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Published on 26.06.15 in Vol 1, No 1 (2015): Jan-Jun

This paper is in the following e-collection/theme issue:

    Original Paper

    Efficacy of a Mobile-Enabled Web App (iCanFit) in Promoting Physical Activity Among Older Cancer Survivors: A Pilot Study

    1School of Public Health, Texas A&M University, College Station, TX, United States

    2Department of Geography, Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States

    3Department of Family Medicine, Baylor Scott & White Health, Temple, TX, United States

    4Department of Statistics, Texas A&M University, College Station, TX, United States

    Corresponding Author:

    Yan Alicia Hong, PhD

    School of Public Health

    Texas A&M University

    1266 TAMU

    College Station, TX, 77843

    United States

    Phone: 1 979 436 9343

    Fax:1 979 436 9591

    Email:


    ABSTRACT

    Background: The benefits of physical activity for cancer survivors are well documented. However, few older cancer survivors are engaged in regular physical activity. Mobile technologies may be an effective method to deliver physical activity promotion programs for older cancer survivors. iCanFit, a mobile-enabled Web-based app, was developed based on formative research and usability testing. This app includes interactive features of physical activity, goal setting and tracking, and receiving personalized visual feedback.

    Objective: The aim of this study is to pilot test the initial efficacy of iCanFit.

    Methods: Older cancer survivors (N=30) were recruited online through our collaborative partnership with a cancer survivor's organization. After the participants completed an online baseline survey, they were asked to use the iCanFit website. Instructional videos on how to use the web app were available on the website. Participants were asked to complete a follow-up survey 2-3 months later. Participants’ physical activity, quality of life, and their experience with iCanFit were measured.

    Results: A total of 30 participants completed the baseline survey, and 26 of them (87%, 26/30) also completed a follow-up survey 2-3 months later. The median age of participants was 69 years (range 60-78). Participants’ quality of life and engagement in regular physical activity improved significantly after the use of iCanFit. Participants indicated a general affinity towards the key function “Goals” in iCanFit, which motivated continued activity. They also provided suggestions to further improve the app (eg, adding a reminder functionality, easier or alternative ways of entering activities).

    Conclusion: The interactive Web-based app iCanFit has demonstrated initial efficacy. Even though our study was limited by a small sample size, convenience sampling, and a short follow-up period, results suggest that using mobile tools to promote physical activity and healthy living among older cancer survivors holds promise. Next steps include refining iCanFit based on users’ feedback and developing versatile functionality to allow easier physical activity goal setting and tracking. We also call for more studies on developing and evaluating mobile and web apps for older cancer survivors.

    JMIR Cancer 2015;1(1):e7

    doi:10.2196/cancer.4389

    KEYWORDS



    Introduction

    More than 87% of American adults have used the Internet. Even among older adults, 88% of those aged 50-64 are online and more than 57% of those older than 64 are online. Furthermore, mobile phone use among older adults has also grown from 11% in 2011 to 18% in 2013 [1]. Literature suggests that older adults are increasingly turning to the Internet for information [2]. Despite high rates of Internet access and mobile device ownership, only a small number of mobile or Web-based apps have been designed specifically for older adults.

    Physical activity is particularly important for older adults, especially for those with chronic conditions including cancer [3]. Regular physical activity has been shown to effectively lessen fatigue and improve overall quality of life for cancer survivors [4]. Web- or mobile-based physical activity interventions have been shown to be effective [5-8], but such interventions have rarely targeted older adults, and web or mobile apps designed specifically for older cancer survivors are especially scarce [9,10].

