For the present study, we obtained anonymous data from medical cannabis users who used the app to track the effectiveness of cannabis to treat headache and/or migraine. Specifically, we obtained data on these individuals’ anonymous ID codes; cannabis treatment session numbers; gender; age; symptoms; self-reported headache/migraine severity before each tracked session of medical cannabis use; self-reported headache/migraine severity after each tracked session of medical cannabis use; cannabinoid content (%THC, %CBD) for the cannabis used in each session; the method of obtaining the cannabinoid content data (ie, licensed dealer vs user-generated); as well as the method of administration and dose for each session. As part of the app terms of use, individuals agree that the data may be used for any purpose deemed appropriate by Strainprint. The Office of Research Assurances at Washington State University determined that this anonymous archival study was exempt from the need for IRB review.

Archival data from Strainprint were obtained. This free medical cannabis app provides individuals with a means of tracking changes in symptom severity as a function of different doses and strains of cannabis. During the initial set-up period, individuals enter basic demographic information (gender [male, female, other] and date of birth) as well as their medical conditions and symptoms. Subsequently, individuals open the app immediately prior to using cannabis to manage their conditions/symptoms. They first select the condition/symptom for which they are about to use cannabis to manage. They are then prompted to enter the strain of cannabis that they are about to use by selecting from a list of over 1,000 strains sold by licensed medical cannabis distributors and cannabis concentrate producers in Canada. The THC and CBD content for each of these strains is prepopulated in the app and was obtained by analyses conducted by one of Health Canada's licensed dealers, with the exception of the cannabis concentrate content data which were obtained from the concentrate manufacturers’ websites. Health Canada enforces strict production guidelines, quality control guidelines and mandatory lab testing from all ministry approved licensed dealers. This mandatory lab testing includes 5 stages of processing: preparation, chromatography, general spectrometry, heavy metal spectrometry, and microbial analysis. Strainprint app users may also enter additional strain names and cannabinoid content (%THC, %CBD) for products that are not prepopulated in the app, but we did not include session data that had user-generated cannabinoid content. Users track their medical cannabis sessions by: 1) rating the severity of each symptom/condition on a scale of 0 (none) to 10 (extreme) before using cannabis, 2) identifying their method of administration (smoke, oil, vape, dab bubbler, dab portable, edible, pill, spray, transdermal, tincture), and 3) indicating the dose (eg, number of puffs). Twenty minutes after cannabis use, individuals are prompted (via a push notification) to re-rate the severity of their symptom(s)/condition(s).

As depicted in Fig 1 , data were obtained from 1,876 cannabis users who collectively used the app 22,491 times to track changes in their headache severity, and from 1,019 users who together used the app 14,091 times to track changes in migraine severity over a 16-month period (February 2017 to June 2018). Given potential differences in onset and efficacy among different routes of administration (eg, oral vs inhaled), only tracked sessions in which individuals indicated administering cannabis via inhalation methods (smoking, vaping, concentrates, dab bubbler, dab portable) were selected (n = 17,856; 79.4% of headache data and n = 11,664; 82.8% of migraine data). Tracked sessions that involved cannabis administration via other methods (eg, tincture, edibles) were excluded from the present study. As the acute subjective effects of inhaled cannabis peak at about 10 to 30 minutes and taper off after 3 to 4 hours,only tracked inhalation sessions for individuals who re-rated their symptoms within 4 hours were included (n = 15,144 tracked headache sessions and n = 10,070 tracked migraine sessions). Finally, we excluded tracked sessions for which THC and CBD values were entered by users due to concerns with the validity and reliability of those data.

The final sample comprised 1,306 medical cannabis users who used the app 12,293 times to track changes in headache and 653 medical cannabis users who used the app 7,441 times to track changes in migraine severity. Descriptive statistics on the samples, the THC and CBD concentrations in the cannabis used, and the number of tracked sessions for headache and migraine are shown in Table 1

Data Analysis

The percentage of tracked sessions in which a reduction in severity, an increase in severity, and no change in severity were reported following cannabis use were computed separately for headache and migraine. Gender differences in these percentages were then examined using chi-square analyses.

16 McArdle JJ Latent variable modeling of differences and changes with longitudinal data. Figure 2 The basic univariate LCS model, with the commonly used symbols in SEM. The variable ‘Rating’ represents severity ratings and is measured at 2 time points (Rating_T1 and Rating_T2). Change (ΔRating) between the 2 timepoints is modeled as a latent variable. The model shown is just identified, which means there are as many estimated parameters as there are pieces of information from the data provided. Thus, model fit indices are not available. For each headache or migraine episode, we used a 2-time points latent change score (LCS) model to examine changes between the severity ratings from before to after the tracked session of medical cannabis use. LCS models use a within-subjects approach to examine changes within people over time.The LCS model is specified using a structural equation modeling (SEM) approach to model the change between “before” and “after” cannabis use as a latent factor. Within the context of SEM, the latent change factor (ΔRATING), is measured by the “after” cannabis use severity rating with a factor loading fixed to 1. This creates a latent factor that captures the change between the “before” and “after” cannabis use severity ratings (see Fig 2 ).

Specifying the LCS model in an SEM framework allows 3 important questions to be addressed. First, the mean of the latent change factor (Δ RATING) provides an estimate of the average change over time. A negative mean of the LCS factor suggests that, on average, participants’ severity ratings decreased from before to after the cannabis use session. Second, the LCS model also estimates the variance of the latent change factor, which indicates the heterogeneity across participants regarding the average difference (ie, the extent to which individuals differed in their change in ratings from before to after cannabis use). Third, the covariance between severity scores from before cannabis use and the latent change factor (Δ RATING) indicates the extent to which the change in severity is proportional to severity scores before cannabis use.

18 Muthén LK, Muthén BO: Mplus User's Guide, 8th ed.Los Angeles, Muthén & Muthén, 1998-2017. Conditional LCS models allow for the addition of predictors to the latent change factor. Estimates for each predictor can be interpreted as beta coefficients that estimate the effect of the predictor variable on the change. When a LCS is positive it indicates that higher values on the predictor variable are associated with less of a decrease in severity ratings from before to after cannabis use, while negative estimates indicate higher values on the predictor variable are associated with more of a decrease in ratings. All LCS models were estimated using Mplus (version 8.2;) with maximum likelihood with robust standard errors to account for nesting of repeated measures within participants.

Univariate LCS models were estimated to test the first 2 study objectives. A baseline model without any predictor variables was first estimated to describe the nature of change in severity ratings from before to after cannabis use (objective 1). Next, 2 conditional models were estimated that added predictor variables to the baseline model (objective 2). Model 1 estimated the effects of time/cannabis use session, gender, type of cannabis (flower = 0 vs concentrate = 1), THC concentration, CBD concentration, and dose on the latent change factor. Model 2 included the same predictor variables and also estimated the interaction of THC X CBD on the latent change factor.

Finally, to address our third objective, for each type of episode (headache/migraine), multilevel modeling (MLM) with repeated measures was used to describe 1) changes in baseline severity ratings across time/cannabis use sessions, and 2) changes in cannabis doses as a function of time/number of sessions. In these unconditional models, cannabis use session was centered at Time 1 so that the intercept (Time 0) represented the first session in each model. The fixed and random linear effects of time/cannabis use session were first estimated. Additional models added fixed and random quadratic effects of time/cannabis use session. All multilevel models were fit using SAS Proc Mixed, with maximum likelihood estimation and incomplete data treated using missing at random assumptions.