Abstract
Introduction
This decentralized, randomized, triple-blind, placebo-controlled study evaluated efficacy and safety of oral cannabigerol (CBG) in Veterans with sleep issues.
Methods
After a 2-week run-in phase, participants received CBG (25 mg daily for 2 weeks, then 50 mg daily for a further 2 weeks) or placebo. The primary endpoint was change in sleep quality via the Medical Outcomes Study Sleep Problems Index II questionnaire (MOS-SS SPI-II). Additional endpoints included change in quality of life measured via the World Health Organization Disability Assessment Schedule, version 2.0 instrument (WHODAS-2.0–12), post-traumatic stress disorder (PTSD) symptoms evaluated via the PTSD Checklist for the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (PCL-5 (PCL-5), and sleep actigraphy data via Fitbit.
Results
A total of 63 participants were randomized to receive CBG (n = 33) or placebo (n = 30). A total of 35 participants completed the study without major protocol deviations (CBG [n = 18]; placebo [n = 17]). MOS-SS SPI-II scores indicated improved sleep with no statistically significant difference between the CBG and placebo groups. Similar patterns were observed for WHODAS-2.0–12 and PCL-5 scores. CBG was well tolerated.
Conclusion
While no firm conclusion on the efficacy of CBG in improving sleep can be made, the favorable safety profile supports future studies with CBG. ClinicalTrials.gov ID: NCT05088018.
Keywords: Cannabigerol, Insomnia, Veterans, Decentralized clinical trial, Sleep
Introduction
The Veteran population is vulnerable to sleep-related disorders, being up to six times more impacted by sleep-related issues than the general population [1]. The US Veterans Health Administration considers sleep issues among Veterans a healthcare crisis; from 2012 to 2018 insomnia diagnoses nearly doubled, and sleep-related breathing disorders increased fourfold [2]. Recent surveys report a high prevalence of disordered sleep, with 11.4% of Veterans reporting clinical insomnia and subthreshold insomnia in 26% [3], while others report even higher proportions [4]. There is abundant clinical evidence that sleep influences pain, fatigue, mood, cognition, and daily functioning. Veterans diagnosed with sleep disorders commonly have comorbidities such as obesity, diabetes, congestive heart failure, depression, post-traumatic stress disorder (PTSD), or traumatic brain injury [4].
Reports indicate that medicinal cannabis is used by Veterans to cope with sleep disturbances and other PTSD symptoms [5, 6]. While delta-9-tetrahydrocannabinol (Δ9-THC) makes up almost 95% of cannabis sales [7], cannabinoids such as cannabidiol (CBD), cannabinol and cannabigerol (CBG) are gaining in popularity for both recreational and clinical use. Whole plant cannabis and varying ratios of Δ9-THC/CBD have been studied for their effects on sleep [8–14]. While CBG exhibits similar activity and affinity characteristics as Δ9-THC and CBD on cannabinoid receptors, CBG has uniquely high (nanomolar to sub-nanomolar) affinity as an α-2 adrenoceptor agonist [15]. Established α-2 agonists such as clonidine show some efficacy in improving sleep in adults with PTSD [16]. While such pharmacology provides support for reports of improved sleep in CBG users [17], clinical efficacy data are sparse.
The aim of the present study was to investigate the efficacy, safety, and tolerability of short-term use of a CBG formulation on insomnia and QoL in Veterans ≥21 years of age resident in California with self-reported problematic sleep. We also investigated the effect of CBG on sleep, activity, and heart rate biometrics collected via a commercial wrist-worn activity tracking device (Fitbit Inspire 2) [18, 19]. Our goal was to establish feasibility and generate preliminary findings that can be used for larger trials and future research.
Methods
Study Design
This was a triple-blinded, randomized, placebo-controlled trial conducted between October 26, 2021, and May 10, 2022, across California. This study was designed to mimic real-world usage of CBG within a population with significant unmet need – US military Veterans. The study design was informed by participant insight panels to understand Veterans’ lived experience and potential obstacles to study recruitment and retention. A fully decentralized design was used to allow for equitable access and participation by this community, who often report skepticism of research and therefore a hesitancy to participate [20]. The use of a remote monitoring technology collected passively reported data in addition to patient-reported outcomes. Routine medications and supplement usage was allowed, as long as usage remained consistent and was reported throughout the study.
Participants were equally randomized, entered a 2-week run-in phase, and then received 25 mg CBG daily or placebo for 2 weeks, with escalation of CBG dosing to 50 mg daily for the final 2 weeks (Fig. 1). A 2-week duration is typical for investigational product sleep intervention studies [21]. The duration and dose escalation for each dosing phase was also deemed appropriate to account for the longer half-life of lipophilic cannabinoids. For example, the half-life of CBD after oral administration may be 2–5 days [22].
Fig. 1.
Study design and schedule of assessments. Following screening, all randomized participants entered a 2-week run-in phase, then received allocated treatment (CBG 25 mg once daily or placebo for 2 weeks with escalation of dosing to 50 mg daily for the final 2 weeks). MOS-SS SPI-II was self-reported at baseline, and on day 14, day 28, and day 42. WHODAS-2.0–12 and PCL-5 were self-reported at baseline (day 0/1 and day 43). Participants maintained daily sleep and evening diaries including allocated medication compliance throughout the study. Fitbit-based outcome measures (sleep and activity duration, heart rate) were monitored from the initial run-in phase to end of study. CBG, cannabigerol; MOS-SS SPI-II, Medical Outcomes Study Sleep Scale Sleep Problems Index II; PCL-5, PTSD Checklist for the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition; WHODAS-2.0–12, 12-item version of World Health Organization Disability Assessment Schedule, version 2.0.
Sleep quality and QoL outcomes were assessed throughout the study via established questionnaires; the Sleep Problems Index II subscale of the Medical Outcomes Study Sleep Scale (MOS-SS SPI-II) [23, 24], the short 12-item form of the World Health Organization Disability Assessment Schedule, version 2.0 instrument (WHODAS-2.0–12) [25], and the PTSD Checklist for the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (PCL-5) [26]. Additional exploratory objectives included evaluation of sleep, activity, and heart rate biometric data, collected via a commercial wrist-worn activity tracking device (Fitbit Inspire 2) [18, 19]. For safety evaluation, subjective study product effects, psychological distress, and adverse events (AEs) were monitored throughout the study.
Participants were recruited through Veterans advocacy organizations, email campaigns, and point-of-sale displays at cannabis dispensaries. A web-based electronic data capture (EDC) system, Curebase Inc, was used for eligibility assessment, consent, randomization, questionnaire deployment, and data collection [27]. Participants were assigned a dedicated clinical research coordinator (CRC) who was unaware of treatment or placebo allocation. Participants also received a Fitbit Inspire 2 to collect sleep, activity, and cardiovascular biometrics.
Ethics
The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice Guidelines, approved by a central Institutional Review Board (Advarra; Pro00056526) and registered at clinicaltrials.gov (NCT05088018). Written informed consent was obtained from each participant, who received no remuneration or reimbursement, although participants were allowed to retain their Fitbit device at the end of the study. Study reporting is in accordance with Consolidated Standards of Reporting Trials (CONSORT) [28] (see CONSORT checklist in online suppl. Materials; for all online suppl. material, see https://doi.org/10.1159/000549902).
