Video and computer games are popular activities, with 72% of adolescents aged 13 to 17 years reporting video game use on either a computer, game console, or portable device. Despite high levels of video and computer game use in adolescence, relatively little scientific literature exists examining the association and effects of video and computer games on adolescents.The objective of this study was to examine the prevalence of video and computer game use among US adolescents and rates of positive screens for obesity, diabetes, high blood pressure (BP), and high cholesterol.A secondary data analysis was conducted using the National Longitudinal Study of Adolescent to Adult Health  data, including adolescents aged 12 to 19 years between 1994 and 2018.P=.02) higher BMI and were more likely to self-report having at least one of the evaluated metabolic disorders: obesity (BMI >30 kg/m2), diabetes, high BP (BP >140/90), and high cholesterol (>240). With increased video or computer game use, there was a statistically significant increase in high BP rates in each quartile, with those with more frequent use also having higher rates of high BP. A similar trend was observed for diabetes, though the association did not reach statistical significance. No significant association was observed between video or computer game use and diagnoses of dyslipidemia, eating disorders, or depression.Respondents (n=4190) who played the most video and computer games had a significantly (Frequency of video and computer game use is associated with obesity, diabetes, high BP, and high cholesterol in adolescents aged 12 to 19 years. Adolescents who play the most video and computer games have a significantly higher BMI. They are more likely to have at least one of the evaluated metabolic disorders: diabetes, high BP, or high cholesterol. Public health interventions designed to target modifiable disease states through health promotion and self-management may support the health of adolescents aged 12 to 19 years. Video and computer games can integrate health promotion interventions in gameplay. This is an important area for future research as video and computer games are integrated into the lives of adolescents. Video and computer games are popular activities, with 72% of adolescents aged 13 to 17 years reporting video game use on either a computer, game console, or portable device [Sedentary behavior throughout the lifespan increases one’s risk for all-cause and cardiovascular disease (CVD) mortality, incident CVD, and type 2 diabetes . Similar=.23, P<.001) and anxiety  [=.13, P<.001) and computer use  were associated with more severe depressive symptoms, and video game playing  was associated with the severity of anxiety [In addition to being associated with chronic health conditions, sedentary screen time is related to psychological conditions. In a study of youth aged 11 to 20 years (n=2282), sedentary screen time  was associated with the severity of depression  . In part anxiety .This study aimed to examine the prevalence of video and computer game use among US adolescents and rates of positive screens for obesity, diabetes, high blood pressure (BP), and high cholesterol. Video and computer games are defined as electronic games played on either a personal computer or a video screen. This does not include smartphone games. The research question that guided this secondary data analysis was the following: To what extent is the frequency of video games and computer game use associated with self-reported chronic health conditions ?A secondary data analysis was conducted in 2022 using the National Longitudinal Study of Adolescent to Adult Health  1994-2018. Add Health was a longitudinal study of US adolescents aged 12 to 19 years, starting in the 1994-1995 school year. This cohort was interviewed in 5 waves between 1994 and 2018. Waves 1 through 5 were collected in 1994-1995, 1996, 2001-2002, 2008-2009, and 2016-2018, respectively.Data were collected from the Add Health longitudinal study, in which all respondents who recorded data for both wave 1 (1994-1995) and wave 5 (2006-2018) were considered eligible. Add Health data came from 80 high schools selected from a sample frame of 26,666. Prior to sampling, schools were sorted by size, school type, census region, level of urbanization, and percent White race. Of the 80 selected, 52 were eligible and agreed to participate. Participating high schools then identified their feeder middle schools, resulting in one school pair. Parental consent was required for students to participate in the study. Some schools required passive consent (assuming parents granted permission for students to participate unless otherwise indicated), and others required active consent . Data were collected from respondents using a combination of physical measurements, biospecimens, and self-reported surveys.Demographic information was collected from the master data set, and exposure and outcome-specific variables were recorded from the respective wave data sets. Video game use variables were collected from wave 1 to capture video and computer game use while in adolescence . Outcome variables were recorded in wave 5 (21-23 years later), including outcomes related to health conditions. Any chronic conditions reported in a previous wave were recorded in wave 5. This allowed for maximum follow-up time through adulthood to capture as many conditions as possible.2), diabetes, high BP, hyperlipidemia, depression, and eating disorders. Obesity was measured using weight and height to calculate BMI, with BMI >30 kg/m2 indicating obesity. Hyperlipidemia was