Impact of Advanced Crohn's Disease Therapies on Sleep Quality
Impact of Advanced Crohn's Disease Therapies on Sleep Quality
The goal of this observational study is to measure changes in sleep quality before and after starting treatment with advance therapy in adult with Crohn disease. The main question it aims to answer is: what is the association between response to therapy and sleeping patterns? Participants who are about to begin advanced treatment will be asked to wear a Fitbit device before end after treatment initiation to monitor sleep patterns and sleep quality, complete a sleep diary and a sleep quality questionnaire. The follow-up period will last 26 weeks, during which disease severity and response to treatment will be assessed.
Inflammatory bowel diseases (IBD) are chronic inflammatory diseases with a relapsing remitting course. The pathogenesis of the disease is hypothesized to be abnormal host immune response to the gut microbiota, triggered by dietary, environmental or stress factors in genetically predisposed individuals. Despite the significant medical progress with the advent of therapy (e.g., TNF, integrin and cytokine inhibitors, jak inhibitors and S1P receptor modulators) there are still many caveats in the treatment of patients with IBD, including loss of response, immunogenicity, immunosuppression and side effects. Furthermore, although current therapies improve health associated quality of life, there is still unmet gap in this area with high rates of disability, sleep disturbances, fatigue, pain and other quality of life domains.
Sleep and circadian rhythm disorders effect the immune system function and are a potential cause of disease flare-ups, which in turn effect sleep pattern and quality, creating a vicious cycle. The interactions between sleep and inflammation are complex. An effective immune system affects sleep, and sleep disorders affect the functioning of the immune system. Patients with sleep disorders showed increase in inflammatory activity. However, it is difficult to dissect the cause and effect for these associations, given their complex interactions.
Studies identified several risk factors associated with sleep disorders in patients with IBD. These include depression, anxiety, active disease, older age (>52 years), CD duration (>12 years), low hemoglobin levels (<12 g/dL), elevated C-reactive protein (CRP), current corticosteroid use, ongoing anti-TNF therapy, opioid use, and smoking.
Previous studies on the association of sleep abnormalities and IBD relied on self-assessment questionnaires, such as the Pittsburgh Sleep Quality Index (PSQI), which are inherently subjective measures of sleep quality. In this study, the investigators will use the Fitbit Inspire 3 to objectively assess sleep quality, leveraging its ability to track sleep duration and stages.
Sleep is a vital physiological process encompassing two primary modes: Non-Rapid Eye Movement (NREM) and Rapid Eye Movement (REM) sleep. NREM sleep, divided into stages N1, N2, and N3. In N1, the body starts to relax; N2 brings deeper relaxation with a slower heart rate; and in N3, the deepest stage. REM sleep, characterized by vivid dreaming and heightened brain activity similar to wakefulness, is essential for cognitive functions like memory consolidation, emotional processing, and learning. Throughout the night, individuals cycle through NREM and REM phases, each contributing uniquely to physical and mental well-being, underscoring the importance of quality sleep for overall health.
Sleep stages can be estimated by monitoring heart rate, which slows progressively from light to deep sleep and fluctuates in REM, as well as movement, which decreases in deep sleep and is minimal in REM due to muscle paralysis. The Fitbit Inspire 3 measures sleep by combining motion sensing, using a 3-axis accelerometer, with heart rate variability (HRV) analysis. The device tracks subtle movements to identify periods of rest versus wakefulness, while HRV data helps estimate sleep stages based on distinct heart rate patterns during light, deep, and REM sleep. Together, these inputs allow Fitbit's algorithm to approximate sleep stages, providing insights into sleep quality and cycles for everyday health tracking, though not as precise as clinical sleep studies.
Sleep quality changes following therapy commencement will be objectively assessed, and associations between treatment response and sleep patterns before and after therapy will be evaluated.
Patients will be asked to wear the Fitbit inspire 3 on their non-dominant hand for consecutive 5 days, in three different time slots (before initiation of new treatment, at week 4 after initiation, and at week 8 after initiation). Data will be extracted from the fit bit application into out registry.
Fitbit information include: sleep onset latency, wake after sleep onset, total sleep time, time in REM, time in light sleep, time in deep sleep.
Patients will complete the PSQI questionnaire and a sleep log, documenting bed time and wake time, facilitating calculations of sleep latency time, time in bed and sleep efficiency.
At the beginning and after 8 weeks participants bowel wall thickness will be assessed by intestinal ultrasound (IUS).
Clinical score will be assessed using the Harvey-Bradshaw index (HBI) at weeks: 0, 5, and 9. Basic medical and demographic data will be collected from the hospital's data sources.
Sample size calculations were based on the hypothesis that therapy response rates are associated with changes in sleep quality, measured both objectively and subjectively. Assuming a 40% clinical response rate, 28 patients are required to detect a statistically significant difference (p<0.05) with 80% power. Allowing for a 10% attrition rate, a total of 30 patients will be enrolled.
Descriptive statistics will be calculated for each variable measured and reported as means, medians, or proportions. Univariate analyses will be performed using paired t tests or Wilcoxon rank-sum tests for means and McNemar tests for categorical variables. Comparison of sleep quality and disease activity between study visits, and evaluation of the overtime trends in these parameters, in accordance with medical treatment will be performed by using the linear mixed model analysis (for three visits) and by the paired sample T test (two visits). Statistical significance is set at P ≤ 0.05.
Inclusion Criteria:
Exclusion Criteria: