach of mobile computing devices, including smartphones and tablet computers, are transforming the communication experience.1,2 An increasing number of adults are using them to run mobile applications (apps) and access the Internet from anywhere,3,4 including to obtain health information.2

While there is no comprehensive theory explaining how mobile interventions improve health (ie, mHealth),1,5,6 they may be effective for several reasons. Mobile devices can enhance engagement with health information1,5 by proactively, unobtrusively, confidentially, and repeatedly reaching out to users, requesting their attention,1,7,8 creating an urgency to respond,9 and delivering advice in real time, on their schedules, 24 hours, 7 days a week, and anywhere.1,8 These properties should elevate the ecological validity of the health information by tailoring it to each user “in-the-moment” when and where it is most meaningful.1,5,7,10 They should create social support through their presence, relevancy, urgency, and interactivity and ability to increase adults’ accountability, deliver emotional support7 and create a sense of volition, choice, and control.11 Moreover, mobile devices can manage time and location dependences, access remote databases,12 and deliver reminders for action. All of these attributes could be used to improve self-efficacy and response efficacy13 and provide cues to action14 to motivate risk-reduction behaviors.

In this project, we conducted the first evaluation of a mobile app that provided sun protection advice to reduce the risk of skin cancer. It is estimated that approximately 2 million nonmelanoma skin cancers (ie, basal and squamous cell carcinoma) and 76 100 cutaneous malignant melanoma (43 890 males; 32 210 females) will be diagnosed in 2014,15 costing $1.4 billion annually for treatment16,17 It was hypothesized that the mobile app would increase sun protection practices and decrease sunburn prevalence by improving sun protection norms, self-efficacy, outcome expectations, and intentions.13

Sample

Participants were recruited from the Knowledge Panel, a survey panel representative of the US adult population administered by GfK Inc. GfK identified panel members who met eligibility requirements (ie, those who were non-Hispanic or Hispanic white, ≥18 years, and a US resident) and invited them to participate through their online system. Adults were screened on smartphone ownership (participation was limited to adults with Android handsets) and eligible individuals signed a consent form and completed the baseline survey online. Recruitment occurred from July 10 to 23, 2012. Participants received credit in the Knowledge Panel system.

Procedures

The trial involved a randomized pretest-posttest controlled design. Potential participants were randomized to receive invitations to join the study. Those who consented and completed the baseline survey in GfK’s online system were enrolled. Participants assigned to the treatment group received instructions through the online system to download, install, and use the Solar Cell mobile app. An online guide was provided, along with e-mail and telephone technical assistance. Seven weeks after randomization, treatment group participants were sent a reminder through the online system to use the mobile app. Ten weeks after the recruitment period began (September 18, 2012), all participants received the invitation for the posttest survey; posttesting concluded on October 3, 2012. A small group of participants failed to indicate they had completed their pretest in the online system, so they did not receive the posttest invitation. However, they were eligible and randomized, so they were recontacted for posttesting in December. Participants received credit in the Knowledge Panel system as an incentive. All procedures and forms were approved by the Western Institutional Review Board.

Solar Cell Mobile App

The Solar Cell mobile app was available for Android smartphones and has been described in detail elsewhere.18 In brief, it provided personalized sun protection advice based on (1) 5-day, hour-by-hour UV Index forecasts issued daily by the National Oceanic and Atmospheric Administration for each 0.5° latitude-longitude grid in North America (approximately 40 × 40 miles), (2) time and location from the phone, and (3) personal information from the user (ie, skin phenotype, height, weight, age, clothing coverage, use of sunscreen and its skin protection factor [SPF], and use of medications increasing sun sensitivity). Using algorithms based on published literature, Solar Cell provided the following advice: (1) risk of sunburn (time until sunburn and level of risk [low, moderate, extreme]), (2) time until reapplication of sunscreen, (3) recommended sun protection practices (use of sunglasses, sunscreen, hats, protective clothing, shade, and going indoors), (4) current forecasted UV Index, and (5) estimated amount of vitamin D produced by the skin. Pop-up screens provided educational information. Visual and audible alerts signaled when users needed to reapply sunscreen, achieved the recommended daily dose of vitamin D, and were at extreme risk of sunburn. Users could indicate when they were in the sun, in the shade, or indoors. Risk of sunburn was adjusted for skin phenotype, use of sunscreen and shade, and being indoors.

