The aim of this study is to evaluate factors associated with the subjective well-being (SWB) and suspected depression measured with WHO-5 among German adults during different phases of the COVID-19 pandemic. Survey data were analyzed from the COVID-19 Snapshot Monitoring (COSMO) study, which collected data from 972, 1013, and 973 participants in time point 1 (19–20 May 2020), time point 2 (15–16 September 2020), and time point 3 (21–22 December 2020), respectively. Descriptive analyses and logistic regression analyses to identify the factors associated with suspected depression (WHO-5 ≤ 50) were conducted. Data showed that the mean WHO-5 scores in three time points were 56.17, 57.27, and 53.93, respectively. The risk of suspected depression was increased by about 1.5 times for females, 2.5–3 times among 18–24 year-olds compared to ages above 65 years, 1.5 times for singles, 2 times for those with chronic illnesses, and 2–3 times for people living in poverty. The main study findings show that German adult SWB is lower than pre-pandemic reference values. Special focus should be placed on vulnerable groups, such as females, younger persons, and people living in poverty who are most prone to a reduction in SWB and therefore suspected depression. The coronavirus disease 2019 (COVID-19) pandemic presented great challenges to all aspects of the health system, resulting in global morbidity and mortality worldwide. Additionally, since the outbreak, people around the world experienced significant impacts on their well-being and mental health. Various measures, such as physical distancing and the suspension of services to intervene in the spread of the coronavirus, brought mental health implications to the public discussion .It is well known that stress affects the psychological and biological systems associated with anxiety and depression ,5,6,7. SWhile psychological support might in parts be provided to patients and healthcare workers during the COVID-19 pandemic, the public’s mental health requires significant attention as well . The preOur aim is therefore to add to this evidence by analyzing the factors associated with SWB and suspected depression in the adult German population during different phases of the pandemic, considering different risk or protective context factors measured in COSMO. The hypothesis is that the SWB of the general population changes over time and is dependent on: (1) the measures taken to contain the outbreak in different phases, such as contact restrictions, school closures, quarantine, and the suspension of services, and (2) different demographic and socioeconomic factors and health conditions.p = 0.05), a sample size of n = 1000 was supposed to be sufficient to detect the correlation coefficients of (at least) r = 0.08 with a sufficient power of 0.8 in each survey wave. The rationale for the study design, population selection, recruitment, and methods are further described in the COSMO study protocol. Participants were recruited via an external study sample provider according to ISO 26362. Before starting the survey, all participants were informed about the study and provided informed consent. They participated voluntarily and obtained remuneration. Ethical approval of the COSMO study was approved by the Institutional Review Board of University of Erfurt (#20200302/20200501).The German COVID-19 Snapshot Monitoring (COSMO) study is a repeated cross-sectional survey study used to capture the broad psychosocial status of the German population during the pandemic. COSMO is a joint project by the University of Erfurt, the Robert Koch Institute (RKI), the Federal Centre for Health Education (BZgA), the Leibniz Institute for Psychology Information (ZPID), the Science Media Centre, the Bernhard Nocht Institute for Tropical Medicine, and the Yale Institute for Global Health . The stuApproximately n = 1000 people aged 18 to 74 years were questioned about their individual psychosocial situation, their knowledge about COVID-19, as well as their attitudes towards several institutions, authorities, and measures to contain the pandemic in each wave. We analyzed the subjective well-being (SWB) via WHO-5 and compared the data from three waves (time point 1 (wave 12 from 19–20 May 2020), time point 2 (wave 21 from 15–16 September 2020), and time point 3 (wave 31 from 21–22 December 2020)). The details of the study, including the study design, data acquisition, settings, and ethical standards are described elsewhere [On 23 March 2020, the German Federal government introduced a strict lockdown to control the spread of the virus with a range of measures, including the enforcement of contact restrictions, social distancing requirements, and the closure of schools and non-essential businesses. On 20 April 2020, smaller shops were allowed to reopen respecting the rules of social distancing. On 6 May 2020, the government announced the further easing of containment measures for all shops and facilities. After 7 weeks of lockdown, the daily new COVID-19 cases reduced to less than 500. Since 26 May 2020, the federal and state governments eased restrictions on public gatherings for up to 10 people or 2 separate households, making it possible to meet in larger groups again. Since August, the number of new infections increased again, with more than 1000 new infections per day. Stricter measures and the “lockdown light” were introduced in 2 November 2020, whereby private gatherings were limited to a maximum of 5 persons from 2 households. Restaurants, facilities, and personal services providers were closed nationwide, although schools remained open. Since 16 December 2020, the lockdown was tightened by virtue of sustained high infection rates and rising death rates. This time, all non-essential shops, as well as schools and daycare centers were closed. The closures were scheduled until the end of January 2021 and extended to early March 2021 [Our study used the data of the COSMO survey collected for time point 1 (19–20 May 2020), to analyze the SWB after the relaxation of the first strict lockdown; time point 2 (15–16 September 2020), to identify the SWB when people were required to maintain social distancing and hygiene measures; and time point 3 (21–22 December 2020), the time people experienced a second hard lockdown during the Christmas season. Based on the design of the COSMO study, each wave consists of approximately 1000 people that are matched to the general German population in terms of age, gender, and distribution across federal states. Following the described sampling strategy, general population characteristics in the three time points were comparable. However, minor invariances between the waves occurred as presented in T.The subjective well-being (SWB) was assessed using WHO-5. WHO-5 is a generic, validated and widely used self-report scale, which may be applied as a screening tool for suspected depression. The psychometric properties, including internal consistency and test–retest reliability were reported in various settings and studies . The WHOT is a recognized screening tool for suspected depression with a cut-off score of 50 or below with a sensitivity of 0.86 and a specificity of 0.81 [T score of 50 or below are classified as persons “screened for depression” and persons with “suspected depression” .According to Bech et al. , transfo of 0.81 . PersonsDemographic variables, such as gender ; age ; education level ); migration background awareness ; household language (German or other than German); relationship status ; age of the participant’s children ; and single parents  were assessed see also .Socioeconomic variables, such as employment ; status as