measured using a biospecimen. High BP was measured using physical measurements. Diabetes, depression, and eating disorders were measured by a self-reported survey of whether or not each participant had been diagnosed with the disorder. Self-reported measures of diabetes, depression, and eating disorders have been found to be reliable and valid in other studies [Variables in the data set were identified as exposures (video or computer game frequency) and outcomes . Wave 1 data were used at the exposure, and wave 5 data were used at the outcome. The exposure variable included self-reported video or computer game play duration and was measured in hours per week. Outcome variables included obesity  and reported metabolic and health outcomes at wave 5 was examined using logistic regression using the following four models: (1) the first model was an unadjusted crude model; (2) the second model adjusted for sociodemographics ; (3) the third model, in addition to sociodemographics, adjusted for socioeconomic characteristics ; and (4) the final model adjusted for sociodemographics, socioeconomic characteristics, and current lifestyle behaviors , accounting for sampling weight and cluster sampling. Additionally, the exposure variable (duration of video or computer game played) was investigated as both a categorical value and a continuous variable in all models. All data were analyzed in 2022.A supplementary analysis was carried out to evaluate the relationship between video or computer game use at enrollment (baseline: wave 1) and reported metabolic and health outcomes at wave 5 while adjusting for current BMI at wave 5 to account for the role of obesity in the development of other metabolic risks.Only cases with complete data at both waves were used for statistical analysis, with no imputation of missing values required. A total of 6504 completed respondent records were identified in wave 1. Of these 6504 records, 6390 were completed through wave 5. Those respondents without complete wave data were removed from our analytic sample, for a total of 114 respondents (1.75%) at this point. Additionally, any records that contained incomplete data for any metric included in this study at any timepoint or wave were also removed. This removed 2200 respondents (33.83%) from our analytic sample. Due to a large amount of missing data across this longitudinal panel, we decided to use listwise deletion to avoid any bias or influence introduced with imputation. Our final analytic database included 4190 respondents.Statistical analysis was performed using SPSS .This study did not require ethics approval as it involved secondary analysis of data available in the public domain.A total of 4190 respondents were included in this analysis. The median (range) hours spent by the respondents on video games and computer games was 1 (0-99) hours per week. Approximately 47% of the respondents did not play video games. The distribution of socioeconomic characteristics of respondents by quartile of the duration of video game played per week is shown in The distribution of lifestyle characteristics of respondents by quartile of the duration of video games played per week is shown in 2), diabetes, high BP, and high cholesterol). The trend for an increased prevalence of high BP with increasing quartiles of video or computer game use was significant. A similar trend was observed for diabetes, though the association did not reach statistical significance. No significant association was observed between video or computer game use and the diagnosis of dyslipidemia, eating disorders, or depression.The distribution of metabolic and health indicators among respondents by quartile of the duration of video game played per week is shown in In brief, the odds for the presence of any metabolic abnormality, obesity, or diabetes were significantly higher in the highest quartile compared to the lowest quartile of video and computer game use at enrollment. Quartile 4 (highest duration spent) compared to quartile 1  had 23%, 27%, and 63% higher odds of reporting or having a metabolic abnormality, obesity, or diabetes, respectively.In the unadjusted model, every additional hour reportedly spent playing video games per week increased the odds of metabolic abnormalities by 1% . The relationship remained significant when adjusted for sociodemographic variables  and additionally for socioeconomic indicators . However, the association was not significant in the fully adjusted model.In the unadjusted model, every additional hour reported to have been spent on playing video games per week increased the odds of obesity by approximately 1% . When adjusted for sociodemographic variables , the strength of the association increased . On additional adjustments for factors such as socioeconomic indicators, the association remained significant . The relationship remained statistically significant even when the current lifestyle of the respondents and cluster sampling were accounted for . However, such a significant association was not found with quartiles of video game playing duration in the final model.In the crude model, the duration of playing video games was not associated with diabetes incidence. However, when adjusted for sociodemographic variables  and additionally for socioeconomic indicators , the associations became significant. The association was attenuated and no longer statistically significant when the final model estimates were adjusted for current lifestyle . Similarly, no significant association was found between quartiles of video game playing duration and the incidence of diabetes in the