Measures

Outcome measures assessed exposure to the midday sun, sun protection practices, and sunburn prevalence in the past 3 months at baseline and posttest, the a priori primary outcomes. The surveys were pretested to ensure that the questions were understandable and easily answered, using cognitive interviewing procedures with non-Hispanic white adults (2 men and 3 women).

Sun Exposure and Sun Protection Practices

Sun exposure and protection practices were assessed with validated open-ended measures from the published literature. Sun exposure was measured by asking participants to report the number of days and number of hours spent in the sun between 10 am and 4 pm (solar noon ± 3 hours) in the past 3 months.19 Participants next reported the number of those days that they practiced each of 7 sun protection behaviors, which were converted to percentage of days engaged in each practice (ie, wearing sunscreen with SPF 15 or higher, sunscreen lip balm with SPF 15 or higher, clothing that protected the skin from the sun, a hat with a wide brim, and sunglasses, keeping time in the sun to a minimum, and staying in the shade). The mean percentage of practicing all sun protection behaviors was also calculated. Sunburn prevalence was assessed with 2 questions: whether participants had ever been sunburned and how many times they were sunburned in the past 3 months (defined as being red and/or painful from exposure to the sun).20

Moderators and Mediators

Potential effect moderators and theoretic mediators were measured at baseline and posttest, again using measures from the literature. Moderators included demographics, skin phenotype (based on hair color, eye color, and skin tanability),21 2-item tanning image scale (“I think I look healthier when I tan”; “I think I look better when I tan” [Cronbach α = 0.86 at baseline; 0.92 at posttest]),22 and personal history of skin cancer (“Has a doctor ever told you that you have had skin cancer?”). Participants who tend to take fewer precautions, such as men and younger adults23 and those who have more sun-sensitive skin and a history of skin cancer might respond more to the mobile app’s advice whereas tanners may resist it.

Theoretic mediators from Social Cognitive Theory24 were assessed by items created by the authors: (1) descriptive norms (“On the average, 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”) and injunctive norms ([A] “Most of my family think getting a sun tan is not a good thing” and [B] “Most of my friends think getting a sun tan is not a good thing” [α = 0.76 at baseline; 0.74 at posttest; 5-point Likert scale where 1 = strongly disagree and 5 = strongly agree]; “Most of my family think people should protect their skin from the sun” and “Most of my friends think people should protect their skin from the sun” [α = 0.67 at baseline; 0.66 at posttest; 5-point Likert scales]), (2) self-efficacy expectations (“I am confident I can [A] avoid getting sunburned while outdoors in the summer sun [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]), and (3) outcome expectations (“It is not so complicated to protect my skin from the sun”; 2 items assessing fit and ease: “Protecting my skin from the sun fits well with my outdoor activities and it is easy to protect my skin from the sun” [α = 0.68 at baseline; 0.71 at posttest; 5-point Likert scales]). Intentions to spend time in the sun to get a tan and a 2-item scale on sun protection—whether or not participant (A) planned (yes/no) and (B) was willing (1 = very unwilling, 5 = very willing) to protect skin from the sun when outdoors in the future (α = 0.75 at baseline; 0.76 at posttest) were assessed.

Statistical Analysis

The effect of Solar Cell was tested by comparing percentage of days practicing sun protection behaviors, time spent outdoors in the midday sun, and sunburn prevalence between the treatment and control groups. Comparisons were performed on posttest values, using analysis of covariance (ANCOVA) and controlling for baseline values and demographic covariates (identified by stepwise elimination at P < .10 [2-tailed]). Initially, comparisons were performed of participants who completed the posttest. Then, missing values were imputed and comparisons rerun to assess effects of loss to follow-up. Potential moderators of Solar Cell’s effect was probed by testing 2-way interactions between the moderator (with levels as appropriate) and treatment group in the ANCOVA models. All tests were performed using P < .05 (2-tailed).

