Parkinson’s disease is a neurodegenerative disorder impacting patients’ movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population’s movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson’s disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% ± 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection. Parkinson’s disease (PD) is a neurological disorder that occurs due to a decrease in the brain dopamine level, impacting patients’ movement. Symptoms of PD include movement abnormalities such as tremor, dysfunction of magnitude, and symmetry of arm swing, and gait characterized by shorter strides and slower movements compared to healthy individuals ,2. TheseThe wrist is the most common and acceptable body location for people to wear sensors on . AdvanceThe contributions of this study are listed as follows: (1) Early detection of PD using wrist accelerometer and passive monitoring, (2) using the document-of-word feature engineering approach to improve the performance over the traditional statistical feature engineering method, (3) identifying the best window size for segmentation of the wrist accelerometer data so that it increases the accuracy of the early detection of PD, and (4) identifying the optimal amount of monitoring time for the early detection of PD. Each of these goals that lead to our contributions in this paper are explained in the following paragraphs.Passive monitoring of individuals over time using an easy-to-wear and comfortable sensor on the wrist could provide us with initial information on whether a person is likely to be in the early stages of a disease with movement disorder. This is the case for patients with PD, because PD is a progressive neurodegenerative disorder of the brain and central nervous system, affecting patients’ motor system, causing reduced movement, tremor, postural instability, and postural rigidity . FatigueThe standard statistical feature engineering method has been the most common feature extraction method in this domain ,21. In tOur third contribution is to test different window size values and identify the best one for data segmentation so that it leads to a classification model with the best performance for the early detection of PD. Choosing the right window size from which features are calculated has been shown to have a significant impact on detecting bradykinesia in patients with PD . ChoosinThe last contribution of this paper is to provide information on what could be the optimal amount of data needed for the early detection of PD when passive monitoring participants using a wrist accelerometer. The inertial sensors’ battery life depends on factors such as the number of sensors utilized, sampling frequency, and recording time, and even in the best-case scenario, when just accelerometer is utilized, there is a constraint on long-term data recording due to these devices’ limited battery lives . TherefoOur main aim in this study is the early detection of PD in a free-living environment using wrist accelerometers. Many research studies have been conducted on PD clinical test automation using wearable technologies in the clinical setting ,27,28,29Our second aim is to investigate the impact of using two feature engineering methods on the early detection of PD: the statistical and document-of-words feature engineering methods. There are only a few research studies in which document-of-words feature engineering was performed. Kheirkhahan et al.  used theFor related work, we consider studies in which a wrist sensor was used to collect data on individuals in free-living conditions, because the mean value of features calculated from data captured in a free-living environment has been shown to be different from that captured in a controlled environment ,39.Monitoring PD patients using wearable sensors and in a free-living environment has been attracting attention recently ,41. MoniIn a research study by San Segundo et al., wrist accelerometry was used to detect tremor in PD both using annotated laboratory data and free-living data . Their rIn a study conducted by Lipsmeier et al., individuals with PD and their healthy age-matched counterparts were monitored in free-living conditions using smartphones to investigate differences in features among participants . Active Zhan et al. conducted a longitudinal study on PD subjects over a period of 6 months in which data were collected actively through five smartphone activities and used to develop a mobile PD score . The scoThe authors of the Parkinson@Home validation study  used accHabets J.G. conducted a study to monitor bradykinesia in patients with PD using a wrist accelerometer . Their dThe study of Williamson et al. on PD  is the mThe following five subsections of this section explain this study data collection’s protocol and methodology. The data were collected from two groups of subjects: (1) healthy elderlies (64.2 ± 7 years and no movement deficiencies) and (2) individuals with various stages of PD (71 ± 6.2). PD patients reported no other illnesses impacting their gait and movement. The Hoehn and Yahr (H&Y) score used in our study is one of the commonly used measures of progression of PD and has been shown to correlate with motor decline happening in PD patients. The data were collected from participants while they had an Actigraph triaxial accelerometer on their non-dominant wrist for seven consecutive days. To be more specific, we used ActiGraph GT3X with ActiLife v6.13.3 Firmware v1.7.1 at 100 Hz. The sensor could be taken off while taking a shower or swimming. The dataset includes three phases of data collection with 6–12 months between each two consecutive phases. The Actigraph device was recorded at 100 Hz with a range of ±8 g. In each phase, the H&R score and relevant clinical measures were recorded for each participant. The data from the first phase of the data collection is used for our analysis in this paper. In this study, an epoch-based segmentation approach was used in which we converted each subject’s wrist movement signal into subsequences of a predefined length. Since a passive data collection was performed without labeling the data, each segment may represent both activities of daily living and symptoms of PD in the case of PD patients, which should be informative when it comes to distinguishing between the PD population and their healthy counterparts. Feature window size has been shown to significantly impact identifying symptoms of PD, including bradykinesia, resting tremor, and freezing of gait ,16,43,44The window lengths considered in this study were as follows: . Each individual time series was split into smaller subsequences using a sliding window of S—second with overlapping windows of size ter data ,16, whilter data . A studyter data . A windoter data . We alsoUsing the epoch-based segmentation approach explained in To be able to compare the statistical feature engineering approach with the document-of-words method, a set of statistical features was used to create the epoch-based statistical features. Below is a list of features included in the analysis:Signal vector magnitude: For the segments of each segmentation strategy, we calculated signal vector magnitude as √(X2 + Y2+ Z2) in which x, y, and z represent the accelerometers’ axes. This measure is dependent from the accelerometer’s orientation. This feature is used by researchers both for identifying PD symptoms [symptoms  and earlsymptoms  to identRMS of acceleration in the anterior posterior (AP), mediolateral (ML), and vertical (Ver) directions: Root mean square (RMS) of acceleration is a time domain feature used in many studies focusing on PD symptom detection or early diagnosis of the disease [RMS values in each direction:  disease ,16,44. Udir represents each of the following directions: {AP, ML, Ver}.The denominator is signal vector magnitude and Movement dispersion feature: this feature is introduced in [z-score values (zi(t) represents z-score of data point, t, in axis i within the nth segment). Then, we removed the outliers, defined as the points for which the standard deviation of either of the axes is greater than two. Then, using the valid points (non-outlier points) of each segment, dispersion in axis i can be calculated as follows in Equations (2) and (3):V(n) represents a set of valid points within the nth segment, Zi(t) represents the z-score of data point t in axis I, and S(n) is the number of the pairs of valid points within the nth frame. Therefore, the vector of dispersion features for each segment looks like {Dx(n), Dy(n), Dz(n)}.duced in  and onlyduced in . First, Acceleration range: This feature is reported among the features used for identifying tremor in PD patients [patients ,23. To cThe average of these features across all subsequences was calculated in the statistical feature engineering method and considered for classification model development. The document-of-words feature representation approach mainly includes three steps: data segmentation, vocabulary generation, and feature calculation, as described in . Every sti denotes the duration of data collection and s is the window size. Data segmentation was performed using the method explained in In the second step, we generated a vocabulary set in which each subsequence was considered as a word. Since it is unlikely to find two identical subsequences with the same feature descriptors, we put all the segments obtained from all subjects together and grouped them using K-medoid clustering . TherefoD = {d0, d1, …, dk} per subject’s data, in which di is a normalized value of word frequency based on the total number of words per individual (Equation (5)):In the last step, every segment was assigned a word, which was assigned to its cluster centroid. Using these words, we could convert every subject’s wrist data into a document of words and construct a vector Feature selection is mainly performed in the analytics domain so that relevant features are selected for model development while irrelevant and redundant features are removed to reduce the model complexity and avoid overfitting. Since genetic algorithm (GA) has been shown to be one of the best algorithms for optimizing the classification of movement data in the healthcare domain, it was used for feature selection in our work. GA is a method inspired by natural selection to solve optimization problems by taking a population of individuals and trying to produce better approximations. The function to optimize here is the performance of our classification models. Each individual was considered to be a binary representation of features. A total of 40 random individuals were generated as the initial population. Our fitness function was defined as a combination of the average accuracy of all classifiers in this study combined with the number of features. The best fitness score is the one with the highest accuracy and the lowest number of features. We ran GA with 50 generations. In each generation, 20 individuals were selected for crossover, half of whom were the fittest and the other half random. This way, we avoided the issue of falling into a local optimum. Crossover was performed four times using random pairs, generating four feature sets. Using crossover, each feature set was generated by the features randomly selected from the parent feature sets. Then, each individual was given a 14% chance of being mutated, creating the new feature sets. More details on how genetic algorithm was applied to our feature set can be found in .To create a model that distinguishes between healthy subjects and patients with PD, we applied different classifiers, including support vector machine (SVM with an RBF kernel), decision tree (DT), random forest (RF), K nearest neighbors (KNN), AdaBoost (AB), and naive Bayes (NB). These classifiers have been used in the gait analysis and document classification literature ,23,48. To evaluate each classification, we performed 100 runs of that classifier with 10-fold cross-validation, such that in each run of cross validation, nine folds  were used for training and the remaining one fold (six subjects’ data) was considered as the validation set. Reported results are based on the average accuracy values and standard deviation around the average. This validation strategy is performed to make sure that the results are robust enough. H0: StatisticalFeatures ≥ meanWordFeaturesmean.H0a: StatisticalFeatures < meanWordFeaturesmean.in which meanStatisticalFeatures and meanWordFeatures are the average accuracy values across all classifiers in the case of using statistical features and