Wearable technologies applied to the monitoring of bipolar disorder: Physiological, behavioural, emotional variables and behavioural patterns - Scoping review

Introduction: Bipolar disorder (BD) is a chronic, recurrent condition that affects overall functioning and is associated with relapses that can lead to significant deterioration. In clinical practice, one of the main challenges is to anticipate decompensations and recognise early warning signs of mood changes, particularly when follow-up depends on infrequent appointments and self-report, which may be inaccurate during manic or hypomanic phases due to reduced illness insight. In this context, digital health and digital phenotyping, through the use of wearable technologies, are emerging as alternatives to objectively monitor physiological, behavioural and emotional variables and behavioural patterns, with the potential to complement clinical follow-up and support timely decision-making. Methodology: A scoping review will be conducted following the Joanna Briggs Institute (JBI) methodology and the PRISMA-ScR guidance. Studies in English or Spanish published between 2016 and 2026 will be included. The population will comprise people with BD type I, BD type II or cyclothymia. Wearables (smartwatches, smart bands, portable sensors/biosensors, smart rings and activity trackers) used in clinical follow-up, home-based monitoring, remote monitoring, digital health, telepsychiatry or applied research will be considered. Searches will be conducted in PubMed, Scopus, Web of Science and BIREME, as well as grey literature. Selection will involve duplicate removal, title/abstract screening and full-text review. Data will be extracted in Excel (device/sensors, type and duration of monitoring, variables, integration with apps/platforms, context, usability/adherence and findings). Expected results: We expect to map and synthesise evidence from the last 10 years on the use of wearables in monitoring BD, identifying the most commonly used devices and sensors, monitored variables (physiological, behavioural, emotional, sleep/activity and behavioural patterns), and contexts of application and technological integration (apps, platforms and clinical systems). We also anticipate describing reported analytical approaches, including the use of algorithms or predictive models for early-change detection, as well as potential benefits such as more continuous monitoring and timely detection of variations, and limitations related to adherence, privacy, methodological heterogeneity and implementation barriers. Finally, the review will identify knowledge gaps and propose future research directions aimed at a more realistic, useful and sustainable clinical implementation.