Real-Time and Long-Term Effects of Medical Marijuana on Older Adults: Protocol for a Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Older adults represent the fastest-growing group of medical marijuana (MM) users in the United States, with chronic pain being the most common reason for use. Despite this trend, scientific evidence remains limited regarding the short- and long-term effects of MM on critical health outcomes, including cognitive function, physical and mental health, and overall quality of life in this population. To better inform clinical practice and public policy, there is a clear need for more rigorous, longitudinal studies that examine the impact of real-world MM products over time. OBJECTIVE: The Study on Medical Marijuana and Its Long-Term Effects on Older Adults (SMILE) is a prospective cohort study that aims to 1) determine MM's short- and long-term effects on pain, physical, emotional, and cognitive functioning, and quality of life in older adults; and 2) identify MM product characteristics and patient subgroups associated with improved outcomes and side effects. METHODS: This study will recruit and follow 440 older adults (50 years or older, ~50% >65, ~50% male) with chronic pain for 12 months, as some initiate MM (MM group, n=330) and others do not (comparison group, n=110). Data collection included quarterly survey questionnaires (focusing longitudinal changes in cannabis use, pain, physical and emotional functioning, side effects, and quality of life); baseline and 12-month cognitive assessments, pain sensory tests, and blood/urine samples for cannabis use; and periodic smartphone- and Fitbit sensor-based measurements to capture detailed MM use patterns, real-time pain, mental health, and objective data on physical activity and sleep. Data will be analyzed using descriptive analyses, generalized linear mixed effects models, and generalized estimating equations models to assess differences in short- and long-term effects between the MM and comparison groups, and subgroups among those initiating MM treatment. RESULTS: Recruitment for the SMILE study began in July 2022 and all data collection is expected to be completed by 2026. As of October 2025, we enrolled 399 participants, with 277 in the MM group and 122 in the comparison group. Data analysis is currently underway, and results are expected to be published starting in 2027. CONCLUSIONS: With multisource data collected in real-time and over 12 months, our study will provide much-needed scientific evidence addressing: 1) whether MM can reduce pain and improve physical and emotional functioning in the short term among older adults; 2) whether effects of MM last for 12 months and demonstrate changes in quality of life or cognition; and 3) whether health benefits and consequences differ by MM product type and whether individual differences (e.g. sex, baseline pain phenotyping) moderate the relationship. Our findings will offer valuable insights for physicians and patients when considering MM as a treatment option, and will help guide more informed, individualized care decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".