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The Scientific World Journal
Volume 2012, Article ID 379752, 14 pages
Research Article

eVITAL: A Preliminary Taxonomy and Electronic Toolkit of Health-Related Habits and Lifestyle

1Asociación Española para el Estudio Científico del Envejecimiento Saludable (AECES), Calle Infante Don Fernando 17, Málaga, 29200 Antequera, Spain
2Asociación Científica PSICOST, Plaza de San Marcos 6, 11403 Jerez, Spain
3Harvard Medical School, c/o Peabody Society, 260 Longwood Avenue, Boston, MA 02115, USA

Received 14 October 2011; Accepted 28 November 2011

Academic Editor: Javier Garcia Campayo

Copyright © 2012 Luis Salvador-Carulla et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objectives. To create a preliminary taxonomy and related toolkit of health-related habits (HrH) following a person-centered approach with a focus on primary care. Methods. From 2003–2009, a working group ( 𝑛 = 6 physicians) defined the knowledge base, created a framing document, and selected evaluation tools using an iterative process. Multidisciplinary focus groups ( 𝑛 = 2 9 health professionals) revised the document and evaluation protocol and participated in a feasibility study and review of the model based on a demonstration study with 11 adult volunteers in Antequera, Spain. Results. The preliminary taxonomy contains 6 domains of HrH and 1 domain of additional health descriptors, 3 subdomains, 43 dimensions, and 141 subdimensions. The evaluation tool was completed by the 11 volunteers. The eVITAL toolkit contains history and examination items for 4 levels of engagement: self-assessment, basic primary care, extended primary care, and specialty care. There was positive feedback from the volunteers and experts, but concern about the length of the evaluation. Conclusions. We present the first taxonomy of HrH, which may aid the development of the new models of care such as the personal contextual factors of the International Classification of Functioning (ICF) and the positive and negative components of the multilevel person-centered integrative diagnosis model.

1. Introduction

Noncommunicable diseases cause 6 out of 10 deaths, and cardiovascular disease alone causes 31.5% of deaths in female and 26.8% in males [1]. Many of the leading causes of death have evidence-based modifiable risk factors [24], but this does not always translate to healthy behavior by individuals. Several studies have shown that risk of mortality or disease decreases stepwise based on the number of healthy habits practiced by an individual [5, 6]. In spite of the fact that major chronic diseases are caused by multiple risks, which when combined are associated with health outcomes, the science of multiple health behavior change and assessment is at an early stage, and factors that facilitate or impede success in investigative or clinical intervention in multiple behavior change are unknown [7].

The developing field of longevity medicine takes a holistic view of health that calls for integrative evaluation of health-related habits (HrHs), both those that increase and decrease risk of disease and those related to general health and well-being, considering the endpoint of years lived without disability and taking into account a person-centered approach [8]. Taxonomies are particularly important in developing fields of study in that they standardize terminology and allow for common understanding of research results; recently proposed examples include the fields of adverse drug reactions [9] and patient-initiated medical errors [10]. In the current study, we present a preliminary taxonomy for HrH, as well as the Spanish version of the eVITAL toolkit for clinical evaluation of the lifestyle and related determinants of longevity of an individual.

2. Methods

Methods and ethics are described in detail elsewhere [11]. In short, the taxonomy and the related eVITAL toolkit were created using a nominal group technique involving a core group of 6 physicians with expertise in various aspects of longevity medicine and 29 health professionals, including physicians, nurses, and psychologists, in a series of four multidisciplinary focus groups. The model used in the creation of the taxonomy was adapted from the International Classification of Functioning, Disability and Health (ICF) [12] and other documents by the World Health Organization (WHO) [1315], as well as the multilevel person-centered integrative diagnosis model [16], and the transtheoretical model of stages of change [17] and related model of multibehavior change [18]. According to the ICF a “domain” is “a practical and meaningful set of related physiological functions, anatomical structures, actions, tasks, or areas of life” [12]. “Dimensions” are the identifiable components of every domain. In some cases mutually exclusive domains could not be categorized and subdomains had to be defined (see below).

Entities were organized hierarchically into constructs, domains, subdomains, dimensions, subdimensions, and individual items, and codes were assigned using a hierarchical tree. In this conceptual model, health behaviors are part of HrH, complex behavioral patterns which are closely related to other determinants of health as well as to specific health conditions. HrH are in turn part of the health lifestyle, which is a key component of the “personal factors” defined in the ICF. These personal factors “are the particular background of an individual’s life and living,” and these factors comprise, among others, “fitness, lifestyle, habits … overall behaviour pattern and character style, individual psychological assets and other characteristics, all or any of which may play a role in disability at any level” [12].

A demonstration study was performed with 11 adult volunteers who completed the evaluation package followed by an open-ended feedback questionnaire. The assessment package was then revised and computerized, the experts involved in the focus groups evaluated the feasibility of the online toolkit using the criteria of applicability, acceptability, and practicality [54], and responses were used to further refine eVITAL.

3. Results

3.1. Domains and Dimensions

The working group and experts revised 7 proposed domains (physical activity, diet, cognition, sleep, stress, psychosocial vitality, and risk behaviors) into the final 6 HrH domains by combining vitality and stress into a single domain combining physical activity and diet into one domain, and dividing “other risk behaviors” into the two domains of substance use and other risk habits (Table 2); the domains of cognition and sleep were unchanged. After discussion regarding the placement of sexuality within the hierarchy, it was decided that, while important for quality of life, sexuality does not meet all of the criteria for domains in terms of contributing to years lived without disability; it was therefore included as a subdimension within the vitality and stress domain. Despite the initial intention to only include evaluation of HrH, the working group decided that the clinical utility of the toolkit would be increased by including an assessment of other determinants and conditions of health specifically related to each basic HrH.

The panel suggested creating an overarching “health lifestyle profile,” with 6 subprofiles related to the 6 basic HrHs. A seventh domain, “Health descriptors,” includes generic descriptors of health related to longevity, such as social and medical determinants of health and current status of health.

The complete taxonomy developed through this process is shown in Table 3. The preliminary taxonomy includes 6 domains or classes (with diet/exercise further divided into three subdomains: generic, diet, and exercise), 43 dimensions or subclasses, and 141 subdimensions. Once the preliminary taxonomy was defined, codes were assigned to each entity and subentity following a hierarchical tree structure. Letters code the main branches or domains: cognition (c), vitality/stress (v), sleep (s), diet/exercise (de), substance use (s), and other risk habits (r). Each letter is followed by a number for the branches, or dimensions, except for the Prochaska stage of change which is coded within each domain by the letter “s” (see Table 3). The complete evaluation schema is shown in Table 4; the toolkit is available online at

Regarding cognition, the working group and expert panels included evaluation tools related to intellectual reserve or to a higher vulnerability to problems with memory or other higher cognitive functions. Tools were selected for the vitality and stress domain to evaluate psychological and social characteristics that are associated with longevity or an improved response to stress and illness. The group decided to include a biologic dimension to this domain due to the evidence linking stress to these components of allostatic load [55]. It was decided that, while diet and exercise have traditionally been considered separate domains, there is sufficient overlap in evaluation, clinical consequences, and intervention strategies that they should be combined. For example, both diet and exercise affect body mass index, which can be combined with activity level to form 16 metabolic types (Table 1). For substance use, the group considered 3 categories: substances that are always harmful such as nicotine and cocaine, those that can be health promoting in moderation such as wine and caffeine, and medication abuse, due to the potential harm done by misuse of all types of substances. For each substance, the following factors were considered: type/form, timing of use, amount consumed, degree of abuse, and related psychosocial and medical problems. The expert group separated substance use habits and non-substance-related risk habits due to differences in assessment, intervention, and evidence related to longevity. This final domain, “other risk habits,” is divided into treatment nonadherence and other risky behaviors; patient error related to treatment nonadherence is not further delineated within this preliminary taxonomy but has recently been described in detail [10].

