In healthy subjects, placebo response and its neural and psychological correlates lack consistency across different routes of administration 12 , 13 , Moreover, the translation of such findings to clinical settings is questionable not only because chronic pain patients exhibit distinct brain anatomy and neurophysiology 15 , 16 , 17 , 18 but also because such patients are repeatedly exposed to a myriad of medical rituals which may bias expectations toward treatment.
Thus, it is likely that the principles of placebo pill analgesia in clinical settings may not be captured by experimental models of placebo. A number of brain imaging studies have examined placebo response in the setting of a RCT. Some of these studies have demonstrated changes in brain functions as a consequence of placebo effects 19 , Others have shown that brain functions may actually predispose chronic pain patients to respond to a placebo treatment.
RCTs comparing a lidocaine patch to placebo treatment in chronic low back pain and duloxetine to placebo treatment in osteoarthritis indicated that placebo response was predicted, respectively, by functional connectivity of the medial prefrontal with the insula 21 and the dorsolateral prefrontal cortex with the rest of the brain Unfortunately, these studies have the caveat of not relying on a no treatment arm to control for the natural history of the patients e.
Similarly controlled studies in chronic pain patients are necessary to investigate placebo responses in clinical settings. This prospective cohort study included four neuroimaging sessions, a large battery of questionnaires assessing personality traits and pain characteristics, and a proper no treatment arm that allowed us to disentangle placebo pill-related analgesia from non-specific effects.
Given that the RCT placebo effect is embedded in predictable psychology and neurobiology and given that metabolic activity and functional connectivity change following placebo response, we designed a comprehensive RCT with two identical treatment periods placebo or active treatment , each followed by a washout period.
The repeat brain imaging sessions were used: 1 to identify functional networks constructed from spontaneous fluctuations in BOLD signal predictive of placebo response prior to placebo exposure; and 2 to test stability of identified networks post-exposure to placebo, as such networks are known to be malleable and may change as a function of learned associations The primary aim of this study was to identify the psychological factors and the brain properties collected prior to placebo treatment that would determine placebo response.
Given previous results, we hypothesized that responses to placebo pills are predetermined by functional connectivity between regions such as the medial and lateral prefrontal cortex, the anterior cingulate cortex, and the subcortical limbic structures, since they have been implicated in both experimental 25 , 26 , 27 and clinical placebo response 20 , 21 , 22 , We also hypothesized that other psychological and neurophysiological determinants, which would be more specific to the clinical setting and to chronic pain patients will also contribute to the prediction of placebo response.
The second aim of this study was to examine the longitudinal effect of treatment exposures on brain properties. Based on prior studies 19 , 20 , we hypothesized that some components of the functional networks predisposing patients to placebo response would change as a consequence of placebo response while others would remain stable.
Consistent with our predictions, we demonstrate-specific psychological factors, anatomical properties, and functional coupling of the lateral prefrontal cortex predetermine placebo pill responses.
We show that components of the response-predictive functional network showed transient properties, dependent on the placebo response. Finally, a fully cross-validated algorithm applied on psychological factors and functional connectivity prior to exposure to placebo treatment successfully predicted response magnitude to placebo pill treatment. Participants visited the lab on six occasions over 8 weeks and underwent identical scanning protocols on four of those visits Fig.
Throughout the duration of the trial, participants used a visual analogue scale, displayed on a smartphone app Supplementary Figure 2 , to rate back pain intensity two times per day in their natural environment. These ecological momentary assessments EMAs represented the primary pain measurement of this study and were used to determine placebo response.
Secondary pain measurements were also collected but only in the lab on each visit. These included a numeric verbal recall of their average pain experienced over the last week pain memory , a numeric rating scale at time of visit NRS, commonly used to quantify pain in clinical trials 28 , the McGill Pain Questionnaire MPQ sensory and affective scales, and the PainDetect.
The covariance matrix across the different pain measurements at baseline is presented in Supplementary Figure 3 and the demographics in Supplementary Table 1.
Placebo pill ingestion diminishes back pain intensity while trial participation non-specifically decreases qualitative pain outcomes. Participants entering and completing the study are indicated. Group by time by Pain intensity vs quality interaction indicated that only the pain intensity was diminished by placebo pill ingestion. Error bars indicate SEM. For each figure, a description of the analyses and p -values are reported in Supplementary Table 7. Our study was designed to test for both the effects of placebo pill ingestion and the effects of placebo response.