    Early detection and improved treatment for cancer have resulted in approximately 13 million survivors being alive in the United States today; this number will increase by nearly a third to almost 18 million by 2022 [11]. A majority of cancer survivors are >60 years of age. Caring for the large number of older cancer survivors presents a significant public health challenge [12]. The benefits of physical activity for cancer survivors have been reinforced through several guidelines [12,13]. However, the level of adherence to physical activity guidelines among cancer survivors is very low. For example, only 4.6% of breast cancer survivors followed physical activity guidelines compared to 12% of women without breast cancer [14]. The level of physical activity among older cancer survivors is believed to be even lower [13]. Thus, there is an urgent need for effective interventions that can reach large numbers of older cancer survivors efficiently. Literature suggests that older cancer survivors are more likely to use the Internet compared to their counterparts without a cancer diagnosis [15]. Once online, cancer survivors are more likely to use the Internet for health-related purposes [16]. Thus, it is potentially efficient and effective to deliver Web- or mobile-based interventions to older cancer survivors.

    Prior studies on promoting physical activity for older adults suggested that goal setting is an effective intervention strategy. Through setting specific and achievable goals and regular feedback, people are motivated to exercise. Additionally, personalized feedback reinforces maintenance of behavioral change [17,18].

    With the aim of promoting physical activity among older cancer survivors, the mobile-enabled website app iCanFit was designed with formative research with key stakeholders [19], along with usability and acceptability testing [20]. The major functions in the iCanFit web app include “Goals” (physical activity, goal setting, and tracking), “Community” (an online network for users), “Tips” (regularly updated tips on healthy living), and “Resources” (active links to reliable health information) (Figure 1). Of these functions, “Goals” is the most important tool (Figure 2). Guided by the Theory of Goal Setting [18], “Goals” motivates participants to exercise regularly through goal setting, activity tracking, personalized feedback, and progress reviews. A new participant will be cued to set up a long-term goal; then be cued to set a short-term, usually weekly, goal. Examples of goals are available to guide participants set specific and tangible goals. Each goal includes type, frequency, and duration of the physical activities. The system will automatically calculate total minutes and energy expenditure for each and all of the activities (Figure 2). On an interactive calendar, participants can enter their activity and log the total number of minutes they exercised on a selected day (Figure 3). Their activity log will be compared to their goals and they will receive tailored messages based on this comparison. Examples of such messages are “Congratulations, you’ve achieved your goal, keep up the good work!” or

    Sorry you did not meet your goal. You may consider setting a more realistic goal. Keep moving!

    The tailored messages (Figure 3) are sent automatically from iCanFit using a pre-designed database that contains >100 messages for different conditions of meeting goals. Finally, “View Progress” allows users to track their progress through various metrics, including total energy expenditure, total minutes exercised, number of days exercised, and comparisons between actual activity and their preset goals (Figure 4). Users have the options to view their progress as bars, lines, and/or as a calendar (Figure 4).

    This study targeted the understudied but growing population of older cancer survivors, and explored the feasibility and initial efficacy of an interactive Web-based app to promote their physical activity. Through a pilot test of pre-post design, we aimed to answer the research question of whether participating in iCanFit is associated with changes in physical activity and quality of life among older cancer survivors.

    Figure 1. Screenshot of iCanFit.
    View this figure
    Figure 2. "Goals" function of iCanFit.
    View this figure
    Figure 3. Activity log of iCanFit.
    View this figure
    Figure 4. "View Progress" function of iCanFit.
    View this figure

    Methods

    Overview of Study Design

    This was a pre-post design study with 2-3 months of follow-up. Online surveys and a mobile-enabled web app were used to collect participant data.

    Participant Recruitment

    All participants were recruited online. Through our collaborative partnership with a cancer survivor's organization, emails were sent out to their list-serve inviting eligible cancer survivors to participate. Inclusion criteria for participation included (1) ≥60 years, (2) having ever been diagnosed with cancer, (3) reporting the ability to do physical activity, and (4) having access to the Internet. Potential participants were instructed to the iCanFit website [21]. On the homepage, there was a screening survey for potential participants. Individuals who completed the screening survey (including the provision of contact information) received a phone call from our research staff to re-verify their eligibility. Eligible participants received instruction on how to participate in the study. Instructional videos on how to use iCanfit were also available on the “Help” page.