Profile of the Sample

Overall, 604 of 1286 individuals invited were enrolled in the trial (see the CONSORT diagram in the Figure). A total of 682 individuals (331 in the treatment group and 351 in the control group) did not consent or were deemed ineligible. Of those enrolled, 150 participants (24.8%; 83 in the treatment group [25.1%] and 67 in the control group [19.1%]) were lost to follow-up at posttest, leaving 454 participants with complete data (222 in the treatment group; 232 in the control group).

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

The participation, loss, and follow-up of participants.

Graphic Jump Location

Participants had a diverse profile (Table 1). However, participants were younger, more educated, and more affluent and lived in large households, and fewer were Hispanic whites than in the US population. Specifically, they ranged in age from 18 to 80 years (68.5% were <45 years) and were well educated. The sample contained 9.6% Hispanic whites but was equally divided by sex. Also, 24.2% had high-risk skin phenotypes (4 or 5 on phenotypic index) and nearly a third had been diagnosed as having skin cancer. The average household size was 3.1 persons; 62.7% had incomes of $50 000 or greater; three-quarters were employed; most were married (nearly half had a child <18 years in their household, and three-quarters were heads of households); and about two-thirds owned their home. Participants were enrolled from 48 states (with exceptions of Idaho and Hawaii): 15.9% from the Northeast; 25.3%, the Midwest; 33.5%, the South; and 25.3%, the West, with 87.6% living in metropolitan areas. Randomization produced groups with no statistically significant differences in demographics, sun protection practices, time spent in the sun, or sunburn prevalence at baseline, except that the treatment group had fewer participants classified as head of household (73.4%) than the control group (80.3%; P = .04). There were very few differences associated with loss to follow-up. Those completing the posttest were older (mean age, 39.58 years; P = .02) and more owned their home (71.4%; P < .05) than those not completing it (mean age, 36.79 years; 62.0%).

Table Graphic Jump LocationTable 1.  Participant Characteristics
Use of Solar Cell Mobile App

Of the 305 people in the treatment group, 232 (76.1%) downloaded Solar Cell, but only 125 (41.0%) used it (ie, ran the app and received the feedback screen) at least once after installing it (downloading and use was detected by web servers). Most of those who used Solar Cell (76.0%) did so 1 to 5 times (16.0%, 6 to 10 times, and 8.0%, ≥11 times). These users created 166 profiles and ran existing profiles 532 times.

Effect of Solar Cell Mobile App on Sun Protection Practices

Solar Cell seemed to weakly affect sun protection practices at posttest (Table 2). Individuals assigned to Solar Cell and completing the posttest reported they used shade a higher proportion of time at posttest than controls. However, individuals assigned to Solar Cell also said they used sunscreen for a smaller proportion of time. No other significant differences were detected. When missing posttest values were imputed, none of the sun protection practices differed significantly by experimental group.

Table Graphic Jump LocationTable 2.  Results of ANCOVA Models Comparing Outcomes by Experimental Group
Effect of Solar Cell Mobile App on Sunburn Prevalence and Time Outdoors in the Midday Sun

There was no statistically significant difference between treatment groups on posttest sunburn prevalence (Table 1). The mobile app did not affect the amount of time users spent outdoors in the midday sun. They did not spend more days or hours in the sun than controls (Table 1).

Moderators of Effect of Solar Cell on Sun Protection Practices

The effect of Solar Cell on sun protection practices was moderated by preferences for a sun tan. Participants with stronger sun tan preferences assigned to Solar Cell reported using protective clothing while outdoors on a greater percentage of days than those in the control group (F = 4.48; P = .03) (see eTable 1 in the Supplement). With lower sun tan preferences, the Solar Cell group reported lower use of protective clothing than controls.