Study Population
Participants were recruited across California through Veteran’s associations and advocacy groups (including the Veterans Cannabis Group, Tactical Patients Group, Operation EVAC, the Santa Cruz Veterans Alliance). Campaigns to raise study awareness included email, printed materials, and advertisements at point-of-sale cannabis product dispensaries.
Full details of inclusion and exclusion criteria are shown in online supplementary Table 1. Participants receiving sleep medications or other psychotropic medications were eligible if medication/dosage were stable. Cannabis product use (Δ9-THC and/or CBD) was allowed, with self-reported use assessed at baseline and throughout the study. It would not be representative to restrict pharmacological use, including mental health-related or even sleep-related medication. Participants reported each morning in a daily diary whether they used drugs, alcohol, and/or sleep aids the night before. This data was collected for post hoc analysis. Participants also reported whether they had stopped, started, or changed the dosage of prescribed medications during the study to account for any mediators or moderators of CBG’s effects. Individuals with obstructive sleep apnea (OSA) were eligible if use of continuous positive airway pressure (CPAP) or alternative PAP devices was established. Participants were excluded if they were receiving cognitive behavioral therapy (CBT) for insomnia, as this is a first-line treatment recommendation for insomnia widely used by the VA [29] and could potentially confound changes observed on the MOS-SS SPI-II survey. Following initial screening, prospective participants completed the MOS-SS SPI-II survey, where a cutoff score of ≥30 served as a proxy indicator of sleep disturbance [30, 31].
Investigational Product
The investigational product was a commercially available highly enriched CBG formulation (Protab™ by LEVEL) extracted from high CBGa expressing Cannabis sativa cultivars prior to senescence (see online suppl. Materials for additional details). Once formulated, tablets are created on a rotary tablet press, with inline production sampling and analysis to ensure product quality control. For the present study, each batch was evaluated by an independent laboratory to ensure that the cannabinoid profile was within established limits and without Δ9-THC. The placebo was prepared in the same facility with the same inactive components but contains 0 mg/serving of CBG. The packaging, labeling, and dosage administration of the placebo formulation were identical to that used with the investigational product to ensure participant blindness.
Due to the lack of robust clinical data on CBG in a human population, there is no well-defined dosing for CBG. Commercially available products have CBG quantities ranging from 2 to 33 mg per dose. We selected a target dosage that mirrored commonly recommended dosages (up to 50 mg daily, formulated as 25 mg tablets). Studies of similar, orally administered cannabinoids such as CBD reported dosages of 400 mg and 800 mg of CBD were well tolerated [22, 32, 33].
After the pretreatment run-in phase, participants were instructed to take one 25 mg tablet daily for 2 weeks (anytime during the day but at least 3 h before bedtime). This caveat was based on empirical reports that CBG might increase dream intensity, and so bedtime dosing was intentionally avoided to reduce any potential adverse impact on dream-state for this Veteran population, due to the high co-occurrence of PTSD.
Following this dosing period, participants were then instructed to take two 25 mg tablets together each day for a further 2 weeks (Fig. 1). By escalating the dosage mid-way through the treatment phase (i.e., after 2 weeks), we hope to better evaluate the effective dose of CBG for the improvement of sleep and study any potential dose-dependent effects. Those allocated to placebo (with identical packaging and labeling to the investigational product) followed similar instructions.
Randomization, Masking, and Data Management
Participants were randomized (1:1) via stratified block randomization. The study was triple-blinded to treatment allocation; participants and researchers remained blinded until final data analysis (see details in online suppl. Material).
Outcome Measures and Endpoints
As previously mentioned, all outcome measures and diaries were collected electronically (as eCOAs or e-diaries) through the Curebase clinical trial management system. Demographics and clinical characteristics were assessed at baseline. A complete list of study assessments at different timepoints is shown in online supplementary Figure 1. Established self-reported questionnaires were used to evaluate sleep, QoL, and PTSD symptoms. Additional details on these instruments are presented in the online supplementary material.
Our primary objective was to evaluate the effect of CBG on sleep quality, evaluated using MOS-SS SPI-II which is scored ranging from 0 to 100 where higher scores indicate greater sleep impairment [23, 24]. While the conventional questionnaire evaluates sleep quality over the past 4 weeks [23, 24, 34], modifications across shorter periods (e.g., over a one or 2-week timeframe) are feasible and validated [30, 35]. For the present study, we chose a 2-week timeframe. Responses were collected at baseline (pre-screening), day 14 and after two and 4 weeks (day 28 and day 42, respectively) (Fig. 1).
QoL was assessed using the WHODAS-2.0–12 [25], scored via the simple scoring method ranging from 0 to 48 (with higher scores indicating poorer QoL) [36, 37]. An exploratory objective was to evaluate the effect of CBG on PTSD symptoms, evaluated via the PCL-5 questionnaire, scored on a scale of 0–80, with higher scores indicating greater PTSD symptoms [26]. Additional measures included evaluation of sleep, activity, and heart rate biometric data, collected via a commercial wrist-worn activity tracking device (Fitbit Inspire 2) [18, 19] (See online suppl. Materials for additional details).
Participants completed a daily evening diary to record their adherence to their allocated treatment (number of tablets and time taken). Participants completed a daily morning diary to record their perceived overall sleep quality as well whether they took any drugs, alcohol, or sleep aids the night before. Participants also completed a “Change in Sleep Management” diary every 2 weeks to report if tobacco and/or cannabis use, medications, or supplements have changed as well as other lifestyle changes that may influence the results (such as starting a new sleep-focused therapy program or changes to their sleeping environment, like having a new baby or pet).
Safety and Tolerability Assessment
Safety monitoring responsibilities were assigned to an independent clinician selected for experience suitable for the Veteran population: a psychiatrist experienced in treating persons who have experienced trauma. AEs could be reported at any time through the study application. This application was also designed to trigger an alert to an on-call clinician when specific PCL-5 responses might indicate the participant is experiencing a crisis (see online suppl. Materials for additional details).
Sample Size
No previous study has reported changes in the primary outcome measure (MOS-SS SPI-II) following cannabinoid therapy. At this time, there is no generally established minimal clinically important difference for MOS-SS SPI-II in a similar population to this study, although 12 and 4.6 point decreases following targeted interventions in Veterans over 16 weeks (yoga and wellness/lifestyle, respectively) have been reported [38]. Sample size was calculated assuming that a mean 10-point improvement in the MOS-SS SPI-II between the two treatment groups would be clinically meaningful, and that the largest SD of pre- and posttreatment scores in either treatment group was 20.95. An initial sample size of 50 participants per arm (accounting for 40% attrition before study completion) was estimated to provide ≥80% power, with an alpha level of 0.05 for a two-sample, two-sided t test.