Table 1: Metabolic classification based on body mass index and physical activity (eVITAL).
Table 2: eVITAL lifestyle profile related to health habits and the Prochaska stage of change in 11 volunteers.
Table 3: Classification system of health-related behaviors (7 domains, 3 subdomains, 43 dimensions, and 141 subdimensions).
Table 4: Evaluation of items included in the eVITAL toolkit(1).
3.2. Assessment Package

The evaluation is divided into four levels of increasing complexity, starting with basic self-assessment tools (Level 0) and progressing through assessments that can be completed in a basic primary care visit by a nonphysician provider (Level 1), in an extended primary care visit requiring physician expertise (Level 2), and in specialty care (Level 3). Within each level the evaluation is divided into two parts: anamnesis (items related to history) and medical exam.

The anamnesis includes 4 templates, 44 inventories, 22 rating scales, 5 sections (sleep, appetite, fatigue, obsessions, and hypochondriasis) of the semi structured interview “Standardized Polyvalent Psychiatric Interview” (SPPI) (also known by its Spanish acronym EPEP) [40], and 6 sub-classification systems (Table 4). We selected assessment instruments that were feasible at each assessment level according to level of complexity and need for trained expertise; when available, we prioritized items that had been standardized in Spain. When instruments were not available, the group designed inventories that should be standardized and validated at a later stage. In all, the full assessment package comprises 1078 items.

The medical exam includes physical exam findings (signs and measures) and laboratory tests. A series of standard indexes have been incorporated. The group designed adjusted indexes of cognitive reserve and body mass index that require future validation (Table 3).

The assessment package uses several possible methods of scoring the evaluation. In the simplest, after evaluating each domain, the rater gives a global impression score of the patient’s profile for that domain in a 3-point Likert scale (good, acceptable, or needs improvement). These scores can be plotted for each of the domains in a health lifestyle profile and compared to the individual’s stage of change for each domain to formulate a plan of care. Figure 1 shows a sample assessment. This type of assessment and its related lifestyle profile can be extended to the dimensions, subdimensions, and types.

Figure 1: Sample lifestyle profile based on eVITAL toolkit. Prochaska stages: precontemplation (P), contemplation (C), preparation (PP), action (A), maintenance (M), termination (T).
3.3. Demonstration Study

Characteristics of the 11 adult volunteers were as follows: mean age 57.45 years (range 43–64), 9 male, marital status: 9 married/1 widow/1 single, 6 with university degrees, all upper-middle income. Problems in HrH were identified in all volunteers: 8 individuals had problems with sleep, 8 with diet, 5 with exercise, 5 with substance use, 2 with other risk habits, and 2 with vitality/stress. Cognitive habits were good or acceptable in all individuals. 10 individuals fulfilled at least one diagnosis from the International Classification of Diseases (ICD-10) [56] in spite of perceiving themselves as “healthy” (Table 2).

After completing the toolkit, 9/11 gave an overall favorable review and 11/11 reported favorable interactions with the professionals administering the evaluation. While there were no specific recommendations for changes from the volunteers, 7/11 reported that the evaluation was quite long.

3.4. Feasibility Study

Upon reviewing the results of the demonstration study, the working group and focus groups revised the basic organization of the assessment package. Then a feasibility questionnaire was sent to the 29 experts involved in the focus groups; 15 responses were received suggesting changes while 14 experts judged the previous package as adequate and provided no further comments. Comments about applicability of the survey were generally positive. In terms of acceptability, there was some concern about generalizability to populations with lower education level and socioeconomic status, as well as whether patients would be able to complete the forms without assistance. Regarding practicality, there was concern about the time required of the clinician, as well as the difficulty of managing all of the data gathered. As the ultimate goal is to integrate eVITAL into use in the primary care system, comments from primary care practitioners, such as the following, were particularly important: “The survey seems too ambitious and impractical for primary care … a tool that you cannot use due to lack of resources (above all, time) loses its practical validity.”