word features, respectively. The feature set used for this hypothesis test was generated using the best windowing strategy/the best combination of windowing strategies identified prior to this step.To reach our second goal, in addition to the observations and results obtained from the classification models, we developed a hypothesis to compare the average performance of the two feature engineering methods. The following hypothesis was developed:Considering one set of features, 100 runs per classifier, and six classifiers, we must have 600 accuracy values per method. H0: Combination_of_Aall ≤ Performance3 SecondsPerformance.H0a: Combination_of_All > Performance3 SecondsPerformance.in which PerformanceCombination_of_All represents the average accuracy obtained using the features calculated from all data granularity levels, and Performance3 Seconds represents the average accuracy of obtained using features calculated out of subsequences of 3 s length. Similar hypotheses were developed for the comparison of each pair of best performing window size/sizes and for the comparison of them with the baseline windowing strategies.To investigate the impact of window size on classification performance, we applied different classification methods using features calculated from subsequences of each windowing strategy , different combinations of all features together, and a reduced feature set selected from the combination of all. The performance of the classifiers based on each single window strategy was considered as baseline. Considering 100 runs of each classifier and 10-fold cross-validation, firstly, all authors investigated the results by observation and identified windowing strategy/strategies that seemed to provide the best performance values. Then, we took the best ones and statistically checked for any significant difference between them and baseline windowing strategies. Here is an example of one hypothesis test developed:To identify the optimal number of days for data collection for early detection of PD, we selected the best windowing strategy/strategies and the best set of features . ClassifH0: performance_1 Day = performance_2 Days = performance_3 Days = performance_4 Days = performance_5 Days = performance_6 Day = performance_7 Day.H0a: not all the performance values from different amounts of data are the same.The null hypothesis states that the classification performance values are not significantly different across different numbers of days from which the data were collected. Using the epoch-based statistical feature representation, the average value of each feature across all subsequences was calculated. First, all possible combinations of the statistical features were investigated by running the classifiers. The best results were obtained using SDS = {RMSX, RMSY, RMSZ, Vector Magnitude, RangeAP, RangeML, RangeVER, DX, DY, DZ}. Then, using this SDS, each classifier was run 100 times using features from various subsequence lengths. The best results were obtained using a reduced set of features containing at least one feature from each windowing strategy. As it appears, the best classification performance was obtained using a reduced set of features containing features from all windowing strategies. This set includes the following features: {DispersionAP_10 s, DispresionAP _60 s, RMSRAP-300, RMSRML-300, signal_vector_magnitude_300 s, signal_vector_magnitude_900 s, RMSRAP-3 s, ARML_3 s, ARAp_3 s, ARVer _3 s}.th person’s wrist data when the signal is split into subsequences of 3 s, 10 s, 1 min, 5 min, and 15 min, respectively. Combining all the words, each individual’s wrist data could be represented by a document of 100 words as follows:To create the features using the document-of-words method, we used the same SDS introduced in the statistical feature method. Various values of subsequence lengths and various numbers of clusters were investigated. The optimum number of clusters/values of k were selected based on the elbow method for each and all windowing strategies. This number ranged between 4 and 32 for each window size. The value of k determines the number of words needed to represent all individuals. After investigating different values of k , the optimum number of clusters for subsequences of 3 s, 10 s, 60 s, 300 s, and 900 s were found to be 22, 22, 20, 20, and 16, respectively. Therefore, each subject’s wrist movement signal could be represented as follows:F = 4.07, p = 0.022. Tukey’s HSD test for multiple comparisons found that the mean accuracy value from the reduced feature set containing features from all granularity levels was significantly higher than that of all other features sets obtained from the other windowing strategies , except for the one containing the combination of all features.For each hypothesis test, we first checked the normality, and since our data were normally distributed, we used ANOVA to compare the average accuracy values based on the different windowing strategies listed in In general, if we look at the results in t = 6.85, df = 4797.8, p-value = 1.27 × 10−6).The normality test was demonstrated that the distribution of accuracy values per group was normal. Therefore, a paired-sample t-test was performed. The test outcome indicates that the average accuracy value of the classification using word features is significantly greater that the average accuracy value of the classification using statistical features  and homogeneity (constant variance) check, we could not use parametric test. Thus, the Kruskal–Wallis non-parametric test was selected and performed, which resulted in a chi-squared = 40.951, with df = 6, and p-value = 2.96 × 10−7. There is enough evidence that the classification performance considering different amounts of data (day-wise) is significantly different.Upon the result of the normality  Early detection of patients with PD using accelerometers worn on the most common and accepted body location, the wrist. Our results indicate that PD can be detected using wrist-worn accelerometry and passive monitoring of individuals with good accuracy values. (2) Application of the document-of-words feature engineering method, which is novel for PD detection using data collected from wrist sensors. Although this method was used for PD detection using data collected from participants’ ankles , the datAP, RMSRML, RMSRVer, DispersionAP, DispresionML, DispersionVer, ARAp, ARML, ARVer}. This set was selected as the best-performing set of statistical features. Various classification models were applied to our dataset using 10-fold cross-validation, and the results were reported per 100 runs of each classifier.In this study, we used two feature engineering methods: epoch-based traditional statistical feature engineering method and the document-of-words approach. Our statistical feature set, which was also used as the subsequence descriptor set in the document-of-words approach, included the following set of ten features: {signal vector magnitude, RMSRAP_10 s, DispresionAP _60 s, RMSRAP-300, RMSRML-300, signal_vector_magnitude_300 s, signal_vector_magnitude_900 s, RMSRAP-3 s, ARML_3 s, ARAp_3 s, ARVer _3 s}. The reason why AR in all three directions using a window size of three is included in the selected feature set could be because of its importance in identifying tremor and bradykinesia [AP from 3 s window sizes could be explained using the same rationale. Dispersion features using a window size of 10 has been shown to contribute positively to the diagnosis of PD [AP_10 s appeared as one of the important features in our reduced statistical feature set. Vector magnitude features from windows of sizes 300 and 900, and RMSRAP-300 and RMSRML-300, can be also seen in the selected subset of features, which could be indicating the amount of hand movement over longer periods of time. Using the document-of-words features, the best performance was obtained using a reduced set of 20 words while the set had words generated from all windowing strategies. This indicates that considering data from each granularity level reveals some useful information about the populations in the study, and that contributes to a better performance of classification tasks.Based on our results, the document-of-words method significantly improves the classification performance compared to the statistical method. Interestingly, our results indicate that the highest performance of both the epoch-based statistical approach and the document-of-words method was obtained when we used a combination of features from all windowing strategies . This set of features was a reduced feature set out of all possible features generated from each windowing strategy. The statistical set of features that contributed to the best classification results is as follows: {Dispersionykinesia . The exiis of PD , and thaThis study is a step towards the early diagnosis of Parkinson’s disease using wrist-worn accelerometry in a real-life setting and would benefit patients and healthcare providers. The document-of-words feature engineering method eliminates the need for precise step/stride segmentation, which is a challenging task specifically for populations with a health condition.It is not easy to compare our work and the results of this study with those from other studies because there has not been a great number of works that have attempted the early detection of PD using wrist-worn sensors and passive monitoring. Moreover, no study has reported on the impact of window size on PD diagnosis. The methods and dataset used in this study have strengths and limitations. One of the strengths of the dataset is that it was passively collected from the most common and acceptable body location of individuals in a free-living condition. Moreover, our methodology does not require participants to wear the sensor on the most-affected wrist. Another strength of this work is that PD can be detected without explicit labeling of the data, e.g., symptoms detection and labeling. We did not have to label the data for activities of daily living and the symptoms of the disease. Furthermore, we had a balanced dataset in terms of the number of patients with PD and healthy elderlies. The other strength of our approach is that we had a set of features that was independent from sensor orientation.Our dataset’s limitations are as follows: a limited number of participants and lack of additional information from each subject .There are several opportunities for future work with the same dataset. Since we have three phases of data collection with 6–12 months in between, we should be able to test our results by combining participants from all three phases and see how that improves the results. Another possible task would be to select our test sets completely independently from the training set by selecting individuals for our test set from the other phase of the study. Another promising next step would be to conduct a longitudinal study and predict each individual’s health status in the third phase based on the first two phases of data. Another next step would be to develop models based on more sophisticated machine learning models to distinguish PD patients with different disease severities.As mentioned in , wrist-wThis research study had four aims, including the early diagnosis of Parkinson’s disease in a free-living environment using wrist accelerometry, investigating the impact of two feature engineering methods on classification models’ performance, investigating the impact of different windowing strategies and their associated features on models’ performance, and investigating the impact of the amount of data on classification models’ performance. Our results indicate the feasibility of using data from wrist-worn accelerometry collected in free-living conditions for the early diagnosis of PD. The performance of our PD diagnosis models were improved using the document-of-words feature engineering method, and a subset of features including at least one feature from each windowing strategy was included. This indicates that including data from different granularity levels adds to our model performance. By comparing our models’ performance values using different amounts of data in terms of days, we noticed an increase in the model performance as the amount of data increased. However, our models’ performance values using three, four, five, and six days of data collection were not significantly different from that of seven days, suggesting the possibility of using three days of data collection for the diagnosis of PD without compromising the diagnosis model performance.