Statistical Analysis
Analyses were performed for both the intention-to-treat (ITT) and the per-protocol (PP) populations. The ITT population included all participants regardless of subsequent compliance or withdrawal. The PP population included all participants without any major protocol deviations (i.e., study withdrawal, loss to follow-up, non-compliance with treatment schedule, or failure to complete study questionnaires, Fig. 2). Safety analyses were conducted in the entire population, tabulating adverse events with descriptive statistics.
Fig. 2.
CONSORT participant flowchart. AE, adverse event; CBG, cannabigerol; ITT, intention-to-treat; PP, per-protocol.
The primary outcome was the mean change in sleep quality during active treatment measured using the MOS-SS SPI-II survey. Mean values at day 14 and day 42 for both treatment groups were computed to generate the mean change score across all participants in each group. The treatment effect was then estimated as the difference in the mean change score for the CBG group minus that for the placebo group, evaluated using Welch’s t test. A similar approach was used for the secondary outcome evaluating QoL via change in WHODAS-2.0–12 and for exploratory PTSD evaluations, although using mean values from baseline to end-of-study (between day 0/1 and day 43).
Planned exploratory analyses included any dose-related effect of CBG on MOS-SS SPI-II, evaluated by assessing the initial early effect after 2 weeks of 25 mg daily treatment (between day 14 and day 28) and comparing this with the overall change after all 4 weeks of treatment. Actigraphy measures were assessed using linear mixed-effects models. All statistical analyses were conducted in R version 4.2.2 (R Core Team).
Post Hoc Analysis
Post hoc analyses were conducted to evaluate characteristics of participants receiving CBG responding to treatment, defined as those individuals with a 10-point improvement (decrease) in their MOS-SS SPI-II score between day 14 and day 42.
Results
Study Population and Participant Disposition
In total, 407 individuals were assessed for eligibility, of which 205 were excluded due to failure to meet eligibility criteria and a further 139 declined participation (Fig. 2). Sixty-three participants were randomized to allocated dosing; CBG (n = 33) and placebo (n = 30) and comprised the ITT population. Only eight (12.7%) participants failed to complete the study. The PP population comprised 35 participants; CBG (n = 18) and placebo (n = 17). Excluded from this analysis set were a further 20 individuals, excluded due to insufficient survey data submission or with major dosing violations (Fig. 2).
Baseline demographics and clinical characteristics were comparable across treatment groups in both the ITT and PP populations (Table 1). The overall study population had a mean age of 44.0 ± 13.6 years (range 23.4–74.1); 77.7% were male. Most participants had longstanding sleep problems, with 70% reporting problems for more than 5 years. On average, participants reported 5.3 ± 1.3 h of sleep each night (range 3.0–10.0), with a mean MOS-SS SPI-II at baseline of 61.4 ± 15.4 (range 31.7–91.7). Mean PCL-5 scores were high in both treatment arms although with substantial variation; over 40% of participants in both groups had scores ≥33, which is the threshold indicative of probable PTSD [39]. Nine (14.3%) participants reported physician diagnosed OSA, all of whom used CPAP. While regular tobacco use was low (15.9%), most participants (85.7%) reported regular use of cannabis products, with 75% reporting daily use (often multiple times daily). While around one-third of patients reported use of Δ9-THC only, almost 50% reported use of both Δ9-THC and CBD. The reported usage of concurrent cannabis was similar across both treatment and placebo groups (Table 1).
Table 1.
Baseline demographics and clinical characteristics of the study cohort
| | Intention-to-treat (ITT) | Per-protocol (PP) | ||||||
|---|---|---|---|---|---|---|---|---|
| CBG (N = 33) | placebo (N = 30) | p valuea | overall (N = 63) | CBG (N = 18) | placebo (N = 17) | p valuea | overall (N = 35) | |
| Age, years | | | 0.3 | | | | 0.5 | |
| Mean±SD | 45.9±15.5 | 41.9±11.0 | | 44.0±13.6 | 46.5±16.6 | 42.1±12.1 | | 44.4±14.6 |
| Median (IQR) | 41.6 (33.5–58.7) | 39.1 (33.7–49.5) | | 40.1 (33.6–55.4) | 40.7 (33.6–58.4) | 39.7 (33.9–50.0) | | 39.9 (33.7–56.6) |
| Range | 23.5–74.1 | 23.4–65.0 | | 23.4–74.1 | 23.5–74.1 | 23.4–65.0 | | 23.4–74.1 |
| Gender | | | 0.8 | | | | 0.6 | |
| Male, n (%) | 26 (78.8) | 23 (76.7) | | 49 (77.7) | 13 (72.2) | 11 (64.7) | | 24 (68.6) |
| Ethnicity, n (%) | | | 0.9 | | | | 0.4 | |
| Caucasian | 24 (72.7) | 19 (63.3) | | 43 (68.3) | 13 (72.2) | 12 (70.6) | | 25 (71.4) |
| Black or African American | 2 (6.1) | 2 (6.7) | | 4 (6.3) | 0 (0.0) | 1 (5.9) | | 1 (2.9) |
| Other | 6 (18.2) | 7 (23.3) | | 13 (20.6) | 5 (27.8) | 4 (23.6) | | 8 (22.9) |
| Missing | 1 (3.0) | 2 (6.7) | | 3 (4.8) | 0 (0.0) | 1 (5.9) | | 1 (2.9) |
| Weight, pounds | | | 0.8 | | | | 0.5 | |
| Mean±SD | 192.3±48.2 | 192.1±45.3 | | 192.2±46.5 | 186.8±53.4 | 199.2±52.8 | | 192.8±52.7 |