We initially tested for the effect of placebo pill ingestion by comparing the analgesia between the 43 patients receiving placebo PTx with the 20 patients comprising the no treatment arm NoTx ; for this, we used the EMAs collected with the phone app.
The effect sizes E. Next, the 63 patients were dichotomized into Resp and NonR based on a permutation test performed on the EMAs collected with the phone app baseline vs. This demonstrates that placebo pill ingestion increased the response rate when considering within-subject pain entries. Importantly, pain levels at baseline were equivalent between PTxResp, PTxNonR, and NoTx groups Table 1 and external factors such as pain intensity during baseline, phone rating compliance, overall treatment compliance, treatment duration, rescue medication usage, and previous medication usage were not related to placebo response Supplementary Table 2.
On average, the magnitude of response in the PTxResp was 1. Results show that NRS collected in the lab correlated with our primary pain outcome Supplementary Figure 3 and captured the effects of placebo pill ingestion and placebo response.
Memory of pain also correlated with our primary pain outcome and strongly dissociated PTxResp and PTxNonR, but also showed a non-specific improvement with exposure to the trial PTx and NoTx arms equally improving. Despite some variability across these measures of pain intensity, the effects of placebo pill ingestion and the effects of placebo response were globally concordant across these outcomes. However, MPQ sensory and affective scales and PainDetect poorly correlated with our primary pain outcome Supplementary Figure 3 , did not differentiate between treatment cohorts, and groupings defined by the pain app showed improvement of symptoms in all groups Fig.
We conclude that the RCT placebo response is composed of two components: 1 a pill ingestion-related response specifically impacting perceived intensity of chronic pain Supplementary Table 3 , and 2 a non-specific response reflecting the effect of time or the mere exposure to healthcare visits that modulates qualitative pain measures Fig.
For the rest of the study, we concentrate on unraveling the mechanisms of the placebo-induced decrease in back pain intensity. In this study, all brain imaging and questionnaires data were analyzed blindly.
We employed cell scrambling to generate two random labeling of patients and all group comparisons were performed three times two times for scrambled codes and one time for real labels. After completing all the analyses, the real labeling was revealed during a public lab meeting.
The results are reported only when the real labeling of patients could be properly identified based on the statistical tests. First, we sought to identify psychological parameters predisposing CBP patients to the placebo pill response from a battery of 15 questionnaires with 38 subscales Supplementary Table 4 collected at visit 1.
Figure 2a shows the covariance across all factors used to assess personality and psychological states. Univariate statistics were used to assess group differences and correlations with the magnitude of response.
Here, the real labeling and the scrambled codes yielded no significant comparisons when correcting for multiple comparisons Fig. Personality traits and psychological factors were strongly linked to the magnitude of response. The red circle marks an outlier on the magnitude of response that was excluded. Our results showed that positive and negative expectations at both visits were not different between PTxResp and PTxNonR and changes in the levels of expectations following treatment 1 representing the update of expectations were not different between the groups Supplementary Table 5.
Thus, although a large body of literature demonstrates the influence of expectations on the placebo response 31 , expectations were not a significant factor in the current study. We secondly sought for anatomical properties predisposing CBP patients to placebo pill response at visit 2 prior to treatment 1.
The volumes of the NAc, amygdala, and hippocampus were first examined because they represent risk factors for developing pathological emotional states 32 , 33 , chronic pain 34 , and placebo response in healthy individuals Given the recent evidence that subcortical volume asymmetry can provide a brain signature for psychopathologies 36 , we followed-up examining inter-hemispheric laterality of the combined volume of these three structures.
Importantly, this result was validated using another brain segmentation software Freesurfer, Supplementary Figure 7. The differences in anatomical properties of the cortex were assessed with gray matter density and cortical thickness Supplementary Figure 8. These anatomical properties also mildly correlated with the magnitude of placebo response Fig.
The identification of brain morphological features, present before treatment and persisting throughout the study, provides evidence for placebo propensity stemming, in part, from stable brain biology. Performing this analysis using scrambled codes for labeling patients generated no significant group differences.