Effect of Using Solar Cell on Sun Protection and Exposure Outcomes

We probed whether the amount of Solar Cell usage was predictive of outcomes, defining use as whether participants ran the mobile app and received the feedback screen, which provided the sun safety advice. Analyses were conducted only within the treatment group on participants completing the posttest. Individuals who used Solar Cell reported a larger mean percentage of time practicing all sun protection behaviors combined than nonusers (Table 3). Use of Solar Cell was unrelated to sunburn prevalence, although means did suggest that participants who used it had fewer sunburns than those who did not (Table 3). Participants who used the app spent a larger percentage of days keeping their time in the sun to a minimum and fewer hours outdoors in the midday sun (but not fewer days) than those who did not use it (Table 3).

Table Graphic Jump LocationTable 3.  Results of ANCOVA Models Comparing Outcomes by Whether Participant Used Solar Cell Mobile App

Solar Cell, when used, had favorable effects on sun protection in some subgroups, specifically those defined by employment, household size, and sex. Participants not employed reported more days wearing wide-brimmed hats when using Solar Cell than those not using it (F = 8.57; P < .01) (eTable 1 in the Supplement); individuals who worked displayed little difference. In large households, participants using Solar Cell reported staying in the shade when outdoors on more days than those not using it (F = 5.81; P < .01) (eTable 1 in the Supplement). Also, using Solar Cell was associated with reporting spending fewer hours outdoors in the midday sun (between 10 am and 4 pm) by women; there was no difference in men (F = 4.88; P = .03) (eTable 1 in the Supplement).

Effect of Solar Cell on Theoretical Mediators

The main effect of Solar Cell on theoretical mediators—injunctive and descriptive norms, self-efficacy and outcome expectations, and intentions—was not statistically significant between treatment and control groups (eTable 2 in the Supplement). However, 2 demographic characteristics moderated the effect of Solar Cell on injunctive norms and self-efficacy expectations. Participants living in nonmetropolitan areas assigned to Solar Cell reported lower injunctive norms for sun protection by family and friends than controls (F = 5.98; P = .01) (Table 3). Individuals with lower income assigned to Solar Cell were more confident they could practice sun safety than controls (F = 3.53; P = .01; those with higher income assigned to Solar Cell were less confident) (Table 3).

The Solar Cell mobile app seemed to promote sun protection practices, especially when it was used. Specifically, it increased use of shade. Shade can substantially reduce exposure to solar UV radiation (UV-R),25 but it needs to be available for it to be used. By contrast, Solar Cell reduced the use of sunscreen, which is not altogether unfavorable. Sunscreen, while a popular practice,23 is frequently not used properly to maximize its protective value.25 Many adults underapply it and/or fail to reapply it to receive its full protective value.26 Thus, health authorities recommend sunscreen be used as a secondary practice after staying indoors or in the shade and wearing protective clothing, hats and eyewear.27 Still, a recent SMS (Short Message Service) text messaging intervention did increase sunscreen use by middle school students.28

Solar Cell may be more effective with some groups than others. Women in the United States seem to practice more sun protection than men, and they may have been more responsive to Solar Cell’s advice.23 The positive impact on individuals who preferred a suntan is a positive outcome, for tanning preferences may make them spend a large amount of time in the sun. When used, Solar Cell also benefited nonworking participants and those in larger households by increasing use of wide-brimmed hats, an uncommon precaution,23 and shade. Nonworking participants may have more time to use and learn Solar Cell’s advice than working individuals. Participants in larger households probably had more children; they may have followed Solar Cell’s advice either over concern for their children’s safety or to set a good example. It was somewhat unexpected that more affluent individuals who used Solar Cell had lower self-efficacy expectations than those who did not use it. Perhaps, more affluent adults were overconfident, and Solar Cell showed them that sun protection was more complicated than they believed, which could make them try harder to take proper precautions.