working in the health sector ; self-employed ; and household size  were assessed. The number of inhabitants of a hometown was classified into 4 categories: ≤20,000 ; 20,001–100,000 (medium-sized town); 100,001–500,000 (city); and >500,000 (big city) inhabitants. Monthly household net income was classified according to the Federal Statistical Office of Germany, the threshold for the risk of poverty in 2019 was EUR 14,109 for a single household, which translates to roughly EUR 1175 per month . There iChronic disease  was re-classified as chronic disease awareness ); COVID-19 infection  was collapsed as disease awareness of yes  or no .T by gender and age groups to show the fluctuation in these subgroups during the COVID-19 pandemic (at 3 time points).Descriptive statistics with frequencies and proportions for categorical variables and unadjusted means and standard deviations (SDs) for continuous variables were calculated. We stratified the WHO-5T and the percentage of participants with WHO-5T ≤ 50 (defined as suspected depression) with its 95% confidence intervals were presented at each time point. To investigate whether there are subgroups that are better or worse off, we performed univariate logistic regressions of suspected depression and reported its odds ratios, including 95% confidence intervals. The potential variables were determined by univariate logistic regression with a p-value < 0.05 to conduct multivariate logistic regression. Thevariables included gender, age, relationship, chronic disease awareness, household size, household net income, migration background awareness, age of children and employment and status as working in the health sector. To further investigate the association between exposure variables and the risk of suspected depression, three multivariate logistic regression models with different adjustments for the covariates were performed. Model 1 was adjusted for gender, age (continuous), relationship, and chronic disease awareness, as these proved to be significant across all three observed COSMO time points. Model 2 was additionally adjusted for household size and household net income that were both significant context factors in two assessed COSMO time points. Model 3 was additionally adjusted for migration background awareness, age of children, employment, and status as working in the health sector. We also performed an overall model selection through a stepwise forward likelihood ratio test to address the leading factors. Sensitivity analysis was performed through a linear regression of the transformed WHO-5 for the predominant predictive factors, as identified by logistic model selections , which were the household size (0.2–0.3%) and household net income (8–9%) . To aligAt the three measurement points from the COSMO study, 972, 1013, and 973 different individuals in the German population were anonymously surveyed, respectively . Over alT scores were 56.17 , 57.27 , and 53.93  in the three time points, respectively. During the pandemic, the lowest average WHO-5T showed in time point 3 in December 2020. According to Bech et al. [T ranges from 0 to 100, representing the spectrum from completely absent (0) to the highest level of SWB (100), and thus indicating a mental burden in December 2020. As depicted in T of males is higher than of females at all time points. Most notably, the difference between males and females was greater at time point 3, compared to the other time points. Considering the age groups, data showed the lowest WHO-5T score for younger people (aged between 18–24 years) during the pandemic in 2020. Interestingly, the elderly population generally had the highest WHO-5T during the pandemic phases. For age groups stratified by gender at each time point -times increased risk of suspected depression , and the odds for suspected depression for ages above 65 years were decreased to 26.8%  compared to those aged between 18–24 years. Singles would have a 1.42 -times greater risk of suspected depression compared to participants in a relationship. Living alone had a significant 1.43 -times higher risk of suspected depression compared to having two persons in a household. Suffering from a chronic disease indicated a 1.60 -times increased risk of suspected depression compared to no chronic disease.According to the results in -5T ≤ 50 ) compareTime point 2p-value: 0.002) among 25–34 year-olds, and lowered to 32.4%  for those older than 65 years. Singles had a significant 1.50 -times increased risk of suspected depression compared to those who were in a relationship. People whose monthly net income was below EUR 1250, had a 2.00 - and 2.60 -times higher risk of suspected depression, compared to the upper-middle class (income between EUR 2250–3999) and the group with high income (income more than EUR 4000), respectively. Having a chronic disease implied a 1.92 -times higher risk of suspected depression compared to no chronic disease.There was no significant difference in the risk of suspected depression between genders. Compared to the ages between 18–24 years, the risk of suspected depression was decreased to 45.5% -times higher risk of suspected depression compared to non-migration. Unemployed individuals had a 1.47 -times increased risk of suspected depression compared to employment. Living alone had a 1.54 -times increased risk of suspected depression compared to having two persons in a household. In comparison to people who lived near or under the poverty line, the more people earned indicated a reduced risk of suspected depression. The detailed proportions and odds ratios are showed in Similar to the results for time points 1 and 2, subjects that are female, younger, single, and suffering from chronic disease, had a higher risk of suspected depression. People with a migration background awareness had a 1.50 -times increased risk of suspected depression in females and nearly a 2 -times higher risk of suspected depression for people with chronic disease. The adjusted R square did not significantly improve from 0.06 in model 1 to 0.07 in model 3. Considering the multivariate results in time point 2  decrease in the risk of suspected depression compared to those living in poverty in models 2 and 3. In model 3, people who worked in the health sector had a significantly decreased risk of suspected depression by 55% . With regard to time point 3 in p-value < 0.05)-times higher risk of suspected depression compared to the rich group. The result of the overall model selection is provided in  point 2 , the adjT or time points 2 and 3 (data not shown). For a single household in time point 2, there was no risk difference between the various incomes; however, in time point 3, people in the lower-middle class and in the upper-middle class had a lowered suspected depression risk by 70% , compared to those living below the poverty line. For a two-person household in time point 2, in comparison to households living below the poverty line, the upper-middle class and the rich group had an approximately 70%  decrease in the risk of suspected depression. We used the group with high income as a reference and the results indicate that the poverty had a 2.90 -higher risk of suspected depression in time point 3. Although the results of the subgroup analysis did not completely align across the different time points, there was still a visible result that lower income levels implied higher odds of developing depression.The subgroup analyses for the household net income stratified by household size assessed the risk of suspected depression by WHO-5T ≤ 50 (indicating suspected depression) was associated with being female, in the age range of 18–24 