    Data Collection Procedures

    Project information was sent out to participants via email with a link to an online survey. Prior to the survey, an informed consent with a detailed project description, benefits, and potential risks of participation was provided. Participants who completed the informed consent were directed to the baseline survey, which took approximately 10 minutes to complete. After the survey, participants were directed to the project site and instructed to create a username and password. The “Help” page also contained instructional videos on how to use the web app. All participants were instructed to use iCanFit for about 8-12 weeks. During that time, 4-6 emails were sent to participants reminding them to continue using iCanFit. The emails were sent automatically and contained personalized information such as participant’s name, duration of the study, and when the follow-up survey would be sent. After 8-12 weeks, participants were sent a link to the follow-up survey, which took approximately 10 minutes to complete. Participants who completed the baseline survey received a $15 gift card and those who completed the follow-up survey received an additional $35 gift card. The study protocol was approved by the Institutional Review Board (IRB) of the Texas A&M University.

    Measures

    The baseline survey included participant demographics, use of mobile tools, quality of life, and current physical activity. The quality of life was measured with 7 items, including self-rated health (1 being poor and 5 being excellent), overall quality of life, fatigue, pain, shortness of breath, stress, and sleep. The response options ranged from 0-10, with 0 being the worst and 10 the best. The Cronbach alpha value for the 7 quality of life items was .79. We also asked the participants how many days in the past 30 days did they not have good physical health, good mental health, or could not do usual activities.

    Participants were asked about their current engagement in regular physical activity with the following 5 response options (1) not engaged in physical activity and have no plan of doing so, (2) not engaged but plan to do so in 3 months or less, (3) engaged occasionally but not on a regular basis, (4) engaged in regular physical activity but started less than 3 months, and (5) engaged in regular physical activity and has been doing so for more than 3 months.

    In the follow-up survey, participants were asked about the same outcomes on quality of life and engagement in regular physical activity, as well as their experience with the program. Some open-ended questions were included to solicit their feedback and suggestions for iCanFit. In addition to the above self-reported subjective data, users’ activities on iCanFit including the number of goals set and achieved were captured by the web app.

    Data Analysis

    All data from the online surveys were downloaded and stored in SPSS 22.0. Descriptive statistics were used for data analysis; paired t test was used to compare pre-post changes and assess level of significance at the P<.05 level. Open-ended responses were entered into Microsoft Word to identify ranges and patterns of responses.


    Results

    Demographics and Mobile Technology Use

    Of the total eligible participants who created an online account and completed the baseline survey (N=30), 87% (26/30) completed the follow-up survey. These 26 participants were the sample to test the efficacy of iCanFit.

    As shown in Multimedia Appendix 1, the median age of the 26 participants in the baseline sample was 69 years (range 60-78). About 70% (18/26) of them were female, 73% (19/26) were white, 77% were married (20/26), 38% (10/26) had some college education, and 42% (11/26) had completed college. Only 2 participants (7%, 2/26) were still in cancer treatment, while the remaining majority weremajority was not, including 46% (12/26) that were being monitored but not in treatment, and 46% (12/26) that were post-treatment survivors. All participants had other chronic conditions, including high blood pressure (46%, 12/26), heart disease (23%, 2/26), arthritis (31%, 8/26), osteoporosis (23%, 6/26), sciatica (12%, 3/26), diabetes (12%, 3/26), chronic back pain (12%, 3/26), and depression or anxiety (8%, 2/26). About 46% (12/26) of participants talked to their health care providers about physical activity almost every time they saw the provider, 27% (7/26) said sometimes they did, and 23% (6/26) said they rarely or never did.

    All of the participants had high-speed Internet access at home, and most of them used multiple mobile tools for Internet access. Specifically, 69% (18/26) used a desktop, 46% (12/26) used a laptop, 46% (12/26) used a tablet, and 81% (21/26) used a mobile phone with app capabilities; and as many as 23% (6/23) of participants used a mobile phone for Internet access every day. Their weekly online hours varied from 1-60 hours (median 10 hours). Most older cancer survivors frequently searched health information online, for example, only 1 person (4%, 1/26) never searched health information online, 8% (3/26) did so twice a month, 19% (2/26) did it once a week, 27% (5/26) did it 2-3 times a week, and 15% (6/26) did it every day. Most participants were active users of social media. For instance, 65% (17/26) of them were on Facebook, 50% (13/26) subscribed to an email listserv, 31% (15/26) used LinkedIn, and 19% (5/26) used an online messenger.

