You are here

  • Home
  • Archive
  • Volume 9, Issue 10
  • Lifestyles and determinants of perceived health in Italian grown-up/adult congenital heart patients: a cross-sectional and pan-national survey

Article Text

Article menu

Download PDFPDF

XML
Cardiovascular medicine
Original research
Lifestyles and determinants of perceived health in Italian grown-up/adult congenital heart patients: a cross-sectional and pan-national survey
  1. Federica Dellafiore1,
  2. Rosario Caruso1,
  3. Cristina Arrigoni2,
  4. Serena Francesca Flocco1,
  5. Alessandro Giamberti3,
  6. Massimo Chessa3
  1. 1 Health Professions Research and Development Unit, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
  2. 2 Department of Public Health, Experimental and Forensic Medicine, Section of Hygiene, University of Pavia, Pavia, Italy
  3. 3 Pediatric and Adult Congenital Heart Disease Center, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
  1. Correspondence to Dr Rosario Caruso; rosario.caruso{at}unimi.it

Abstract

Objectives To provide the first epidemiological lifestyle descriptions of the Italian grown-up/adult congenital heart disease (GUCH/ACHD) population by identifying the determinants of poor perceived health status.

Design Cross-sectional pan-national survey.

Setting Italian GUCH/ACHD patients who were members of the Italian Association of GUCH/ACHD.

Primary and secondary outcome measures To discuss these lifestyle descriptions through an ad hoc developed questionnaire and health perceptions (ie, mental and physical health perception) through a short form health survey (SF-12).

Results 629 patients included; many investigated GUCH/ACHD lifestyles were determined similar to those of the general population — with the exception of the smoking habits, which were lower. The odds of the occurrence of inadequate physical health perceptions increased by more than two times in patients undergoing antiarrhythmic therapies (OR adjusted=2.045; 95% CI=1.201 to 3.479; p=0.008; n=629), more than 1.5 times in patients taking oral anticoagulants (OR adjusted=1.638; 95% CI=1.038 to 2.585; p=0.034; n=629) and roughly 1.7 times in patients treated with antiplatelets (OR adjusted=1.743; 95% CI=1.024 to 2.966; p=0.041; n=629). The odds of the occurrence of inadequate mental health perceptions increased by 1.7% for every year that the patients aged (OR adjusted=1.017; 95% CI=1.002 to 1.032; p=0.025; n=629).

Conclusion Particular attention should be paid to these ageing patients’ increasing psychological needs, and additional research is needed to identify associations between their lifestyles and clinical outcomes.

  • congenital heart disease
  • adult cardiology
  • mental health

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2019-030917

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Strengths and limitations of this study

    • The study results require caution regarding their generalisation, as lifestyle trajectories over time were not described due to the cross-sectional approach taken for the data collection process.

    • The self-report approach taken during the clinical information collection requires caution, as a direct assessment of the respondents’ actual clinical conditions was not performed to collect data.

    • The sampling was performed using the contacts of the Italian Association of grown-up/adultcongenital heart disease (AICCA); therefore, a slight overestimation of the healthy lifestyle is possible, acknowledging that the AICCA patient–members may become more engaged in their treatment than may other patients.

    • The questionnaire used in this survey to investigate lifestyles (ad hoc developed questionnaire) was validated for content and face validity, while the questionnaire that investigates health perceptions possesses well-known validity proprieties (SF-12).

    • The surveyed patients equally represent Northern, Central and Southern Italy.

