Introduction: People living with type 2 diabetes undertake a range of tasks to manage their condition, collectively referred to as self-management. Interventions designed to support self-management vary in their content, and efficacy. This systematic review will analyse self-management interventions for type 2 diabetes drawing on theoretical models of patient workload and capacity. Methods and analysis: Five electronic databases (Medline, Embase, CENTRAL, CINAHL and PsycINFO) will be searched from inception to 27th April 2021, supplemented by citation searching and hand-searching of reference lists. Two reviewers will independently review titles, abstracts and full texts. Inclusion criteria include Population: Adults with type 2 diabetes mellitus; Intervention: Randomised controlled trials of self-management support interventions; Comparison: Usual care; Outcomes: HbA1c (primary outcome) health-related quality of life (QOL), medication adherence, self-efficacy, treatment burden, healthcare utilization (e.g. number of appointment, hospital admissions), complications of type 2 diabetes (e.g. nephropathy, retinopathy, neuropathy, macrovascular disease) and mortality; Setting: Community. Study quality will be assessed using the Effective Practice and Organisation of Care (EPOC) risk of bias tool. Interventions will be classified according to the EPOC taxonomy and the PRISMS self-management taxonomy and grouped into similar interventions for analysis. Clinical and methodological heterogeneity will be assessed within subgroups, and random effects meta-analyses performed if appropriate. Otherwise, a narrative synthesis will be performed. Interventions will be graded on their likely impact on patient workload and support for patient capacity. The impact of these theoretical constructs on study outcomes will be explored using meta-regression. Conclusion This review will provide a broad overview of self-management interventions, analysed within the cumulative complexity model theoretical framework. Analyses will explore how the workload associated with self-management, and support for patient capacity, impact on outcomes of self-management interventions. Registration number: PROSPERO CRD42021236980.

Biohacking, considered to include technology such as wearables, lifestyle changes and nutrition to allow one to optimise their health, is growing in popularity. However, the definition of and insights according to those involved in these practices remains elusive and unexplored. Technological advancements, including the internet, have given rise to globally connected communities and various health-related consumer technologies that measure health metrics from the comfort of ones own home. While health-related information sharing and technology-assisted health tracking may appear beneficial, it also affords many opportunities for harm through the spread of misinformation and the use of potentially inaccurate devices. Adopting a qualitative approach using thematic analysis, this study focused on identifying the practices and topics discussed publicly on the Twitter social media associated with the hashtags #biohacking and #biohacker. The main topics were physical fitness, nutrition, mental health, self-development, genetics, and neuroscience. Most of the biohacking practices were found to be health-centric and include practices such as dietary or herbal supplements or chip implants that could interact with medical investigations and treatments. This highlights that biohacking practices should be included as part of a proper medical history to allow healthcare providers to recommend safe and appropriate therapies, and to avoid supplement-drug interactions and adverse events. Implications for biohacking are vast and minimising harms, whilst optimising benefits at the individual and population level requires a better understanding of how biohacking practices are conceptualised. This will help inform healthcare decision-makers, policymakers, and industries associated with the practices identified.