3.5. Development of the Toolkit

These comments were taken into account in developing the electronic toolkit eVITAL. The open access preliminary version of the toolkit is available at

4. Discussion

Although there is an increasing interest in the comprehensive assessment of HrHs and their relationship to longevity [57], this study presents the first attempt at classifying HrHs to date using the longevity model with the endpoint of years lived without disability. The ICF indicates the relevance of HrH and lifestyle as main components of the “personal contextual factors,” but these factors have not been defined or coded to date [58].

The transtheoretical model of stages of change [17] with the related multibehavioral assessment [18] is the main integrative approach to HrH. Despite the limited evidence regarding the effectiveness of stage-based interventions as a basis for behavior change or for facilitating stage progression [59], multiple behavioral assessment provides a composite index of overall behavior change and includes overarching outcome measures such as quality of life, related biometrics, and cost [60]. For example, a composite index for evaluating change in physical activity and diet showed that interventions focused only on exercise achieved a larger amount of behavior change than an intervention combining both physical activity and nutrition [60].

The eVITAL expert panel opted for a global impression rating of every major HrH and the graphical representation of the resulting lifestyle (health profile), instead of using composite indexes. Similar global ratings have been shown to be practical both in routine clinical practice [61] and in eHealth tools [62].

Unexpectedly given the income and the education level of the volunteers in our pilot sample, we found numerous HrHs in the “needs improvement” category, along with illnesses both related and unrelated to HrH. We diagnosed one case each of prostate cancer and Sjögren’s syndrome, as well as a high proportion of sleep, diet, and exercise problems. This pilot may indicate the relevance of designing both population- and primary care-based epidemiological studies of health lifestyles which include all basic habits and related conditions, as opposed to focusing specifically on targets such as nutrition or exercise.

4.1. Study Strengths

This study is unique in its integrative approach to the evaluation of HrH and the focus on the middle-aged adult population. It begins to address the barriers to health promotion in the primary care setting recently identified in Spain [63] by providing an innovative approach to the assessment of individuals. This preliminary taxonomy fills an existing gap in the assessment of HrH. The eVITAL toolkit is freely available online for use in clinical and research settings, with the hope that this and other groups will continue to gather information on its utility and contribute to further refinement.

4.2. Study Limitations

The taxonomy has received adequate consensus, and the related tools included in eVITAL are those deemed by group to be most useful in the development of an integrated understanding of the HrH and lifestyle of an individual in the Spanish cultural context. However, the clinical utility of the toolkit as a whole will have to be validated in the future. The current, computerized version of eVITAL has not undergone the type of demonstration study reported here for the earlier assessment package prototype; this remains to be performed prior to widespread integration into clinical practice. The greatest limitation of the toolkit at this time is the concern raised about the feasibility of widespread use of eVITAL, most notably in populations with lower education level and socioeconomic status and in the primary care system. While it is important to gather enough information to develop a complete understanding of a patient’s health lifestyle profile, the system we propose must be feasible within the existing health care system. eVITAL will continue to be adjusted to work toward this goal.

5. Conclusion

To our best knowledge this is the first toolkit of lifestyle and health-related habits based on a formal taxonomy of HrH. This taxonomy may improve the assessment of lifestyle in health sciences, enhance the development of a classification of HrH and personal factors in the context of the WHO family of classifications, and develop this construct in new models of care such as person-centered medicine and diagnosis [16, 64].


BMI:Body mass index
ICD:International Classification of Diseases
ICF:International Classification of Functioning, Disability and Health
HrH:Health-related habits
SPPI:Standardized Polyvalent Psychiatric Interview
WHO:World Health Organization.

Conflict of Interests

The authors declare that there are no conflicts of interest.