It is disappointing that Solar Cell did not reduce sunburns, although neither did a recent text messaging intervention with adolescents.28 A recent meta-analysis showed variation in success of mobile interventions using text messaging, with those focused on smoking cessation and physical activity being most successful.29 The lack of impact on sunburn prevalence may have occurred because use of Solar Cell was lower than expected, despite extensive usability testing, clear expectation that enrollees use it, and advice that adults indicated they desired (eg, estimates of the risk of sunburn).18 Intervention attrition and declining and/or low use has been observed with other technology-based interventions (eg, web-based interventions3033) and with mobile interventions,3438 despite the apparent enthusiasm for health-related mobile apps. Unfortunately, commercial data indicate that most people who download apps fail to use them regularly.39 In our formative research,18 some individuals predicted that they would use the mobile app to learn sun protection and then discontinue use, a trend observed with a diabetes mellitus self-management mobile app.40 Some participants also may have tried Solar Cell and felt they already knew its advice. Increased use of Solar Cell was associated with improvements in sun protection practices and less time spent in the midday sun, so future research on implementation strategies for mobile interventions is an important consideration both in randomized clinical trials and when evidence-based interventions are translated more broadly.4143

Fortunately, there was no evidence that providing advice on sunburn risk on a mobile app adjusted in real time for UV level and sun protection actions caused adults to spend more time outdoors and increase their high-risk UV-R exposure. This unfavorable adverse effect has been observed with sunscreen44 and personal UV meters.45 However, Solar Cell, when used, seemed to motivate participants to try intentionally to reduce their time in the sun. The advice in Solar Cell was designed to help individuals make more informed decisions regarding sun exposure and sun protection by not only displaying the risk of sunburn but also advocating sun protection practices appropriate for the real-time UV-R level and showing how taking these precautions decreased risk of sunburn, also in real time. The combination of tailored sun protection and real-time personal exposure information provided by the mobile app may be one way to avoid this undesirable adverse effect of sun protection technologies.46 Consistent with this conclusion, feedback on UV-R exposure provided online to students and teachers in a primary school in Australia resulted in lower sun exposure of students.47

There were several strengths in the trial. The sample was large and recruited nationwide; randomization created equivalent groups; and few differences were associated with loss to follow-up, all implying that the evaluation was unbiased. However, there were notable shortcomings. Generalizability may be limited by the racial and education composition of the sample. The trial enrolled only non-Hispanic and Hispanic whites; however, the incidence of skin cancer is far higher among non-Hispanic whites than in other racial/ethnic groups and is increasing in Hispanic whites.48,49 Likewise, the sample had high education, but smartphone ownership reflects this trend. Outcome measures were assessed by self-report but we used validated, reliable measures. The measures of sun protection practices and time spent outdoors were newly validated19 and used for one of the first times in a trial. As discussed herein, the inability to get most intervention group participants to use Solar Cell was a major weakness. GfK would not provide us direct contact with participants. It was difficult to assist them with technical problems and we were only able to remind participants to download and use the mobile app 1 time after randomization.

Smartphone mobile apps have potential to deliver disease prevention interventions to a large and growing segment of the US population, engage them proactively, confidentially, and repeatedly, and provide real-time personalized advice when and where they need it. Solar Cell, one of the first sun safety mobile apps evaluated in a randomized clinical trial, may help adults with high-risk skin types or who spend a lot of time outdoors make effective prevention decisions that reduce dangerous doses of UV-R.

Accepted for Publication: September 18, 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.2014.3889.

Author Contributions: Dr Buller and Ms Liu had full access to all of 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.

Administrative, technical, or material support: All authors.

Study supervision: D. B. Buller, Shane, Kane.

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

Funding/Support: The research reported in this paper was supported by a contract from the National Cancer Institute (HHSN261201100108C).

Role of the Sponsor: The National Cancer Institute 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: We are indebted to Craig Long at the National Oceanic and Atmospheric Administration for his help in obtaining the daily UV Index forecast data.