final model.The associations between video or computer game use, high BP, and high blood cholesterol were much less evident. While compared to those in Q1 (those who did not play the games), Q2, 3, and 4 had higher odds of reporting high BP or high blood cholesterol, a linear dose-response relationship was absent.Chronic disease was described as reporting any one of the following conditions: heart disease, kidney disease, stroke, and heart failure at wave 5. No association was observed between the risk for the prevalence of a chronic disease and the duration of video or computer game use .A supplementary analysis that adjusted for current obesity in evaluating the association between video or computer game use and cardiometabolic risks is presented in 2; diabetes; high BP; and high cholesterol) than those who did not report playing games over the 2 decades of follow-up. No significant association was observed for diabetes, hypertension, dyslipidemia, eating disorders, or depression.The objective of this study was to examine the prevalence of video and computer game use among US adolescents and assess its association with obesity and associated metabolic disorders longitudinally. Respondents who played the most video and computer games at enrollment had a significantly higher BMI and were more likely to report having at least one of the evaluated metabolic disorders  range between 1.19 and 1.38 [The associations between video and computer game use and obesity are of public health importance. The raw mean difference in BMI between the lowest and highest quartiles of video game use was approximately 1 kg/mand 1.38 ,11. Howeand 1.38 . We acknand 1.38 ,14. The It is noted that obesity usually precedes the development of metabolic abnormalities. This could explain why the association of video and computer game use with obesity was statistically significant, but the trend did not reach statistical significance for other metabolic disorders. The trend for an increased prevalence of high BP with increasing video or computer games aligns with current literature . ElevateSimilarly, there was an observed potential for an increased likelihood of diabetes, which did not reach statistical significance. Current literature suggests inconsistent findings exist on the relationship between video games and diabetes. One cross-sectional study of adolescents aged 14-18 years (n=307) found no association between video games and an elevated risk of diabetes , and ano=.13, P<.001) and computer use  were associated with more severe depressive symptoms [No significant association was observed between video or computer game use and the diagnosis of dyslipidemia, eating disorders, or depression. Current literature has mixed findings on the relationship between video and computer games and depression. One study (n=126) found mobile phone and TV viewing were associated with higher levels of depression a year later . Howeversymptoms . No knowr use β=., P<.001 The Add Health study is robust, yet the limitations of large databases and secondary data analyses are present. First, the quantification of exposures and outcomes is accurate within the limitations of the self-reported nature of this data. Second, the long retrospective period of this study is both to its benefit and a potential limitation. With more than 20 years between exposure (wave 1) and outcome (wave 5), there is adequate time to assess the development of the outcomes of interest into adulthood; however, this also opens the study up to more than 20 years of unmeasured confounding and loss to follow-up. However, this does not diminish the observed association. Third, in our categorization of video game use, quartile 1 included all respondents who reported that they did not play video games at enrollment. This approximately represented 48% of the respondents. The rest of the respondents were distributed between the remaining quartiles, resulting in a skewed distribution of respondents among the quartiles. We also acknowledge that the available data does not allow for a trajectory analysis of important confounders such as diet and physical activity. Thus, this analysis assumes that these variables were stable during the course of the follow-up and that residual confounding cannot be discounted. Finally, video game technology as well as social understanding around the topic has greatly changed in the past 20 years. These limitations, however, highlight areas for future study, as the vast majority of them are related to the secondary data source used. A purpose-built study design and data collection would address these limitations and help to examine the relationship found.The frequency of video and computer game use in adolescents aged 12 to 19 years is longitudinally associated with obesity, with trends for higher prevalence of diabetes, high BP, and high cholesterol after 20 years of follow-up. Adolescents who play the most video and computer games have a significantly higher BMI in adulthood. They are more likely to have developed at least one of the evaluated metabolic disorders: diabetes, high BP, and high cholesterol. Obesity, diabetes, high BP, or high cholesterol are all modifiable health conditions. As such, public health interventions designed to target modifiable disease states through health promotion and self-management may support the health of adolescents aged 12 to 19 years. Potentially, video and computer games can integrate health promotion interventions into gameplay. This is an important area for future research as video and computer games are integrated into the lives of adolescents.