Placebo pill response is predetermined by subcortical limbic volume asymmetry and sensorimotor cortex thickness. The placebo pill responders displayed a rightward asymmetry in overall volume of these three subcortical limbic structures after controlling for peripheral gray matter volume, age, and sex at visit 2. We thirdly tested whether brain functions provided useful information to determine placebo pill response in our RCT.
Building on previous findings, we derived placebo-related networks of interest from results in OA patients exposed to placebo treatment in an RCT Fig. We performed a modularity analysis segregating the functional networks into 6 communities, and restricted all analyses to the default mode network DMN , sensorimotor SM , and frontoparietal FP communities, due to their overlap with placebo-related networks observed in OA.
Subcortical limbic regions were added along with the PAG because of their involvement for placebo response and in pain chronification Supplementary Figure 9. No other exploratory analyses were performed outside of this initially planned strategy.
Placebo pills response identifies a lateral prefrontal functional network with invariant and transient components. The connectivity matrices of our data were restricted to communities overlapping with these networks of interest: default mode network DMN , frontoparietal F-P , sensorimotor SM , and subcortical limbic.
These results therefore demonstrate the existence of a lateral prefrontal-functional network, whose components either stably or transiently determine the likelihood of placebo pill response.
Here again, the scrambled codes yielded no significant group differences. We then examined the variability of each of these anatomical and functional brain measurements in patients of the NoTx arm. We observed no changes across the visits, indicating stability of the measure without placebo effects Supplementary Figure We further tested if the mere exposure to placebo pills, regardless of the response, impacted these brain measurements by comparing the PTx group with the NoTx group.
These analyses revealed absence of pill exposure effects on anatomical and functional brain measurements Supplementary Figure We used machine learning to determine whether placebo response could be predicted from brain imaging and questionnaires data collected prior to placebo treatment.
We implemented a nested leave-one-out cross-validation LOOCV procedure where placebo outcome of each patient was predicted using an independent training sample. Within each n -1 patients training sample set, the model parameters were tuned using tenfolds cross-validations.
The optimized model showing the least error was then applied to the left-out patient, repeated for every patient. We initially used data from the questionnaires to classify the patients into response groups binary variable approach. Within each training sample set, the scores of the normalized 38 subscales were used to build the support vector machine SVM classifier.
SVM classification achieved an accuracy of 0. Sensitivity of this approach was 0. The ROC curve shows specificity and sensitivity of the model. The red circle marks the predicted magnitude of response for the outlier, which was excluded for assessing the error of the model. The model was trained in n -1 patients in an inner loop using tenfolds cross-validations for tuning the LASSO parameters, and then tested in the unseen held out patient, repeated for every patient.
As with the previous approach, we initially predicted the magnitude of response using just the combination of questionnaire data Fig. The model was no longer able to predict the magnitude of response after removing these psychological parameters. This not only indicates their importance for response, but also reveals that neither the traditional psychological measures reported in healthy controls under placebo conditioning e.
Next, we sought to determine if rsfMRI collected prior to placebo pill ingestion could predict the magnitude of response. These connections were used to train a predictive model using tenfolds cross-validations for tuning the LASSO parameters, which was tested in the left-out patient Fig. The resultant network consisted of a combination of 19 weighted connections predicting the magnitude of placebo analgesia. Although this set of edges was predictive of magnitude of response prior to the first placebo treatment, the prediction did not generalize to rsfMRI data collected at other visits post treatment.
This is likely due to the small number of connections included in our model, which are either changing in time as a consequence of learning and adjustment of expectations with the introduction of a placebo treatment or due to inherent variability of the measurements themselves.
Applying LASSO regression on features from brain anatomy generated a predictive model, which did not correlate with the actual magnitude of response. The model was therefore not considered significant and is not reported.
We tested whether predictive models from psychological factors and brain functions were independent or if they instead predicted redundant information. Furthermore, a linear regression entering the value of the predicted magnitude of response from rsfMRI and the value of the predicted analgesia from the questionnaires data revealed that both models explained independent variance of the actual response, suggesting that they are complementary to one another Fig.
Thus, although the questionnaires were strong predictors of the magnitude of response, they should not be considered as proxy for the brain imaging data, and vice versa. We finally tested whether our models were specific for placebo pill analgesia or if they were predicting unspecific improvement of symptoms. There were no differences on any of these parameters in the NoTx group suggesting that our results were specific for placebo response Supplementary Figure Second, we tested whether our multivariate classifier based on psychological factors Fig.