    Physical Activity and Quality of Life

    The differences in quality of life and physical activity at baseline and follow-up are shown in Table 1. The mean self-rated health score (1-5) increased from 3.23 to 3.81, the overall quality of life score increased from 8.12 to 8.50, fatigue-related quality of life score increased from 6.04 to 6.77, pain-related quality of life score increased from 8.85 to 9.11, shortness of breath-related quality of life score increased from 8.65 to 9.04, stress-related quality of life score increased from 6.27 to 7.04, and sleep quality also increased from 6.19 to 7.00. All of the quality of life-related changes were significant (P<.05). The number of days without good physical health, good mental health, or reporting they could not do usual activities did not have significant changes due to flooring effects of the numbers.

    Table 1. Efficacy of iCanFit on promoting physical activity and quality of life among older cancer survivors.
    View this table

    In terms of self-reported physical activities at baseline, 3 participants (12%, 3/26) were not engaged in physical activity and had no plan of doing so and 5 participants (19%, 5/26) were not engaged in physical activity but planned to do so in less than 3 months. All of these 8 participants were engaged in physical activity in the follow-up. The number of participants engaged in regular physical activity also increased from 11 (42%, 11/26) to 15 (58%, 15/26).

    Experience With iCanFit

    Participants’ use of and experience with iCanFit is depicted in Table 2. Most participants accessed iCanFit with multiple mobile devices including 58% (15/26) via desktop, 38% (10/26) via laptop, 23% (6/26) via tablet, and 27% (7/26) via mobile phones. Participants used iCanFit with varying frequencies; 12% (3/26) used it once every 2 weeks, 62% (16/26) used it once a week, 19% (5/26) used it 2-3 times per week, and 8% (2/26) used it 4-5 times a week. Their weekly median time on iCanFit was 25 minutes (range 5-60). About 65% (17/26) of participants had talked to their family and friends about iCanFit, and 27% (7/26) used iCanFit with their family or friends.

    Table 2. User experience with iCanFit (follow-up, N=26).
    View this table

    Table 2 also demonstrates that participants reported an overall positive experience with iCanFit. On the scale of 0-5, the mean (SD) value of overall ease of using iCanFit was 4.1 (1.7), 3.9 (2.1) for "Goals", 4.2 (0.9) for "Healthy Tips", 4.5 (0.8) for "Resources", and 4.1 (0.9) for "Community".

    Participants also shared their experience of using iCanFit. Most participants made comments like “Great program, it helps me track my exercise.” Their favorite functions included the graphic display of their progress and the personalized feedback of their physical activities. For example, one shared “I like it sends me feedback right after I enter my activities,” and “It’s great to see a bar chart comparing my goals and my activities.” Participants who did not exercise regularly or have an exercise plan prior to the intervention reported using iCanFit to set goals and track their activities. For example, one participant reported, “It motivates me to exercise more so I can meet or exceed my goals,” and “After some weeks of using it, I know how much I can do on a weekly basis.”

    Participants also shared suggestions on further improving the programs. Nearly half of the participants had frustration with forgetting to enter activities. “I often forget to enter my activities, and at the end of the week, I would totally forget what I’ve done,” and “It’d be better if I don’t need to enter my activity because I easily forget.” Some suggested using automatic function; for example, a participant had proposed a pedometer “If you could link my pedometer to iCanFit, then I don’t need to enter activity.” Participants also commented that they mostly used the "Goals" function and didn’t use the other functions (Healthy tips, Resources and Community) very often.

    Within2-3 months of using iCanFit, participants set a total of 289 goals, with a mean of 11.1 per participant. Nearly half of these goals were met. The most typical activities included walking, jogging, aerobics, and gardening.