We thank profs. Deng and Zhang for their comment on our manuscript.First, profs. Deng and Zhang mentioned that it was unknown why we selected randomized controlled trials (RCTs) and observational studies simultaneously in the same meta‐analysis. A high quality meta‐analysis based on RCTs is regarded as the highest level of evidence‐based medical evidence, and the formulation of many clinical guidelines also follows this standard, but this does not mean that we should unconditionally believe in RCT‐based meta‐analysis, especially when the results of RCTs are different from those of observational efficacy comparison trials. The implementation of RCTs is limited by the scale of funds and usually can only ensure the statistical efficiency of the primary outcome indicators. Considering the operability and other factors, the included population is often different from that in clinical practice, the external applicability of evidence is limited, and the direct application to clinical practice sometimes cannot achieve the expected effect. The population included in the observational efficacy comparison study is closer to clinical practice, but since there is no randomization, there are many factors that may interfere with the final results. When answering a clinical question, do not just believe in RCTs. Observational efficacy comparative studies also need to be included in the evaluation. Moreover, if the results of RCTs are inconsistent with those of observational studies, it is more necessary to carefully evaluate the reasons for the differences. Sometimes the conclusions of observational studies are more credible.p < .0001 and stroke . However, that difference was not found in nonfatal myocardial infarction , cardiovascular‐related mortality , and all‐cause mortality .Second, both profs. Deng and Zhang have noted that two eligible published studies were missed in our meta‐analysis. One of the missed studies compares major cardiovascular events in patients with gout and concurrent cardiovascular disease and chronic kidney disease who receive febuxostat or allopurinol.n Figure   Nonfatal stroke; (B) nonfatal myocardial infarction; (C) cardiovascular death; and (D) death from any cause.Third, we agree with profs. Deng and Zhang that another probably missing critical point is the quality assessment of the included studies. Study quality was independently assessed (by Gao L. G. and Cheng R.) according to a tool that was specifically developed for the randomized studies based on the Delphi Consensus.The Newcastle Ottawa Scale (NOS) was used to assess the quality of the included nonrandomised studies.We have benefited a lot from this discussion. This meta‐analysis was updated and supplemented according to valuable opinions, which further makes up for the shortcomings of this analysis. We appreciate the authors' comments on our study. Your valuable suggestions will be of great help to our future scientific research work.The authors declare no conflict of interest.


The prevalence of smoking among young adults aged 19–28 years old in the United States persists at rates of 14.3%. Young adults underutilize pharmacotherapy for smoking cessation, and the use of e-cigarettes has increased. We analyzed comments from online smoking-cessation support groups to understand young-adult smokers’ views of pharmacotherapy and e-cigarettes, to provide a more in-depth insight into the underutilization of pharmacotherapy. A qualitative analysis was performed on comments about pharmacotherapy and e-cigarettes from participants enrolled in online smoking-cessation support groups in 2016–2020. A codebook was developed with a deductive approach to code the comments, followed by thematic analysis. Eighteen themes were identified, with four dominant themes: interest, benefit, knowledge, and flavor. Participants expressed less interest in both nicotine-replacement therapy and e-cigarettes; moreover, they expressed unfamiliarity with and misconceptions about pharmacotherapy, and recognized the enticing flavors of e-cigarettes. Participants often felt e-cigarettes were not useful for smoking cessation, but the flavors of e-cigarettes were appealing for use. Participants had mixed opinions about the use of e-cigarettes for smoking cessation, but predominantly felt e-cigarettes were not useful for smoking cessation. The use of social media may be an effective way to address misconceptions about pharmacotherapy for smoking cessation and increase willingness to accept assistance. Though the prevalence of cigarette smoking among young adults has been decreasing over the past several years, as many as 14.2% of young adults age 19-28 in the United States (USA) still currently smoke cigarettes . Early sWatkins et al. found that young adults are 3.6 times more likely to use electronic cigarettes (e-cigarettes) or “vapes”, also known as electronic nicotine-delivery systems (ENDS), than evidence-based pharmacotherapy . One posCommune Smokefree Social was implemented utilizing the online social-media platform, Facebook. Commune Smokefree Social was an adaptation of the Tobacco Status Project, which had shown short-term efficacy in a randomized trial , but since nicotine dependence isn’t my main issue with quitting, I don’t know that it would be right for me.”Despite more participants expressing the higher frequency of disinterest in NRT, there were some participants that expressed a willingness to consider using NRT, with the highest interest being in nicotine lozenges.“Haven’t tried any over the counter method but it [lozenge] does seem effective because you really just miss the feeling of a cigarette in your mouth too.”Posts about ENDS revealed the concern about the safety and health risks of ENDS use, which contributed to overall disinterest. Conversations about ENDS differed from those regarding NRT, in that participants were particularly focused on safety when discussing ENDS. Several individuals shared their skepticism about joining the ENDS trend and concerns over the potential occurrence of “popcorn lung” . Comments about ENDS and NRT were similar in that many individuals were less interested because of doubts regarding efficacy for quitting. In general, more participants expressed disinterest towards ENDS than interest. Individuals who expressed interest shared that ENDS were an appealing alternative compared to more foul-smelling cigarettes. There was an association between a sense of shame with smoking cigarettes and the general notion that ENDS were a more socially acceptable way to mask a nicotine addiction.“I’d probably grab the e-cig. It’s easier to hide and some of the flavors are good.”Participants who had personal experience with NRT, bupropion, or varenicline tended to report beneficial outcomes with their use compared to participants with no experience. Participants expressed mixed impressions of nicotine patches and nicotine gum; many expressed that these forms of pharmacotherapy were useful, while others expressed that they were not beneficial. Individuals found the nicotine patch and gum to be helpful in preventing nicotine cravings. Those who stated that nicotine gum was useful had personal experience with utilizing gum to help them quit, particularly to help satisfy the hand–mouth action, preventing further cravings for more cigarettes.“I like gum because it also satisfies my mouth’s need to be active without shoving a bunch of food into it.”Some participants stated that the nicotine patch was not effective in keeping their hands distracted from reaching another cigarette. Individuals also expressed that nicotine patches were not effective often, because their use was limited by the side effects and inability to keep patches on for a longer duration. Participants who stated that nicotine gum was not useful often expressed an aversion to the peppery taste. “I did but it [nicotine patch] made me sick and gave me a rash that would last for weeks each time I used one. I’ve known it [nicotine patch] to work for other people though.”The attitudes and perceptions around ENDS shared many similarities with those around NRT. There was a strong correlation with friend and family influences that led participants to believe ENDS were an ineffective aid to quit smoking cigarettes. Participants also mentioned that ENDS helped curb their craving for a cigarette, similar to their thoughts about certain NRTs. Other individuals expressed that ENDS were ineffective as an alternative due to the throat burn and overall feeling of being unsatisfied. In contrast, many participants mentioned that they tried ENDS, but expressed concerns over them perpetuating the dependence due to their similarity to cigarettes. “E-cigs don’t work for me, they’re too similar and its [sic] too easy to spend all day vaping.”Participants expressed more familiarity with nicotine patches and gums and less familiarity with other forms of NRT, such as nicotine inhalers and nasal sprays. Many of those who lacked knowledge about NRT and medications expressed less interest in utilizing these options to aid in smoking cessation. There were many misconceptions about NRT or medication use, such as the belief that NRT would cause more dependence on nicotine or cause a new dependence. Some participants expressed distaste for the utilization of smoking-cessation medications because they viewed it as a replacement rather than an aid to quit smoking. There was a common belief that nicotine is a harmful substance and that utilizing NRT would be counterintuitive to quitting smoking. “Not interested in replacing the intake method... I don’t want nicotine in any form to rule my life, and I am afraid I will simply just use the patch or the gum or the lozenge indefinitely.”The co-occurrence of knowledge and cost appeared, which suggested that participants were unaware of options that would help NRT and medications become more affordable, such as insurance coverage. Many individuals did not know that certain smoking-cessation pharmacotherapy options are covered under insurance plans.