The classifier accuracy was considered non-significant 0. Finally, we applied the regression model predicting the magnitude of response Fig. In this case, the coefficient of correlation between the predicted and the actual analgesia was stronger in the PTx group Fig. This is the first brain imaging RCT specifically designed to study chronic pain patients receiving placebo pills compared to a no treatment arm.
Daily pain ratings from a smart phone revealed that patients receiving placebo pills showed stronger pain reduction and a higher response rate compared to patients in the no treatment arm, indicating that placebo pills successfully induced analgesia that could not be explained by the natural history of the patient or the mere exposure to the study. Our results show a multiplicity of biological systems, partially overlapping with complex inter-relationships, underlying placebo pill response.
The identified systems seem to encompass brain properties known to be involved in chronic pain maintenance or in the transition to chronic pain e. Given the moderate to large effect size of RCT placebo effect also observed here , our results imply that gaining a better understanding of placebo pill response has important clinical utility. Our results demonstrate the psychological, functional, and anatomical determinants of the placebo response and suggest that once patients begin a placebo treatment, their individual pain relief may be predicted in the context of a RCT.
Even various measures of pain intensities—daily ratings, memory, and NRS—provided slightly different information about the extent of analgesia and pain fluctuations. This highlights the importance of examining a multiplicity of pain-related outcomes as the analgesic properties of any given treatment may not be constrained to a single dimension of the pain experience, and the measures used to capture analgesia may be differentially influenced by different factors.
Our behavioral results stress the importance of moving away from a single, cross-sectional pain measurement, and demonstrate that distinct dimensions respond differentially to placebo pill ingestion, impacting mainly perceived magnitude but not its qualities. It is however possible that placebo might only impact the intensity of pain while a successful active pharmacological treatment would improve both intensity and qualities.
This remains an open but important area of inquiry. Our study design included two washout periods in order to determine stability and within-subject re-occurrence of response. The use of EMAs allowed us to determine that placebo analgesia started on the first day of treatment, but the return to baseline levels of pain started only several days after washout. Thus, the washout periods were proven too short to test the re-occurrence of response.
Instead, our data showed a carryover effect of the placebo response after discontinuation of treatment. Associating a psychological profile with placebo pill response in CBP departs from the literature regarding placebo in healthy subjects.
None of the often-cited personality traits in placebo literature 35 , 41 , 42 , 43 —optimism, anxiety, extraversion, neuroticism—successfully differentiated placebo responders from non-responders in our chronic pain patients. In our patients, placebo pill response was driven primarily by a combination of a greater openness to experience, increased emotional awareness, decreased distraction about pain and discomfort, augmented capabilities in describing inner experiences, and higher sensitivity to non-painful situations.
Our results reveal that placebo response can be predicted from an ability to recognize subtle cues in the body regarding emotional and physical well-being, to remain attentive to these cues and emotions by not ignoring or suppressing them, and to choose to accept these states as opposed to becoming worried or burdened by them.
These factors of personality were able to differentiate PTxResp and PTxNonR as well as predict the magnitude of placebo response in new patients. These results are critical, as questionnaires are easy to administer and may be sufficient to predict placebo pill response in chronic pain. In healthy individuals, the placebo response recruits endogenous pain pathways acting upon the opioid system to regulate descending inhibition from the rACC 9 through the PAG 25 , a mechanism that can be reversed by naloxone As such, levels of activation in the DLPFC and the OFC are believed to represent the strongest predictors of experimental placebo response in healthy controls The present results indicate that these systems are also part of the placebo pill response in CBP patients, although direct correspondences between functional networks and regional activity remain uncertain.
The coupling of the DLPFC and rACC with anti-nociceptive circuitry is also consistent with our previous observation that these regions were predictive of placebo response in OA patients Therefore, there are close correspondences in the mechanisms underlying placebo pill response across different types of pain chronic back pain observed in this study, chronic knee pain 22 , and acute experimental pain and in different settings RCT vs.
Because the model was not stable in the post-treatment visits, its capacity for predicting magnitude of response in a new set of chronic pain patients remains to be determined in further studies.