    Discussion

    Principal Findings

    To our knowledge, there has been little systematic research of mobile-enabled, Web-based apps for older cancer survivors. As one of the first pilot studies, the initial efficacy of iCanFit as a web app has been demonstrated in this study. Significant improvement in quality of life and engagement in physical activity all indicate that this, and similar mobile-enabled, web apps may, be able to have positive effects for older cancer survivors. Positive user experiences with iCanFit, coupled with improvements in health-related items all point to the promising utility of this interactive web app. The already large, parallel advances in similar technology including mobile and web apps, fitness trackers, and other devices, may potentially benefit millions of older adults with chronic conditions.

    Limitations

    Our study has the following limitations. First, the use of convenience sampling, the sample being mostly drawn from a cancer survivor's organization in Texas, and the relatively highly educated sample (eg, most with some college education or above) limits generalizability to all older cancer survivors in the country. In addition, there was a voluntary bias in the sampling as the older cancer survivors who signed up for this study might have been those who were already physically active. Second, our measures of quality of life were mainly developed from our own research and might not be comparable to other studies with validated quality of life scales. The pilot study described here aimed to test the initial efficacy of iCanFit and it took participants <10 minutes to complete the online survey. Third, the quality of life measures focused on physical and mental health functioning and not a more broad application of multiple quality of life domains. Even so, lifestyle factors such as physical activity were the focus of this study. Future studies need to include validated, multi-dimensional qualify of life scales to increase its comparability to other studies. Fourth, the measure of physical activity was dependent on self-entry and self-report, which may incur recall or social desirability biases. Future research should thus include alternative forms of data entry. For example, the recent popularity of accelerometer and sensors can be used to capture users’ physical activity to reduce users’ burden and potential errors and biases in data entry. Finally, our study did not have a control group and had only one follow-up. Future studies need to include a larger sample, a control group, and long-term follow-ups to further establish the efficacy of the intervention. While the vast majority of older adults have at least one chronic condition, nearly three quarters have ≥2 [22]. Thus, effective interventions must be multi-faceted and recognize that older adults are likely to have co-morbidities which bring greater complexity to adopting and maintaining healthy lifestyles [23,24]. In this study, all older cancer survivors had multiple chronic conditions, which is important to consider when determining their specific needs and barriers to physical activity [25,26]. For example, more tailored messages about how to manage chronic conditions and tips on easy access to opportunities for physical activity and enhanced communication with healthcare providers may be helpful [27-29]. We also need to explore innovative multicomponent interventions to tailor the needs of older adults [30]. Online and mobile health programs designed for older adults need to tailor for their literacy level, cognitive function, and physical abilities [31].

    Conclusions

    Since the population of older cancer survivors is expected to grow; interventions to improve quality of life among this population are timely and will continue to be needed at even greater levels. The high rates of Internet access and mobile phone ownership throughout the United States and globally have driven greater searches for health-related information online via various mobile devices. Thus, the utility of this timely research is of global interest. Our next steps will be focused on two aspects: building the mobile app with support for communication with smart devices (eg, devices with sensors), and disseminating this mobile-enabled app to greater numbers of older cancer survivors.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Demographics, health conditions, and use of mobile tools among participants (baseline and follow-up).

