A systematic review of measurement burst research designs for studying mental health

Measurement burst designs, involving short intensive data collections repeated over extended intervals, offer a powerful framework for capturing both short-and long-term changes and advancing multi-timescale understanding of development. Although increasingly applied in psychological and health research, a systematic synthesis of their use in mental health contexts has been lacking. This review addresses that gap by examining how measurement burst designs have been applied in mental health research, summarising their methodological features, synthesising key findings, and offering recommendations for future research. Systematic searches of Web of Science, PubMed, and PsycINFO, supplemented by manual searches using project names and reference chaining, identified 89 studies (91 analyses) employing measurement burst designs to mental health outcomes. Most (88%) were published in the past decade, predominantly in the United States, and with a focus on emotional well-being or substance use. Daily diaries were the most common method (74%), although 42% of analyses incorporated ecological momentary assessment (EMA), experience sampling method (ESM), or ambulatory assessments, either alone or alongside diaries. Designs varied considerably in the number of bursts (2-9), intervals (weeks to nearly a decade), prompt schedules (event-contingent, random, or fixed), prompt frequency (1-10 prompts/day), incentives, and approaches to handling and reporting of attrition, compliance, and missing data. Measurement burst designs have substantially advanced multi-timescale research on mental health; but key gaps remain. Future research should broaden mental health domains and populations studied, develop validated and multi-method assessments, optimise design through pilot testing, adopt standardised reporting practices, and fully leverage the nested structure of burst data.