The procedure was nevertheless informative regarding the neurophysiological contributors to the placebo response. Unfortunately, the pain trajectories showed sustained effects of placebo analgesia during washout periods that were proven too short to be informative regarding within subject replication of placebo effects.
Several pitfalls have been raised when trying to predict complex behaviors like the placebo response Here, many of these potential confounds were accounted for by incorporating novel methodological strategies such as: including a no-treatment arm documenting the natural history of the patients, using smart phone technology accounting for natural fluctuations of pain outside of the clinical setting, collecting multiple pain outcome measures, performing analyses blindly using one real code and two scrambled codes to minimize bias, and utilizing machine learning methods to estimate predictability in a fully cross-validated procedure.
Some have argued that clinical trials do not provide an appropriate context to study the psychobiology of placebo because they are contaminated by uncontrollable confounds and that instead placebo should be studied in a controlled environment, such as a laboratory Despite the complexity of the phenomenon, our results challenge this assumption as placebo response could be partially predicted in chronic pain patients. Precisely, machine learning applied to psychological factors or functional connectivity showed that magnitude of response was predictable.
The predictions from both models were not correlated to one another and they were independent predictors of analgesia. Importantly, the joint prediction from both models was more accurate in the PTx arm compared to the NoTx, suggesting a certain level of specificity unique to placebo analgesia.
Together, our results contribute to the placebo literature by demonstrating the existence of psychological and neurobiological principles determining the placebo response in RCTs. During that time, participants with chronic low back pain CBP were initially recruited from the general population and clinical referrals via hospital databases and advertising in the community.
To meet inclusion criteria, individuals had to be 18 years or older with a history of lower back pain for at least 6 months. This pain should have been neuropathic radiculopathy confirmed by physical examination was required , with no evidence of additional co-morbid chronic pain, neurological, or psychiatric conditions. Individuals had to agree to stop any concomitant pain medications and had to be able to use a smartphone or computer to monitor pain twice a day.
Finally, for safety precautions, clinical measurements taken at visit 1 were required to be within the pre-specified healthy range as determined by the standards utilized by Northwestern University Feinberg School of Medicine Laboratory Services Department and all participants passed the MRI safety screening requirements at each scanning visit. Informed consent was obtained from all participants on their first visit. Supplementary Figure 1 consort diagram illustrates the flow of patients through the clinical trial.
From the initial chronic back pain CBP patients recruited in the study, 4 individuals were assessed for eligibility but met exclusion criteria before consenting. Of the enrolled patients, 43 failed to meet the inclusion criteria at visit 1 or during the 2-week baseline period between visits 1 and 2. The inclusion of an active treatment group was used to ensure that the double blind for placebo treatment was maintained for the duration of the study and that no deception took place during the informed consent process i.
Therefore, the 5 participants randomized in the active treatment group were not analyzed. The final sample size included 20 CBP patients randomized to the no treatment group and 43 CBP patients randomized to the placebo treatment group; demographics for these individuals can be found in Supplementary Table 1. The number of patients recruited was based both on the power analysis and on our previous experience with attrition rates in studies with similar patient populations; the final sample sizes based on the following effect size estimates were approved by the sponsor NCCIH prior to starting the study.
For responders, we anticipated a mean decrease of 30 units on a 0— scale, with an estimated standard deviation of 15; this results in an effect size estimate of 2. In non-responders, the mean decrease in pain was anticipated to be negligible and we did not expect to have enough power to detect this.
In addition, it ensured adequate sample sizes even assuming some attrition in each group. The design was setup to track placebo response in time and to test the likelihood of response to multiple administrations of placebo treatment in order to optimize accuracy in the identification placebo response. The overall protocol included four scanning sessions collected before and after each treatment period. The randomization scheme was performed using 2 kinds of blocks, each with 8 patients; the first block assigned 5 patients to placebo and 3 to no treatment, and the second block assigned 5 patients to placebo, 2 to no treatment, and 1 to active treatment.
Each patient ID was randomly attached to a randomization code. The initial randomization included codes for the first 80 patients. It was followed by a second randomization of 50 additional codes about 6 months later. After these procedures, study coordinators picked up the blinded agent from NUCATS for storage and dispensing; all drugs were stored at room temperature in a locked cabinet within the lab and monitored daily for temperature changes, bottle counts, and expiration dates.