    PDF File (Adobe PDF File), 111KB

    References

    1. Fox S, Rainie L. The Web at 25. Pew Research Center; 2014.   URL: http://www.pewinternet.org/files/2014/02/PIP_25th-anniversary-of-the-Web_0227141.pdf [accessed 2015-02-28] [WebCite Cache]
    2. Smith A. Older Adults and Technology Use. Pew Research Center; 2014.   URL: http://www.pewinternet.org/files/2014/04/PIP_Seniors-and-Tech-Use_040314.pdf [accessed 2015-02-28] [WebCite Cache]
    3. Fact Sheet for Health Professionals on Physical Activity Guidelines for Older Adults.: US Department of Health and Human Services; 2008.   URL: http://www.cdc.gov/nccdphp/dnpa/physical/pdf/PA_Fact_Sheet_OlderAdults.pdf [accessed 2010-12-21] [WebCite Cache]
    4. Luctkar-Flude MF, Groll DL, Tranmer JE, Woodend K. Fatigue and physical activity in older adults with cancer: a systematic review of the literature. Cancer Nurs 2007;30(5):E35-E45. [CrossRef] [Medline]
    5. Fanning J, Mullen SP, McAuley E. Increasing physical activity with mobile devices: a meta-analysis. J Med Internet Res 2012;14(6):e161 [FREE Full text] [CrossRef] [Medline]
    6. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs 2013;28(4):320-329 [FREE Full text] [CrossRef] [Medline]
    7. Kuijpers W, Groen WG, Aaronson NK, van Harten WH. A systematic review of web-based interventions for patient empowerment and physical activity in chronic diseases: relevance for cancer survivors. J Med Internet Res 2013;15(2):e37 [FREE Full text] [CrossRef] [Medline]
    8. Wijsman CA, Westendorp RG, Verhagen EA, Catt M, Slagboom PE, de Craen AJ, et al. Effects of a web-based intervention on physical activity and metabolism in older adults: randomized controlled trial. J Med Internet Res 2013;15(11):e233 [FREE Full text] [CrossRef] [Medline]
    9. Vollmer DD, Fair K, Hong YA, Beaudoin CE, Pulczinski J, Ory MG. Apps seeking theories: results of a study on the use of health behavior change theories in cancer survivorship mobile apps. JMIR Mhealth Uhealth 2015;3(1):e31 [FREE Full text] [CrossRef] [Medline]
    10. Bantum EO, Albright CL, White KK, Berenberg JL, Layi G, Ritter PL, et al. Surviving and thriving with cancer using a Web-based health behavior change intervention: randomized controlled trial. J Med Internet Res 2014;16(2):e54 [FREE Full text] [CrossRef] [Medline]
    11. Siegel R, DeSantis C, Virgo K, Stein K, Mariotto A, Smith T, et al. Cancer treatment and survivorship statistics, 2012. CA Cancer J Clin 2012;62(4):220-241 [FREE Full text] [CrossRef] [Medline]
    12. Doyle C, Kushi LH, Byers T, Courneya KS, Demark-Wahnefried W, Grant B, 2006 Nutrition‚ Physical ActivityCancer Survivorship Advisory Committee, et al. Nutrition and physical activity during and after cancer treatment: an American Cancer Society guide for informed choices. CA Cancer J Clin 2006;56(6):323-353. [Medline]
    13. Kushi LH, Doyle C, McCullough M, Rock CL, Demark-Wahnefried W, Bandera EV, American Cancer Society 2010 NutritionPhysical Activity Guidelines Advisory Committee. American Cancer Society Guidelines on nutrition and physical activity for cancer prevention: reducing the risk of cancer with healthy food choices and physical activity. CA Cancer J Clin 2012;62(1):30-67 [FREE Full text] [CrossRef] [Medline]
    14. Smith SG, Chagpar AB. Adherence to physical activity guidelines in breast cancer survivors. Am Surg 2010 Sep;76(9):962-965. [Medline]
    15. Flynn KE, Smith MA, Freese J. When do older adults turn to the internet for health information? Findings from the Wisconsin Longitudinal Study. J Gen Intern Med 2006 Dec;21(12):1295-1301 [FREE Full text] [CrossRef] [Medline]
    16. Chou WS, Liu B, Post S, Hesse B. Health-related Internet use among cancer survivors: data from the Health Information National Trends Survey, 2003-2008. J Cancer Surviv 2011 Sep;5(3):263-270. [CrossRef] [Medline]
