More than 17 million cancer survivors reside in the United States, and simulation models predict that the survivorship population will increase to 22 million by January 2030 [1]. The growing prevalence of cancer survivors represents a significant health care challenge especially since they have higher risk for treatment-related morbidities (eg, cardiovascular disease) and cancer than the general population [2-4]. Obesity is considered a major risk factor for chronic disease, modifiable with energy-restricted, high-quality diets, and consistent physical activity [5]. Yet the prevalence of obesity continues to increase rapidly among cancer survivors [6] despite medical recommendations to maintain a healthy weight [7]. The prevalence of obesity among adult cancer survivors has increased 10% since 1997, a significantly faster rate than among those without a history of cancer [6]. However, several issues restrict cancer survivors from accessing nutrition services, including inadequate reimbursement coverage, providers’ heavy clinical load, and providers’ limited nutrition or behavior change training [8,9]. To increase access to nutrition care, digital technology support for weight management and health promotion (eHealth) is being developed to facilitate healthy lifestyle change [10-12] and patient-clinician communication [13,14].

Many eHealth interventions for cancer survivors, delivered through smartphone apps and internet websites, promote a high-quality diet and physical activity through behavior change techniques [15], such as goal-setting [16-21], self-monitoring of behavior [16-27], modeling of behavior [28], and behavioral feedback [16-19,21,24,26,28,29]. eHealth interventions have shown some promise for assisting cancer survivors with health-promoting behavior change and weight loss [30,31] yet the one size fits all approach is unlikely to be effective for this population [32]. Individuals in the increasingly culturally and linguistically diverse survivor population may have different medical experiences, as well as different digital access and engagement [33]. The few studies [34-37] that have investigated associations among eHealth use, sociodemographic characteristics, and medical history examined the general population rather than cancer survivors. In these studies [34-37], researchers found poorer engagement in eHealth practices among adults who are older, male, in a lower annual income bracket, less healthy, or without a regular provider. Even less is known about how different care experiences are associated with different types of eHealth practices. However, a recent study [38] found that negative medical experiences (ie, low perceived patient-centeredness) were associated with greater engagement in self-management eHealth practices only among those with less education and not among those with more education, suggesting that eHealth use can vary as a function of sociodemographic factors and medical experiences. Further investigation is warranted to understand how and why different combinations of these factors are associated with varied eHealth practices among cancer survivors with BMI in overweight or obese categories. Latent class analysis is a statistical approach that allows an investigation of how the intersection of several patient-related factors are associated with eHealth use. This type of analysis is useful when there are several variables that can contribute to heterogeneity, such as that observed among cancer survivors and can facilitate understanding to guide optimization of eHealth promotion among different underlying cancer survivor subgroups.

The primary objective of the study was to determine whether distinct classes can be identified based on sociodemographic characteristics, medical history, and medical experiences (eg, patient-related factors) of cancer survivors with BMI in overweight or obese categories. We also investigated whether class membership was associated with eHealth practices for weight management and patient-clinician communication among cancer survivors with BMI in overweight or obese categories.

Despite the substantial investment in advancing eHealth to extend patient care [46], there is insufficient evidence about how sociodemographic factors, medical history, and medical experiences affect how different groups of cancer survivors use eHealth. As obesity is both prevalent and a significant risk factor for future multimorbidity among cancer survivors, our study objective was to characterize patterns of eHealth use among distinct classes of cancer survivors with BMI in overweight or obese categories. Three classes emerged: younger with no comorbidities; younger with comorbidities; older with comorbidities. People in the older with comorbidities class were less likely to use eHealth self-management technologies than those in the younger-no comorbidities class. However, when compared to those in the younger with comorbidities class, people in the older with comorbidities class were more likely to share health information from an eHealth device with a health professional.