| Median (IQR) | 185.0 (158.0–220.0) | 177.5 (162.0–220.1) | | 185.5 (160.5–220.0) | 176.5 (155.5–217.0) | 185.0 (165.0–227.0) | | 178.0 (159.0–220.0) |
| Range | 105.0–320.0 | 133.0–320.0 | | 105.0–320.0 | 105.0–320.0 | 133.0–320.0 | | 105.0–320.0 |
| BMI, kg/m2 | | | >0.9 | | | | 0.5 | |
| Mean±SD | 28.8±6.5 | 29.0±6.3 | | 28.9±6.4 | 28.1±6.8 | 29.8±7.5 | | 28.9±7.1 |
| Median (IQR) | 27.4 (23.7–32.9) | 27.7 (24.6–32.3) | | 27.4 (24.3–32.5) | 27.0 (23.8–31.1) | 28.0 (24.4–32.6) | | 27.4 (24.0–32.3) |
| Range | 18.6–47.3 | 20.8–47.3 | | 18.6–47.3 | 18.6–47.3 | 20.8–47.3 | | 18.6–47.3 |
| Obstructive sleep apnea | | | 0.7 | | | | 0.7 | |
| Physician diagnosis, n (%) | 4 (12.1) | 5 (16.7) | | 9 (14.3) | 2 (11.1) | 3 (17.6) | | 5 (14.3) |
| Use of CPAP, n (%) | 4 (12.1) | 5 (16.7) | | 9 (14.3) | 2 (11.1) | 3 (17.6) | | 5 (14.3) |
| Duration of sleep problem, n (%) | | | 0.2 | | | | 0.7 | |
| ≥10 years | 12 (36.4) | 15 (50.0) | | 27 (42.8) | 7 (38.9) | 6 (35.3) | | 13 (37.1) |
| 5–10 years | 9 (27.3) | 8 (26.7) | | 17 (27.0) | 6 (33.3) | 6 (35.3) | | 12 (34.3) |
| <5 years | 9 (27.3) | 7 (23.3) | | 16 (25.4) | 3 (16.7) | 5 (9.4) | | 8 (22.9) |
| Missing | 3 (9.1) | 0 (0.0) | | 3 (4.8) | 2 (11.1) | 0 (0.0) | | 2 (5.7) |
| Average hours of sleep each night | | | 0.4 | | | | 0.9 | |
| Mean±SD | 5.2±1.2 | 5.5±1.4 | | 5.3±1.3 | 5.5±1.4 | 5.6±1.6 | | 5.5±1.5 |
| Median (IQR) | 5.0 (4.0–6.0) | 5.0 (5.0–6.0) | | 5.0 (4.0–6.0) | 6.0 (4.2–6.0) | 5.0 (5.0–6.0) | | 5.0 (4.5–6.0) |
| Range | 3.0–8.0 | 4.0–10.0 | | 3.0–10.0 | 3.0–8.0 | 4.0–10.0 | | 3.0–10.0 |
| Regular tobacco use, n (%) | 4 (12.1) | 6 (20.0) | 0.5 | 10 (15.9) | 2 (11.1) | 4 (23.5) | 0.4 | 6 (17.1) |
| Regular cannabis product use, n (%) | 27 (81.8) | 27 (90.0) | 0.5 | 54 (85.7) | 17 (94.4) | 14 (82.4) | 0.3 | 31 (88.6) |
| Frequency, n (%) | | | 0.5 | | | | 0.3 | |
| Once or twice a week | 1 (3.0) | 0 (0.0) | | 1 (1.6) | 1 (5.6) | 0 (0.0) | | 1 (2.9) |
| About every other day | 4 (12.1) | 2 (6.7) | | 6 (9.5) | 2 (11.1) | 1 (5.9) | | 3 (8.6) |
| About once a day | 13 (39.4) | 12 (40.0) | | 25 (39.7) | 10 (55.6) | 5 (29.4) | | 15 (42.9) |
| Multiple times a day | 9 (27.3) | 13 (43.3) | | 22 (34.9) | 4 (22.2) | 8 (47.1) | | 12 (34.3) |
| Missing | 6 (18.2) | 3 (10.0) | | 9 (14.3) | 1 (5.6) | 3 (17.6) | | 4 (11.4) |
| Cannabis product type, n (%) | | | 0.10 | | | | 0.06 | |
| Δ9-THC only | 14 (42.4) | 8 (26.7) | | 22 (34.9) | 9 (50.0) | 2 (11.8) | | 11 (31.4) |
| CBD only | 1 (3.0) | 0 (0.0) | | 1 (1.6) | 0 (0.0) | 0 (0.0) | | 0 (0.0) |
| Both Δ9-THC and CBD | 12 (36.4) | 19 (63.3) | | 31 (49.2) | 8 (44.4) | 12 (70.6) | | 20 (57.1) |
| Missing | 6 (18.2) | 3 (10.0) | | 9 (14.3) | 1 (5.6) | 3 (17.6) | | 4 (11.4) |
| MOS-SS SPI-II | | | 0.8 | | | | 0.2 | |
| Mean±SD | 61.2±17.7 | 61.6±12.6 | | 61.4±15.4 | 56.8±18.0 | 61.4±8.6 | | 59.0±14.2 |
| Median (IQR) | 59.4 (49.4–72.8) | 60.8 (53.2–70.3) | | 60.6 (50.0–70.8) | 51.4 (44.2–67.6) | 61.1 (55.0–67.8) | | 60.0 (49.7–68.1) |
| Range | 31.7–91.1 | 37.8–91.1 | | 31.7–91.7 | 33.9–91.1 | 45.6–75.0 | | 33.9–91.1 |
| WHODAS-2.0–12 | | | 0.9 | | | | >0.9 | |
| Mean±SD | 24.2±7.8 | 25.6±10.5 | | 24.8±9.1 | 23.7±6.9 | 24.2±9.2 | | 23.9±7.9 |
| Median (IQR) | 24.0 (19.0–30.0) | 24.0 (18.0–31.0) | | 24.0 (18.0–30.0) | 24.0 (17.5–29.2) | 24.0 (17.8–28.0) | | 24.0 (17.2–29.2) |
| Range | 12.0–46.0 | 12.0–45.0 | | 12.0–46.0 | 13.0–35.0 | 12.0–40.0 | | 12.0–40.0 |
| PCL-5 | | | 0.8 | | | | 0.8 | |
| Mean±SD | 32.1±18.9 | 33.2±19.1 | | 32.6±18.9 | 30.9±18.3 | 31.4±19.3 | | 31.2±18.5 |
| Median (IQR) | 30.0 (17.0–43.0) | 31.0 (19.2–45.8) | | 30.0 (17.5–45.5) | 31.0 (16.2–46.0) | 30.0 (20.0–38.0) | | 30.0 (17.0–44.0) |
| Range | 6.0–75.0 | 1.0–71.0 | | 1.0–75.0 | 11.0–65.0 | 1.0–75.0 | | 1.0–75.0 |
| PCL-5 score ≥33, n (%) | 14 (42.4) | 13 (43.3) | | 27 (42.9) | 8 (44.4) | 6 (35.3) | | 14 (40.0) |
CBD, cannabidiol; IQR, interquartile range MOS-SS SPI-II, Medical Outcomes Study Sleep Scale Sleep Problems Index II; PCL-5, PTSD Checklist for the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition; SD, standard deviation; Δ9-THC, Delta-9-tetrahydrocannabinol; WHODAS-2.0–12, 12-item version of World Health Organization Disability Assessment Schedule, version 2.
a p values calculated via Pearson’s Chi-squared test, Wilcoxon rank sum exact test, Wilcoxon rank sum test or Fisher’s exact test.
Effect on Sleep
MOS-SS SPI-II scores numerically declined (indicating improved sleep) in both treatment groups between day 14 and day 42, evident in both the ITT (online suppl. Table 2; Fig. 2) and PP populations (Table 2; Fig. 3). For the primary endpoint, the mean difference of within-group change between groups in the ITT population was 4.4 (95% CI: −4.1 to 13.0) in favor of placebo (p = 0.3). Similar results were seen in the PP population, where the mean difference between groups was 3.8 (95% CI: −4.8 to 12.0) in favor of placebo (p = 0.4) (Table 2; Fig. 3). There were no substantial differences in changes in MOS-SS SPI-II scores observed after the first 2 weeks of treatment compared with changes after all 4 weeks of treatment in either the ITT (online suppl. Table 2) or the PP population (Table 2).
Table 2.