    Introduction

    Roughly 85% of children with congenital heart disease (CHD) live to adulthood and thus represent the population of grown-up/adult congenital heart disease (GUCH/ACHD) patients.1 More precisely, the GUCH/ACHD rate has been growing steadily, even if accurate data on the size and characteristics of this population remain lacking.2 Even if the large variety of CHD and its clinical problems require specific focus for improving the GUCH/ACHD clinical condition, this population also shares a number of important communalities mainly related to the modifiable risk factors of a decline in clinical status. Among these modifiable risk factors, lifestyle plays a pivotal role in improving the overall health conditions associated with GUCH/ACHD.3 Further, the descriptions of these patients’ socio-demographic and clinical determinants of their perceived health statuses play a paramount role in framing a comprehensive understanding of the actual weaknesses present in the educational plans and follow-ups. Specific interventions in the determinants of poor perceived health status may be useful for achieving the most favourable outcomes in the GUCH/ACHD population, as has been previously described for other chronic conditions, such as diabetes and acquired heart diseases.4–7

    Thus far, the determinants of perceived health status among GUCH/ACHD patients remain poorly described on a global scale, despite their possible influence on the patients’ overall adherence to follow-ups and, consequently, on patients’ outcomes.8 In Italy, neither lifestyles nor determinants of perceived health status among GUCH/ACHD patients have been previously for large samples, thus undermining the possibility of planning mid-term and long-term strategies for patients who report poor perceived health or inadequate lifestyles. For these reasons, this study aimed to provide the first epidemiological lifestyle descriptions of the Italian GUCH/ACHD population by identifying the determinants of poor perceived health status.

    Material and methods

    Design, study population and data collection

    This cross-sectional survey was promoted by an Italian GUCH/ACHD centre and supported by the Italian Association of GUCH/ACHD (AICCA). The study design and reporting method aligned with the ‘STrengthening the Reporting of OBservational studies in Epidemiology’ checklist (online supplementary file 1). Data were collected through the list of contacts available in the AICCA’s repository between March 2017 and October 2017. More precisely, data were collected using a computer/mobile-assisted, web interviewing survey, and participants were sampled via the repertory of contacts of AICCA contacts and stratified to reflect the geographical population by macro-area (ie, Northern, Central and Southern Italy).

    Supplemental material

    According to the study protocol, all patients were informed on the study’s aim and provided consent flagging an electronic form, whereas it was considered implicit due to their voluntarily and anonymously completion of the questionnaire.

    Validation of the questionnaire on lifestyles

    The development of this study’s lifestyle questionnaire was based on a previous survey of the Italian National Institute of Public Health on the chronic disease population,9 adapted using a process of face and content validity to detect the peculiarities of GUCH/ACHD. Accordingly, the questionnaire’s validation required the involvement of a multidisciplinary panel of experts (n=14) to ascertain the new questionnaire’s face and content validity. The panellists (nine females; 35.7%) were aged a median of 44.6 years (IQR=7.4 years), had a minimum of 4 years of experience in CHD field, and comprised the following professions: cardiac surgeons (n=2), clinical nutritionists (n=2), clinical psychologists (n=2), clinical cardiologists (n=2), clinical nurses (n=2), experts of public health (n=2), experts in instrument development with a background in nursing at the doctoral level (n=2). Precisely, face validity explored the panellists’ understanding of each item and their comments about the overall concept they purported to measure through an assessment executed using open-ended questions. Conversely, content validity refers to the ‘quantitative’ agreement among panellists regarding how pertinent each item is in relation to the aim of its measurement.

    Content validity encompassed the panellists’ quantitative assessments using the content validity ratio (CVR), and the content validity index for item and scale level (I-CVIs and S-CVI). The CVR may potentially range between −1 (perfect disagreement among panellists) and +1 (perfect agreement among panellists), while I-CVIs and S-CVI range between 0 (no content judged as appropriate) and +1 (content totally judged as appropriate). As per the critical CVR cut-offs for determining adequate/inadequate content indices (ie, the lowest level of CVR such that the level of agreement was greater than 50%), recent research was proposed to consider critical CVR values as the statistics arising from binomial distribution that are applied to the panel sizes.10 A critical CVR value for 14 panellists is equal to 0.57110; as per I-CVIs, an adequate index must be equal or superior to 0.75, while S-CVI must be equal or superior to 0.70.11

