Confirmatory Factor Analysis of the Center for Epidemiologic Studies Depression Scale in Oncology With Examination of Invariance Between Younger and Older Patients

COVID-19 Lockdown and Changes in Dietary and Lifestyle Behaviors in a French Longitudinal Cohort

BACKGROUND

The COVID-19 pandemic has imposed local lockdowns resulting in strong disruptions in our lifestyles and dietary behaviors. This study aimed to determine how the lockdown in France affected these behaviors and weight during the lockdown and in a one month follow up period of time after the end of the lockdown.

METHODS

The study design was a longitudinal cohort, among French adults. A total of 593 participants (68.6% female), with a mean age of 42.2 years (SD = 15.2) completed a self-reported questionnaire on four occasions spaced one month apart, from the beginning of the lockdown starting 17 March 2020, until one month after its end (mid-June 2020). Clusters of participants were formed using the non-supervised k-means algorithm.

RESULTS

The mean weight gain after one month of lockdown was 0.56 kg (SD = 0.6). The cluster analysis exposed three different patterns of behavioral changes, despite no significant differences in age or BMI between clusters. These three groups have experienced different weight change dynamics over the follow-up duration. The first cluster (n = 210) reported fewer changes in sleep quality and quantity and less change in snacking frequency (p ≤ 0.001). The second cluster (n = 200) reported significantly lower levels of stress than the other clusters (p ≤ 0.001). The third cluster (n = 183) differed from the others, with a more degraded quality of sleep reported throughout the lockdown (p ≤ 0.01). However, changes in eating behaviors and body weight were not significant.

CONCLUSIONS

During the lockdown, behavioral changes occurred, both health-favorable and non-health-favorable, yet they had a minor impact on eating behaviors and reported body weight once the restrictive measures were lifted. The identification of three patterns suggests that, in such constraining situations, personalized recommendations should be provided.

Higher stress and symptom severity are associated with worse depressive symptom profiles in patients receiving chemotherapy

PURPOSE

In a sample of oncology patients, identify subgroups of patients with distinct depressive symptom profiles and evaluate for differences in demographic and clinical characteristics, levels of stress and resilience, and the severity of common co-occurring symptoms.

METHODS

Patients (n = 1327) had a diagnosis of breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding four weeks; and were scheduled to receive at least two additional cycles of chemotherapy. Demographic and clinical characteristics, stress, resilience, and co-occurring symptoms were evaluated at enrollment. Depressive symptoms were evaluated using the Center for Epidemiological Studies-Depression (CES-D) scale a total of six times over two cycles of chemotherapy. Latent profile analysis (LPA) was used to identify subgroups of patients (i.e., latent classes) with distinct depressive symptom profiles using the six CES-D scores.

RESULTS

Based on the findings from the LPA, 47.3% of the patients were classified as "None"; 33.6% as "Subsyndromal"; 13.8% as "Moderate"; and 5.3% as "High". Compared to None class, patients in the Subsyndromal, Moderate, and High classes had a lower functional status, a higher comorbidity burden, and a self-reported diagnosis of depression or back pain. Those patients with higher levels of depressive symptoms reported higher levels of stress, lower levels of resilience, and increased severity of co-occurring symptoms.

CONCLUSIONS

Inter-individual variability in depressive symptoms was associated with demographic and clinical characteristics, multiple types of stress and levels of resilience, as well as with the increased severity of multiple co-occurring symptoms. The risk factors associated with worse depressive symptom profiles can assist clinicians to identify high risk patients and initiate more timely supportive care interventions.