Change in MOS-SS SPI-II scores across the study period (per-protocol population)
| Analysis population | Observed values, mean ± SD | Changea, mean ± SD | Treatment effect (95% CI)b | p valuec | ||||
|---|---|---|---|---|---|---|---|---|
| CBG | placebo | CBG | placebo | |||||
| Intention-to-treat (ITT) | n | | n | | | | | |
| Pre-screening/baseline | 33 | 61.2 ± 17.7 | 30 | 61.6 ± 12.6 | | | | |
| Day 14 | 33 | 52.5 ± 19.6 | 30 | 55.3 ± 13.2 | –8.6 ± 11.5 | –6.5 ± 9.9 | –2.1 (–7.7, 3.5) | 0.5 |
| Day 28 | 33 | 46.9 ± 17.7 | 30 | 48.6 ± 12.0 | –4.4 ± 13.8 | –6.7 ± 10.6 | 2.3 (–4.4, 8.9) | 0.5 |
| Day 42 | 33 | 42.4 ± 18.5 | 30 | 43.9 ± 13.1 | –3.5 ± 9.5 | –4.7 ± 9.8 | 1.2 (–4.2, 6.6) | 0.6 |
| Change [day 42 – day 14]d | | | | | –7.0 ± 17.1 | –11.4 ± 13.0 | 4.4 (–4.1, 13)d | 0.3 |
| Difference of Change Scores ([day 42 – day 14] – [day 28 – day 14])e | | | | | –3.5 ± 9.5 | –4.7 ± 9.8 | 1.2 (–4.2, 13)e | 0.6 |
| Per-protocol (PP) | n | | n | | | | | |
| Pre-screening/baseline | 18 | 56.8 ± 18.0 | 17 | 61.4 ± 8.6 | | | | |
| Day 14 | 18 | 47.0 ± 17.7 | 17 | 54.7 ± 9.7 | –9.8 ± 11.3 | –6.7 ± 9.8 | –3.1 (–10.0, 4.2) | 0.4 |
| Day 28 | 18 | 42.5 ± 16.6 | 17 | 46.4 ± 11.2 | –4.6 ± 13.4 | –8.3 ± 8.5 | 3.7 (–4.0, 11.0) | 0.3 |
| Day 42 | 18 | 38.0 ± 18.7 | 17 | 41.9 ± 10.3 | –4.5 ± 9.5 | –4.5 ± 8.9 | 0.07 (–6.3, 6.4) | >0.9 |
| Change [day 42 – day 14]d | | | | | –9.0 ± 15.6 | –12.8 ± 8.4 | 3.8 (–4.8, 12.0)c | 0.4 |
| Difference of Change Scores ([day 42 – day 14] – [day 28 – day 14])e | | | | | –4.5 ± 9.5 | –4.5 ± 8.9 | 0.07 (–6.3, 6.4)e | >0.9 |
CBG, cannabigerol; CI, confidence interval; MOS-SS SPI-II, Medical Outcomes Study Sleep Scale Sleep Problems Index II; SD, standard deviation.
aRepresents change from previous time-point unless otherwise stated.
bEstimated as difference in the mean endpoint for the CBG group minus the mean endpoint for the placebo group.
c p value for differences between groups calculated using the Welch two-sample t test.
dPrimary outcome endpoint.
eExploratory outcome endpoint.
Fig. 3.
MOS-SS SPI-II, WHODAS-2.0–12, and PCL-5 scores throughout the study (per-protocol population). MOS-SS SPI-II was self-reported at screening and on day 14, day 28, and day 42; WHODAS-2.0–12 was self-reported at day 1 and day 43; and the PCL-5 was self-reported at day 0 and day 43. Following screening, all randomized participants entered a 2-week run-in phase (through day 14). Participants then received CBG 25 mg daily or placebo for 2 weeks (from day 15 to day 28) with escalation of CBG dosing to 50 mg daily (from day 29 to day 42). MOS-SS SPI-II, Medical Outcomes Study Sleep Scale Sleep Problems Index II; PCL-5, PTSD Checklist for the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition; SD, standard deviation; WHODAS-2.0–12, 12-item version of World Health Organization Disability Assessment Schedule, version 2.0.
Post hoc analysis was performed for participants receiving CBG in the ITT population considered having a clinically meaningful response (in terms of observed decline in MOS-SS SPI-II scores of at least 10 points) during active treatment from day 14–42 (online suppl. Fig. 3). “Responders” (n = 8) were younger (mean age 35.2 years ± 11.8) compared to “non-responders” (n = 17) with an average age of 50.8 years ± 16.2 (p = 0.011). No differences in other characteristics or activity biometrics were observed.
Effect on QoL and PTSD Symptoms
WHODAS-2.0–12 scores declined in both groups across the study period (indicating improved QoL) although such improvements were small (Table 3; Fig. 3). In the ITT population, the mean difference of within-group change between groups was 1.9 (95% CI: −1.1 to 5.5) in favor of placebo (p = 0.2) (online suppl. Table 2; Fig. 2). A similar pattern was observed in the PP population, with a mean group difference of 2.2 (95% CI: −0.83 to 5.3) in favor of placebo (p = 0.15) (Table 3). Improvements in PTSD symptoms (decline in PCL-5 scores) were also observed in both groups, with greater reductions apparent in the placebo group in both the ITT (online suppl. Table 2; Fig. 2) and the PP populations (Table 3; Fig. 3).
Table 3.
Change in WHODAS-2.0–12 and PCL-5 scores across the study period (per-protocol population)
| Measure/analysis population | Observed values, mean±SD | Change, mean±SD | Treatment effect (95% CI)a | p valueb | ||||
|---|---|---|---|---|---|---|---|---|
| CBG | placebo | CBG | placebo | |||||
| WHODAS-2.0–12 | ||||||||
| Intention-to-treat (ITT) | n | | n | | | | | |
| Day 1 | 33 | 24.2±7.8 | 30 | 25.6±10.5 | | | | |
| Day 43 | 33 | 22.7±8.5 | 30 | 22.6±8.9 | | | | |
| Change [day 43 – day 1]c | | | | | −0.8±5.8 | −2.7±4.5 | 1.9 (−1.1, 4.9)c | 0.2 |
| Per-protocol (PP) | n | | n | | | | | |
| Day 1 | 18 | 23.7±6.9 | 17 | 24.2±9.2 | | | | |
| Day 43 | 18 | 23.1±9.1 | 17 | 21.5±7.9 | | | | |
| Change [day 43 – day 1]c | | | | | −0.7±5.0 | −2.9±3.6 | 2.2 (−0.83, 5.3)c | 0.15 |
| PCL-5 | ||||||||
| Intention-to-treat (ITT) | n | | n | | | | | |
| Day 0 | 33 | 32.1±18.9 | 30 | 33.2±19.1 | | | | |
| Day 43 | 33 | 26.0±17.7 | 30 | 23.5±14.9 | | | | |
| Change [day 43 – day 1]d | | | | | −5.8±10.8 | −8.7±13.4 | 2.9 (−3.9, 9.7)d | 0.4 |
| Per-protocol (PP) | ||||||||
| Day 0 | 18 | 30.9±18.3 | 17 | 31.4±19.3 | | | | |
| Day 43 | 18 | 25.4±18.8 | 17 | 23.8±13.5 | | | | |
| Change [day 43 – day 0]d | | | | | −5.5±10.5 | −7.6±13.0 | 2.1 (−6.0, 10.0)d | 0.6 |
CBG, cannabigerol; CI, confidence interval; PCL-5, PTSD Checklist for the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition; SD, standard deviation; WHODAS-2.0–12, 12-item version of World Health Organization Disability Assessment Schedule, version 2.
aEstimated as difference in the mean endpoint for the CBG group minus the mean endpoint for the placebo group.
b p value for differences between groups calculated using the Welch two-sample t test.
cSecondary outcome endpoint.
dExploratory outcome endpoint.