    The first round of content validity was performed in June 2016 and was based on the questionnaire proposed by the Italian National Institute of Public Health.9 Thus, the panellists were asked to propose modifications insofar as adequate for developing a questionnaire for the specific GUCH/ACHD population. After four rounds of consulting the panellists and amending the questionnaire, all CVR values were higher than 0.65, I-CVIs were equal or higher than 0.80 and S-CVI was equal to 0.75. Further, the questionnaire was preliminarily tested on a small group of six patients to evaluate the clarity of each item. Patients were asked to respond to a 3-point Likert scale (1=completely not understandable, 2=somewhat understandable, 3=completely understandable) and an open-ended question to investigate the need for an eventual rewording of the terminology. We computed the Fleiss’ kappa to determine the level of quantitative agreement between patients, which was 0.75 and indicated consensus in their defining of the questionnaire as understandable, although some minor amendments to the items’ wording were requested as per the answers to the open-ended question. Finally, the questionnaire validation process was concluded in January 2017 (online supplementary file 2). The survey required roughly 30 min for its completion, and no respondent received compensation for participating in this study.

    Supplemental material

    Measurements

    The online survey collected the main socio-demographic and clinical characteristics of GUCH/ACHD patients, including their lifestyles and perceived health statuses.

    Specifically, each participant’s socio-demographic and clinical characteristics include sex, age, family composition, working role, educational background, provenience, body mass index (BMI) and therapeutic plan. The lifestyles investigations revealed patients’ dietary habits, substance use or abuse (eg, smoking, drugs) and physical and sexual activities. Conversely, their perceived health statuses were assessed using SF-12,12 which has notably been successfully developed as a shorter version of the short form health survey 36 (SF-36) in nine European countries and demonstrates adequate validity in measuring the physical and mental components of health.12 Thus, these components were respectively labelled physical component summary (PCS) and mental component summary (MCS), both of which were scored from 0 to 100 using the procedure indicated by the authors of reference, wherein a higher value indicates a more favourable health perception.12 Further, it is possible to dichotomise the scores through an adequate versus an inadequate health perception if we consider the median split strategy13 and acknowledge that the median scores of general Italian population, clustered by different age ranges, were previously described.14

    Statistical analysis

    We calculated the response rate by considering the invitations sent to the contacts provided by the AICCA. All the collected variables were preliminary checked for possible missing data, outliers or errors using an analysis of frequency distribution. Categorical variables were described using frequency and percentage, while quantitative variables were assessed for normality via skewness and kurtosis analysis, followed by Shapiro-Wilk test. According to the quantitative variable distributions, we employed mean±SD or median and IQR to describe these variables. The univariate analysis was based on multiple comparisons of the lifestyles, ongoing treatments, PCSs and MCSs between the subgroups defined by the socio-demographic characteristics. According to the nature of each variable, the comparisons were performed using the following possible tests: χ2 test or Fisher exact test (when appropriate), Mann–Whitney U test or Kruskal–Wallis H test (for non-normally distributed variables), t-test or one-way analysis of variance (for normally distributed variables). Variables exhibiting significant differences were evaluated to be used as predictors of inadequate PCS and MCS. A median split approach was used to dichotomise PCS and MCS,13 by considering the median values previously described among Italians aged between 18 and 44 years.14 Accordingly, PCS scores lower than 52.5 were considered as indicating inadequate physical health, while MCS scores lower than 51.2 were considered as indicating inadequate mental health. Subsequently, PCS and MCS were employed as dichotomous outcomes in two logistic regression (LR) models. The LR models were assessed for the possible collinearity between the independent variable by checking the strength of their bivariate associations, which should not exceed 0.45.15 Maximum likelihood estimation was used to determine the unknown LR model parameters though the generalised linear model function of R, while the goodness of fit as determined with the Hosmer-Lemeshow test (non-significant P indicates a good fit), and Nagelkerke’s pseudo-R 2. The independent variables were simultaneously entered into the models to examine each variable’s relatively unique contribution to health perception. Significance levels were set using α=5%. Overall, missing data referred to the socio-demographic section were managed using pairwise deletions, while no missing data were expected in the answering of the questionnaire, as all the questions were mandatory to complete the survey. Statistical analysis was run through Statistical Package for the Social Sciences V.22 (IBM Corporation) and R Statistical Package (R Foundation for Statistical Computing).