When the time comes, many patients may be unable to make the transition from audio-only to video telehealth due to digital inexperience. This study explores the The aim of this study is to evaluate video capabilities, eHealth literacy, and engagement with video telehealth among hospitalized patients.The study design is a cross-sectional observational study of adult inpatients at the University of Chicago Medical Center. We assessed self-reported rates of audio versus video telehealth usage as well as the participants’ self-reported willingness to use video telehealth for future health care visits. We used a multivariable binary logistic regression to determine the odds ratio for being unwilling to use video telehealth, adjusted for age, sex, race or ethnicity, educational level, eHL literacy scale (eHEALS), health literacy , technology access, internet access, and video capability.Of the 297 enrolled participants, median age was 58 years, most  identified as Black, half  were female, one-quarter  lacked home internet access, and one-third  had inadequate eHL.Patients with low eHL reported greater participation in audio-only telehealth over video telehealth, of which the former may lose its flexible pandemic reimbursement policy. This may widen the existing health disparities as older adults and patients with low eHL face challenges in accessing video telehealth services. Low eHL is associated with lack of web-based skills, lower rates of video telehealth usage, and lower willingness to use video technology. The study results raise the question of how to improve video capability among patients who, despite having access to smartphones and laptops, face challenges in using telehealth optimally. Telehealth emerged as a ubiquitous communication tool for providers and patients during the COVID-19 pandemic . The numCoverage for audio-only telehealth is slated to end after the public health emergency concludes . Given tUnderstanding the mediating factors behind video telehealth use and the unwillingness to use video telehealth can help inform telehealth policy going forward. Given the near ubiquity of smartphone ownership across demographics, there is a potential for video telehealth to have a broad reach ,11. Howesecond digital divide within video telehealth, which is primarily characterized by skills and capabilities rather than access [The intersection of health literacy and digital literacy may be particularly relevant in understanding the n access . eHealthn access . Prior rn access . To dateWe conducted a cross-sectional observational study among adult inpatients at the University of Chicago Medical Center, as part of the University of Chicago Hospitalist Project, a larger ongoing quality-of-care study . We survEligibility criteria included hospitalization on general medicine services, being 18 years or older, being English speaking, and consenting to the primary study.The University of Chicago Biological Sciences Division Institutional Review Board approved this protocol (#IRB16-0763). The participants were consented for primary data collection and secondary analysis of research data. Study data are deidentified.The primary outcome variables were self-reported audio-only and video-enabled telehealth usage and willingness to use video telehealth, captured by binary survey responses. The participants were asked if they had a telehealth appointment prior to the pandemic (defined as before March 2020), if they had a telehealth appointment since the pandemic (defined as after March 2020), and whether they were willing to use video telehealth visits.As mentioned, technology access and home internet access were self-reported and captured by binary survey responses. To assess technology capabilities, the participants were asked if they knew how to perform given web-based tasks, including the capability to use video without needing help.The participants were categorized as having either low eHL or adequate eHL based on the eHealth literacy scale (eHEALS), a validated 8-item questionnaire with each item scored on a 5-point Likert scale . The resHealth literacy (HL) was based on the brief health literacy screen, which is a 3-item survey scored on a Likert scale from 0 to 4 . Low HL P values. Statistical significance was set at P<.05.We assessed self-reported telehealth usage (audio-only versus video-enabled), willingness