Maryska L. G. Janssen-Heijnen

Supplementary Figure from Longitudinal Associations between Inflammatory Markers and Fatigue up to Two Years after Colorectal Cancer Treatment

Supplementary Table from Longitudinal Associations between Inflammatory Markers and Fatigue up to Two Years after Colorectal Cancer Treatment

Supplementary Figure from Longitudinal Associations between Inflammatory Markers and Fatigue up to Two Years after Colorectal Cancer Treatment

Supplementary Table from Longitudinal Associations between Inflammatory Markers and Fatigue up to Two Years after Colorectal Cancer Treatment

Background:Fatigue is often reported by colorectal cancer survivors and largely impacts their quality of life. Inflammation has been linked to fatigue mainly in patients with breast cancer. Therefore, we investigated how inflammation is longitudinally associated with fatigue in colorectal cancer survivors, up to 2 years posttreatment.Methods:A total of 257 patients from the ongoing Energy for life after ColoRectal cancer cohort study were included in the analysis. Plasma levels of IL6, IL8, IL10, TNFα, high-sensitivity C-reactive protein (hsCRP), and fatigue were measured at 6 weeks, 6, 12, and 24 months posttreatment. Fatigue was measured through the validated Checklist Individual Strength (CIS; total, 20–140), consisting of four subscales – subjective fatigue (8–56), motivation (4–28), physical activity (3–21), and concentration (5–35), and the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 fatigue subscale (0–100). Linear mixed-models were used to assess the confounder-adjusted longitudinal associations between inflammatory markers and overall fatigue along with the subscales.Results:Mean levels of CIS fatigue decreased from 62.9 at 6 weeks to 53.0 at 24 months. In general, levels of inflammatory markers also decreased over time. No statistically significant longitudinal associations were found between IL6, IL8, IL10, TNFα, and fatigue. Higher levels of hsCRP were associated with more CIS fatigue (β per SD 3.21, 95% confidence interval (CI), 1.42–5.01) and EORTC fatigue (β 2.41, 95% CI, 0.72–4.10).Conclusions:Increased levels of hsCRP are longitudinally associated with more posttreatment fatigue in colorectal cancer survivors.Impact:These findings suggest that low-grade inflammation may play a role in fatigue reported by colorectal cancer survivors up to 2 years posttreatment.

Supplementary Figure from Longitudinal Associations between Inflammatory Markers and Fatigue up to Two Years after Colorectal Cancer Treatment

Supplementary Figure from Longitudinal Associations between Inflammatory Markers and Fatigue up to Two Years after Colorectal Cancer Treatment

Supplementary Table from Longitudinal Associations between Inflammatory Markers and Fatigue up to Two Years after Colorectal Cancer Treatment

Purpose: Sleep problems are a frequent concern of colorectal cancer (CRC) survivors. Research on modifiable lifestyle factors that may mitigate sleep problems is sparse. Therefore, we investigated how various body composition parameters are longitudinally associated with sleep problems from 6 weeks up to 24 months post-treatment. Methods: In a prospective cohort of 396 stage I-III CRC survivors, home-based repeated measurements were conducted at diagnosis and at four post-treatment time points. The insomnia scale of the EORTC QLQ-C30 (range: 0–100) was used to measure sleep problems. Anthropometric measurements of adiposity (BMI, fat percentage, waist-hip ratio) and of muscle mass and muscle function (mid-upper arm muscle circumference, handgrip strength) were employed. Linear mixed models were applied to analyze overall longitudinal associations, and hybrid models were used to disentangle inter- and intra-individual components. Results: At 6 weeks post-treatment, 47.0% of participants reported sleep problems and symptom severity was at its highest; a decline was observed thereafter. In confounder-adjusted models, no statistically significant overall longitudinal associations of different body composition parameters with sleep problems were found. Intra-individual analyses revealed that increases in BMI were related to less sleep problems over time ( β per 1 kg/m ² : -2.8, 95% CI -4.4; -1.2). Conclusions: BMI increases in the first 24 months post-treatment were associated with decreased sleep problems. These findings must be interpreted with caution due to the observational design, yet might suggest a potential link between weight regain and sleep problems among CRC survivors recovering from the physical and mental impact of cancer treatment.

Nature-based interventions (NBIs) in healthcare settings have the potential to enhance physical and mental wellbeing of patients, healthcare staff, and visitors but are often underutilized. Knowledge about factors influencing effective implementation of NBIs in healthcare settings is scarce. This study aimed to develop a framework for identifying implementation factors relevant to NBIs in hospitals, long-term care facilities for elderly, and rehabilitation centers. A two-round Delphi study was conducted. In the first round, potential implementation factors were identified based on input from 33 experts from the following groups: healthcare managers, landscape designers, nature and health consultants, and healthcare staff. These factors were then supplemented with additional factors identified from previous studies and the Consolidated Framework for Implementation Research (CFIR). In the second round, 21 experts rated the importance of these factors. The items that reached expert consensus were included in the new implementation framework. In the first round, an initial set of 99 implementation factors was identified. Most factors derived from experts and literature were design related. These factors were complemented with factors derived from the CFIR. In the subsequent round, 98 implementation factors were considered important by experts and retained. These factors were incorporated into a new framework, the Green Implementation Framework (GreenIF). The GreenIF provides an overview of factors that contribute to the successful design and implementation of NBIs in healthcare settings and can be used to enhance their usage and maximize their benefits.