    Patient and public involvement

    Neither patients nor the public were involved in the designing of this research, although both will be informed of the survey results via the AICCA network. Accordingly, the authors will employ the AICCA’s support to disseminate the study results.

    Results

    The response rate was 89.7% (626 responses out of 698 invitations). Missing data were reported only for the provenience (n=3; 0.4%), BMI (n=1), education (n=3; 0.1%) and occupation (n=2; 0.3%). Table 1 illustrates the participants’ socio-demographic characteristics, lifestyles, ongoing treatments and health perceptions. Roughly one of every five patients (n=106; 18.1%) declared to be lowly adherent to their ongoing medical treatment. Patients were primarily treated with oral anticoagulants (n=162; 25.9%), antiarrhythmic drugs (n=121; 19.3%), diuretics (n=104; 16.6%), antiplatelet therapy (n=87; 13.9%), antihypertensive drugs (n=81; 12.9%) and dietary supplements (n=160; 25.6%).

    View this table:
    Table 1

    Descriptive characteristics of socio-demographics, lifestyles, ongoing treatment and health perception (n=629)

    The physical health (PCS12) scores reached a mean (SD) equal to 48.69±8.96, which was higher than that of mental health (MCS12; 45.56±10.99). Overall, the adequate physical and mental health scores were 53.6% (n=337) and 63.8% (n=401), respectively.

    Differences of the variables described in table 1 between adequate and inadequate physical health were significant considering diuretics (χ2=22.9; d.f.=1; p<0.001), antiarrhythmic drugs (χ2=28.1; d.f.=1; p<0.001), anticoagulants (χ2=21.2; d.f.=1; p<0.001), antiplatelet drugs (χ2=11.1; d.f.=1; p=0.001), antihypertensive drugs (χ2=10.4; d.f.=1; p=0.001), BMI (χ2=8.4; d.f.=3; p=0.033) and age (t=4.1; d.f.=610; p<0.001). Conversely, differences of the same variables between adequate and inadequate mental health were significant considering diuretics (χ2=20.1; d.f.=1; p<0.001), antiarrhythmic drugs (χ2=22.7; d.f.=1; p<0.001), anticoagulants (χ2=19.2; d.f.=1; p<0.001), antiplatelet drugs (χ2=10.2; d.f.=1; p=0.002), antihypertensive drugs (χ2=9.4; d.f.=1; p=0.003) and age (t=−2.3; d.f.=610; p=0.011).

    As table 2 indicates, the odds of inadequate physical health perception increased by more than two times in patients receiving antiarrhythmic therapy (OR adjusted=2.045; 95% CI=1.201 to 3.479; p=0.008; n=629), more than 1.5 times in patients receiving oral anticoagulants (OR adjusted=1.638; 95% CI=1.038 to 2.585; p=0.034; n=629) and roughly 1.7 times in patients treated with antiplatelets (OR adjusted=1.743; 95% CI=1.024 to 2.966; p=0.041; n=629). Conversely, as per table 3, the odds of inadequate mental health perception increased by roughly 2% for each year a participant aged (OR adjusted=1.017; 95% CI=1.002 to 1.032; p=0.025; n=629).

    View this table:
    Table 2

    Determinants of inadequate scores of PCS12 (outcome)

    View this table:
    Table 3

    Determinants of inadequate scores of MCS12 (outcome)

    Discussion

    This study represents the first overview in Italy of ACHD/GUCH patients’ lifestyles by identifying determinants of poor perceived health status. Our results are strategic considering that they allow a comparison between the general population’s lifestyles and those of ACHD/GUCH patients. These possible comparisons are particularly worthy considering that the majority of patients born with CHD are expected to survive into adulthood, being exposed to the general risks of inadequate lifestyles (ie, modifiable cardiovascular risks).16 Further, this study contributes to identify potentially important determinants of poor physical and mental health and focuses attention onto patients who present those determinants.