    17. Lyons EJ, Lewis ZH, Mayrsohn BG, Rowland JL. Behavior change techniques implemented in electronic lifestyle activity monitors: a systematic content analysis. J Med Internet Res 2014;16(8):e192 [FREE Full text] [CrossRef] [Medline]
    18. Locke EA, Latham GP. Building a practically useful theory of goal setting and task motivation. A 35-year odyssey. Am Psychol 2002 Sep;57(9):705-717. [Medline]
    19. Hong Y, Dahlke DV, Ory M, Hochhalter A, Reynolds J, Purcell NP, et al. Designing iCanFit: A Mobile-Enabled Web Application to Promote Physical Activity for Older Cancer Survivors. JMIR Res Protoc 2013;2(1):e12 [FREE Full text] [CrossRef] [Medline]
    20. Hong Y, Goldberg D, Dahlke DV, Ory MG, Cargill JS, Coughlin R, et al. Testing Usability and Acceptability of a Web Application to Promote Physical Activity (iCanFit) Among Older Adults. JMIR Human Factors 2014 Oct 13;1(1):e2. [CrossRef]
    21. iCanFit.   URL: http://www.icanfit.org/ [accessed 2015-06-09] [WebCite Cache]
    22. Ory MG, Ahn S, Jiang L, Smith ML, Ritter PL, Whitelaw N, et al. Successes of a national study of the Chronic Disease Self-Management Program: meeting the triple aim of health care reform. Med Care 2013 Nov;51(11):992-998. [CrossRef] [Medline]
    23. Ory MG, Smith ML, Resnick B. Changing behavior throughout the life-course: Translating the success of aging research. Transl Behav Med 2012 Jun;2(2):159-162 [FREE Full text] [CrossRef] [Medline]
    24. Ory MG, Smith ML, Patton K, Lorig K, Zenker W, Whitelaw N. Self-management at the tipping point: reaching 100,000 Americans with evidence-based programs. J Am Geriatr Soc 2013 May;61(5):821-823. [CrossRef] [Medline]
    25. Jankowski CM, Ory MG, Friedman DB, Dwyer A, Birken SA, Risendal B. Searching for maintenance in exercise interventions for cancer survivors. J Cancer Surviv 2014 Dec;8(4):697-706. [CrossRef] [Medline]
    26. Risendal B, Dwyer A, Seidel R, Lorig K, Katzenmeyer C, Coombs L, et al. Adaptation of the chronic disease self-management program for cancer survivors: feasibility, acceptability, and lessons for implementation. J Cancer Educ 2014 Dec;29(4):762-771. [CrossRef] [Medline]
    27. Forjuoh SN, Lee C, Wang S, Hong Y, Ory MG. Patient-physician discussion of physical activity and environmental barriers. Prev Med 2011 Sep;53(3):209-210. [CrossRef] [Medline]
    28. Adelman RD, Greene MG, Friedmann E, Ory MG, Snow CE. Older patient-physician discussions about exercise. J Aging Phys Act 2011 Jul;19(3):225-238. [Medline]
    29. Forjuoh SN, Ory MG, Wang S, des Bordes JK, Hong Y. Using the iPod Touch for Patient Health Behavior Assessment and Health Promotion in Primary Care. JMIR Mhealth Uhealth 2014;2(1):e14 [FREE Full text] [CrossRef] [Medline]
    30. McHugh J, Lee O, Aspell N, Lawlor BA, Brennan S. A shared mealtime approach to improving social and nutritional functioning among older adults living alone: study protocol for a randomized controlled trial. JMIR Res Protoc 2015;4(2):e43 [FREE Full text] [CrossRef] [Medline]
    31. Agree EM, King AC, Castro CM, Wiley A, Borzekowski DL. "It's got to be on this page": age and cognitive style in a study of online health information seeking. J Med Internet Res 2015;17(3):e79 [FREE Full text] [CrossRef] [Medline]

    Edited by G Eysenbach; submitted 28.02.15; peer-reviewed by J des Bordes, J Cho, J Fanning; comments to author 20.03.15; revised version received 18.04.15; accepted 24.05.15; published 26.06.15

    ©Yan Alicia Hong, Daniel Goldberg, Marcia G Ory, Samuel D Towne Jr, Samuel N Forjuoh, Debra Kellstedt, Suojin Wang. , 26.06.2015.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on http://cancer.jmir.org/, as well as this copyright and license information must be included.