Funding for this study was provided by IMSERSO, the Spanish Ministry of Health and Social Policies and Equality. The participation of Caroly Walsh was funded by NIMH/NIH R25 MH071286 (Dr. Kerim Munir, PI). The funding body was not involved in study design, development of the eVITAL database, paper writing, or decision to submit for publication. The authors would like to acknowledge Dr. Kerim Munir and the members of the eVITAL group. Other members of the group are Jorge Moreno, Teresa Magallanes, Cristina Romero, Jaime Tarradellas, Juan Carlos Durán, Miriam Poole, Juan Carlos García-Gutiérrez, Félix Abad, Arun Mansukani, Francisco Cabello, Monserrat Manuvens, Eduard Estivill, Francisco Segarra, Javier Albares, Francisco Gil, María José Abellán, Saturnino Reyes, Carmen Vargas, Francisco Pradas, Carlos Mampel, Luisa Tejonero, and José Almenara Barrios.


  1. World Health Organization, The Global Burden of Disease, 2004 Update, Geneva, Switzerland, 2008.
  2. T. A. Pearson, S. N. Blair, S. R. Daniels et al., “AHA guidelines for primary prevention of cardiovascular disease and stroke: 2002 update: consensus panel guide to comprehensive risk reduction for adult patients without coronary or other atherosclerotic vascular diseases. American Heart Association Science Advisory and Coordinating Committee,” Circulation, vol. 106, pp. 388–391, 2002. View at Google Scholar
  3. L. B. Goldstein, R. Adams, M. J. Alberts et al., “Primary prevention of ischemic stroke: a guideline from the American Heart Association/American Stroke Association Stroke Council: cosponsored by the Atherosclerotic Peripheral Vascular Disease Interdisciplinary Working Group; Cardiovascular Nursing Council; Clinical Cardiology Council; Nutrition, Physical Activity, and Metabolism Council; and the Quality of Care and Outcomes Research Interdisciplinary Working Group: the American Academy of Neurology affirms the value of this guideline,” Stroke, vol. 37, pp. 1583–1633, 2006. View at Google Scholar
  4. M. J. Kelley and D. C. McCrory, “Prevention of lung cancer: summary of published evidence,” Chest, vol. 123, no. 1, pp. 50S–59S, 2003. View at Google Scholar · View at Scopus
  5. L. B. Yates, L. Djoussé, T. Kurth, J. E. Buring, and J. M. Gaziano, “Exceptional longevity in men: modifiable factors associated with survival and function to age 90 years,” Archives of Internal Medicine, vol. 168, no. 3, pp. 284–290, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. M. J. Stampfer, F. B. Hu, J. E. Manson, E. B. Rimm, and W. C. Willett, “Primary prevention of coronary heart disease in women through diet and lifestyle,” The New England Journal of Medicine, vol. 343, no. 1, pp. 16–22, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. J. J. Prochaska, C. R. Nigg, B. Spring, W. F. Velicer, and J. O. Prochaska, “The benefits and challenges of multiple health behavior change in research and in practice,” Preventive Medicine, vol. 50, no. 1-2, pp. 26–29, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Salvador-Carulla, A. Cano Sánchez, and J. Cabo-Soler, Longevidad: Tratado Integral Sobre Salud en la Segunda Mitad de la Vida, Editorial Médica Panamericana, Madrid, Spain, 2003.
  9. C. Bousquet, B. Trombert, A. Kumar, and J. M. Rodrigues, “Semantic categories and relations for modelling adverse drug reactions towards a categorial structure for pharmacovigilance,” in Proceedings of the AMIA Annual Symposium, pp. 61–65, 2008.
  10. S. Buetow, L. Kiata, T. Liew, T. Kenealy, S. Dovey, and G. Elwyn, “Patient error: a preliminary taxonomy,” Annals of Family Medicine, vol. 7, no. 3, pp. 223–231, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. F. Alonso, C. O. Walsh, and L. Salvador-Carulla, “Methodology for the development of a taxonomy and toolkit to evaluate health-related habits and lifestyle (eVITAL),” BMC Research Notes, vol. 3, article 83, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. World Health Organization, International Classification of Functioning, Disability and Health (ICF), 2001.