Effect on Activity Tracking Measures
While MOS-SS SPI-II scores decreased in both groups through the course of the study, wrist actigraphy data indicated that sleep duration, efficiency, minutes asleep, minutes awake, minutes of REM, and overall time in bed were relatively constant (Fig. 4). One finding of interest was a potential physiological effect of CBG dose timing on resting heart rate. For all time periods except late-night dosing, participants in the ITT population receiving CBG had a lower mean heart rate (2 h post-dosing) than those taking placebo. In those reporting afternoon dosing (noon–5 PM), a statistically discernible lower mean heart rate (p = 0.017) was observed (online suppl. Fig. 4). As this is the first randomized placebo-controlled study of CBG in humans, the physiological effects of CBG merit further study.
Fig. 4.
Per-participant daily sleep summaries (per-protocol population). Wrist actigraphy data for sleep duration, efficiency, minutes asleep, minutes awake, minutes of REM, and overall time in bed. Light lines show individual participant summaries, and the bold lines represent smoothed group averages based on loss fit. CBG, cannabigerol; REM, rapid eye movement.
Safety and Adverse Events
No serious AEs considered possibly related to the study medication were reported. A total of 5 nonserious AEs considered possibly related to CBG were recorded from 5 participants; headache, lethargy, gastric upset, nausea, and hypersomnia (one episode for each). All were considered mild, although 2 participants withdrew from further participation. One participant in each treatment group reported mild dermatitis/skin irritation related to the Fitbit device.
A total of 10 emergency alerts triggered by extreme responses to PCL-5 questions addressing negative beliefs or risk-taking/self-harm were reported, involving a total of 9 (14%) participants. None required emergency or routine medical services, with no study withdrawals (see online suppl. Material).
Discussion
While cannabis and Δ9-THC/CBD formulations have been studied in a range of conditions, including sleep disturbance [8–14], clinical data for CBG is sparse, with most studies evaluating effects in vitro or in animal models [15]. Although some investigators have reported on CBG use and safety/tolerability [17], to our knowledge this is the first randomized placebo-controlled study formally evaluating CBG efficacy and safety. We found that while use of CBG tended toward improvement in sleep (as reported by a numerical decline in MOS-SS SPI-II scores), a similar pattern was observed in the placebo arm, with no statistically significant differences between the two groups. Similarly, both groups demonstrated improved QoL, though any tendency toward improvements was far smaller. Baseline PCL-5 scores in the study population were high, with over 40% of participants in each group with scores ≥33 (indicative of PTSD) at inclusion. The pretreatment run-in phase allowed evaluation of any potential impact of study involvement and participant engagement on outcomes, where some improvement in sleep scores before any active treatment was apparent. This improvement continued across the subsequent 4 weeks of treatment in both groups, and where decline in MOS-SS SPI-II scores was numerically greater in the placebo group. Substantial placebo responses in randomized studies evaluating cannabinoids for analgesia/pain relief are well recognized, whereby high treatment expectations may impact self-reported outcome measures such as those used in the present study [40, 41]. Biometric monitoring or other real-time, real-world evidence collection is of continued interest for future studies, as sleep biometrics did not show measurable differences between the treatment and placebo groups in this study despite the participant-reported perceived benefit.
As previously noted, this study was designed for a population with significant unmet need and to mimic the real-world usage of CBG. A limitation of the present study was that, except for OSA, we did not consider medical comorbidities or associated concomitant medications and did not account for these in our randomization or analyses. It was not feasible nor representative to restrict any pharmacological use, including mental health-related or even sleep-related medication. Concomitant use of other pharmaceuticals and supplements were allowed, as long as dosage remained stable throughout the study and participants were asked to report if their use of tobacco, cannabis, medications, supplements changed or if lifestyle changes occurred that impacted their sleep. The 2-week run-in phase prior to treatment allowed the establishment of individual baseline values, which should allow a more robust individualized change to be evaluated despite the use of supplements or medications. However, we cannot exclude the potential for concomitant medications or supplements influencing the sleep outcomes for this study.
Likewise, the use of non-CBG cannabis products was allowed (legal within California), with over 75% reporting daily use of Δ9-THC and/or CBD. As use could continue throughout the study, this could have masked any additional benefit of CBG. Not restricting cannabis use was a necessary compromise to ensure representative study recruitment and retention in this population [42]. Cannabis usage was comparable across both study groups. An efficacy-focused study design that restricts concomitant medications and cannabis use would require a much larger sample population, though the authors agree this would more clearly elucidate the effects of CBG.
Due to significant challenges in recruiting enough eligible participants, a protocol amendment modified inclusion criteria so that a prior sleep apnea diagnosis was allowed, but only if the individual had sustained CPAP use prior to entering the study. The recruitment period was also extended for an additional 5 months to mitigate recruitment challenges. Future studies focused on the Veteran population may anticipate similar recruitment challenges.
Nevertheless, the present study does provide valuable information. CBG was well tolerated with no serious AEs reported. While safety data are limited for CBG, Russo et al. [17] have reported AEs in more than 50% of subjects regularly using CBG products. In the present study, only five out of 33 participants receiving CBG (15.2%) each experienced one nonserious AE (all considered mild). No single AE predominated, spanning a range of symptoms, all consistent with those previously reported by Russo et al. [17]. This is reassuring, and supports future studies evaluating the CBG formulation. An additional consideration is dosing and treatment duration. Although we did not observe any dose-related response, studies evaluating CBD show greater effects at higher dosing [22, 32, 33], and it is possible that higher CBG dosing (beyond 50 mg) may achieve better study outcomes. Dosing beyond 4 weeks could also be considered.
Decentralized studies are increasingly proposed to address inequitable access to clinical trial participation, but pose challenges with study retention. A recent cross-study evaluation of 100,000 participants in eight remote digital health studies reported a median participant retention of only 5.5 days [43]. The present study was entirely decentralized, yet >87% of study participants were retained for the full 6-week duration. This affirms the feasibility of decentralized randomized trials in underrepresented populations such as Veterans. In addition to the self-reported sleep and QoL outcome measures, we also evaluated passively collected data via Fitbit on sleep quality, activity tracking and heart rate measures. Actigraphy data are particularly useful in decentralized studies and may provide a useful adjunct to conventional PSG and sleep diaries [44–46].