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PubMed   |  Link to Article
Buller  DB, Andersen  PA, Walkosz  BJ,  et al.  Compliance with sunscreen advice in a survey of adults engaged in outdoor winter recreation at high-elevation ski areas. J Am Acad Dermatol. 2012;66(1):63-70.
PubMed   |  Link to Article
Diffey  BL.  When should sunscreen be reapplied? J Am Acad Dermatol. 2001;45(6):882-885.
PubMed   |  Link to Article
World Health Organization. Ultraviolet radiation and the INTERSUN programme: sun protection: simple precautions in the sun.http://www.who.int/uv/sun_protection/en/. Accessed January 29, 2014.
Hingle  MD, Snyder  AL, McKenzie  NE,  et al.  Effects of a short messaging service-based skin cancer prevention campaign in adolescents. Am J Prev Med. 2014;47(5):617-623.
PubMed   |  Link to Article
Head  KJ, Noar  SM, Iannarino  NT, Grant Harrington  N.  Efficacy of text messaging-based interventions for health promotion: a meta-analysis. Soc Sci Med. 2013;97:41-48.
PubMed   |  Link to Article
Leslie  E, Marshall  AL, Owen  N, Bauman  A.  Engagement and retention of participants in a physical activity website. Prev Med. 2005;40(1):54-59.
PubMed   |  Link to Article
Eysenbach  G.  The law of attrition. J Med Internet Res. 2005;7(1):e11.
PubMed   |  Link to Article
Glasgow  RE, Nelson  CC, Kearney  KA,  et al.  Reach, engagement, and retention in an Internet-based weight loss program in a multi-site randomized controlled trial. J Med Internet Res. 2007;9(2):e11.
PubMed   |  Link to Article
Buller  DB, Floyd  AHL. Internet-based interventions for health behavior change. In: Noar SM, Harrington NG, eds. EHealth Applications: Promising Strategies for Behavior Change. 1st ed. New York, NY: Routledge; 2012:59-78.
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
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.
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
Becker  S, Miron-Shatz  T, Schumacher  N, Krocza  J, Diamantidis  C, Albrecht  UV.  mHealth 2.0: experiences, possibilities, and perspectives. JMIR Mhealth Uhealth. 2014;2(2):e24.
PubMed   |  Link to Article
Cohen  DK, Nardone  B, Cotton  M, West  DP, Kundu  RV.  Use of a mobile application to characterize a remote and global population of acne patients and to disseminate peer-reviewed acne-related health education. JAMA Dermatol. 2014;150(6):660-662.
PubMed   |  Link to Article
Localytics. Localytics app stickiness index: Q2 2014. Localytics Web site.http://www.localytics.com/resources/app-stickiness-index-q2-2014/. Updated 2014. Accessed September 2, 2014.
Tatara  N, Arsand  E, Skrøvseth  SO, Hartvigsen  G.  Long-term engagement with a mobile self-management system for people with type 2 diabetes. JMIR mHealth Uhealth. 2013;1(1):e1.
PubMed
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
Autier  P, Boniol  M, Doré  JF.  Sunscreen use and increased duration of intentional sun exposure: still a burning issue. Int J Cancer. 2007;121(1):1-5.
PubMed   |  Link to Article
Carli  P, Crocetti  E, Chiarugi  A,  et al.  The use of commercially available personal UV-meters does cause less safe tanning habits: a randomized-controlled trial. Photochem Photobiol. 2008;84(3):758-763.
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
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

Figures

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

The participation, loss, and follow-up of participants.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Participant Characteristics
Table Graphic Jump LocationTable 2.  Results of ANCOVA Models Comparing Outcomes by Experimental Group
Table Graphic Jump LocationTable 3.  Results of ANCOVA Models Comparing Outcomes by Whether Participant Used Solar Cell Mobile App