Each person assigned to treatment received a mixture of blue and bi-colored pills. This way, neither the participants nor the researchers knew which treatment the participant had received. For those assigned to the no-treatment group, no blind was maintained, as both study staff and participants knew that they were not receiving the study agent. Visit 1: Participants were screened for eligibility and consented on visit 1.
Following informed consent, a blood sample was drawn for a comprehensive chemistry panel, a complete blood count, and a pregnancy test if applicable , vital signs were taken blood pressure, heart rate, respiration rate, height, and weight , and a medical professional completed a physical examination and took a comprehensive pain history. Participants were then asked to complete a battery of 29 questionnaires regarding basic demographics, pain, mood, and personality Supplementary Table 4.
Once submitted, questionnaire answers were finalized in the database and were rendered un-editable by both participants and study staff.
To best avoid questionnaire fatigue due to the number of questionnaires administered, participants were allowed to take breaks and walk around the testing room, although they were required to complete all questionnaires at the designated visit. Any remaining information, including clinical data collected at the visit, were entered manually into the database by study staff.
The relevant information was verified via double-data entry by different staff members at a later time. Certain antacids can make it harder for your body to absorb this medicine. Ask a doctor or pharmacist before using other cough or cold medicines that may contain similar ingredients.
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Top of the page. SM can be dangerous and can cause waste of resources, pharmaceutical reactions and possible increase of antimicrobial resistance [ 4 ].
Nowadays, the arbitrary use of over-the-counter drugs among the youth, especially students is on the rise, and the possibility of SM in health sciences students is higher due to easy access to pharmaceutical information resources and relatively adequate familiarity with different kinds of drugs [ 5 ]. Numerous studies around the world have analyzed the prevalence of SM among students. In a study carried out among medical and pharmacy students in Jordan in , the prevalence of SM was found to be In another study on health sciences students in Kuwait, the prevalence of SM was reported to be Further, a study in India showed the prevalence of SM among medical students to be Most of the students reported they used self-medication because of insignificance of illness and having adequate pharmaceutical information, and about half of them used the former prescriptions.
The most prevalent medications were antipyretics, antitussives and analgesics. About half of students believed SM was a part of self-care and had to be encouraged [ 7 ]. The most common reasons for SM were pain, urinary and throat infections and cold. The most prevalent medications used were non-steroid anti-inflammatory drugs [ 8 ]. Moreover, a study performed on Iranian health sciences students showed a prevalence of The most common medications used included antitussives, cold drugs, analgesics and antihistamines.
The most common illness lading to SM was reported to be cold [ 9 ]. Considering the increasing growth of SM around the world as well as inadequate information about the prevalence of SM among the health sciences students in Kermanshah, the current study was carried out to investigate the prevalence of SM and its related factors among the health sciences students.
This university has six schools, including medicine, pharmacy, dentistry, nursing and midwifery, paramedical sciences and public health, located in the west of Iran. The study sample, based on the results of Ramazani et al.
Sampling was performed via stratified random sampling method, and sampling classes comprised of the university faculties. According to the number of students in each faculty, a percentage of them in each faculty was selected as study sample.
To this end, a list of students in each faculty was first obtained and encoded. Then, using random numbers table, students among different specialties and levels in the faculty were chosen and included in the study. SM in this study was meant the use of any over-the-counter medication over the past 6 months.
The inclusion criterion was informed consent for participation in the study. Data in this study were collected by a two-section questionnaire.
The first section was about demographic information such as age, gender, marital status, residence, health insurance, specialty, faculty and monthly income.
The second section was a researcher-made questionnaire on SM, which analyzed SM, its causes, illnesses leading to SM, use of medications and information resources of SM.
The validity and reliability of the questionnaire had been analyzed and confirmed by Ramazani et al. The current study also evaluated the validity of the scale by content validity.
The questionnaire was given to 12 experts to evaluate, and their corrective comments were applied. It is noteworthy that these students were not included in the study. To collect the data, the researcher first obtained permission from the Ethical Review Committee of KUMS and then referred to the faculties of university for sampling. Then, their written informed consent for participation in the study was obtained. Finally, the questionnaires were given to them and collected after completion.
