Identification and potential use of clusters of patients with colorectal cancer and patients with prostate cancer in clinical practice: Explorative mixed methods study

Background: A steady increase in colorectal and prostate cancer survivors and patients with these cancers is expected in the upcoming years. As a result of primary cancer treatments, patients have numerous additional complaints, increasing the need for cancer aftercare. However, referrals to appropriate cancer aftercare remain inadequate, despite a wide range of aftercare options. Caregivers and patients often do not know which aftercare is the most appropriate for the individual patient. Since characteristics and complaints of patients within a diagnosis group may differ, predefined patient clusters could provide substantive and efficient support for professionals in the conversation about aftercare. By using advanced data analysis methods, clusters of patients who are different from one another within a diagnosis group can be identified. Objective: This study had a 2-fold objective: (1) to identify, visualize, and describe potential patient clusters within the colorectal and prostate cancer population and (2) to explore the potential usability of these clusters in clinical practice. Methods: First, we used cross-sectional data from patients with colorectal cancer and patients with prostate cancer provided by the population-based PROFILES (Patient-Reported Outcomes Following Initial Treatment and Long-Term Evaluation of Survivorship) registry, which were originally collected between 2008 and 2012. To identify and visualize different clusters among the 2 patient populations, we conducted cluster analyses by applying the K-means algorithm and multiple-factor analyses. Second, in a qualitative study, we presented the patient clusters to patients with prostate, patients with colorectal cancer, and oncology professionals. To assess the usability of these clusters, we held expert panel group interviews. The interviews were video recorded and transcribed. Three researchers independently performed content-directed data analyses to understand and describe the qualitative data. Quotes illustrate the most important results. Results: We identified 3 patient clusters among colorectal cancer cases (n=3989) and 5 patient clusters among prostate cancer cases (n=696), which were described in tabular form. Patient experts (6/8, 75%) and professional experts (17/20, 85%) recognized the patient clustering based on distinguishing variables. However, the tabular form was evaluated as less applicable in clinical practice. Instead, the experts suggested the development of a conversation tool (eg, decision tree) to guide professionals through the hierarchy of variables. In addition, participants suggested that information about possible aftercare initiatives should be offered and integrated. This would also ensure a good overview and seemed to be a precondition for finding suitable aftercare. Conclusions: This study demonstrates that a fully data-driven approach can be used to identify distinguishable and recognizable (ie, in routine care) patient clusters in large data sets within cancer populations. Patient clusters can be a source of support for health professionals in the aftercare conversation. These clusters, when integrated into a smart digital conversation and referral tool, might be an opportunity to improve referral to cancer aftercare. Trial Registration: Netherlands Trial Register NL9226; https://trialsearch.who.int/Trial2.aspx?TrialID=NL922

Lifestyle-related effects of the web-based Kanker Nazorg Wijzer (Cancer Aftercare Guide) intervention for cancer survivors: a randomized controlled trial

PURPOSE: The web-based Kanker Nazorg Wijzer (Cancer Aftercare Guide) responds to the needs of cancer survivors and oncology care providers to improve the counseling related to self-management of lifestyle and psychosocial challenges. In present study, overall intervention effects and the effects of using specific components were evaluated on vegetable, fruit, whole grain bread, and fish consumption, physical activity (PA), and smoking behavior. METHODS: Cancer survivors from 21 Dutch hospitals were recruited for a randomized controlled trial (N = 432). Intervention effects after 6 months were evaluated using multilevel linear regression analysis (complete cases and intention-to-treat). By conducting moderation analyses, additional effects of following the behavior-related modules were explored. The false discovery rate correction was applied to account for multiple testing. RESULTS: After 6 months, 409 participants completed follow-up (dropout = 11.5 %). Indications were found that access to the intervention may result in increases of moderate PA and vegetable intake. The moderate PA increase was meaningful: 74.74 min p/w higher increase in the intervention condition. Effect sizes of moderate PA (d = .25) and vegetable (d = .37) consumption were comparable to prior effective interventions. Visiting behavior-related modules affected moderate PA, fruit, and fish consumption. However, after correction for multiple testing, significances expired. No significant intervention effect was found on smoking behavior due to low numbers of smokers. IMPLICATIONS FOR CANCER SURVIVORS: Although the effectiveness was only shown only to a limited extend, this study provided several indications that this theory-based, comprehensive, and personalized eHealth intervention provides valuable content to complement usual cancer aftercare

Long-term effects of a web-based cancer aftercare intervention on moderate physical activity and vegetable consumption among early cancer survivors: a randomized controlled trial

BACKGROUND: The number of cancer survivors is growing. Negative physical and psychosocial consequences of cancer treatment can occur during survivorship. Following healthy lifestyle recommendations is beneficial to increase quality of life and to reduce the risk of cancer recurrence and comorbidities. To meet individual needs, web-based interventions can supply a large population of cancer survivors with easily accessible and personalized information. Evidence concerning the long-term effects of web-based cancer aftercare interventions on lifestyle outcomes is limited. The present study evaluates the 12-month effects of a fully automated web-based cancer aftercare intervention. We investigated whether the previously determined 6-month effects on moderate physical activity and vegetable intake were maintained over 12 months. Possible moderator effects of using specific intervention modules, gender, age, and education were also explored. METHOD: A two-armed randomized controlled trial was conducted using online self-report questionnaires among survivors of various types of cancer (N = 462). The intervention group had access to the online intervention for 6 months, and the control group received access after 12-months. Multilevel linear regression analyses (complete cases and intention-to-treat) were conducted to explore 12- month effects. RESULTS: A significant intervention effect after 12 months was found for moderate physical activity (complete cases: B = 128.475, p = .010, d = .35; intention-to-treat: B = 129.473, p = .011). Age was the only significant moderator (p = .010), with the intervention being effective among participants aged younger than 57 years (B = 256.549, p = .000, d = .59). No significant intervention effect remained for vegetable consumption after 12 months (complete cases: B = 5.860, p = .121; intention–to-treat: B = 5.560, p = .132). CONCLUSION: The online cancer after care intervention is effective in increasing and maintaining moderate physical activity in the long term among early cancer survivors younger than 57 years. Short-term increases in vegetable consumption were not sustained in the long term. These findings indicate the value and potential of eHealth interventions for cancer survivors. Based on the study results, web-based self-management interventions could be recommended for younger cancer survivors (<57 years of age) as a possible method to increase physical activity. TRIAL REGISTRATION: Dutch Trial Register NTR3375. Registered 29 March 2012