“Just didn’t like the fact it [nicotine gum] was $60 per box and I didn’t know it [nicotine gum] was covered by insurance at the time.”In contrast, there was an even mixture of participants who were aware of ENDS and those who were unaware of this nicotine-delivery system. Many individuals who knew about ENDS were those who had prior experience with them. Participants believed that the use of candy or fruit flavors was a smart marketing strategy and were enticing, and expressed that the ENDS flavors made their smoking experience more palatable and enjoyable than smoking cigarettes. Many shared that their utilization of ENDS was not for purposes of smoking cessation and felt that ENDS were harmless. “I enjoy the flavors and love how not stinky it is compared to cigs. I hope to gradually reduce my use but I enjoy it.”However, there were a few participants who expressed disgust towards the flavors used in the ENDS juices and their potential for harm.To our knowledge, this is the only qualitative analysis, to date, that describes perceptions surrounding smoking-cessation pharmacotherapy and ENDS in young adults attempting to quit smoking on social media. Our data analysis identified gaps in knowledge, which were mainly misconceptions about NRT and smoking-cessation medications. Our findings were similar to Duarte et al.’s focus-group study with non-college-educated young adults, which revealed an overall negative perception about cessation treatments due to side effects, and a misunderstanding of how these pharmacologic therapies work . Our anaParticipants expressed interest in ENDS as an alternative to tobacco cigarettes because of the enticing and customizable flavors of ENDS. This was consistent with a survey conducted by Farsalinos et al., which found that the most important factor for the maintenance of ENDS use was the variety of flavorings, which assisted with reduction of tobacco cigarette use . HoweverIn addition, non-pharmacological methods may help decrease young-adult smoking, including motivational cognitive behavioral therapy or social-media campaigns that de-normalize the tobacco industry ,39,40,41This qualitative analysis of Facebook posts is subject to inherent limitations. The themes identified were based on the content of the posts, and the coders may have brought biases in determining the themes; however, this was mitigated by blinded coding and having a third reviewer. Additionally, this intervention was limited to the young-adult population in the Bay Area. We cannot generalize our findings to other populations. Lastly, engagement among participants varied, so it is possible that the common themes that we identified may have been based on the more active participants. Many young-adult smokers ready to quit in online support groups expressed a lack of interest and knowledge in and about smoking-cessation pharmacotherapy. This was mainly driven by misconceptions about its use, side-effect concerns, a preference to quit without assistance (“cold turkey”), and a lack of recognition of nicotine dependence. In general, there were mixed opinions about ENDS, but more participants ready to quit expressed that ENDS would not be beneficial in smoking cessation. Social-media-based approaches for smoking cessation have been found to be an effective intervention, and their use may serve as a vehicle to deliver educational information regarding smoking-cessation pharmacotherapy to young adults. It is pertinent to identify effective methods to decrease smoking rates in young adults through online support groups. This may be achieved by integrating information for available smoking-cessation strategies to encourage tobacco abstinence, to prevent further risks of tobacco-related conditions and mortality. Social-media-based approaches should also address the barriers or misconceptions about pharmacologic agents that have been identified in our study and other literature, which may be significant barriers to young adults quitting smoking. As smoking among young adults is a major public-health issue across the world, efforts to use social media to mitigate incorrect, pre-perceived notions about smoking-cessation pharmacotherapy, and to promote the effectiveness of evidence-based smoking-cessation pharmacotherapy methods, are needed.


This meta-analysis aimed to determine the prognostic impact of microscopically positive margins (R1) on primary gastrointestinal stromal tumors.A literature search was performed using PubMed, Embase, Web of Science, and Cochrane Library for studies up to 23 November 2020. The pooled disease-free survival (DFS) and overall survival (OS) between R1 and negative margins (R0) were estimated using a random-effects model.2 = 0%; reference: R0). This negative impact of R1 disappeared with the use of adjuvant Imatinib . R1 was related to poor DFS in gastric GISTs , which was attenuated in the subgroup of adjuvant Imatinib . Rectal GIST with R1 margin who even received adjuvant Imatinib still had poor DFS . Patients who underwent R1 resection had similar OS compared with those underwent R0 resection regardless of the use of adjuvant Imatinib.Twenty studies with 6,465 patients were included. Compared with R0 resection, R1 was associated with poor DFS in patients who did not receive adjuvant Imatinib (HR: 1.62, 95% CI: 1.26–2.09; P = 0.48, IR1 was associated with poor DFS for primary GISTs, which was attenuated by adjuvant therapy with Imatinib. Similar result was observed in the gastric GISTs subgroup. Rectal GIST patients with R1 resection had poor DFS even when they received adjuvant Imatinib. The R1 margin did not influence the OS of GISTs. Gastrointestinal stromal tumors (GISTs) are one of the most common mesenchymal tumors, accounting for 80% of tumors that arise from the gastrointestinal tract . The rarpost hoc study based on the EORTC 62024 randomized trial suggested that tumor rupture rather than R1 margin significantly influenced the overall survival (OS) of GIST regardless of the acceptance of adjuvant Imatinib  through PubMed, Embase, Web of Science, and Cochrane Library to identify studies that reported the relationship between the status of surgical margins and prognosis of gastrointestinal stromal tumor. The search included studies up to 23 November 2020. Attempts have been made to obtain additional eligible studies by searching the references of relevant studies. This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines .Eligible studies were identified by two investigators (ZL and HY) according to the following criteria: (1) Participants (P): The patients were diagnosed pathologically and immunohistochemically as primary GISTs without metastasis or other cancers; (2) Interventions (I) and comparisons (C): All the patients underwent surgery and outcomes between R1 and R0 resection margin were compared; (4) Outcomes (O): DFS and/or OS were available or able to be calculated by sufficient data in the studies. When duplicate studies based on similar populations were identified, only the newest or largest study was included. Any discrepancies were resolved by discussion with a third investigator (XG).The name of the first author, year of publication, country, sample size, tumor site, recurrence events, adjuvant therapy, follow-up, DFS, disease-specific survival, and OS were extracted independently by two investigators (SL and JZ). If the hazard ratio (HR) and 95% confidence interval (CI) were not provided in the studies, we calculated these data from available data or from the Kaplan–Meier survival curves using the methods reported by Tierney et al. . A thirdTwo authors (ZL and ZZ) independently assessed the quality of all included studies using the Newcastle–Ottawa Quality Assessment Scale (NOS) with the highest score of nine , and any2 statistics. Subgroup analysis was conducted to identify the source of heterogeneity. The random-effects model was used by default because of the nature of the included studies. The estimated results of the fixed-effects model are also provided for reference. Sensitivity analysis was performed to validate the stability of the model by sequentially omitting each study. Potential publication bias was assessed using the Begg’s and Egger’s tests. Statistical analyses were performed using R software 3.6.1  with the meta package (4.13-0) . Statistical heterogeneity was evaluated using the chi-square test and Iackage 4.