to use video visits and technology access, as well as demographic characteristics, across eHL levels using descriptive statistics. Chi-squared tests were used to calculate We then used a multivariable binary logistic regression to determine the odds ratio for being unwilling to use video telehealth, adjusted for age, sex, race or ethnicity, educational level, eHL, HL, technology access, internet access, and technology capability. All analyses were performed using STATA version 16.1 (StataCorp LLC).Of the 297 adults included in this study, the majority identified as Black , half identified as female , and the median age was 58 years , smartphone ownership , laptop ownership , tablet ownership , and video capability  and fewer households without internet access  compared with participants with low eHL .Fewer participants with low eHL reported a video telehealth appointment compared with those with adequate eHL , low eHL (P=.003), lack of video capability , and lack of home internet access (P=.03; P=.48; Using a multivariable binary logistic regression, we found that unwillingness to use video technology for telehealth visits was significantly associated with older age (s P=.03; . In contTelehealth adoption increased across the board during the pandemic. However, our study found significant disparities in telehealth usage when comparing the 2 telehealth modalities. Patients with low eHL reported greater participation in audio-only telehealth over video telehealth, of which the former may lose its flexible pandemic reimbursement policy. This has the potential to widen the existing health disparities as older adults and patients with low eHL face challenges in transitioning to video telehealth services. The use of audio-only visits was similar across most demographic subgroups; however, the rates of video visits and self-reported ability to use video technology varied significantly by eHL level.Our findings are consistent with prior studies showing disparities in audio-only versus video telehealth use during the pandemic ,8,9. VidBeyond the gaps in broadband access and technology ownership, importantly, we show that gaps in digital skills, specifically low electronic health literacy, were also a driver in video telehealth disparities. Low eHL and lack of video capability reduced the likelihood of being willing to engage in video-enabled telehealth. After multivariable adjustment, the willingness to use video telehealth was significantly lower among participants who were older, lacked home internet access, had low eHL, and needed help using video technology. These results are consistent with prior literature that demonstrate how patients still face challenges in knowing how to use video technology to improve their health despite having access to smartphones and internet ,17,26.There are several limitations to this study. This was an urban, single-site study; therefore, the results may not be generalizable to rural locations where a greater proportion of patients may lack broadband access. The study includes only English-speaking patients, so the findings may differ among patients with limited English proficiency who may face additional challenges in telehealth use. Additionally, the study uses patient self-reported measures for telehealth use and technology ownership. While this could introduce recall and social desirability bias, our study design has several strengths. The self-reported nature of the study allows for additional information on patient telehealth preferences that is not captured in claims data. Moreover, the survey was administered over the phone, which reduces the chance of bias from internet surveys that self-select for patients with greater technology capability.Our study contributes to the literature by showing the positive association of adequate eHL with the patients’ willingness to engage in video visits, and conversely, the negative relationship between low eHL and video engagement. The study results raise the question of how to improve video capabilities and skills among patients who have access to smart devices but face challenges in accessing the internet and operating video equipment. Moving forward, additional research should investigate what interventions will increase digital skills, particularly eHealth literacy, in patients who encounter barriers to using video telehealth.