years, single, and suffering from a chronic illness in all time points.In this study, based on the COSMO data, we analyzed SWB measured using WHO-5 in repeated cross-sectional surveys within the general German adult population between the ages of 18 to 74 years. Our main result is that the SWB changes over time and the worst SWB is presented in December 2020. Possible explanations for this could be the most serious COVID-19 situation, the highest daily confirmed cases, and the strict restrictions during that time. WHO-5There are various, mostly self-assessed indices to measure the well-being available. The COSMO study assessed SWB via WHO-5, which is a globally recognized and well-studied SWB instrument, which is not only validated in its German version , but is T, which increased further to about 40% during the more restrictive time in December 2020 of our observation period , travel restrictions, misinformation or disinformation, or/and the postponement of religious activities. Many of these and other factors are interrelated and may lead to lower levels of well-being during the pandemic. Based on the study results, we can observe reduced SWB compared to pre-pandemic times, which could be explained by various factors, such as mitigation and containment measures, leading to stress and adverse socioeconomic and health impacts. In our study, nearly one-third of the general population reported a poor WHO-5n period . Similar in 2016 . A possi in 2016 ,31. WithIn this study, we mainly used an indicator of suspected depression as a proxy-variable for psychological distress to interpret the study results. Sociodemographic factors, such as gender, age, ethnicity, and social roles, contribute to a certain psychological impact. Apart from gender and age, ethnic minorities tend to experience more psychological distress by virtue of stigmatization, involving lower self-esteem and access to social resources . RelatioThe study results additionally implicate that younger people have higher odds of suspected depression in all three time points. Nearly 50% of people between 18–24 years old had lower well-being, and with an age increase, the SWB increased with it. Young adults may have age-specific challenges during COVID-19, accounting for more exposure to media, problematic Internet use, ennui due to recreation facility closures, lack of in-person contact, uncertainty of future careers, role confusion , a change in social identification, or being poorly prepared for being an adult, such as financial self-insufficiency, owing to curtailed vocational training or entry-level employment ,35,39. MIn accordance with our study, female participants had a higher risk of suspected depression, which is consistent with the results from the WHO Global Burden of Disease Study from 2015, where the prevalence of depression was more common in women (5.1%) than in men (3.6%) . These fSocioeconomic status (SES) is differentiated, among other things, by education level, employment status, income level, migration, and disparities in health and life expectancy. Our study found that a lower SES, a markedly lower income regarded as poverty, manifested profoundly reduced well-being during the pandemic. Unemployment also implicated higher odds of suspected depression in time point 3 in December 2020. Other studies also found that with a lower SES background, such as racial minority and joblessness, there would be a decrease in SWB and increased rates of mental disorders accounting for discrimination and disparities ,40,47,48Within the scope of our study, we were able to identify potential groups that were particularly vulnerable during the ongoing COVID-19 pandemic, as evidenced by poorer SWB and an increased risk in suspected depression. When planning possible interventions and support measures, these groups may therefore benefit from specific consideration as well as mid-term monitoring of their mental health status.Firstly, this survey was repeated nationwide across Germany and was population-based, which represented the current status of the public perceptions and psychological impact. We acquired the potential protective and risk factors associated with one’s SWB. Most existing evidence focused on one-time cross-sectional studies and identified the short-term psychological adverse effects of COVID-19. In this study, we attempted to capture the fluctuation of COVID-19′s impact on the general society over time. Secondly, WHO-5 is a validated, widely used, and a highly practical tool that can be applied for the screening of suspected depression, and to assess SWB over time. With the high applicability of this measurement, our study could be compared to other research using this scale. As for the limitations, the cross-sectional design of snapshot data in this study was not able to assess the actual causalities between the risk factors and the mental health outcomes, but rather the observations. Secondly, COSMO data did not weight up the employment status and household size variables, which might trigger underrepresented groups. It should also be noted that the data from COSMO did not collect the information of pre-existing or previous mental illness, which might need further subgroup analyses to address the results. Thirdly, this survey was voluntary and web-based, concerning the current situation of those who participated in it. People who declined or were not willing to respond may differ from those who did in their outcomes .This study reports lower SWB in our German adult study population during the pandemic, compared to pre-pandemic reference values. We were also able to identify some potentially protective factors as well as some risk factors associated with one’s SWB. The vulnerable groups who showed a higher risk of suspected depression were females, younger people (aged between 18–24 years), chronically ill people, people living alone, people with a migration background, or a lower socioeconomic status. Hence, these groups might particularly benefit from timely social support and psychological interventions, such as telecare systems, a 24 h available hotline consultation, and pandemic-specifically trained healthcare professionals to foster resilience resources ,55. It i


To compare the diagnostic value of serum markers human epididymal protein 4 (HE4) and cyclooxygenase-1 (COX-1) combined with transvaginal color Doppler sonography (TVCDS) in ovarian cancer (OC) treated with Bushen Yiqi Quyu prescription. n = 116) and control group (n = 116). The control group was treated with essential Western medication, and the observation group was treated with essential Western medication and Bushen Yiqi Quyu prescription. The clinical efficacy of the two groups was compared. The levels of HE4 and COX-1 were compared between the two groups before and after treatment. The ultrasonic features of TVCDS were compared between the two groups before and after treatment. The ROC curve was drawn to compare the sensitivity, specificity, and accuracy of single and combined detection of HE4, COX-1, and TVCDS in the observation group.  A total of 232 OC patients treated at the hospital from January 2018 to October 2020 were randomly divided into an observation group ( The total effective rate of the observation group was significantly higher than that of the control group. After treatment, HE4 and COX-1 levels in both groups were considerably lower than those before treatment, and in the observation group, they decreased significantly than in the control group. HE4 and COX-1 were positively correlated with the clinical stage of OC. The higher the clinical stage, the higher the levels of HE4 and COX-1. After treatment, there was no significant difference in tumor location and the boundary between the two groups. There were statistically significant differences in tumor echo, nature, morphology, calcification, internal blood flow, and lymph node metastasis, and the difference in the observation group was more evident than in control group. The sensitivity, specificity, accuracy, positive detection rate, and negative detection rate of combined detection were higher than those of single detection.  