Commentary on Coelho <i>et al</i> .: Ecological momentary assessments may be key to the future of cannabis studies

Ecological momentary assessment has significant potential for tracking cannabis use with unprecedented precision. While limitations remain, the future integration of this methodology into longer-term, comprehensive studies and digital health tools could advance our understanding of cannabinoid dose-effects, inform health policies, and refine predictive models for cannabis-related harms and health outcomes. The study of cannabis use and cannabis-associated health outcomes currently centres primarily around two distinct methods of research: experimental trials using fixed doses of Δ-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), and observational studies of naturalistic cannabis use. The former method has the advantage of producing accurate dose-effect data, but is limited to acute dosing in controlled, clinical environments. And while the latter allows us to track real-world cannabis consumption over extended periods of time, previous attempts to accurately record dosing have proven rudimentary and imprecise. The ecological momentary assessment (EMA) methodology presented by Coelho et al. [1] could help to fill this critical gap in the research by allowing the collection of precise, time-sensitive, and ecologically relevant cannabinoid dosing data across a variety of product types and modes of administration. Importantly, as alternative forms of use have become increasingly popular [2], it has become more necessary than ever for a meaningful index of cannabis use to aggregate all such products and routes. There are several points to consider when moving forward with this proposed methodology. First, it is imperative to consider and assess the generalizability across populations of interest, from otherwise relatively healthy consumers to populations displaying comorbid conditions (e.g. physical or mental health disorders) or other vulnerability factors. In addition, while there is evidence of overlap in medical and non-medical reasons for use [3], the conditions in which medical and non-medical cannabis users utilize cannabis (e.g. alone or with company, exact measured dosing or ad libitum, etc.) and report their cannabis use will also likely differ, as will the effects that these populations experience. It is also important to consider how this tool may be used in longer studies. The present study was conducted for 14 days to reduce the burden on participants, but the major cannabis-related harms of interest, such as cannabis use disorder, development or worsening of psychotic symptoms, etc., typically occur only over much longer periods of use. Longer follow-up would also be desirable for evaluating the safety and efficacy of self-directed medical cannabis use, such as in the management of chronic pain or insomnia. Such longer-term studies would need to limit the burden on participants, by making data input as fast and simple as possible (e.g. allowing users to save product characteristics to be automatically input again), and possibly having distinct periods of EMA data entry, for example, 1 or 2 weeks every 3 months, alternating with traditional retrospective data collection, such as timeline followback (TLFB) or enhanced TLFB (eTLFB) [4, 5]. In addition, this methodology would be a more powerful research tool when incorporated into more comprehensive studies of cannabis use and related effects. Cannabis use data could, for example, be combined with other substance use data, biometric health data (e.g. sleep, heart rate, blood pressure, etc.), and validated measures of physical and mental health, affect, quality of life, and functioning. Beyond research applications, this kind of EMA tool piloted by Coelho et al. has remarkable potential for personal health tracking. Whilst there are limitations to the precision and accuracy of the THC and CBD dosing data collected, owing to inaccurate product labelling [6] or estimations of quantities used [7], differences in bioavailability and pharmacokinetic profile of different routes of administration [8], etc., cannabis use data could be leveraged to assess and potentially notify users and/or their carers of changes in specific components of their patterns of use. These changes could be predictive of a change in health status; in the present study, greater THC use in a single session than what was average for that individual was associated with a greater risk of negative consequences, whilst overall THC use was not. This pilot study by Coelho et al. represents an important step forward – a proof of concept for the more precise tracking of cannabinoid use, aggregated across product types and routes of administration, in near real time. This methodology can be considered a prototype that can be further optimized to enhance its accuracy and applicability; for example, by scaling cannabinoid content by cannabis type and administration method based on administration efficiency, to calculate estimated quantities of bioavailable THC and CBD, in addition to exact dosing data [9]. As digital health and research tools become more widely adopted, the ability to capture and analyse cannabis use patterns in a nuanced manner will be instrumental in understanding its health effects over time. These findings have the potential to shape evidence-based health policies, refine dosing recommendations and improve our capacity to predict cannabis-related health outcomes. Lucy Chester: Conceptualization (equal); writing—original draft (lead); writing—review and editing (equal). François-Olivier Hebert: Conceptualization (equal); writing—review and editing (equal). Didier Jutras-Aswad: Conceptualization (equal); writing—review and editing (equal). Lucy Chester is the recipient of a postdoctoral fellowship from the Institut Universitaire sur les Dépendances (IUD). Didier Jutras-Aswad is supported by a senior research scholar award from the Fonds de Recherche du Québec - Santé. D.J.A. received study materials from Cardiol Therapeutics for clinical trials funded by the Quebec Ministry of Health and Social Services. Data sharing is not applicable to this article as no new data were created or analysed for this commentary.

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