Combined pulmonary fibrosis and emphysema (CPFE) is a clinical syndrome characterized by upper lobe emphysema and lower lobe fibrosis manifested by exercise hypoxemia, normal lung volumes, and severe reduction of diffusion capacity of carbon monoxide. It has varying prevalence worldwide with a male predominance, and with smoking history of more than 40 pack-years being a common risk factor. The unique imaging features of CPFE emphasize its distinct entity, aiding in the timely detection of pulmonary hypertension and lung cancer, both of which are common complications. High-resolution computed tomography (HRCT) is an important diagnostic and prognostic tool, while lung cancer is an independent factor that alters the prognosis in CPFE patients. Treatment options for CPFE are limited, but smoking cessation, usual treatments of pulmonary fibrosis and emphysema, and avoidance of environmental exposures are encouraged.  Chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) have long been considered mutually exclusive disorders. However, in recent years, the coexistence of emphysema and pulmonary fibrosis has emerged as a new entity. Historically, these two diseases found concomitantly in the same patient were first described almost half a century ago , and it In light of the increased interest in this new entity over the past few years, this article will present a comprehensive overview of the specific features of CPFE and comparative evidence describing COPD and IPF. CPFE is a clinical syndrome that affects heavy smokers and is characterized by a combination of upper lobe emphysema and lower lobe fibrosis. Symptoms of CPFE include exercise-induced and later resting hypoxemia, normal lung volumes, and a significant reduction in the diffusing capacity, frequently associated with pulmonary hypertension. It is a heterogeneous condition affecting a diverse population of individuals.COPD prevalence estimates vary across the world in the general population, ranging from 1382.6 cases per 100,000 in Latin America and 1821.5 cases per 100,000 in Eastern Europe to 3017.5 cases per 100,000 in Western Europe and 3558.4 cases per 100,000 in North America [Patients with CPFE are generally older with a heavy smoking history. The majority of studies have shown that almost all CPFE patients have a significant smoking history, suggesting that smoking may be the predominant risk factor for this condition ,9. The sNumerous studies have found a male predominance in CPFE syndrome , but morAfter the sixth decade of life, CPFE seems to affect males more frequently . PatientThe exact mechanisms leading to the development of both emphysema and fibrotic lesions are not fully understood. Whether the coexistence of two different entities is just a “cohabitation” or if there is a result of shared mechanisms that led to the creation of both is still up for debate. However, the pathogenesis of CPFE is multifactorial, occurring in susceptible individuals after exposure to environmental triggers such as smoking or dust inhalation. Emphysema and pulmonary fibrosis are both recognized as primarily driven by smoking , and mosEnvironmental exposures such as noxious particles or gases other than tobacco, asbestos, silica dust, and agrochemical chemicals  may contSince nonsmokers also develop CPFE, which is especially true in connective tissue diseases (CTDs) ,25,26, tAnother potential risk factor for the development of both entities is oxidative stress ,29. In tIn addition, accelerated lung aging has been suggested as a potential mechanism for the onset of pulmonary fibrosis and pulmonary emphysema ,32, and It is reasonable to assume that the lung parenchyma depicts distinct patterns of injury and repair, displaying various phenotypes of lesions determined by the balance between apoptosis, proteolysis, and fibrosis. Patients with overexpression of genes related to connective tissue synthesis, structural components of the cytoskeleton, and cell adhesion typically exhibit a fibrogenic phenotype similar to that observed in patients with IPF; however, a different inflammatory response to smoking-associated cellular damage  results in the destruction of the lung parenchyma, leading to pulmonary emphysema . Gene exStudies suggest that the development of CPFE results from the interaction between environmental exposure, such as smoking, and genetic predisposition in susceptible individuals. COPD and IPF share genetic variants in the MMP9, MUC5B, FAM13A, DSP, and TERT genes ,38. SincDespite sharing age-related alterations, mitochondrial dysfunction, excessive oxidative stress, resulting in pathological tissue repair, the prevalence, pathology, and clinical behavior of IPF and COPD are notably distinct. This is likely due to substantial disparities in genetic background and epigenetic modifications between the two diseases, which result in distinct target cell types and molecular responses to environmental triggers. As such, the mechanisms that contribute to the age-related preference for IPF and COPD, or the combination, currently remain unknown .A summary of the factors associated with CPFE is represented in COPD and IPF share common features at both the micro- and macroscopic levels, demonstrating patchy distribution of pathological areas, consisting of