Bushen Yiqi Quyu prescription has certain curative effects in the treatment of OC patients, which can significantly reduce the level of tumor markers and improve the symptoms of OC patients. The combined detection of HE4, COX-1, and TVCDS has high sensitivity, specificity, and accuracy, which can effectively detect OC and reduce missed diagnosis and misdiagnosis. Ovarian cancer (OC) is one of the common malignant tumors of the female reproductive system, and its incidence varies in the world. In China, the incidence of OC is high and on the rise, second only to endometrial cancer and cervical cancer . ClinicaHE4 is a new marker for OC. Studies have found that HE4 is highly expressed in OC cells, predominantly serous and ovarian endometrioid tumors, and currently plays an essential role in the early diagnosis, prognosis, and disease monitoring of OC . COX-1 iThe treatment of OC is mainly surgery, supplemented by radiotherapy and chemotherapy. However, long-term use of chemotherapy drugs can damage normal cells and cause a series of adverse reactions, seriously affecting the normal life of patients . StudiesThe purpose of this study was to observe the diagnostic value of HE4 and COX-1 combined with TVCDS in OC patients treated with Bushen Yiqi Quyu prescription.n = 116) and control group (n = 116). Inclusion criteria: (1) diagnosed by histopathology; (2) met the Western medicine diagnostic criteria , and comparison between groups was performed by the χ2 test. An ROC curve was drawn to calculate sensitivity and specificity. p < 0.05 was considered statistically significant.SPSS 22.0 software was used for statistical analysis. Measurement data were expressed by mean ± standard deviation, and comparison between groups was performed by the The total effective rate of the observation group was 83.62%, significantly higher than that of the control group 62.93% . The resBefore treatment, there was no significant difference in serum HE4 and COX-1 levels between two groups. After treatment, HE4 and COX-1 of the observation group were (106.01 ± 46.58) pmol/L and (11.05 ± 3.11) U/mL, respectively. The levels of HE4 and COX-1 in the control group were (156.32 ± 47.17) pmol/mL and (26.03 ± 4.68) U/mL, respectively. After treatment, the levels of HE4 and COX-1 were significantly lower than before treatment. After treatment, the levels of HE4 and COX-1 in the observation group were lower than those in the control group .The results showed that the levels of HE4 and COX-1 were positively correlated with the clinical stage of OC. The higher the clinical stage, the higher the levels of HE4 and COX-1 .Before treatment, there was no significant difference in ultrasound indexes between the two groups. After treatment, there were statistically significant differences in echo, nature, morphology, calcification, internal blood flow, and lymph node metastasis between the two groups, and the difference in the observation group was more obvious than in the control group .The sensitivity, specificity, accuracy, positive rate, and negative rate of COX-1 in the diagnosis of OC were 65.38%, 81.58%, 70.69%, 87.93%, and 55.17%, respectively. The sensitivity, specificity, accuracy, positive rate, and negative rate of HE4 in the diagnosis of OC were 73.08%, 84.21%, 76.72%, 90.48%, and 60.38%, respectively. The sensitivity, specificity, accuracy, positive rate, and negative rate of TVCDS in the diagnosis of OC were 67.95%, 86.84%, 74.14%, 91.38%, and 59.90%, respectively. The sensitivity, specificity, accuracy, positive rate, and negative rate of combined detection were 92.31%, 92.11%, 92.24%, 96.00%, and 85.37%, respectively. The results showed that the combined detection was higher than the single detection, and the difference in sensitivity, accuracy, and negative detection rate was statistically significant .OC is one of the common malignant tumors of the female reproductive system and has the highest mortality rate among gynecological tumors. As the early clinical symptoms are not apparent, it is easy to cause missed diagnosis and misdiagnosis, so most OC patients are found with the advanced stage at the time of diagnosis commonly . OC is dFallopia multiflora has the effect of reinforcing the kidney for supplementing essence and detoxification. Herba Hedyotidis and Scutellaria baicalensis can clear heat and remove toxicity and promote urination. Glycyrrhizae Radix et Rhizoma Praeparata cum Melle has the effect of benefiting qi for nourishing yin and activating Yang [Scutellaria baicalensis can effectively inhibit the proliferation and induce apoptosis of OC cells. Ruanet al. [Poria cocos extract can inhibit the occurrence and development of OC by interfering with mitochondrial function, galactose, and fatty acid metabolism. All these remedies can improve the clinical symptoms of OC patients and promote their recovery by boosting blood circulation and removing blood stasis, resolving static blood for relieving pain, diuresis, tonifying the kidney and spleen, tonifying qi, and moistening the lung.According to our findings, the total effective rate in the observation group was significantly higher than that in the control group. HE4 and COX-1 levels in the observation group were predominantly higher than those in the control group after therapy. After treatment, there were significant differences between the two groups in tumor echo, nature, morphology, calcification, internal blood flow, and lymph node metastasis. This is because Rehmanniae Radix Praeparata, Corni Fructus, and Dioscoreae Rhizoma have a nourishing role. Corni Fructus can tonify the liver and kidney and cure frequent urination, backache, dysmenorrhea, and vaginal bleeding. Dioscoreae Rhizoma can nourish the spleen and strengthen the kidney. Rehmanniae Radix Praeparata can nourish Yin and blood. Ligustri Lucidi Fructus can nourish kidney Yin. Codonopsis Radix can tonify qi and enhance immunity. Chuanxiong Rhizoma can promote qi, activate blood, and dispel wind for dispelling pain. Solanum nigrum can remove heat and eliminate toxicity and dissipate blood stasis for detumescence. Curcumae Rhizoma can remove blood stasis and relieve pain and protect the liver and kidney. Poria can nourish the heart to tranquilize, reinforce Qi, and strengthen the spleen. Scutellariae Barbatae Herba can clear heat and remove toxicity and boost blood circulation for removing blood stasis and detumescence for relieving pain. ing Yang –24. Choiing Yang  found thanet al.  found thEarly screening of OC is of great significance. Serum markers play an important role in diagnostic oncology. In recent years, many potential markers have been identified and used individually or in combination to improve specificity and sensitivity, especially in the early stages of disease . TVCDS iIn conclusion, Bushen Yiqi Quyu prescription in this study has good therapeutic effects in treating OC, which can significantly reduce the serum indexes of patients and improve the tumor deterioration. The application of TVCDS combined with HE4 and COX-1 in the treatment of OC is optimum and significantly higher than each single detection, which has potential of clinical application for the early diagnosis of OC.