    This study highlights that associations exist between antiarrhythmic drugs, oral anticoagulants, antiplatelet drugs and lower physical health status. To the best of our knowledge, this study provides the first empirical evidence of these associations in the GUCH/ACHD population. Previous research has demonstrated similar results in patients with paroxysmal atrial fibrillation, wherein patients treated with antiarrhythmic drugs reported lower physical health than did patients treated with radiofrequency ablation.17 Even if the literature does not fully address the question of whether or not physical health is directly associated with antiarrhythmic drugs in patients with atrial fibrillation,17 there is room in the clinical practice for monitoring the physical health trajectories of patients treated with antiarrhythmic drug over time. Similar associations with a decreased quality of life and physical health were described in patients treated with anticoagulants and/or antiplatelet drugs,18 even if little is currently known in GUCH/ACHD population about the effects of these drugs on physical and mental health statuses.19 These associations should be studied in future research, and possible manifestations of side effects should be monitored to understand whether the worsening of one’s physical health is related to the drug-related side effects or other factors, such as the psychological burden related to one’s need for medical therapy in terms of posology and adherence.

    As described above, our results profile patients who are at greater risk for achieving inadequate physical health in consideration of their ongoing treatments. Conversely, mental health status seems to worsen as individuals age, which may be related to many GUCH/ACHD patients’ previously described fear of ageing, that is enhanced by a sense of uncertainty20; in other words, the GUCH/ACHD population’s psychological needs increase with age.

    An unexpected result was related to the rates of reported physical activities, which were consistent with those described for the general population.21 Our initial expectation was to identify a lower rate of physical activity in GUCH/ACHD patients, because past clinicians restricted patients’ activity due to concerns that increased activity might be risky for patients’ health.22 Over the last 10 years, the recommendations from a consensus of an international expert panel endorsed by the European Society of Cardiology encourage that all CHD patients regularly exercise; while these recommendations are mainly based on expert opinion, there nevertheless exists scarce evidence for the effects of exercise training.23 Our results confirm the shifting of paradigm from the restriction of physical activities to its support of educational advices. Accordingly, the steady progress in GUCH/ACHD diagnostics emphasise the life-long benefits of regular physical activity for general health as well as in complex CHD (adequately adherent to the follow-ups).24

    Overall, our sample reported an adequate BMI that was slightly lower than previous epidemiological, self-report BMI assessments of the Italian general population aged between 30 and 45 years.25 The other socio-demographic characteristics are consistent with the current data regarding general population.25 26 Concerning lifestyles, the rate of smokers among the GUCH/ACHD sample (10.4%) appears to be encouragingly lower than that reported in general population (21.4%)27 as well as that determined by a recent description in Malta.28 This result may be related to the clinicians’ high sensibility when providing regular anti-smoking advice and education. Cannabis consumption, on the other hand, appears to be consistent with and slightly higher than the general population’s rate of self-reported consumption.29 This result is also in line with previous evidence of younger adults with CHD, whose behaviours were described as being more strongly influenced by peer relationships than the awareness of one’s clinical condition.30 Clinicians should address this aspect with increasing attention to reduce illicit drug consumption by administering to the GUCH/ACHD population detailed information that highlights the risk of increased systolic blood pressure, orthostatic hypotension and ischaemic stroke.31

