To evaluate medication adherence, available social support and types of accessible support among patients with drug-sensitive tuberculosis (DS-TB). Coping mechanism(s) in TB management and associations between accessible support(s) and medication adherence were also explored.

Prospective questionnaire-guided cross-sectional study.

The WHO-certified TB directly observed therapy (DOT) clinic of two tertiary hospitals in Ibadan, southwest Nigeria.

Adult patients with DS-TB attending the TB-DOT clinic of the hospitals.

Primary outcomes were level of adherence to TB regimens, available social support and type(s) of accessible support. Secondary outcomes were coping mechanisms in TB management, as well as associations between accessible support(s) and medication adherence.

Overall, 152 (89.4%) had optimal adherence. Available social supports were ranked as family (168; 98.8%) >healthcare providers (166; 97.6%) >government (119; 70.0%) >non-governmental organisation (NGO) (118; 69.4%) >religious affiliations (81; 47.6%) >friends (65; 38.2%) >patient association (3; 1.8%). Emotional (168; 98.8%) and financial (137; 80.6%) supports were mostly accessible from family, while drug support (118; 69.4%) was largely received from NGO. Seeking emotional support from family or friends (159; 93.5%) was the most effective coping mechanism. Medication adherence was higher among patients who received health information (p=0.02) and drug support (p=0.03) from the NGO. There was no difference in medication adherence of patients with or without emotional (p=0.20) and financial support (p=0.53) from family, as well as from friends (p=0.31 and p>0.99, respectively).

Nearly ninety per cent of DS-TB patients had optimal adherence. Family was the most available source of social support for TB management, with one hundred and approximately eighty-two per cent respectively citing emotional and financial support as most accessible from the family. Emotional support from family or friends was the most effective coping mechanism in TB management. There is generally a need for initiation and implementation of an expanded social support system for DS-TB patients as a critical step towards achieving the WHO End-TB strategy milestone target.

Intermittent physiological monitoring and early warning scores (EWS) are limited in their ability to detect deteriorating patients in a timely manner. Wearable physiological sensors allow continuous remote monitoring and may be more timely and accurate in the identification of those at risk, compared with manual collection. This study aims to determine if wearable physiological sensors can be used for the early detection of postoperative deterioration, while being acceptable to patients and healthcare staff.

This is a prospective observational cohort study that will recruit adults undergoing major surgery in Benin, India, Ghana, Guatemala, Mexico, Nigeria, Rwanda and the UK. Participants will wear wearable physiological chest and limb sensors before, during and after surgery for up to 10 days or until discharge. In this ‘shadow-mode’ study, continuous physiological observations collected using the devices will not be made available to clinical teams. No changes in participant care will result. Standard of care clinical data will be collected contemporaneously. Continuous sensor data will be used to design algorithms to predict deterioration and specific complications in this population. Usability and feasibility testing, through focus groups, interviews and questionnaires, will be undertaken with healthcare professionals and people undergoing surgery.

Our stakeholder panel are directly involved in all aspects of this study, which will be conducted in accordance with the principles of the International Conference on Harmonisation Tripartite Guideline for Good Clinical Practice (ICH GCP) in addition to the principles of the ethics committee(s)/Institutional Review Boards (IRBs) who have reviewed and approved this study. Artificial intelligence (AI) prediction models will be reported in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis+Artificial Intelligence (TRIPOD+AI) and Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI) reporting guidelines frameworks.

NCT06565559.