Chemotherapy is the main treatment for patients with lung squamous cell carcinoma (LUSC). However, how chemotherapy affects their immune system is rarely reported. This study was aimed to compare the differences in the immune microenvironment of LUSC patients with or without chemotherapy.A total of 494 LUSC samples were obtained from The Cancer Genome Atlas (TCGA) database. The immune cell infiltration was evaluated by the ssGSEA algorithm, and the tumor subtype was assayed by ConsensusClusterPlus. The differences in tumor mutation burden (TMB) and clinical information between the two types were then compared. Additionally, the differentially expressed genes (DEGs) between two types were analyzed and hub genes were validated in the GEO database.CD19, CTLA4, FCGR3B, CD80, IL-10, etc. were also validated in the GSE37745 dataset.LSCC samples in TCGA were divided into three subtypes. Then, combining the tumor subtype and immune scores, the samples were divided into hot and cold tumors. Regardless of whether LUSC patients received chemotherapy, the survival of the hot tumor group was not significantly prolonged compared with that of the cold tumor group. For LUSC patients who received chemotherapy, the TMB value in hot tumor group was significantly higher. Total 501 DEGs were identified between two groups. The high expressions of hub genes CD19, CTLA4, and FCGR3B, may serve as biomarkers to distinguish cold and hot tumors in LUSC.Chemotherapy does not affect the survival and prognosis of LUSC patients, but it significantly increases the TMB value of patients with hot tumor. The DEGs, especially hub genes, such as  Chemotherapy does not affect the survival of LUSC patients.2. Chemotherapy significantly increases the TMB value of LUSC patients with hot tumor.CD47, SIRPA, and other immune checkpoint genes can serve as biomarkers to help identify the immune microenvironment of LUSC patients.3. CD19, CTLA4, and other hub genes can serve as biomarkers to help identify the immune microenvironment of LUSC patients.4. Lung cancer is one of the most common malignant tumors with a 5-year overall survival rate of 16%–20% , which cPresently, chemotherapy remains the standard treatment for LUSC . As is kThe establishment of The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database has helped to generate many large-scale cancer genomic datasets and enabled comprehensive bioinformatics analyses . TherefoThe Illumina HiSeq 2000 gene expression data  of lung squamous cell carcinoma (LUSC) were downloaded from TCGA database. There are 550 samples, including 501 tumor samples and 49 normal samples. Among the tumor samples, 494 samples with clinical survival and prognostic information were retained as the training set. Based on the clinical information of these samples, we further divided them into the chemotherapy group and the non-treatment group and performed a subgroup analysis on the TMB and survival of the two groups, respectively. Additionally, the GSE37745 –12 datasCells in the tumor microenvironment can cluster into different types, and there are robust cell infiltration patterns among these cells. In this study, gene set variation analysis for microarray (GSVA) version 1.36.3  in R3.6.Based on the obtained immune cell proportion, the tumor subtypes were analyzed for all samples using the ConsensusClusterPlus version 1.54.0  in R3.6.ESTIMATE score, immune score, stroma score, and tumor purity were calculated using the estimate package  in R3.6.Based on the mutation information of tumor samples downloaded from TCGA database, the mutation of each gene in each sample was analyzed, and the genes with high-frequency mutation were displayed. Then using the maftools package version 2.6.05  of R3.6.For the tumor samples in TCGA, significant DEGs between hot and cold groups were screened using the limma package version 3.34.7  in R3.6.CD274), PD1 (PDCD1), CTLA-4 (CTLA4), Tim3 (HAVCR2), CD278 (ICOS), LAG3, CD47, CD73, TIGIT, BTLA, myd1 (SIRPA), 4-1BB (TNFRSF9), OX40 (TNFRSF4), and B7-H4 (VTCN1). The expression differences between cold and hot tumor groups were compared.Based on the gene expression level in TCGA samples, the expression levels of specific immune checkpoint genes were extracted, including PD-L1 , titin (TTN), and CUB and sushi multiple domains 3 (CSMD3). Then TMB values of tumor samples were calculated. The results showed that the TMB value in the hot tumor group was significantly higher than that in the cold tumor group (The genes with high-frequency mutations are shown in With FDR < 0.05 and |log2 fold change (FC)| > 1, 501 DEGs were identified between cold and hot tumor groups, which were significantly correlated with 25 biological processes and 13 KEGG signaling pathways. As shown in ICOS and CD73. For the other 12 genes, their expression levels in the hot tumor group were significantly higher than that in the cold tumor group, except for VTCN1.The expression levels of 14 immune checkpoint genes were extracted from the LSCC samples in TCGA database. As shown in ), Fc fragment of IgG receptor IIIb (FCGR3B), CD80, interleukin 10 (IL10), CD28, CD247, CD69, zeta chain of T cell receptor-associated protein kinase 70 (ZAP70), and interferon gamma (IFNG) , germline, copy number variation, and structural variation . TMB is A total of 501 DEGs were identified between two groups, which were enriched in immune response-related functions. In the present study, 12 immune checkpoint genes were found to be significantly differentially expressed between hot and cold tumor groups. Among those immune checkpoint genes, both CD47 and SIRPA were remarkably upregulated in the hot tumor group. CD47 is an integrin-associated protein and is overexpressed in many cancer cells . SIRPA iin vitro or in vivo experiments are needed to validate our findings.Moreover, ten hub genes, such as CD19, CTLA4, FCGR3B, and CD80, were validated in the GEO database. Interestingly, these genes were all upregulated in the hot tumor group. Additionally, among the ten genes, five were CD molecular, such as CD19, CD80, and CD28. CD19 is a transmembrane glycoprotein of the immunoglobulin superfamily and is broadly expressed in B-cell malignancies. CD80 can be expressed in immune cells as well as some cancer cells. Moreover, it could interact with both coinhibitory (CTLA4) and costimulatory (CD28) receptors to regulate the immune response . SpecialCD47, SIRPA, and other immune checkpoint genes, as well as CD19, CTLA4, and other hub genes, can serve as biomarkers to help identify the immune microenvironment of LUSC patients, so as to better screen people who are suitable for continuing immunotherapy.In conclusion, chemotherapy does not affect the survival of LUSC patients in our study, but it significantly increases the TMB value, suggesting that subsequent immunotherapy may further improve the efficacy and improve the prognosis. The original contributions presented in the study are included in the article/HQ and JL contributed to the conception/design of the work, the collection and analysis of data, and the writing and edit of the article. The remaining authors provided editing and writing assistance. All authors contributed to the article and approved the submitted version.This study was funded by the Western Medicine Guide Project of Shanghai Committee of Science and Technology (grant no. 21Y11913500).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.