-0 (13). (4.13-0) .As shown in 2 = 8%). Sensitivity analysis was performed by omitting each study sequentially, and the estimated results did not differ significantly, indicating the stability of the model  but not in subgroup of RCT . However, patients of two series of the three in the subgroup analyzing data from RCTs received adjuvant Imatinib.Two of the 17 studies (three series) analyzed data from randomized controlled trials (RCT) and the remaining 15 were observational studies. Thus, subgroup analysis according to the type of study  in the subgroup without adjuvant Imatinib, while the status of resection margin had no significant impact on DFS in the adjuvant Imatinib subgroup .Thus, another subgroup analysis was performed according to the use of adjuvant Imatinib .Tumor site is another key prognostic factor for GISTs. The eligible studies were categorized into three subgroups: stomach, rectum, and mixed sites. The mixed sites included studies that analyzed more than one tumor site. The results of this subgroup analysis . However, a relatively high heterogeneity was observed in the gastric subgroup without adjuvant Imatinib , which made the result of this subgroup less reliable. Notably, rectal GIST patients with R1 resection had poor DFS even when they received adjuvant Imatinib . In the mixed sites group, R1 resection was correlated with poor DFS compared with R0 resection  for patients without adjuvant Imatinib, while the status of resection margin did not impact DFS for patients receiving adjuvant Imatinib .The results differed when tumor site and Imatinib use were both taken into consideration (2 = 0%), regardless of whether they received adjuvant Imatinib  or not   Figure 4As shown in The present study found that R1 resection was associated with poor DFS for primary GISTs. Subgroup analysis was performed according to study type, use of adjuvant Imatinib, and tumor site. DFS did not worsen for patients who underwent R1 resection in the subgroup of RCT. However, patients of two of the three series in the RCT subgroup received adjuvant Imatinib. To illustrate this point, in the subgroup analysis of the use of adjuvant Imatinib, the negative influence of R1 resection on DFS was attenuated by adjuvant Imatinib. Similar effect of adjuvant Imatinib in DFS was observed in the subgroup of gastric GISTs. Rectal GIST patients who underwent R1 resection had poor DFS even when they received adjuvant Imatinib. Patients who underwent R1 resection had similar OS compared with those underwent R0 resection regardless of the use of adjuvant Imatinib.Although surgical margin was removed from the 2014 edition of the European Society for Medical Oncology (ESMO) guidelines  as a proHowever, the Imatinib in these studies was not specifically given to those who had R1 margins, and the mechanism by which Imatinib attenuated the negative survival impact of R1 requires further exploration. Interestingly, Shannon et al.  in theirFurther subgroup analysis of this meta-analysis according to tumor site and use of adjuvant Imatinib showed that gastric GISTs with R1 margin had poor DFS which was attenuated in the subgroup of adjuvant Imatinib. Notably, R1 margin was associated with poor DFS of rectal GISTs that even received adjuvant Imatinib. The relatively lower malignancy of GISTs in the stomach  and high2 = 50%), which makes the result of this subgroup less reliable and requires further exploration. Fourth, risk factors that are collinear with the R1 margin were not analyzed in the current study. In summary, further high-quality case-controlled observational trials with a balanced baseline are needed.The current study has some limitations. First, the majority of the included studies were retrospectively designed such that bias was inevitable in the process of this meta-analysis. Second, adjuvant Imatinib was not given specifically to those who experienced R1 margin, so the mechanism of Imatinib attenuating the negative survival impact of R1 needs further exploration. Third, a relatively high heterogeneity was observed in the gastric subgroup without adjuvant Imatinib ; 2. CAMS Innovation Fund for Medical Sciences (2020-I2M-C&T-B-027); 3. Beijing Bethune Charitable Foundation (WCJZL202106); 4. Beijing Xisike Clinical Oncology Research Foundation (Y-HS2019-43).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This study supplies a comprehensive strategy for discovering potential serum biomarkers of RA and provides a different perspective for comprehending the pathological mechanism of RA, identifying potential therapeutic targets, and disease management.Rheumatoid arthritis (RA) causes serious disability and productivity loss, and there is an urgent need for appropriate biomarkers for diagnosis, treatment assessment, and prognosis evaluation. To identify serum markers of RA, we performed mass spectrometry (MS)-based proteomics, and we obtained 24 important markers in normal and RA patient samples using a random forest machine learning model and 11 protein–protein interaction (PPI) network topological analysis methods. Markers were reanalyzed using additional proteomics datasets, immune infiltration status, tissue specificity, subcellular localization, correlation analysis with disease activity-based diagnostic indications, and diagnostic receiver-operating characteristic analysis. We discovered that ORM1 in serum is significantly differentially expressed in normal and RA patient samples, which is positively correlated with disease activity, and is closely related to CD56dim natural killer cell, effector memory CD8 Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic erosive arthritis, which affects 1% of the world’s population and usually occurs in middle-aged and elderly women , 3.Current common laboratory indicators are rheumatoid factor (RF), anti-cyclic citrullinated peptide antibody (ACPA), erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) . HoweverIt is known that proteins have an important role in RA pathogenesis with the formation of a new epitope; compared with other techniques such as transcription, proteomics can study the posttranscriptional modification of proteins and their interactions in cells and tissues . PreviouIn this study, our comprehensive analysis of serum proteins in RA from multiple perspectives is shown Figure 1Human serum specimens were collected from the First Affiliated Hospital of Guangzhou University of Chinese Medicine . The study was approved by the Chinese Clinical Trial Registry (ChiCTR), and the registration number is ChiCTR2100043294. This study was approved by the local research ethics committee; the approval document number of the ethics committee is NO. JY[2020]264. All subjects in this study were asked for consent. Human serum samples included 8 healthy control and 7 patients at different time points of 0 and 6 after administration. All samples were frozen and stored at −80°C in aliquots of polyethylene tubes until use.Bicinchoninic acid (BCA) assay was used for protein quantification, taking 500 μg of protein and using High Select™ Top14 Abundant Protein Depletion Resin to remove high-abundance proteins. After adding the supernatant obtained in the previous step to dithiothreitol (DTT) at a final concentration of 10 mM, polypeptide samples were prepared using the filter-aided sample preparation (FASP) method from previous literatures , 12. In p-values were obtained by using the t-test, and the p-values of comparisons were adjusted for multiple comparisons to preserve an error rate of 5%. To identify the significant pair(s), we used multiple comparisons. The Benjamini–Hochberg method was used for the multiple comparisons.Tryptic peptides were separated using a 60-min total data collection for peptide separation, with the Easy-nLC 1200 system connected online to an Orbitrap Eclipse mass spectrometer equipped with FAIMS Pro (Thermo). Scans were collected in data-dependent top-speed mode with dynamic exclusion at 90 s. Raw data were analyzed using MaxQuant version 1.6.17.0 search against the Human Fasta database, with label-free quantification and match-between-run functions enabled. The identification of proteins that were differentially expressed between patients with RA and HCs was conducted based on the empirical Bayes method using the limma R package. The Machine learning was combined with PPI network analysis to screen differential proteins. The random forest classifier (RFC) was used to perform feature extraction on protein sample expression data, screen out the top 30 key proteins based on classification features, and use MeanDecreaseGini to plot. 11 topological analysis methods, including Degree, Edge Percolated Component (EPC), Maximum Neighborhood Component (MNC), Density of Maximum Neighborhood Component (DMNC), Maximal Clique Centrality (MCC), and six centralities  based on shortest paths were used to measure the true efficiency of nodes in the PPI network to evaluate their importance.R (version 3.6.3)’s ggplot2 package and clusterProfiler package were used to analyze the enrichment analysis of 24 differential proteins, including KEGG pathway, biological process (BP), cell component (CC), and molecular function (MF).https://pubmed.ncbi.nlm.nih.gov/) was used to search, and clinical samples with healthy people were selected as the control group. A total of 5 datasets were collected, including 155 healthy controls and 183 RA patients. The differentially expressed proteins were summarized in each dataset, and candidate proteins with the number of repetitions greater than 2 were selected. An NCBI gene ID was assigned to each protein based on gene symbol, and we kept proteins with the NCBI gene IDs for subsequent analysis.