extracellular matrix proteins or inflammatory infiltrates with fibrotic regions alternating with regions of normal alveolar tissue or areas with interstitial leukocyte accumulation . The disCompared to IPF and COPD, patients with CPFE typically have a mean age of 65–70 years, and males predominantly account for 73–100% of cases [Cough and dyspnea are frequent symptoms in CPFE, COPD, and IPF. Typically, chronic cough and sputum production, as a hallmark of COPD, occur many years before airflow limitation . In contIn almost all patients, progressive shortness of breath is the most common symptom and is typically more severe, especially during physical exertion. Exertional dyspnea is the clinical expression of inefficient ventilation and increased dead space ventilation in hypoperfused areas . Other common respiratory signs and symptoms, such as cough, wheezing, cyanosis, and fatigue, may also manifest in some patients. On physical examination, patients with CPFE typically have inspiratory crackles referred to as “velcro sounds” produced by the underlying pulmonary fibrosis, found in 87–100% of patients, and almost half of them have finger clubbing . In addiCPFE is distinguished from pure emphysema and IPF in terms of pulmonary function by the unexpected presence of relatively normal lung volumes in contrast to a severely diminished diffusing capacity (DLCO), as presented in The blood gas analysis of CPFE differs from that of COPD and resembles that of IPF more closely. Severe decreases in arterial oxygen saturation and hypoxemia during exercise are highly prevalent in CPFE, particularly when complicated by severe PH ,54. TherImaging demonstrates a variety of findings on chest high-resolution computed tomography (HRCT) dominated by the coexistence of emphysema in the upper zones of the lungs and pulmonary fibrosis in the lower zones . The fibBesides the UIP pattern, several other patterns have been reported, such as non-specific interstitial pneumonia (NSIP) ; respiraA distinctive imaging feature of CPFE that does not appear to be present in patients with IPF or emphysema alone is the thick-walled large cyst pattern resulting from the combination of emphysema and SRIF , i.e., dIt has been found that the presence of these large cystic lesions with thick walls in CPFE patients suggests greater severity of emphysema compared to patients with no such lesions .Both clinicians and radiologists should remember that HRCT is an important prognostic tool since the extent of fibrosis has a more significant impact than emphysema , and pulAs smoking-related diseases, emphysema and IPF have been identified as independent risk factors for developing lung cancer ,62.CPFE patients appears to have a higher incidence of lung cancer (6.1–46.8%)  comparedPulmonary hypertension (PH) is a significant complication of COPD, IPF, and CPFE and is associated with a lower survival rate . ApproxiWhile PH in COPD or IPF patients is generally mild to moderate, in most CPFE cases, it is moderate to severe . This sePH becomes a dominant clinical feature and a major complication of CPFE, resulting in severe dyspnea, markedly impaired gas transfer (DLCO), and exercise hypoxemia, all of which contribute to a poor prognosis . Given tStreptococcus pneumoniae is considered beneficial [Currently, there is limited evidence regarding CPFE treatment; therefore, management strategies are based solely on data derived from studies on COPD and IPF patients with concurrent CPFE. Encouraging general measures such as smoking cessation and avoiding environmental exposures may slow down disease progression ,72. Becaneficial . Although inhaled bronchodilators with or without corticosteroids have shown to be effective in CPFE patients with significant airflow limitation ,74, the Currently, only two drugs, pirfenidone and nintedanib, have proven efficacy in both IPF and fibrotic ILD, reducing the progression rate by half ,76,77. HRegarding therapy for pulmonary hypertension, there is a significant discrepancy between controlled and uncontrolled data. As a result, there is limited evidence regarding the use of pulmonary vasodilators, leading to limited treatment options . HoweverThe approach to treating lung cancer in CPFE patients is similar to other patients. Unfortunately, the combined severity of emphysema and underlying fILD likely contribute to the greater complication rates among CPFE individuals receiving treatment . MoreoveCPFE is a distinct entity that sums up the pathogenic pathways found in both COPD and IPF, which determine the impaired regeneration of the lung parenchyma after damage. More research is needed to understand the coexistence of the divergent phenotypes of response to injury driven by environmental factors in CPFE. This entity has a wide variety of imaging and histopathological appearances. From the clinical perspective, CPFE combines the effects of emphysema and fibrosis, resulting in patients with increased symptoms that frequently associate with severe comorbidities such as lung cancer and pulmonary hypertension, which impose a poor prognosis and increased mortality. Treatment options are scarce, as they derive from studies on COPD and IPF patients with concurrent CPFE, and include similar recommendations with limited supporting evidence.