We measure for the first time the associations between subjective patient experiences of feeling "high" and treatment outcomes during real-time flower consumption sessions. Our study uses data from the mobile health app, Releaf App™, through which 1,882 people tracked the effects of flower on a multitude of health conditions during 16,480 medical cannabis self-administration sessions recorded between 6/5/2016 and 3/11/2021. Session-level reported information included plant phenotypes, modes of administration, potencies, baseline and post-administration symptom intensity levels, total dose used, and real-time side effect experiences.
View Article and Find Full Text PDFObjectives: Many annual, nationally representative US surveys that assess cannabis use do not collect information on product characteristics despite varying health risks and benefits. Capitalizing on a rich dataset of primarily medical users, the purpose of this study was to describe the degree of potential misclassification in clinically relevant cannabis use measures when primary mode of use is recorded but not product type.
Methods: Analyses consider user-level data from the Releaf App™ database on product types, consumption modes, and potencies in a non-nationally representative sample of 26,322 cannabis administration sessions occurring in 2018, across 3,258 users.
Background: Little is known about the frequency with which different combinations of phytochemicals (chemovars) arise in Cannabis flower or whether common chemovars are associated with distinct pharmacodynamics and patient health outcomes. This study created a clinically relevant, user-friendly, scalable chemovar indexing system summarizing primary cannabinoid and terpene contents and tested whether the most frequently consumed chemovars differ in their treatment effectiveness and experienced side effects.
Methods: Between 09/10/2016 and 03/11/2021, 204 people used the freely available, educational mobile software application, Releaf App, to record 6309 real-time consumption sessions using 633 distinct Cannabis flower products, unique at the user level, with terpene and cannabinoid potency information.
Objectives: We measure for the first time how commercially available flower products affect feelings of fatigue.
Methods: A total of 1,224 people recorded 3,922 flower self-administration sessions between June 6, 2016, and August 7, 2019, using the Releaf App. Usage sessions included real-time subjective changes in fatigue intensity levels prior to and following consumption, flower characteristics (labeled phenotype, cannabinoid potency levels), combustion method, and any potential experienced side effects.
J Clin Gastroenterol
April 2022
Goals: We measure for the first time how a wide range of cannabis products affect nausea intensity in actual time.
Background: Even though the Cannabis plant has been used to treat nausea for millennia, few studies have measured real-time effects of common and commercially available cannabis-based products.
Study: Using the Releaf App, 886 people completed 2220 cannabis self-administration sessions intended to treat nausea between June 6, 2016 and July 8, 2019.
Background: An observational research design was used to evaluate which types of commonly labeled Cannabis flower product characteristics are associated with changes in momentary feelings of distress-related symptoms.
Methods: We used data from 2306 patient-directed cannabis administration sessions among 670 people who used the real-time Cannabis effects recording software, Releaf App, between June 6, 2016, and February 23, 2019, for tracking the effects of Cannabis flower consumption. Fixed effects multivariable panel regression techniques were used to establish overall relief by symptom type and to determine which labeled product characteristics (e.
Objective: Few studies to date have measured the real-time effects of consumption of common and commercially available Cannabis products for the treatment of headache and migraine under naturalistic conditions. This study examines, for the first time, the effectiveness of using dried Cannabis flower, the most widely used type of Cannabis product in the United States, in actual time for treatment of headache- and migraine-related pain and the associations between different product characteristics and changes in symptom intensity following Cannabis use.
Methods: Between 06/10/2016 and 02/12/2019, 699 people used the Releaf Application to record real-time details of their Cannabis use, including product characteristics and symptom intensity levels prior to and following self-administration; data included 1910 session-level attempts to treat headache- (1328 sessions) or migraine-related pain (582 sessions).
: Scientific research on how consumption of whole, natural flower affects low mood and behavioral motivations more generally is largely nonexistent, and few studies to date have measured how common and commercially available flower used may affect the experience of "depression" in real-time. : We observed 1,819 people who completed 5,876 cannabis self-administration sessions using the ReleafApp™ between 06/07/2016 and 07/08/2019, with the goal of measuring real-time effects of consuming flower for treating symptoms of depression. Results: On average, 95.
View Article and Find Full Text PDFComplement Ther Med
October 2019
The prior medical literature offers little guidance as to how pain relief and side effect manifestation may vary across commonly used and commercially available cannabis product types. We used the largest dataset in the United States of real-time responses to and side effect reporting from patient-directed cannabis consumption sessions for the treatment of pain under naturalistic conditions in order to identify how cannabis affects momentary pain intensity levels and which product characteristics are the best predictors of therapeutic pain relief. Between 06/06/2016 and 10/24/2018, 2987 people used the ReleafApp to record 20,513 cannabis administration measuring cannabis' effects on momentary pain intensity levels across five pain categories: musculoskeletal, gastrointestinal, nerve, headache-related, or non-specified pain.
View Article and Find Full Text PDFFederal barriers and logistical challenges have hindered measurement of the real time effects from the types of cannabis products used medically by millions of patients in vivo. Between 06/06/2016 and 03/05/2018, 3,341 people completed 19,910 self- administrated cannabis sessions using the mobile device software, ReleafApp to record: type of cannabis product (dried whole natural Cannabis flower, concentrate, edible, tincture, topical), combustion method (joint, pipe, vaporization), Cannabis subspecies (C. indica and C.
View Article and Find Full Text PDFThe Releaf App mobile software application (app) data was used to measure self-reported effectiveness and side effects of medical cannabis used under naturalistic conditions. Between 5/03/2016 and 12/16/2017, 2,830 Releaf App users completed 13,638 individual sessions self-administering medical cannabis and indicated their primary health symptom severity rating on an 11-point (0-10) visual analog scale in real-time prior to and following cannabis consumption, along with experienced side effects. Releaf App responders used cannabis to treat myriad health symptoms, the most frequent relating to pain, anxiety, and depressive conditions.
View Article and Find Full Text PDF: We use a mobile software application (app) to measure for the first time, which fundamental characteristics of raw, natural medical flower are associated with changes in perceived insomnia under naturalistic conditions. : Four hundred and nine people with a specified condition of insomnia completed 1056 medical cannabis administration sessions using the Releaf App educational software during which they recorded real-time ratings of self-perceived insomnia severity levels prior to and following consumption, experienced side effects, and product characteristics, including combustion method, cannabis subtypes, and/or major cannabinoid contents of cannabis consumed. Within-user effects of different flower characteristics were modeled using a fixed effects panel regression approach with standard errors clustered at the user level.
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