References

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Ekwueme  DU, Guy  GP  Jr, Li  C, Rim  SH, Parelkar  P, Chen  SC.  The health burden and economic costs of cutaneous melanoma mortality by race/ethnicity-United States, 2000 to 2006. J Am Acad Dermatol. 2011;65(5)(suppl 1):S133-S143.
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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.
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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.
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Shoveller  JA, Lovato  CY.  Measuring self-reported sunburn: challenges and recommendations. Chronic Dis Can. 2001;22(3-4):83-98.
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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.
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Buller  DB, Cokkinides  V, Hall  HI,  et al.  Prevalence of sunburn, sun protection, and indoor tanning behaviors among Americans: review from national surveys and case studies of 3 states. J Am Acad Dermatol. 2011;65(5)(suppl 1):S114-S123.
PubMed   |  Link to Article
Bandura  A.  Health promotion by social cognitive means. Health Educ Behav. 2004;31(2):143-164.
PubMed   |  Link to Article
Buller  DB, Andersen  PA, Walkosz  BJ,  et al.  Compliance with sunscreen advice in a survey of adults engaged in outdoor winter recreation at high-elevation ski areas. J Am Acad Dermatol. 2012;66(1):63-70.
PubMed   |  Link to Article
Diffey  BL.  When should sunscreen be reapplied? J Am Acad Dermatol. 2001;45(6):882-885.
PubMed   |  Link to Article
World Health Organization. Ultraviolet radiation and the INTERSUN programme: sun protection: simple precautions in the sun.http://www.who.int/uv/sun_protection/en/. Accessed January 29, 2014.
Hingle  MD, Snyder  AL, McKenzie  NE,  et al.  Effects of a short messaging service-based skin cancer prevention campaign in adolescents. Am J Prev Med. 2014;47(5):617-623.
PubMed   |  Link to Article
Head  KJ, Noar  SM, Iannarino  NT, Grant Harrington  N.  Efficacy of text messaging-based interventions for health promotion: a meta-analysis. Soc Sci Med. 2013;97:41-48.
PubMed   |  Link to Article
Leslie  E, Marshall  AL, Owen  N, Bauman  A.  Engagement and retention of participants in a physical activity website. Prev Med. 2005;40(1):54-59.
PubMed   |  Link to Article
Eysenbach  G.  The law of attrition. J Med Internet Res. 2005;7(1):e11.
PubMed   |  Link to Article
Glasgow  RE, Nelson  CC, Kearney  KA,  et al.  Reach, engagement, and retention in an Internet-based weight loss program in a multi-site randomized controlled trial. J Med Internet Res. 2007;9(2):e11.
PubMed   |  Link to Article
Buller  DB, Floyd  AHL. Internet-based interventions for health behavior change. In: Noar SM, Harrington NG, eds. EHealth Applications: Promising Strategies for Behavior Change. 1st ed. New York, NY: Routledge; 2012:59-78.
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
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.
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
Becker  S, Miron-Shatz  T, Schumacher  N, Krocza  J, Diamantidis  C, Albrecht  UV.  mHealth 2.0: experiences, possibilities, and perspectives. JMIR Mhealth Uhealth. 2014;2(2):e24.
PubMed   |  Link to Article
Cohen  DK, Nardone  B, Cotton  M, West  DP, Kundu  RV.  Use of a mobile application to characterize a remote and global population of acne patients and to disseminate peer-reviewed acne-related health education. JAMA Dermatol. 2014;150(6):660-662.
PubMed   |  Link to Article
Localytics. Localytics app stickiness index: Q2 2014. Localytics Web site.http://www.localytics.com/resources/app-stickiness-index-q2-2014/. Updated 2014. Accessed September 2, 2014.
Tatara  N, Arsand  E, Skrøvseth  SO, Hartvigsen  G.  Long-term engagement with a mobile self-management system for people with type 2 diabetes. JMIR mHealth Uhealth. 2013;1(1):e1.
PubMed
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
Autier  P, Boniol  M, Doré  JF.  Sunscreen use and increased duration of intentional sun exposure: still a burning issue. Int J Cancer. 2007;121(1):1-5.
PubMed   |  Link to Article
Carli  P, Crocetti  E, Chiarugi  A,  et al.  The use of commercially available personal UV-meters does cause less safe tanning habits: a randomized-controlled trial. Photochem Photobiol. 2008;84(3):758-763.
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
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

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Supplement.

eTable 1. Statistically significant moderators of experimental group and mobile app use effects on sun safety outcomes at posttest

eTable 2. Results of ANCOVA models comparing theoretical mediators by experimental group

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