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Interest in Health Behavior Intervention Delivery Modalities Among Cancer Survivors: A Cross-Sectional Study

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Interest in Health Behavior Intervention Delivery Modalities Among Cancer Survivors: A Cross-Sectional Study

Emily C Martin et al. JMIR Cancer.

Abstract

Background: Effective, broad-reaching channels are important for the delivery of health behavior interventions in order to meet the needs of the growing population of cancer survivors in the United States. New technology presents opportunities to increase the reach of health behavior change interventions and therefore their overall impact. However, evidence suggests that older adults may be slower in their adoption of these technologies than the general population. Survivors' interest for more traditional channels of delivery (eg, clinic) versus new technology-based channels (eg, smartphones) may depend on a variety of factors, including demographics, current health status, and the behavior requiring intervention.

Objective: The aim of this study was to determine the factors that predict cancer survivors' interest in new technology-based health behavior intervention modalities versus traditional modalities.

Methods: Surveys were mailed to 1871 survivors of breast, prostate, and colorectal cancer. Participants' demographics, diet and physical activity behaviors, interest in health behavior interventions, and interest in intervention delivery modalities were collected. Using path analysis, we explored the relationship between four intervention modality variables (ie, clinic, telephone, computer, and smartphone) and potential predictors of modality interest.

Results: In total, 1053 respondents to the survey (56.3% response rate); 847 provided complete data for this analysis. Delivery channel interest was highest for computer-based interventions (236/847, 27.9% very/extremely interested) and lowest for smartphone-based interventions (73/847, 8.6%), with interest in clinic-based (147/847, 17.3%) and telephone-delivered (143/847, 16.9%) falling in between. Use of other technology platforms, such as Web cameras and social networking sites, was positively predictive of interest in technology-based delivery channels. Older survivors were less likely to report interest in smartphone-based diet interventions. Physical activity, fruit and vegetable consumption, weight status, and age moderated relationships between interest in targeted intervention behavior and modality.

Conclusions: This study identified several predictors of survivor interest in various health behavior intervention delivery modalities. Overall, computer-based interventions were found to be most acceptable, while smartphones were the least. Factors related to survivors' current technology use and health status play a role in their interest for technology-based intervention versus more traditional delivery channels. Future health behavior change research in this population should consider participants' demographic, clinical, and lifestyle characteristics when selecting a delivery channel. Furthermore, current health behavior interventions for older cancer survivors may be best delivered over the Internet. Smartphone interventions may be feasible in the future following further adoption and familiarization by this particular population.

Keywords: behavioral intervention; cancer survivor; diet; physical activity; smartphone; technology.

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Simple slopes showing relationship between BMI and interest in getting in shape interaction and interest in clinic-based intervention.
Figure 2
Figure 2
Simple slopes showing relationship between BMI and interest in getting in shape interaction and interest in telephone-based intervention.
Figure 3
Figure 3
Simple slopes showing relationship between physical activity and interest in getting in shape interaction and interest in telephone-based intervention.
Figure 4
Figure 4
Simple slopes showing relationship between fruit and vegetable consumption and interest in weight control and interest in telephone-based intervention.
Figure 5
Figure 5
Simple slopes showing relationship between physical activity and interest in getting in shape and interest in computer-based intervention.
Figure 6
Figure 6
Simple slopes showing relationship between fruit and vegetable consumption and interest in getting in shape interaction and interest in computer-based intervention.
Figure 7
Figure 7
Simple slopes showing relationship between age and interest in healthy eating interaction and interest in smartphone-based intervention.
Figure 8
Figure 8
Simple slopes showing relationship between BMI and interest in getting in shape interaction and smartphone-based intervention.

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References

    1. DeSantis CE, Lin CC, Mariotto AB, Siegel RL, Stein KD, Kramer JL, Alteri R, Robbins AS, Jemal A. Cancer treatment and survivorship statistics, 2014. CA Cancer J Clin. 2014;64(4):252–271. doi: 10.3322/caac.21235. - DOI - DOI - PubMed
    1. American Cancer Society Cancer Facts and Figures. 1998. [2015-10-13]. http://pbadupws.nrc.gov/docs/ML0716/ML071640135.pdf .
    1. Paskett ED, Dean JA, Oliveri JM, Harrop JP. Cancer-related lymphedema risk factors, diagnosis, treatment, and impact: a review. J Clin Oncol. 2012 Oct 20;30(30):3726–3733. doi: 10.1200/JCO.2012.41.8574. - DOI - PubMed
    1. Cormie P, Newton RU, Taaffe DR, Spry N, Galvão DA. Exercise therapy for sexual dysfunction after prostate cancer. Nat Rev Urol. 2013 Dec;10(12):731–736. doi: 10.1038/nrurol.2013.206. - DOI - PubMed
    1. Cella D, Davis K, Breitbart W, Curt G, Fatigue C. Cancer-related fatigue: prevalence of proposed diagnostic criteria in a United States sample of cancer survivors. J Clin Oncol. 2001 Jul 15;19(14):3385–3391. - PubMed

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