A brain–computer interface (BCI) based on kinesthetic motor imagery has a potential of becoming a groundbreaking technology in a clinical setting. However, few studies focus on a visual-motor imagery (VMI) paradigm driving BCI. The VMI-BCI feature extraction methods are yet to be explored in depth. In this study, a novel VMI-BCI paradigm is proposed to execute four VMI tasks: imagining a car moving forward, reversing, turning left, and turning right. These mental strategies can naturally control a car or robot to move forward, backward, left, and right. Electroencephalogram (EEG) data from 25 subjects were collected. After the raw EEG signal baseline was corrected, the alpha band was extracted using bandpass filtering. The artifacts were removed by independent component analysis. Then, the EEG average instantaneous energy induced by VMI (VMI-EEG) was calculated using the Hilbert–Huang transform (HHT). The autoregressive model was extracted to construct a 12-dimensional feature vector to a support vector machine suitable for small sample classification. This was classified into two-class tasks: visual imagination of driving the car forward versus reversing, driving forward versus turning left, driving forward versus turning right, reversing versus turning left, reversing versus turning right, and turning left versus turning right. The results showed that the average classification accuracy of these two-class tasks was 62.68 ± 5.08%, and the highest classification accuracy was 73.66 ± 6.80%. The study showed that EEG features of O1 and O2 electrodes in the occipital region extracted by HHT were separable for these VMI tasks. BCI canMany earlier studies have achieved remarkable results on the MI mental activity  ,16,17 anCompared with the above-mentioned MI mental activity, visual motor imagery (VMI) is a kind of mental activity that is easy to learn and control. It requires subjects to view a specific picture or scene in their brain from a third-person perspective ,8,9,10. Compared with the traditional MI-BCI, few studies focus on VMI-BCI . Neuper Traditional EEG feature extraction methods are autoregressive (AR) model, adaptive model, wavelet transform, and common space model. The Fourier transform wavelet method uses sinusoidal components of different frequencies to fit the original signal. It is difficult for the wavelet method to achieve a high resolution in both the time domain and the frequency domain . CompareThe rest of the article is organized as follows. Section 22.1Using the kinesthetic and visual-motor imagery questionnaire (KVMIQ) ,33,34,35Informed consent: Informed consent has been obtained from all individuals included in this study.Ethical approval: The research related to human use has been complied with all the relevant national regulations, institutional policies, and in accordance the tenets of the Helsinki Declaration and has been approved by the Medical Ethics Committee of Kunming University of Science and Technology.2.22.2.1To control the car or robot to move forward, move backward, turn left, and turn right naturally by mental activity, four VMI tasks were designed: imagine the car moving forward, reversing, turning left, and turning right as shown in 2.2.2Subjects were allowed to have enough sleep before the experiment to maintain a good mental state. Before the experiment, the main tester, the EEG acquisition equipment, was connected, and the relevant software was launched. Subjects were seated in comfortable chairs, about 70 cm away from the visual cue screen, with their hands flat on the table. The tester wears a brain cap and injects the brain electric cream into the subjects until the electrode impedance falls to a suitable range. In the beginning, the subjects completed the visual observation and visual imagery tasks according to the prompts of 2.2.3The experimental setup is shown in 2.32.3.1The collected visually imagined EEG data were examined visually, and the data of five subjects with serious EEG contamination were removed. Then, the EEG data of each subject, collected under different tasks, were extracted. The duration of each task was 5 s. To prevent interference of other signals, only the data segments from 1 s  is the original signal, Ci(t) is the IMF component obtained by the ith screening, N is the number of screening times, and Rn(t) is the final residual component.The HHT method includes empirical mode decomposition (EMD) and Hilbert spectrum analysis (HSA). The EMD obtains the intrinsic mode function (IMF) using the average of the upper and lower envelopes of the time series. The EMD process is as follows: first, all of the maximum and minimum points of the input signal are obtained. Then, the maximum value point and the minimum value point are fitted using a cubic spline. The curve of the upper and lower envelopes is obtained, and the mean value function is calculated to obtain the difference one pole . From thone pole :(1)s,,5), respThe average instantaneous energy is obtained from the instantaneous amplitudes according to equations  and 7)  7) 22]::22]:(6)The left side of equations  and 7)  7) is thAR1, …, O1AR6, O2AR1, …, O2AR6}. Then, the SVM suitable for small sample classification is used for feature classification [After EMD decomposition of the EEG signals of VMI, the IMF of each order is shown in fication . The cla3P < 0.05), whereas the main effects of other leads are insignificant.To verify the VMI movement direction as a mental strategy for BCI users, we carried out the feature extraction and pattern classification of EEG collected during car forward driving and reverse driving of VMI. P < 0.05).4Many earlier studies have made a remarkable research progress on MI-BCI ,16,17; hThe experimental paradigm is different from that of Nataliya et al. , which rThe EEG pattern induced by the VMI task is also closely related to the performance of subjects’ imaginative mental activities . To thisFor the feature extraction of VMI-BCI, the EEG power spectrum estimation method is mainly used in the existing research ,20,41,42P = 0.045, P < 0.05). The first reason for these results is that VMI is related to memory, whereas memory is related to the prefrontal cortex and the endothelial layer of the lateral interior of the parietal lobe (LIP area) [When extracting the EEG features of VMI using HHT, the selection and combination of electrodes have an impact on the classification accuracy of VMI tasks. IP area) . The eleThe EEG features of electrodes Fp2, F7, Fcz, P3, and Pz in VMI have been analyzed, and the correlation between channel EEG and VMI ,15,19,20Compared with the traditional MI-BCI with more research, the task based on EEG recognition VMI is more challenging. The research is still insufficient, and the classification accuracy needs to be further improved. For this reason, feature selection and extraction and electrode combination optimization should be studied.In our future work, we will attempt to extract the correlation between channel EEGs in VMI as a feature to optimize the combination of electrodes and to improve the classification accuracy of VMI tasks. We will construct an online real-time VMI-BCI brain-controlled robot system with few channel EEGs.5Contrary to the traditional MI-BCI paradigm, this study designed a new VMI-BCI paradigm: visual imagination of a car moving forward, reversing, turning left, and turning right. These four mental strategies can control the car or robot to move forward, backward, left, and right. EEG features are extracted from HHT with high temporal and spatial resolution, and the designed VMI task is identified using an SVM suitable for small sample classification. Studies have shown that in the designed experimental paradigm, the above EEG extracted features at the O1 versus O2 electrode positions in the occipital region can distinguish visual imagination tasks (average classification accuracy can reach 73.66 ± 6.80%). Visual imagination car has the strongest connection between the electrode pairs O1 and F7, O2 and F8, O2 and Fp2, and Cz and F7 in reversing, turning left, turning right, and driving forward. The visual imagination driving car based on EEG is expected to be a BCI strategy, and VMI-BCI can be used as a window to observe brain function. This article can provide ideas for online real-time VMI-BCI brain-controlled robot systems based on less-channel EEG.