In conclusion, to our knowledge, this is the first randomized placebo-controlled study reporting CBG efficacy and safety. While the small sample size and notable placebo effect obscured any potential efficacy on sleep or QoL, CBG was well tolerated. These results support further investigation of the physiological and psychological effects of CBG in future studies.
Acknowledgments
The authors sincerely appreciate our co-sponsor Veterans Cannabis Group and Jeremy Freitas for their time, effort, and feedback throughout the study design phase. They also thank staff at Curebase Inc. for their role in logistical study support. The authors appreciate and acknowledge Irina Angel, M.D. for their role as the independent study clinician and their dedication to study participants. We are particularly grateful for the Veterans that volunteered their time for our participant panels to share their experiences and guide our study design. The authors also thank Iain O’Neill (freelance on behalf of nymbly) for providing medical writing support.
Statement of Ethics
The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice Guidelines, approved by a central Institutional Review Board (Advarra; Pro00056526) and registered at clinicaltrials.gov (NCT05088018). Written informed consent was obtained from each participant.
Conflict of Interest Statement
C.R.E. is an employee, shareholder, and fiduciary officer of Metta Medical (dba LEVEL). C.E.W. is an employee of nymbly, which was contracted by Metta Medical for the conduct of this study. E.J.D., B.J.K., and M.T. are external consultants declaring personal fees from nymbly/Metta Medical (dba LEVEL). No other potential conflicts of interest relevant to this article are reported.
Funding Sources
This study and costs associated with development of this manuscript were supported by Metta Medical (dba LEVEL).
Author Contributions
C.E.W. and C.R.E. conceptualized and designed the study. E.J.D. developed the statistical analysis plan. B.G.K. performed statistical analysis. C.E.W. wrote the initial manuscript draft. M.T. cleaned and preprocessed Fitbit data for statistical analysis. All authors contributed to further manuscript revision and approved the final version.
Funding Statement
This study and costs associated with development of this manuscript were supported by Metta Medical (dba LEVEL).
Data Availability Statement
The data that support the findings of this study are not publicly available due to privacy restrictions but are available from the corresponding author upon request.
Supplementary Material.
References
- 1. Alexander M, Ray MA, Hébert JR, Youngstedt SD, Zhang H, Steck SE, et al. The national veteran sleep disorder study: descriptive epidemiology and secular trends, 2000-2010. Sleep. 2016;39(7):1399–410. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Folmer RL, Smith CJ, Boudreau EA, Hickok AW, Totten AM, Kaul B, et al. Prevalence and management of sleep disorders in the veterans health administration. Sleep Med Rev. 2020;54:101358. [DOI] [PubMed] [Google Scholar]
- 3. Byrne SP, McCarthy E, DeViva JC, Southwick SM, Pietrzak RH. Prevalence, risk correlates, and health comorbidities of insomnia in US military veterans: results from the 2019-2020 national health and resilience in veterans study. J Clin Sleep Med. 2021;17(6):1267–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Colvonen PJ, Almklov E, Tripp JC, Ulmer CS, Pittman JOE, Afari N. Prevalence rates and correlates of insomnia disorder in post-9/11 veterans enrolling in VA healthcare. Sleep. 2020;43(12):zsaa119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Metrik J, Bassett SS, Aston ER, Jackson KM, Borsari B. Medicinal versus recreational cannabis use among returning veterans. Transl Issues Psychol Sci. 2018;4(1):6–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Metrik J, Jackson K, Bassett SS, Zvolensky MJ, Seal K, Borsari B. The mediating roles of coping, sleep, and anxiety motives in cannabis use and problems among returning veterans with PTSD and MDD. Psychol Addict Behav. 2016;30(7):743–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Cannabis Data Company (BDSA) . Retail sales tracking. In: Share of market cannabinoids by sales (Jan, 2021 through June); 2022. Available from: https://bdsa.com/products/retail-sales-tracking/ (Accessed August 28, 2023). [Google Scholar]
- 8. Babson KA, Sottile J, Morabito D. Cannabis, cannabinoids, and sleep: a review of the literature. Curr Psychiatry Rep. 2017;19(4):23. [DOI] [PubMed] [Google Scholar]
- 9. Choi S, Huang BC, Gamaldo CE. Therapeutic uses of cannabis on sleep disorders and related conditions. J Clin Neurophysiol. 2020;37(1):39–49. [DOI] [PubMed] [Google Scholar]
- 10. Russo EB, Guy GW, Robson PJ. Cannabis, pain, and sleep: lessons from therapeutic clinical trials of sativex, a cannabis-based medicine. Chem Biodivers. 2007;4(8):1729–43. [DOI] [PubMed] [Google Scholar]
- 11. Shannon S, Lewis N, Lee H, Hughes S. Cannabidiol in anxiety and sleep: a large case series. Perm J. 2019;23:18–041. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Kuhathasan N, Dufort A, MacKillop J, Gottschalk R, Minuzzi L, Frey BN. The use of cannabinoids for sleep: a critical review on clinical trials. Exp Clin Psychopharmacol. 2019;27(4):383–401. [DOI] [PubMed] [Google Scholar]
- 13. Suraev AS, Marshall NS, Vandrey R, McCartney D, Benson MJ, McGregor IS, et al. Cannabinoid therapies in the management of sleep disorders: a systematic review of preclinical and clinical studies. Sleep Med Rev. 2020;53:101339. [DOI] [PubMed] [Google Scholar]
- 14. Walsh JH, Maddison KJ, Rankin T, Murray K, McArdle N, Ree MJ, et al. Treating insomnia symptoms with medicinal cannabis: a randomized, crossover trial of the efficacy of a cannabinoid medicine compared with placebo. Sleep. 2021;44(11):zsab149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Nachnani R, Raup-Konsavage WM, Vrana KE. The pharmacological case for cannabigerol. J Pharmacol Exp Ther. 2021;376(2):204–12. [DOI] [PubMed] [Google Scholar]
- 16. Naguy A. Clonidine use in psychiatry: Panacea or panache. Pharmacology. 2016;98(1–2):87–92. [DOI] [PubMed] [Google Scholar]
- 17. Russo EB, Cuttler C, Cooper ZD, Stueber A, Whiteley VL, Sexton M. Survey of patients employing Cannabigerol-Predominant cannabis preparations: perceived medical effects, adverse events, and withdrawal symptoms. Cannabis Cannabinoid Res. 2022;7(5):706–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Fitbit inspire 2. Available from: https://www.fitbit.com/global/us/products/trackers/inspire2 (accessed August 28, 2023).
- 19.Fitbit device API. Available from: https://dev.fitbit.com/build/reference/web-api/intraday/ (accessed August 28, 2023).