“rheumatoid arthritis” and “proteomics” were used as keywords, PubMed  ssGSEA method was used for immune cell infiltration analysis, the degree of infiltration of 28 immune cells was calculated, and https://gtexportal.org/). GTEx is a tissue-specific database of gene expression and regulation derived from simultaneous transcriptome sequencing and genetic analysis of samples from multiple human tissues and organs.Tissue specificity data were obtained from GTEx (https://compartments.jensenlab.org/). COMPARTMENTS is a powerful website for predicting the subcellular localization of proteins, helping us to better understand candidate proteins.Subcellular localization was performed through COMPARTMENTS (R (version 3.6.3) was used for statistical analysis and visualization, the ggplot2 package (version 3.3.3) was used for visualization, and the statistical method used was Spearman. A heatmap was used to display the pairwise correlation (spearman correlation) of all elements between the two tables (protein expression table and diagnostic index change table). Statistical significance was marked on the heatmap.We used the pROC package for diagnostic ROC analysis and used the ggplot2 package for visualization. The value of the area under the curve (AUC) is between 0.5 and 1. The closer the AUC is to 1, the better the diagnostic effect. The AUC has a low accuracy when it is 0.5–0.7, it has a certain accuracy when it is 0.7–0.9, and it has a high accuracy when it is above 0.9; key proteins were further screened based on an AUC greater than 0.7.http://www.r-project.org/). Data visualization was generated by the clusterProfiler package (version3.14.3) of R and Microsoft Office 2019.The statistical analysis in this study was performed by the differential enrichment test of R platforms . The differential enrichment test of R software was used to study the differential expression of proteins. Test-diff was used to perform a differential enrichment test based on protein-wise linear models and empirical Bayes statistics using limma. False discovery rates were estimated using the fdrtool package (version 1.2.17). Analyzing 262 candidate proteins, according to the RFC machine learning method and 11 topological analysis methods , cell composition (CC), and molecular function (MF) and arrange them in ascending order of p-value<0.001); Cholesterol metabolism ; Vitamin digestion and absorption .” The first 3 BPs with the smallest p-value were extracellular structure organization “; platelet degranulation ; chylomicron assembly .” The first 3 CCs with the smallest p-value were “blood microparticle ; cytoplasmic vesicle lumen ; vesicle lumen .” The first 3 MFs with the smallest p-value were “lipoprotein particle receptor binding ; cholesterol transporter activity ; intermembrane cholesterol transfer activity ”.24 proteins overlapping the reference datasets were analyzed. The first 3 KEGG pathways with the smallest p-value were “Complement and coagulation cascades , and To further confirm the underlying function of potential targets, we used Fisher’s exact test to calculate the enrichment significance of each term in KEGG, biological process (BP), cell composition (CC), and molecular function (MF) and arrange them in ascending order of p-value were “Complement and coagulation cascades ; Staphylococcus aureus infection ; ECM-receptor interaction .” The first 3 BPs with the smallest p-value were “acute inflammatory response ; complement activation ; regulation of protein processing .” The first 3 CCs with the smallest p-value were “blood microparticle ; cytoplasmic vesicle lumen ; vesicle lumen ”. The first 3 MF with the smallest p-value were “lipoprotein particle binding ; protein-lipid complex binding ; antioxidant activity ”.The 30 proteins overlapping the reference datasets were analyzed. The first 3 KEGG pathways with the smallest +T cell, activated dendritic cell, CD56bright natural killer cell, CD56dim natural killer cell, central memory CD4+T cell, central memory CD8+T cell, effector memory CD8+T cell, eosinophil, gamma delta T cell, immature B cell, macrophage, mast cell, myeloid-derived suppressor cell, monocyte, natural killer cell, natural killer T cell, plasmacytoid dendritic cell, regulatory T cell, follicular helper cell, type 1 T helper cell, type 17 T helper cell, and type 2 T helper cell. As shown in +T cell, and natural killer cell was significantly different between HC and RA . Through Spearman correlation analysis, we found that 13 of the 24 differential genes were significantly related to the degree of immune cell infiltration, which are ORM1, HP, TTR, CRP, FGB, FN1, APOA2, APOC3, SAA1, FGG, FGA, APOB, and VWF; we believe that these 13 genes are potential biomarkers for the treatment of RA and related to immune cell infiltration, as shown in We found that the degree of immune cell infiltration in RA patients is generally lower than that in normal people. We analyzed 28 types of immune cells and obtained data on 23 types of immune cells, which are activated B cell, activated CD4As shown in As shown in p<0.001). After using Spearman correlation analysis between 24 proteins and 13 diagnostic indicators , the correlation analysis between CP and 11 diagnostic indicators except CRP and GH was statistically significant, as shown in In terms of predicting disease activity, CP (AUC=0.837), TTR (AUC=0.776), ORM1 (AUC=0.796), HP (AUC=0.816), SERPINA1 (AUC=0.796). They have a certain accuracy in the diagnosis and prediction of disease activity, but they are not statistically significant compared with other diagnosis results.As shown in p = 3.25 * 10-32, Fisher test) and dataset iii . Collectively, we showed the reliability and accuracy of our protein list, and it is in accordance with previous publication. We will upload this information together. The above evidence demonstrates the reliability and accuracy of our analysis.Through diverse-perspective analysis, our study provides novel insights into RA-related proteomics research. Using random forest machine learning methods and analysis of 11 other topological analysis methods, our proteomics data selected 24 potential differential proteins in normal and RA patient samples. When searching the PubMed database for 24 potential proteins, at least 19 (79.17%) were found in RA-related reports Table 3.FN1, a multidomain extracellular matrix (ECM) protein, binds to multiple compounds on the cell surface, including collagen , fibrin In addition, our research reevaluated the importance of 24 candidate proteins in terms of immune infiltration, disease activity, experimental diagnosis correlation analysis, and diagnostic ROC analysis. After a comprehensive analysis, both HP and ORM1 exceled in this aspect.+T cells, and natural killer cells.HP has many physiological functions. It can combine with free hemoglobin to prevent oxidative damage to various organs  and acti+T cells, and natural killer cells. ORM has many activities including, but not limited to, acting as an acute-phase reactant and disease marker, modulating immunity, binding and carrying drugs, maintaining the barrier function of the capillary, and mediating the sphingolipid metabolism (Orosomucoid 1 (ORM1) is different from HP in that it has less research. When we searched in PubMed, there were only 3,555 related articles Table 3,tabolism . In RA ptabolism . Althougtabolism . Based otabolism . In geneHowever, one of the disadvantages in this experiment design is that proteins are the performers of functions; it is necessary to consider combining the results of transcriptome and metabolomics for multidimensional research. Also, the serum proteome has its limitations, and the perspective of synovial proteomics is worth further studying. If possible, the systematic research on the differentially expressed proteins should be screened out, combined with animal experiments, clinical trials, etc., using molecular biology techniques to verify and enrich the rationality of high-throughput screening results.The innovation of this study is that we refer to the results of other RA proteomic studies to make our findings more convincing. Our analysis of differential proteins, combined with disease activity, is more clinically meaningful and valuable. In general, these rich data are expected to stimulate subsequent hypothesis-driven research, deepen our understanding of the pathogenesis of RA, provide potential biomarkers, and provide new strategies to facilitate the diagnosis and treatment of this harmful disease.The data presented in the study are deposited in the PRIDE and ProteomeXchange repository. ProteomeXchange accession number PXD032912.The studies involving human participants were reviewed and approved by the First Affiliated Hospital of Guangzhou University of Chinese Medicine Ethics Committee.[2020]264. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.GC, LW, and CH conceived and designed the experiments. ZD and JX conducted the experiments and wrote the manuscript. LiZ, YX, ML, JY, LuZ, and HD provided some of the data. LL, MZ, JH, LW, and GC reviewed and revised the manuscript. CH and ZD have contributed equally to this work. All authors contributed to the article and approved the submitted version.This study was funded by the Guangzhou Science Technology and Innovation Commission Technology Research Projects (No. 201904010336) and the Innovative and Strong Hospital Project of The First Affiliated Hospital of Guangzhou University of Chinese Medicine (No. 211010010705).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