Promoting physical activity is an important part of patient care in primary care and has been investigated in many studies with a wide range of intervention characteristics, often including external support. It is unclear, however, if promoting physical activity is effective.To investigate the effectiveness of behaviour change interventions to promote physical activity in primary care.This is a systematic review and meta-analysis to evaluate physical activity promotion in a primary care setting.EMBASE, MEDLINE, PsycInfo, and the Joanna Briggs Institute Database were searched for ‘physical activity’, ‘interview’, ‘motivation’, ‘primary care’, and equivalent words to identify randomised controlled trials with physical activity as the outcome at patient level.The review identified 25 eligible studies. The quality appraisal showed that most studies reported insufficient details regarding randomisation, group allocation, blinding, and fidelity of intervention delivery. The included studies reported a wide range of interventions with varying numbers of follow-up visits or phone calls. The overall effect size for interventions with a 6-month follow-up interval was 0.04 , and for interventions with a 12-month follow-up interval it was 0.19 (95% CI = 0.03 to 0.36). Only one intervention based on three motivational interviewing sessions achieved a moderate effect.Counselling to promote physical activity in primary care has a limited effect on patients’ behaviour and it might not, on its own, be enough to change physical activity behaviour. In addition, adults should complete muscle-strengthening activities at least twice a week, and avoid sedentary behaviour for long, uninterrupted periods.Current PA guidelines4 Physical inactivity is associated with considerable costs for healthcare systems, particularly in high-income countries.5 These costs are predicted to increase over the next decades because of the ageing of the population.6However, only about 30% of adults are sufficiently physically active in Europe, with figures ranging from 23% in Sweden to 44% in the Netherlands.7,8 A systematic literature review showed that barriers to PA counselling included lack of incentives for the primary care physicians, time constraints, the perception of insufficient knowledge and training, and the lack of a counselling protocol on behaviour change.8 A survey indicated that about 75% of primary care physicians found it difficult to provide lifestyle modification counselling.7Primary care physicians are often the first point of contact for people to discuss their health. A consultation may present a suitable opportunity for patients to discuss PA levels, as this falls within the remit of primary care physicians.9 A meta-analysis of 43 randomised controlled trials (RCTs) investigating behaviour change techniques for weight management and PA across settings showed that goal setting and self-monitoring were positively associated with intervention effect in the short and long term; exploring pros and cons of behaviour change produced inverse effects.10 In addition, giving feedback, setting graded tasks, and adding objects to the environment (such as a diet logbook) were associated with positive long-term effects.10 The study did not find any differences in effects when comparing different settings or weight management with PA.At present, it is unclear which behaviour change strategies and support mechanisms primary care physicians should use to promote PA in their counselling sessions. There is a broad spectrum of behaviour change techniques, with at least 93 techniques available.et al showed that a wide range of behavioural counselling interventions were effective.11 In addition, a health economics analysis indicated that most PA interventions set in primary care were cost-effective.12 However, both studies included interventions with characteristics that are not usually available in a primary care context . Therefore, it remains unclear which interventions would be successful in supporting PA engagement when delivered in primary care settings. This systematic review of the literature and meta-analysis aimed to investigate interventions to promote PA that were delivered within a primary care context to evaluate their effectiveness. The research objectives were:to identify the types of behaviour change interventions that take place in primary care practices to support engagement in physical activities;to evaluate the effectiveness of behaviour change interventions delivered in a primary care context; andto determine which type of intervention is associated with moderate or large effect sizes.In relation to primary care specifically, Noordman The protocol for this systematic literature review was published on PROSPERO (CRD42020154879). Searches were performed in Ovid (databases were combined) for EMBASE (1974 to 15 October 2019), MEDLINE (1946 to 15 October 2019), PsycInfo (1906 to week 1 October 2019), and Joanna Briggs Institute EBP database (current to 15 October 2019).Search terms  were: primary care OR family practi* OR GP OR general practi* OR physician* OR primary health AND interview* OR advice OR consultation* OR promotion* OR counselling OR counselling AND motivation OR behaviour* change* OR behaviour* change* OR lifestyle change* AND physical activit* OR exercise* OR physiotherap* OR physical therap*. Where possible, the search was limited to humans. The search was repeated for the years 2019 and 2020 on 30 October 2020 for articles up to that date.Included were peer-reviewed RCTs investigating behaviour change consultations promoting PA engagement in a primary care setting; studies whose outcome parameter include PA levels; studies with outcomes at the patient  level; articles reporting primary research studies in English, German, Italian, Spanish, French, or Dutch; and eligible studies retrieved through the reference lists of literature reviews.Excluded were studies investigating interventions without reporting behaviour change consultations; studies examining consultations not pertaining to behaviour change and PA; and abstracts, protocols, editorials, discussion papers, and comments (unless relating to one of the included studies).All records identified were imported into Mendeley, and duplicate records were removed. Title and abstracts were screened by one author to determine whether or not they met the eligibility criteria. The abstracts that did not meet the eligibility criteria were rejected and numbers were recorded. If the eligibility was uncertain, the article was retained, and its full text retrieved to determine eligibility.Full-text articles for all candidate-eligible studies based on titles and abstracts were retrieved and assessed by two co-authors to determine eligibility. Any uncertainties concerning the appropriateness of reviews for inclusion were resolved through discussion with a third reviewer. Reasons for non-eligibility were recorded.Data from the selected articles were extracted by one author using a custom-designed form. Data extracted included author; year and country of publication; study characteristics, including design, and inclusion and exclusion criteria; participants; intervention characteristics, including frequency and duration; and outcome measures (primary and secondary) and effect of consultation on outcome measures (if possible).13 Any disagreements were resolved through discussion with a third reviewer.Two authors appraised the quality of the included studies independently using the Cochrane Collaboration’s tool for assessing risk of bias in RCTs.For the first research objective, a descriptive analysis was completed to report who delivered the intervention  and what type of behaviour change consultations were delivered. For the second research objective, two meta-analyses were completed for interventions, with a follow-up assessment at 6 and 12 months. These timepoints were chosen as they were the most commonly reported ones. As some studies included >1 PA measure -hours per week), the analysis was completed for results with the smallest effect size to provide a conservative estimate of the overall effect size. Effect sizes were based on standard mean differences for two samples. When >1 intervention was tested, effect sizes for each individual intervention were used in the meta-analyses. For the meta-analysis, effect sizes were weighted by sample size. For the third research objective, studies with moderate or large effect sizes were identified and their characteristics described.14 In total, 1701 articles were identified. Following titles and abstracts screening, 1604 articles were excluded. After full-text examination of the remaining 97 articles, 73 were excluded. The review included 25 articles.15–39 The characteristics of the included studies are reported in Supplementary Table S1. The studies were published between 1995 and 2020, with five articles each from the US,15,16,29,32,33 the Netherlands,20,21,22,27,28 and the UK,25,30,34,35,36 four from Australia24,37–39 two from Germany,23,31 and one each from Canada,17 Finland,26 Mexico,18 and Spain.19 The sample sizes ranged from 20 to 4317 participants.The screening process and reasons for exclusion of full-text articles are