    Limitations

    This study’s findings are subject to some important limitations. First, the impossibility of collecting reliable clinical data (eg, CHD classification, diagnostics) undermined the possibility of drawing solid, inferential associations between lifestyles (behaviours) and clinical outcomes. For this reason, this study has mainly provided descriptive information to frame new knowledge of the Italian GUCH/ACHD population’s lifestyles. Second, we suspect the possibility that the levels of risky behaviours were underestimated and the levels of healthy behaviours were overestimated according to the potential social desirability effect’s occurrence in the participants’ responses. However, the choice to anonymously collect data should have limited the probability that the social desirability effect would occur. Third, data collection was performed using the AICCA network, which may have introduced a bias in the sampling procedure because patients from the AICCA — that is, patients who have learnt skills of observation, description and symptom handling, thus increasing their basic knowledge of health problems — may have more likely been ‘activated’ than general patients and because they were not representative of the general population. We believe this sampling bias is generally marginal in this study, as the AICCA holds the contacts of real-world patients from the majority of CHD centres in Italy, and not all patients in the AICCA network actively participate in the association’s initiative. Fourth, this study has included no information on the possible manifestations of drug-related side effects or other factors that may interact through the relationship between drugs and physical health, which such information might help effectively interpret. Overall, considering this study’s limitations, we suggest that caution be taken when generalising the results. This study’s strengths are related first to the fact that patients roughly equally represent Northern, Central and Southern Italy and second to the prudent approach used to analyse the data.

    Conclusions

    Thus far, this study represents the first lifestyle descriptions of the Italian GUCH/ACHD population, and we have identified a number of similarities and differences among the general Italian population. Clinicians should address the issue of illicit drug consumption (especially cannabis) more deeply, and particular attention should be paid to accommodating the increasing psychological needs resulting from these patients’ age progression. More research is needed to identify the associations between lifestyles and clinical outcomes to determine additional details for homogeneous subgroup stratifications in consideration of patients’ clinical information, such as CHD classification.