Among cancer survivors with comorbidities, older adults were more likely than younger adults to share their eHealth data with a health care provider in order to facilitate patient-clinician communication. Our finding supports the supposition that eHealth is a promising tool to facilitate patient-clinician communication for older cancer survivors with comorbidities. In comparison, those in the younger with comorbidities class were less likely to have a regular provider, have health insurance, feel involved in medical decisions, or feel they understood next steps of care. They were also more likely to identify as Black or Hispanic individuals and have a lower education level. The characteristics observed in the younger with comorbidities class were consistent with previous reports that Black and Hispanic participants receive less health care than non-Hispanic White participants, and that cancer survivors with lower education are less likely to discuss health-promoting behaviors [47-49]. We also observed that the younger with comorbidities class did not emerge within the 2-class model. This observation suggests that minority groups among cancer survivors with BMI in overweight or obese categories can easily go unnoticed and underrepresented in health care despite having different medical experiences and being at increased risk of having a medical condition, relative to non-Hispanic White adults. A valuable opportunity exists for clinicians and researchers to identify strategies that will improve the medical experiences of underserved minority groups, while leveraging eHealth technology to facilitate health-promoting behaviors.

Compared to those in the older with comorbidities class, cancer survivors in the younger with no comorbidities class were more likely to use a computer to research health information, use a tablet or smartphone to track progress on a health-related goal, and watch health-related videos on YouTube—all types of self-management eHealth behaviors. These differences seemed to be largely driven by the combination of age and medical history as the 2 classes shared similar characteristics for other sociodemographic factors and medical experiences. However, those in the older with comorbidities and younger with no comorbidities classes showed no differences for other eHealth behaviors, such as (1) having health-related apps on their devices, (2) accessing health records for test results, (3) using electronic devices to treat a condition with clinicians, and (4) participating in a health-related support group. Collectively, these results demonstrate that although younger age and better health status jointly predict greater engagement in using eHealth for self-management, there is no generational divide in having health-related apps, accessing electronic health records, and sharing eHealth data with clinicians among cancer survivors with BMI in overweight or obese categories. Our results show agreement with mixed evidence that age is associated with eHealth use [34,35,37], and echo findings indicating that better health was associated with greater eHealth use to track health and goals [34,35].

The strengths of this study include the use of a large nationwide sample drawn from NCI HINTS which allowed us to use weightings to generate nationally representative estimates. Although the sample analyzed for the current study comprised less than 5% of the NCI HINTS study sample, the estimates are reflective of the population of cancer survivors with BMI in overweight or obese categories. Despite several eHealth weight management interventions in survivor populations, this is the first study to investigate how eHealth is used to manage health and relate to health care providers [50]. An additional strength was the ability to investigate different forms of eHealth usage separately, rather than in aggregate, which allowed us to identify who was more likely to use specific eHealth features to promote weight management and patient-clinician communication. A few limitations should be noted as well. We were unable to determine whether eHealth use would differ for diet, physical activity, or smoking behaviors since the NCI HINTS items did not distinguish between types of health-promoting behaviors. Another limitation is that eHealth use and cancer status were self-reported and, therefore, susceptible to recall bias. Although the data were weighted to generate nationally representative estimates, generalizability may still be limited by reliance on participant self-selection. Replication is warranted using different nationally representative study samples with further investigation on environmental factors, such as rural-urban differences [51]. Additionally, the temporal relationship between patient-related factors and eHealth use has yet to be established.

There is growing interest in the development and usability of eHealth to guide health-promoting behaviors for cancer survivors [52-54], particularly as there is limited access to nutrition services at cancer centers [9]. This study provides new evidence about the feasibility and usability of eHealth among cancer survivors with BMI in overweight or obese categories by investigating how sociodemographic factors, medical history, and medical experiences co-vary with eHealth behaviors. While our results suggest that all cancer survivors use eHealth, some groups engage with eHealth technologies in different ways. Thus, this study highlights the importance of considering the eHealth needs and usage patterns of different types of cancer survivors when developing digital interventions to support health promotion and patient-clinician communication. Our study also reveals that race/ethnicity, as well as medical attributes and experiences, predict eHealth use—lending support to the idea that sociodemographic, medical history, and clinician interactions can collectively influence eHealth engagement. Further efforts to develop eHealth recommendations tailored for different groups of cancer survivors are needed to optimize survivors’ ability to use digital tools to promote health behaviors and reduce treatment-related morbidities and obesity.