shown in 15 which did not show follow-up data, reported data loss. Fourteen studies used an intention-to-treat analysis;17,19,21,24,26–28,30,31,34,35,36,38,39 the other studies did not report how they approached the missing data in the analyses. Further bias might have been introduced in 18 studies either by not reporting fidelity data, or through low fidelity to the intervention.Most studies lacked details on randomisation and allocation concealment, as well as blinding of clinicians, researchers, and participants, though blinding was not possible in most study designs . All stu15,17–19,24,32,33,37,39 in 11 by practice nurses,20–22,25–29,30,34,36 and in five by both.16,23,31,35,38 Fifteen studies evaluated a PA intervention16–19,23,25,27–29,31,33–37 and 10 a lifestyle intervention.15,20,21,22,24,26,30,32,39,40 Three studies included a single behaviour change consultation as intervention,16–18 and 11 studies a baseline behaviour change consultation with follow-up visits or phone call.19–28,36 Three studies evaluated an intervention comprising telephone consultations,29–31 and six studies tested interventions that included behaviour consultation visits, as well as additional support mechanisms such as assessment of motivational readiness report, posters, or pedometers.32–37 Only two studies reported on the training for the practice staff but not on the implementation at the patient level,38,39 and one study included an intervention consisting of two physical examinations plus an optional behaviour change consultation.15In nine studies, the intervention was delivered by primary care physicians,22,29Though all interventions were consultation based, they still included a wide range of formats, types, and support mechanisms, with different follow-up periods. Therefore, the authors decided to complete the meta-analyses for studies with equal follow-up periods to enable a comparison of effects of the different interventions at a set timepoint. Due to a lack of detail in reporting, effect sizes could not be calculated for two of the studies.18,19,28,30,37 the overall effect size was 0.04 . Effect sizes and CIs are presented in 23,30,32,34,36,37 The overall effect size was 0.19 (95% CI = 0.03 to 0.36)  .The effect of primary care counselling to increase PA levels was small for most studies, and better in studies designed to change behaviour over a longer period of time (12 months) than in studies with a shorter follow-up period (6 months). No further patterns identifying a successful intervention could be detected regarding specific intervention characteristics, such as counselling strategy, population, training of intervention staff, or theoretical underpinning of the intervention.et al,32 which included participants diagnosed with type 2 diabetes. The intervention was delivered by the primary care physician, and included three motivational interviewing sessions based on a personal report outlining the computer-assessed motivational readiness to increase PA and make dietary changes. The tailored report provided feedback to the participant, addressed behaviour change barriers, and listed two or three dietary and/or PA self-management goals that the participant had chosen as target behaviour. The participants were also given a 30-page planning guide with additional information about a healthy lifestyle. The physician received a summary of the participant’s report for the counselling visit to discuss goals.The only study that achieved a medium-effect size was by Christian Physical activity promotion may have a limited effect if restricted to primary care settings, despite different consultation approaches being used. Some studies included interventions investigating single counselling sessions; others had follow-up visits or telephone calls. Different support mechanisms, such as tailored reports, goal setting, or activity prescriptions were added, and a range of health psychology approaches were used as theoretical underpinning of the counselling element. There was no clearly superior counselling strategy, and only seven out of 24 interventions increased PA levels significantly more than their control interventions.The effect sizes in the individual studies were generally small, and a meta-analysis of interventions with a 6- or 12-month follow-up period confirmed these findings. The difference in results between the meta-analyses with 6- and 12-month follow-up data also indicated that interventions developed for a long-term behaviour change (here 12 months) might be more effective that those developed for a shorter-term follow-up. Because of the lack of reporting on details regarding the content of the counselling sessions, it remains unclear if the prospect of a 12-month follow-up affected the counselling approach.et al.32 Their intervention design included characteristics  that have been shown to support behaviour change in overweight and obese people.10 The findings of the review by Samdal et al showed that goal setting and self-monitoring were significantly associated with a positive intervention effect both in the short and long term.10 This would suggest that interventions to increase PA might work better for certain subgroups, as the sample of the Christian et al32 study included people with type 2 diabetes.The only study including an intervention that showed a moderate effect size was by Christian This systematic literature review is the first to investigate effect sizes of PA promotion counselling in primary care settings. Though only interventions based on counselling were included, the review examined different approaches without external support that might not be available for primary care patients. The meta-analysis contained studies based on the length of the follow-up interval (6 and 12 months), but these included a wide range of intervention characteristics.40 Without these, the findings lack the required context to conclude whether the intervention itself was inefficient, or whether the implementation of the intervention was unsuccessful. Any future RCTs should include a well-designed process evaluation that follows Medical Research Council guidelines.40 Furthermore, because of different followup periods, not all studies could be included in the meta-analyses and there were not enough studies to compare the effect of different counselling approaches. Another limitation was the number of literature databases used for the search; this was due to time and resource constraints. Though the literature databases used in this review included large scientific databases for medical research, additional articles might have been identified by searching a wider range of databases.Overall, the quality of the included studies was acceptable, though some studies did not report sufficient details on randomisation, blinding of participants, and intervention deliverers (primary care physicians and praxis nurses). Fidelity reporting was lacking in many studies and it was therefore not always clear whether the small effect was due to the intervention itself, or whether the intervention had not been implemented as intended. Process evaluation and adherence reporting are an essential part of a RCT.41,42 Linking primary care counselling with additional elements of PA support might lead to larger effects on PA behaviour. A more comprehensive approach to behaviour change, with multiple support mechanisms, would also better reflect the behaviour change wheel by Michie et al,9 which suggests that a comprehensive behaviour change support system rather than one source is required to support the person to change their behaviour. Three components, motivation (brain processes that energise and direct behaviour), capability (a person’s capacity to engage in the targeted activity), and opportunity , are required to achieve a positive behaviour change.9 A successful intervention should focus on all three components to provide a supporting context for the individual to adopt a healthy lifestyle.The review excluded interventions that contained elements not delivered in a primary care context, such as exercise classes, external support , and/ or community groups. In addition, further motivation support strategies such as fitness trackers can support self-monitoring and exercise adherence.9 to develop interventions and report these in sufficient detail to allow replication of the research. The RCTs testing the interventions need to include a process evaluation to assess the implementation of the intervention and to clarify causal mechanisms and context factors. The combined information from the intervention development reporting and the results of the RCT, as well as the process evaluation, could then enable a detailed analysis of which intervention components enable behaviour change mechanisms.The findings indicate that counselling to promote PA in primary care has a limited effect on patients’ behaviour. Strategies to increase PA levels should include a more comprehensive approach, with multiple mechanisms to support motivation, capability, and opportunity, rather than a single point of encouragement for behaviour change in primary care. Future interventions should use a comprehensive approach as outlined in Michie’s behaviour change wheel