    References

    1. ↵
      2. Baumgartner H ,
      3. Bonhoeffer P ,
      4. De Groot NMS , et al
      . ESC guidelines for the management of grown-up congenital heart disease (new version 2010). Eur Heart J 2010;31:2915–57.doi:10.1093/eurheartj/ehq249
      OpenUrlCrossRefPubMedWeb of Science
    2. ↵
      2. van der Linde D ,
      3. Konings EEM ,
      4. Slager MA , et al
      . Birth prevalence of congenital heart disease worldwide: a systematic review and meta-analysis. J Am Coll Cardiol 2011;58:2241–7.doi:10.1016/j.jacc.2011.08.025
      OpenUrlFREE Full Text
    3. ↵
      2. Flocco SF ,
      3. Caruso R ,
      4. Dellafiore F , et al
      . [The effect of the transition care model on health perception among adolescents with congenital heart disease: a quasi-experimental study]. G Ital Cardiol (Rome) 2018;19:386–93.doi:10.1714/2922.29372
      OpenUrl
    4. ↵
      2. Dellafiore F ,
      3. Conte G ,
      4. Baroni I , et al
      . Gender differences in heart failure self-care behaviors: do we know enough? Minerva Med 2018;109:401–3.doi:10.23736/S0026-4806.18.05579-9
      OpenUrl
    5. ↵
      2. Caruso R ,
      3. Arrigoni C ,
      4. Magon A , et al
      . Health determinants in Italian type 2 diabetes mellitus (T2DM) patients: a critical gender differences analysis. J Res Gend Stud 2017;7:93–108.
      OpenUrl
    6. ↵
      2. Dellafiore F ,
      3. Arrigoni C ,
      4. Pittella F , et al
      . Paradox of self-care gender differences among Italian patients with chronic heart failure: findings from a real-world cross-sectional study. BMJ Open 2018;8:e021966.doi:10.1136/bmjopen-2018-021966
    7. ↵
      2. Caruso R ,
      3. Magon A ,
      4. Baroni I , et al
      . Health literacy in type 2 diabetes patients: a systematic review of systematic reviews. Acta Diabetol 2018;55:1–12.doi:10.1007/s00592-017-1071-1
      OpenUrl
    8. ↵
      2. Dontje ML ,
      3. Feenstra M ,
      4. de Greef MHG , et al
      . Are grown-ups with congenital heart disease willing to participate in an exercise program? Congenit Heart Dis 2014;9:38–44.doi:10.1111/chd.12069
      OpenUrlCrossRefPubMedWeb of Science
    9. ↵
      1. Istituto Superiore di Sanit
      . Questionario di Valutazione Degli Stili di vita. Rome, 2015. Available: http://old.iss.it/binary/ofad/cont/questionario giovani in forma.1225957648.pdf [Accessed 5 Oct 2018].
    10. ↵
      2. Wilson FR ,
      3. Pan W ,
      4. Schumsky DA
      . Recalculation of the critical values for Lawshe’s content validity ratio. Meas Eval Couns Dev 2012;45:197–210.doi:10.1177/0748175612440286
      OpenUrlCrossRef
    11. ↵
      2. Polit DF ,
      3. Beck CT ,
      4. Owen SV
      . Is the CVI an acceptable indicator of content validity? appraisal and recommendations. Res Nurs Health 2007;30:459–67.doi:10.1002/nur.20199
      OpenUrlCrossRefPubMedWeb of Science
    12. ↵
      2. Kodraliu G ,
      3. Mosconi P ,
      4. Groth N , et al
      . Subjective health status assessment: evaluation of the Italian version of the SF-12 health survey. results from the MiOS project. J Epidemiol Biostat 2001;6:305–16.doi:10.1080/135952201317080715
      OpenUrlCrossRefPubMed
    13. ↵
      2. DeCoster J ,
      3. Gallucci M ,
      4. Iselin A-MR
      . Best practices for using median splits, artificial categorization, and their continuous alternatives. J Exp Psychopathol 2011;2:197–209.doi:10.5127/jep.008310
      OpenUrl
    14. ↵
      2. Gandek B ,
      3. Ware JE ,
      4. Aaronson NK , et al
      . Cross-Validation of item selection and scoring for the SF-12 health survey in nine countries: results from the IQOLA project. International quality of life assessment. J Clin Epidemiol 1998;51:1171–8.doi:10.1016/s0895-4356(98)00109-7
      OpenUrlCrossRefPubMedWeb of Science
    15. ↵
      2. Dormann CF ,
      3. Elith J ,
      4. Bacher S , et al
      . Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 2013;36:27–46.doi:10.1111/j.1600-0587.2012.07348.x
      OpenUrlCrossRefWeb of Science
    16. ↵
      2. Deen JF ,
      3. Krieger EV ,
      4. Slee AE , et al
      . Metabolic syndrome in adults with congenital heart disease. J Am Heart Assoc 2016;5.doi:10.1161/JAHA.114.001132
    17. ↵
      2. Reynolds MR ,
      3. Walczak J ,
      4. White SA , et al
      . Improvements in symptoms and quality of life in patients with paroxysmal atrial fibrillation treated with radiofrequency catheter ablation versus antiarrhythmic drugs. Circ Cardiovasc Qual Outcomes 2010;3:615–23.doi:10.1161/CIRCOUTCOMES.110.957563