While e-cigarettes usually contain nicotine, their addictive potential is not yet fully understood. We hypothesized that if e-cigarettes are addictive, users will experience typical symptoms of addiction.The aim of our study was to investigate whether and how e-cigarette users report signs of addiction.We identified 3 large German-language e-cigarette online forums via a systematic Google search. Based on a netnographic approach, we used deductive content analysis to investigate relevant posts in these forums. Netnography has the advantage of limiting the social desirability bias that prevails in face-to-face research, such as focus groups. The data were coded according to the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) criteria for tobacco use disorder, adapted for e-cigarettes. The DSM-5 criteria were used to portray a broad spectrum of possible experiences of addiction.Overall, 5337 threads in 3 forums were screened, and 451 threads containing relevant information were included in the analysis. Users reported experiences consistent with the DSM-5 criteria, such as craving e-cigarettes, excessive time spent vaping, and health issues related to e-cigarette use. However, our analysis also showed that users reported the absence of typical tobacco use disorder criteria, such as successful attempts to reduce the nicotine dosage. For most themes, reports of their absence were more frequent than of their presence. The absence of perceived addiction was mostly reported in contrast to prior tobacco smoking.This is the first study to use a netnographic approach to explore unfiltered self-reports of experiences of e-cigarette addiction by users in online forums. As hypothesized, some but not all users reported subjective experiences that corresponded to the criteria of tobacco use disorder as defined by the DSM-5. Nevertheless, subjective reports also indicated that many e-cigarette users felt in control of their behavior, especially in contrast to their prior use of tobacco cigarettes. The finding that some e-cigarette users subjectively experience addiction highlights the need for effective cessation programs to support users who experience their e-cigarette use as burdensome. This research can guide the refinement of instruments to assess e-cigarette addiction and guide cessation programs.RR2-10.1186/s40359-021-00682-8 Electronic cigarettes, or e-cigarettes, are handheld electronic devices that heat a liquid to create vapor that users inhale . AlthougAlthough e-cigarettes are often marketed as a less harmful alternative to tobacco -11, schoFor this paper, substance-related addiction was defined as a “recurring desire to continue taking the drug despite harmful consequences” . CurrentPrevious studies provide some evidence for e-cigarettes’ addictive potential. Browne and Todd  found thGiven this research gap, we hypothesized that if e-cigarette consumption led to addiction, users would report signs of tobacco use disorder. Thus, the aim of our study was to shed light on whether and how e-cigarette users subjectively perceive typical signs of addiction according to the DSM-5 criteria for tobacco use disorder. To answer this question within the Evaluation of the Addictive Potential of e-Cigarettes (EVAPE) project, we used a netnographic approach  and analThe study design was based on a netnographic approach, which applies ethnographic techniques to online social interactions. This approach was first developed and applied in a sociological research context and is increasingly used in epidemiology and health research .Previous research has shown that online forums are important spaces for e-cigarette users to communicate about relevant topics with like-minded people ,25. WithWe used a 3-step procedure to collect the data for this study. First, 1 study team member (DS) conducted a search for the relevant online forums via Google search using different combinations of the German versions of the keywords “e-cigarette” AND “(online) forum” . A searcIn the second step, we searched the identified forums for previously defined keywords. For each criterion, we defined 1 to 3 keywords that described the respective theme as accurately as possible . The iniIn the third step, 2 team members (DS and TG) screened all the threads, which were posted between April 9 and June 18, 2021. To assess whether a thread was relevant to the theme, we viewed short threads in their entirety. For long threads (≥5 pages), we based the content assessment on the title of the thread and the post in which the keyword was mentioned. Threads without posts on relevant themes were excluded at this stage. We excluded posts from users who explicitly reported also smoking conventional cigarettes  from our analysis.We chose a deductive thematic approach, as described by Mayring , as the After data piloting, we decided to use positive  and negative  coding for each theme. For example, a forum user could report a craving for e-cigarettes (a positive code), or an absence of craving (a negative code). The program Maxqda  was used to code the data. According to the research standards for our deductive thematic approach , we desiQuotes chosen as examples for this paper were paraphrased and translated to English by 1 author (MA) and checked for accuracy by 2 authors (DS and SS).In addition to the qualitative analysis, we collected quantitative data on the frequency of reported signs.Specific ethical aspects need to be considered when researching online communities . Since wFrequently discussed topics in the forums were e-cigarette types and setups, different kinds of liquids and flavors, and general lifestyle. The forums were active, with the biggest forum having 1,458,441 posts and approximately 400 daily new posts by 14,751 users (forum statistics at time of data extraction in February 2021). The users’ nicknames, as well as their posts, indicated that the majority were male and had smoked tobacco cigarettes before their e-cigarette use. As described above, we excluded posts from users who explicitly reported being current dual users from our analysis.Of the 5337 threads initially screened in the 3 forums, 451 contained relevant information regarding addiction and were included in the data analysis . We idenAttempts to reduce the nicotine dosage were frequently discussed in the forums. User #12 described their unsuccessful attempt: “I started off with a lower nicotine dosage but I did not get a buzz from it. So now I vape a higher dosage and don’t want to go back.” On the other hand, more users related successful attempts to reduce the amount they consume. User #398 reported, “I was a heavy smoker but when I started vaping I was able to reduce from 18mg to 7mg and have been vaping this mix for some years now.”Many users reported almost uninterrupted e-cigarette use throughout the day. This behavior was often subjectively perceived as being deliberately chosen. For instance, user #137 wrote, “I vape incessantly, in the morning, when I work, on the way to work, and when I go out in the evening.” Other users stated that they spent a great deal of time consuming in specific situations, such as while drinking alcohol.We did not code a negative code for this theme, because the only relevant post mentioning a low time expenditure fitted the theme “loss of control” more accurately.Different posts showed that even though users experienced health problems, they continued to use e-cigarettes. User #10 wrote, “I get a swelling under my eyes whenever I vape more than usual. A few days after that I can still feel the swelling in my cheeks as well as under the eyes.” Other symptoms described by users were headaches, inflammation, rash, swelling, dizziness, and digestive problems. Moreover, problems were reported to be associated with specific liquids and nicotine dosages. The ingredients propylene glycol and vegetable glycerin were prominently discussed as reasons for allergic reactions and other health issues.In contrast, other users perceived that their physical health improved after they switched from tobacco to e-cigarettes. User #71 reported, “My quality of life really improved when I stopped normal  smoking. When working out I breathe more easily and I can taste and smell much better.” Moreover, improvements in mental health were also described. User #85 said that their panic attacks had stopped (“I used to get panic attacks and was super nervous but not anymore!”) since switching to an e-cigarette, and user #36 related that they slept better: “I wake up before my alarm clock.” No user reported improvements in health due to e-cigarette use without describing a contrast with tobacco.Some users reported explicit cravings for e-cigarettes, such as frequent thoughts about e-cigarettes or seeking out favorable opportunities to use e-cigarettes. Descriptions of craving in the context of specific situations or habits were also found. For example, user #54 reported, “There are specific moments where I feel craving for my e-cigarette. Mostly when I’m alone or when I had a really exhausting day I want to reward myself with vaping.” Another user stated, “Honestly, I am addicted to vaping. Wherever I go, be it my office or in my free time, I instantly look for a place where I can vape.”On the other hand, more users described a weak or missing desire for e-cigarette use. For these users, it seemed easy to abstain, regardless of the situation. User #282 explained, “When I still smoked I would always go for a smoke after lunch. But now I can sit and relax after lunch and enjoy the time.”Users described withdrawal symptoms when they could not use e-cigarettes for a long time or when the nicotine dosage was subjectively too low. Withdrawal symptoms such as physical unrest, nervousness, irritability, and difficulty concentrating were specified. For instance, user #57 reported, “I was using a 18mg dosage. I was very