The obtained data were analyzed by SPSS software using descriptive statistics frequency, mean and standard deviation and inferential statistics chi-square test.
To assess the association between SM and demographic characteristics, chi-square test was run. Among participants in this study, The mean age of the respondents was In terms of academic level, As for major, 83 Also, The conditions to run the chi-square test were not met in order to analyze the relationship between SM and major Table 1.
The SM rate was As for residence, the SM rate was higher in the students living with their families The data in this part did not have the conditions for running the chi-square test. The prevalence of SM varies in different countries and regions, ranging from The difference in the SM rates may be due to differences in demographic characteristics of the study samples, research methodology, data collection tools and working definition of SM.
We tend to think the high prevalence of SM may be due to poor implementation of pharmaceutical rules. However, this rule is overlooked by many pharmacies due to various reasons like lack of adequate supervision by the concerned authorities.
In the present study, the most prevalent medication categories used were common cold drugs, analgesics and antibiotics. In various studies conducted on SM, different drug classes have been used. In this regard, the most prevalent pharmaceutical categories used included analgesics [ 3 , 14 , 15 , 17 , 18 , 19 , 21 ], antipyretics [ 3 , 14 , 18 , 21 ], antibiotics [ 14 , 15 , 16 , 18 ], antihistamines [ 15 ], antipruritics [ 15 ], and non-steroid anti-inflammatory drugs [ 19 ].
Most studies performed on the prevalence of SM have only mentioned the name of pharmaceutical category not the medications of the given category. However, some studies have reported the name of medications in addition to their pharmaceutical class.
In these studies, the most commonly used medications are paracetamol [ 10 , 17 , 19 ] and amoxicillin and metronidazole [ 12 ]. By taking a quick look at the above drug categories, analgesics and antibiotics are more prominent. Every medicine has its own side effects, and its arbitrary use can be perilous.
For example, some of the side effects of analgesics are hematologic, metabolic, digestive, neural, cardiovascular, hepatic, optic and respiratory complications [ 22 ]. This situation is also the same for antibiotics, and arbitrary use of these drugs can be truly disastrous. Antibiotics, even prescribed by the doctors, act like a double-edged sword and can cause unwanted side effects in the patient. The most prevalent side effects of antibiotics are hypersensitivity reactions, anaphylactic reactions, microbial resistance, immune dysfunction and neurologic, renal, cardiac, hematologic and hepatic complications [ 20 , 21 ].
The authorities of faculties should make use of new training methods like distant education to provide the students with the necessary warnings about arbitrary use of different medications, especially antibiotics [ 23 ].
In the current study, the most prevalent reasons for SM in the opinion of students were prior experience about the illness, non-seriousness of the illness and availability of drugs. In the current research, the most prevalent illnesses subjected to SM were common cold and headache. Other studies performed on SM have reported fever [ 3 , 14 , 15 , 21 , 24 ], headache [ 3 , 14 , 15 , 17 , 19 , 21 , 24 , 25 ] and cold as the most prevalent illnesses.
If SM is carried out by the qualified people who are adequately familiar with the medications, it can be safely used for the treatment of mild diseases like common cold. In this regard, the World Health Organization has accepted SM as a part of self-care process and believes it can reduce the pressure on the healthcare system, especially in areas with limited medical facilities. However, it should be noted that easy access to various medications, especially antibiotics and analgesics can risk the health of individuals and society.
Some risks associated with SM include addiction and drug dependence, drug interactions, lower or higher consumption of allowable amount, microbial resistance, and emergence of new microbes and occurrence of different pharmaceutical complications [ 21 ]. In this study, SM among the male and female students was not different, which is in line with some studies in this regard [ 11 , 12 , 13 , 24 ]. However, some studies have reported higher SM in females [ 19 , 25 ], while some others have shown higher SM in males [ 15 ].
We believe SM is a problem that is not associated with gender and is observed in every person, whether a man or a woman. Moreover, the prevalence of SM was the same in the married and single students. Shah et al. We think SM is a cultural phenomenon that is influenced by various factors, and these factors can get an individual to embark on SM, irrespective of being married or single.
In addition, the findings showed SM was higher among the students living with their families than those living in the dormitory, but the difference was not statistically significant. In our opinion, SM may occur at any place, in the family or in the dormitories.
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