      OpenUrlAbstract/FREE Full Text
    18. ↵
      2. Hasan SS ,
      3. Teh KM ,
      4. Ahmed SI , et al
      . Quality of life (QOL) and international normalized ratio (Inr) control of patients attending anticoagulation clinics. Public Health 2015;129:954–62.doi:10.1016/j.puhe.2015.05.014
      OpenUrl
    19. ↵
      2. Fteropoulli T ,
      3. Stygall J ,
      4. Cullen S , et al
      . Quality of life of adult congenital heart disease patients: a systematic review of the literature. Cardiol Young 2013;23:473–85.doi:10.1017/S1047951112002351
      OpenUrlCrossRefPubMed
    20. ↵
      2. Oliver JM ,
      3. Gallego P ,
      4. Gonzalez AE , et al
      . Impact of age and sex on survival and causes of death in adults with congenital heart disease. Int J Cardiol 2017;245:119–24.doi:10.1016/j.ijcard.2017.06.060
      OpenUrl
    21. ↵
      2. Massidda M ,
      3. Cugusi L ,
      4. Mathieu A
      . Physical activity levels and health-related quality of life in young Italian population. J Sports Med Phys Fitness 2015;55:506–12.
      OpenUrl
    22. ↵
      2. Tutarel O ,
      3. Gabriel H ,
      4. Diller G-P
      . Exercise: friend or foe in adult congenital heart disease? Curr Cardiol Rep 2013;15:416.doi:10.1007/s11886-013-0416-9
    23. ↵
      2. Hirth A ,
      3. Reybrouck T ,
      4. Bjarnason-Wehrens B , et al
      . Recommendations for participation in competitive and leisure sports in patients with congenital heart disease: a consensus document. Eur J Cardiovasc Prev Rehabil 2006;13:293–9.doi:10.1097/01.hjr.0000220574.22195.d6
      OpenUrlPubMed
    24. ↵
      2. Bassareo PP ,
      3. Saba L ,
      4. Solla P , et al
      . Factors influencing adaptation and performance at physical exercise in complex congenital heart diseases after surgical repair. Biomed Res Int 2014;2014:862372.doi:10.1155/2014/862372
    25. ↵
      2. Krul AJ ,
      3. Daanen HAM ,
      4. Choi H
      . Self-reported and measured weight, height and body mass index (BMI) in Italy, the Netherlands and North America. Eur J Public Health 2011;21:414–9.doi:10.1093/eurpub/ckp228
      OpenUrlCrossRefPubMed
    26. ↵
      1. ISTAT
      . Rapporto Annuale 2018. Rome, 2018. Available: https://www.istat.it/storage/rapporto-annuale/2018/Rapportoannuale2018.pdf [Accessed 23 Oct 2018].
    27. ↵
      2. Lugo A ,
      3. Zuccaro P ,
      4. Pacifici R , et al
      . Smoking in Italy in 2015-2016: prevalence, trends, roll-your-own cigarettes, and attitudes towards incoming regulations. Tumori 2017;103:353–9.doi:10.5301/tj.5000644
      OpenUrl
    28. ↵
      2. Caruana M ,
      3. Grech V
      . Lifestyle habits among adult congenital heart disease patients in Malta. Congenit Heart Dis 2016;11:332–40.doi:10.1111/chd.12366
      OpenUrl
    29. ↵
      2. Zuccato E ,
      3. Castiglioni S ,
      4. Senta I , et al
      . Population surveys compared with wastewater analysis for monitoring illicit drug consumption in Italy in 2010-2014. Drug Alcohol Depend 2016;161:178–88.doi:10.1016/j.drugalcdep.2016.02.003
      OpenUrl
    30. ↵
      2. Reid GJ ,
      3. Webb GD ,
      4. McCrindle BW , et al
      . Health behaviors among adolescents and young adults with congenital heart disease. Congenit Heart Dis 2008;3:16–25.doi:10.1111/j.1747-0803.2007.00161.x
      OpenUrlCrossRefPubMed
    31. ↵
      2. Goyal H ,
      3. Awad HH ,
      4. Ghali JK
      . Role of cannabis in cardiovascular disorders. J Thorac Dis 2017;9:2079–92.doi:10.21037/jtd.2017.06.104
      OpenUrl

    Footnotes

    • Twitter @@utenterox

    • Contributors RC, FD, CA, SFF, AG and MC: conception and design. FD, SFF, AG and MC was particularly involved in the acquisition of data; RC and CA in analysis and interpretation of data. RC, FD, CA, SFF, AG and MC have substantially contributed in drafting the manuscript and in providing critical revision of important intellectual content. Each author gave their final approval of the version to be published. Each author should have participated sufficiently in the work to take public responsibility for appropriate portions of the content.

    • Funding This research was partially supported by ‘Ricerca Corrente’ funding from Italian Ministry of Health to IRCCS Policlinico San Donato.

    • Competing interests None declared.

    • Patient consent for publication Obtained.

    • Ethics approval Study protocol approved by the Ethical Committee of San Raffaele Hospital, Prot. N. 111/INT/2016.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available upon reasonable request.