- 20. Webster CE, Emerson CR, Daza EJ. The veterans ECS21 (exploring cannabigerol for sleep) study: a protocol for the first randomized clinical trial of cannabigerol (CBG), a decentralized study designed for U.S. veterans in California. medRxiv. 2020. [Google Scholar]
- 21. De Crescenzo F, D'Alò GL, Ostinelli EG, Ciabattini M, Di Franco V, Watanabe N, et al. Comparative effects of pharmacological interventions for the acute and long-term management of insomnia disorder in adults: a systematic review and network meta-analysis. Lancet. 2022;400(10347):170–84. [DOI] [PubMed] [Google Scholar]
- 22. Millar SA, Stone NL, Yates AS, O’Sullivan SE. A systematic review on the pharmacokinetics of cannabidiol in humans. Front Pharmacol. 2018;9:1365. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Hays RD, Stewart AL. Sleep measures. In: Stewart AL, Ware JE, editors. Measuring functioning and well-being: the Medical Outcomes Study approach. Durham (NC): Duke University Press; 1992. p. 235–59. [Google Scholar]
- 24. Spritzer KL, Hays RD. MOS sleep Scale: a manual for use and scoring, version 1.0. Los Angeles, CA; 2003. Available from: https://labs.dgsom.ucla.edu/hays/files/view/docs/surveys/sleep/sleepman-112603.pdf (accessed August 28, 2023). [Google Scholar]
- 25. World Health Organization . Measuring health and disability: manual for whom disability assessment schedule WHODAS 2.0. Geneva: WHO Press; 2010. Available from: https://www.who.int/publications/i/item/measuring-health-and-disability-manual-for-who-disability-assessment-schedule-(-whodas-2.0 (accessed August 28, 2023). [Google Scholar]
- 26. Blevins CA, Weathers FW, Davis MT, Witte TK, Domino JL. The posttraumatic stress disorder checklist for DSM-5 (PCL-5): Development and initial psychometric evaluation. J Trauma Stress. 2015;28(6):489–98. [DOI] [PubMed] [Google Scholar]
- 27.Curebase - distributed clinical trials. Available from: https://www.curebase.com/ (accessed August 28, 2023).
- 28. Schulz KF, Altman DG, Moher D; CONSORT Group . CONSORT 2010 statement: updated guidelines for reporting parallel group randomized trials. Ann Intern Med. 2010;152(11):726–32. [DOI] [PubMed] [Google Scholar]
- 29. US Department of Veterans Affairs . Digital cognitive behavioral therapy for insomnia. VA Health Systems Research; 2024. Available from: https://www.hsrd.research.va.gov/impacts/digital-cbt-for-insomnia.cfm (accessed September 30, 2025). [Google Scholar]
- 30. Nakamura Y, Lipschitz DL, Landward R, Kuhn R, West G. Two sessions of sleep-focused mind-body bridging improve self-reported symptoms of sleep and PTSD in veterans: a pilot randomized controlled trial. J Psychosom Res. 2011;70(4):335–45. [DOI] [PubMed] [Google Scholar]
- 31. Nakamura Y, Lipschitz DL, Donaldson GW, Kida Y, Williams SL, Landward R, et al. Investigating clinical benefits of a novel sleep-focused mind-body program on gulf war illness symptoms: a randomized controlled trial. Psychosom Med. 2017;79(6):706–18. [DOI] [PubMed] [Google Scholar]
- 32. Kirkland AE, Fadus MC, Gruber SA, Gray KM, Wilens TE, Squeglia LM. A scoping review of the use of cannabidiol in psychiatric disorders. Psychiatry Res. 2022;308:114347. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Arnold JC, McCartney D, Suraev A, McGregor IS. The safety and efficacy of low oral doses of cannabidiol: an evaluation of the evidence. Clin Transl Sci. 2023;16(1):10–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Hays RD, Martin SA, Sesti AM, Spritzer KL. Psychometric properties of the medical outcomes study sleep measure. Sleep Med. 2005;6(1):41–4. [DOI] [PubMed] [Google Scholar]
- 35. Nakamura Y, Lipschitz DL, Kuhn R, Kinney AY, Donaldson GW. Investigating efficacy of two brief mind-body intervention programs for managing sleep disturbance in cancer survivors: a pilot randomized controlled trial. J Cancer Surviv. 2013;7(2):165–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Federici S, Bracalenti M, Meloni F, Luciano JV. World Health Organization disability assessment schedule 2.0: an international systematic review. Disabil Rehabil. 2017;39(23):2347–80. [DOI] [PubMed] [Google Scholar]
- 37. Axelsson E, Lindsäter E, Ljótsson B, Andersson E, Hedman-Lagerlöf E. The 12-item self-report world health organization disability assessment schedule (WHODAS) 2.0 administered via the internet to individuals with anxiety and stress disorders: a psychometric investigation based on data from two clinical trials. JMIR Ment Health. 2017;4(4):e58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Davis LW, Schmid AA, Daggy JK, Yang Z, O'Connor CE, Schalk N, et al. Symptoms improve after a yoga program designed for PTSD in a randomized controlled trial with veterans and civilians. Psychol Trauma. 2020;12(8):904–12. [DOI] [PubMed] [Google Scholar]
- 39. US Department of Veterans Affairs (VA) . PTSD checklist for DSM-5 (PCL-5). Available from: https://www.ptsd.va.gov/professional/assessment/adult-sr/ptsd-checklist.asp (accessed August 28, 2023). [Google Scholar]
- 40. Gedin F, Blomé S, Pontén M, Lalouni M, Fust J, Raquette A, et al. Placebo response and media attention in randomized clinical trials assessing cannabis-based therapies for pain: a systematic review and meta-analysis. JAMA Netw Open. 2022;5(11):e2243848. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Fisher E, Moore RA, Fogarty AE, Finn DP, Finnerup NB, Gilron I, et al. Cannabinoids, cannabis, and cannabis-based medicine for pain management: a systematic review of randomised controlled trials. Pain. 2021;162(Suppl 1):S45–66. [DOI] [PubMed] [Google Scholar]
- 42. Weygandt J, Moody M, Sajjadi NB, Greiner B, Ford AI, Mazur A, et al. Discontinuation and nonpublication of clinical trials for the pharmacologic treatment of posttraumatic stress disorder among military veterans. J Trauma Stress. 2023;36(2):325–32. [DOI] [PubMed] [Google Scholar]
- 43. Pratap A, Neto EC, Snyder P, Stepnowsky C, Elhadad N, Grant D, et al. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. NPJ Digit Med. 2020;3(21):21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Te Lindert BHW, Blanken TF, van der Meijden WP, Dekker K, Wassing R, van der Werf YD, et al. Actigraphic multi-night home-recorded sleep estimates reveal three types of sleep misperception in insomnia disorder and good sleepers. J Sleep Res. 2020;29(1):e12937. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Williams JM, Taylor DJ, Slavish DC, Gardner CE, Zimmerman MR, Patel K, et al. Validity of actigraphy in young adults with insomnia. Behav Sleep Med. 2020;18(1):91–106. [DOI] [PubMed] [Google Scholar]
- 46. Rösler L, van der Lande G, Leerssen J, Vandegriffe AG, Lakbila-Kamal O, Foster-Dingley JC, et al. Combining cardiac monitoring with actigraphy aids nocturnal arousal detection during ambulatory sleep assessment in insomnia. Sleep. 2022;45(5):zsac031. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are not publicly available due to privacy restrictions but are available from the corresponding author upon request.