confused why I was becoming more aggressive and hyperactive. And then I realized it was because I was vaping nicotine-free for half the day.”On the other hand, users also reported the absence of withdrawal symptoms, even when it was not possible to use e-cigarettes for a long time or when they used a lower nicotine dosage. For example, user #7 said, “While I work, I cannot vape for the whole day. But that’s fine by me.”Users reported (1) breaking off contact with people in whose presence e-cigarette use was undesired, (2) experiencing negative reactions from people in their immediate environment  to e-cigarettes, and (3) experiencing situations that escalated due to personal e-cigarette use. For example, user #493 wrote, “Once I started vaping, my circle of friends changed. I broke off contact with those who do not tolerate vaping when we meet.”Especially in contrast to when they used tobacco cigarettes, some users reported that reactions within their social environment to their e-cigarette use were positive. For example, user #41 related, “In the beginning my friends and family were worried. But now they see how much better my health is, they don’t see it as a problem.” Users also frequently reported being complimented on certain liquid flavors by their friends or colleagues.When discussing their dosage and liquid mixtures, some users recounted the development of tolerance. This means that their “buzz” or nicotine high ceased, causing them to increase their dosage. For example, user #89 admitted, “I have really become increasingly tolerant.” Another user (user #11) asked the forum, “I don’t miss cigarettes but lately I have been vaping much more than usual and I do not get a buzz anymore. Am I the only one who gets more craving after vaping for some months?”Only one user (user #51) described negative physical reactions to the usual nicotine dose: “I am realizing the effects of more nicotine...I have trouble sleeping, higher blood pressure, palpitations and so on.”Dauernuckler, the German word for someone who is constantly sucking on their e-cigarette, and admitted, “I’m actually very annoyed by it but I can’t stop.”A few users revealed that they used their e-cigarettes almost continuously, even though they did not intend to. As a consequence, they subjectively perceived this behavior as a problem or burden. For example, user #355 described themselves as a In contrast, only one user reported that they managed to control the amount they vaped by keeping a constant nicotine level: “I vape 18mg just to avoid vaping constantly.”One user (user #243) reported they had to give up an activity because of e-cigarette use: “Since I spend some money on my setup, I had to stop my hobby for the time being.” We did not code a negative code for criterion 7 in cases when users saw e-cigarette use as their new hobby, since we did not interpret substance consumption as a new social activity.This study is the first to show that e-cigarette users report subjective experiences of addiction based on the DSM-5 criteria for tobacco use disorder. This was the result of a netnographic analysis of 451 threads from 3 large German-language online forums. In summary, some but not all users reported subjective experiences that corresponded to the criteria of a tobacco use disorder as defined by the DSM-5. Our aim was not to diagnose forum members but to use the DSM criteria to explore subjective experiences of addiction. Our chosen method of accessing the field allowed us to take an unfiltered look at user reports. Our findings are consistent with previous qualitative and quantitative research on e-cigarette addiction, as discussed below.The criterion we coded positively most often was “expenditure of time.” While using any substance involves a certain amount of time, an excessive preoccupation with the consumption process is an important aspect of the definition of addiction. Indeed, specific questions regarding time are included in instruments such as the Fagerström test of e-cigarette dependence .Furthermore, our analysis showed that many users continued their e-cigarette use despite experiencing health problems such as chest pain, skin swelling, or gastrointestinal symptoms. This reflects clinical observations of e-cigarette users’ health . SimilarMoreover, we found that users reported experiencing cravings and withdrawal when they were not able to use e-cigarettes. This result confirms longitudinal studies conducted in the United States ,35. LikeIn the forums we investigated, some users described unsuccessful attempts to reduce the amount of nicotine they consumed. Indeed, they reported increasing their nicotine dosage over time. As of yet, only one study exists on nicotine dosing patterns in e-cigarette users over time, by Browne and Todd , who shoNevertheless, we also found that the absence of signs of addiction according to the DSM-5 criteria was frequently reported by users. This especially applied to the theme “attempts to reduce.” The majority of posts coded for this theme described successful attempts to reduce either e-cigarette use or nicotine dosage. One reason for this could be that in the forums, experienced members recommended starting with a high nicotine dosage when switching from tobacco to e-cigarettes, in order to avoid relapse. Furthermore, more posts discussed an absence of craving for e-cigarettes than its presence. The same was true for the themes “health problems,” “social problems related to use” and “withdrawal” but with less pronounced differences in frequencies. Importantly, we found that an absence of these experiences was mostly reported in contrast to the previous use of tobacco cigarettes. Likewise, Schilling et al  reportedRegarding social acceptability, our analysis showed that while some users did experience social problems related to use, e-cigarette use was mostly accepted in their environments, especially in contrast to smoking. Users also reported using their e-cigarettes in different locations, such as their offices. This reflects previous research indicating that e-cigarette use is seen as more socially acceptable than smoking and is therefore possible in spaces in which smoking is restricted or undesirable .Finally, we did not find any posts corresponding to the DSM-5 criteria “neglecting responsibilities” or “hazardous use.” This finding is in line with an ongoing debate on the appropriateness of the DSM-5 criteria for tobacco use disorder. Sokya and Baumgärtner  criticizIn sum, our study offers a comprehensive and broad overview of subjective experiences of addiction, adding to the existing literature, which so far has mostly focused on specific symptoms. Our results indicate that some users perceive e-cigarettes to be highly addictive and reflect on their incessant use and craving. Since our research indicates which experiences are perceived as burdensome by users, this knowledge could be a starting point for the refinement of instruments to measure e-cigarette addiction, as well as tools to identify users who are at risk for e-cigarette addiction. Even though e-cigarettes are perceived as less addictive or harmful than tobacco cigarettes by users, our study clearly demonstrates that users do experience addiction. Hence, more research on effective strategies for e-cigarette cessation and efforts to support users with effective cessation programs are required.The evidence that there are users who experience signs of addiction is a piece of the overall puzzle of e-cigarette dependence. The interdisciplinary EVAPE project aims to provide further valuable insights based on neurobiological and longitudinal epidemiological data, as well as focus groups with e-cigarette users .This study has some limitations. First, we relied on user self-reports. A systematic review demonstrated that self-reports of smoking are less accurate than biological measures, as smokers underreport their behavior . FurtherAnother limitation is the possibility that the analysis included posts by users who use e-cigarettes in addition to tobacco . While we excluded posts containing explicit mentions of dual use, we could not exclude all posts by dual users. Since dual users have reported higher levels of addiction than exclusive e-cigarette users in previous research , this shThis study is the first to use a netnographic approach to explore self-reported signs of dependence in e-cigarette users. In contrast to other forms of qualitative research, such as interviews or focus group discussions, netnography approaches in the context of research on online forums have had to rely on limited responses, with no opportunity to clarify meanings, and hence bear the risk of misinterpreting responses. On the other hand, this approach avoids biases related to personal or group interviews, such as interviewer bias or social desirability bias . HoweverThe netnographic approach used in this study allowed us to explore unfiltered self-reports of addiction by e-cigarette users in online forums. Some but not all users reported subjective experiences that corresponded with the DSM-5 tobacco use disorder criteria. Nevertheless, we also found that users discussed the absence of such signs, and in most cases, reports of absence were more frequent than reports of presence. While it is not possible to derive a clinically reliable diagnosis of tobacco use disorder from such self-reports, our study clearly showed that some users reported signs that were comparable to the criteria for tobacco use disorder in the DSM-5.