Purpose: Medication nonadherence is one of the most significant obstacles to tuberculosis (TB) control worldwide. Identification of the factors associated with medication nonadherence is important. However, few related studies have been carried out in Tibet. This study aimed to explore factors influencing medication nonadherence to pulmonary TB (PTB) treatment in Tibet, China, from the patient perspective. Patients and Methods: In this qualitative study, seventeen PTB patients in Tibet were recruited by purposive and maximum variation sampling methods. In-depth semistructured interviews were conducted to collect data on factors influencing medication nonadherence, and Colaizzi’s seven-step method was used to analyze the data. Results: The medication nonadherence of PTB patients in Tibet was influenced by one or a combination of the following four factors. First, patient-related factors included a lack of knowledge of PTB treatment, poor self-management capability, poor self-regulation capability and misperception of health condition. Second, a medication-related factor was medication side effects. Third, health service-related factors included the poor treatment skills of doctors in primary hospitals and a lack of directly observed treatment (DOT). Last, sociocultural factors included the effect of traditional Tibetan medicine, lack of family member support and discrimination. Conclusion: Multiple interplaying factors influenced medication nonadherence during PTB treatment in Tibet, and the main influencing factors were a lack of knowledge about PTB treatment, poor self-management capability, and the effect of traditional Tibetan medicine. TB health workers in Tibet should provide permanently viewable PTB treatment knowledge materials to PTB patients when oral health education is conducted, find feasible alternative strategies to DOT and establish links to traditional Tibetan medicine hospitals.

Background: While the exact mechanisms are not fully understood, there are significant links between sleep quality, anxiety, depressive symptoms, and cognitive emotion regulation. This research examines how sleep quality affects anxiety and depressive symptoms, as well as the potential of cognitive emotion regulation strategies (CERS) to moderate the impact of sleep quality on these symptoms. Methods: The Chinese version of the Pittsburgh Sleep Quality Index (CPSQI), the Cognitive Emotion Regulation Questionnaire (CERQ), the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder Scale-7 (GAD-7) were all completed online by students from two colleges in China’s Xizang region. Results: The study included 4325 subjects. The prevalence of poor sleep quality, anxiety symptoms, and depression symptoms was 45.69%, 36.81%, and 51.86%, respectively. We observed significant direct effects on poor sleep and severity of anxiety/depression: c’1 = 0.586 (0. 544–0.628), and c’2 = 0.728 (0.683–0.773). Adaptive CERS only had a mediating effect on the relationship between sleep quality and depression symptoms, with a1b3 = −0.005 (−0.011–−0.001). The link between poor sleep quality and the intensity of anxiety and depression was significantly affected by the indirect effects of maladaptive CERS: effect a2b2 = 0.126 (0.106–0.147), and effect a2b4 = 0.145 (0.123–0.167). Conclusions: Individuals who experience poor sleep quality are more likely to have increased levels of anxiety and depression. However, enhancing sleep quality led to a decrease in anxiety and depression levels. Adaptive CERS did not predict anxiety, but they did predict depression. Multiple maladaptive CERS could increase levels of anxiety and depression. To prevent mental stress, it is crucial to examine sleep problems among college students, understand their cognitive strategies, promote the adoption of adaptive CERS, and reduce the reliance on maladaptive CERS.