  13. World Health Organization, Health Promotion Glossary, World Health Organization, Geneva, Switzerland, 1998.
  14. World Health Organization Noncommunicable Disease and Mental Health Cluster, Active Ageing: A Policy Framework, World Health Organization, 2002.
  15. World Health Organization, Promoting Mental Health: Concepts, Emerging Evidence, Practice, World Health Organization, Geneva, Switzerland, 2004.
  16. I. Salloum and J. Mezzich, “Person-centered diagnosis,” International Journal of Integrated Care, vol. 10, pp. 75–78, 2010. View at Google Scholar
  17. J. O. Prochaska and W. F. Velicer, “The transtheoretical model of health behavior change,” American Journal of Health Promotion, vol. 12, no. 1, pp. 38–48, 1997. View at Google Scholar · View at Scopus
  18. J. O. Prochaska, S. Butterworth, C. A. Redding et al., “Initial efficacy of MI, TTM tailoring and HRI's with multiple behaviors for employee health promotion,” Preventive Medicine, vol. 46, no. 3, pp. 226–231, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Del Ser Quijano, F. Sánchez Sánchez, M. J. Garcia De Yébenes, Á. Otero Puime, M. V. Zunzunegui, and D. G. Muñoz, “Spanish version of the 7 Minute screening neurocognitive battery. Normative data of an elderly population sample over 70,” Neurología, vol. 19, no. 7, pp. 344–358, 2004. View at Google Scholar · View at Scopus
  20. P. R. Solomon, A. Hirschoff, B. Kelly et al., “A 7 minute neurocognitive screening battery highly sensitive to Alzheimer's disease,” Archives of Neurology, vol. 55, no. 3, pp. 349–355, 1998. View at Publisher · View at Google Scholar · View at Scopus
  21. T. S. Altepeter, R. I. Adams, W. L. Buchanan, and P. Buck, “Luria memory words test and Wechsler memory scale: comparison of utility in dicriminating neurologically impaired from controls,” Journal of Clinical Psychology, vol. 46, no. 2, pp. 190–193, 1990. View at Google Scholar · View at Scopus
  22. A. L. Benton, The Revised Visual Retention Test, Psychological Corporation, New York, NY, USA, 1974.
  23. R. M. Reitan, “Validity of the Trail Making test as an indicator of organic brain damage,” Perceptual & Motor Skills, vol. 8, pp. 271–276, 1958. View at Google Scholar
  24. L. Dilks, J. Marceaux, B. Mayeaux, D. Turner, J. Bourassa, and M. Bourgeois, “Validity study of the western psychological services finger-tapping test,” American Journal of Psychological Research, vol. 2, pp. 8–13, 2006. View at Google Scholar
  25. M. F. Scheier, C. S. Carver, and M. W. Bridges, “Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the life orientation test,” Journal of Personality and Social Psychology, vol. 67, no. 6, pp. 1063–1078, 1994. View at Google Scholar · View at Scopus
  26. J. Alonso, E. Regidor, G. Barrio, L. Prieto, C. Rodriguez, and L. De La Fuente, “Population-based reference values for the Spanish version of the Health Survey SF-36,” Medicina Clínica, vol. 111, no. 11, pp. 410–416, 1998. View at Google Scholar · View at Scopus
  27. G. Vilagut, M. Ferrer, L. Rajmil et al., “The Spanish version of the Short Form 36 Health Survey: a decade of experience and new developments,” Gaceta Sanitaria, vol. 19, no. 2, pp. 135–150, 2005. View at Google Scholar · View at Scopus
  28. M. P. Beaudet, “Depression,” Statistics Canada, vol. 7, no. 4, pp. 11–22, 1996. View at Google Scholar · View at Scopus
  29. O. Dalgard, “Explanation of the Oslo-3 Social Support Scale (OSS-3),” In: System, E.U.P.H.I., (Ed.), 2006.
  30. A. S. Zigmond and R. P. Snaith, “The hospital anxiety and depression scale,” Acta Psychiatrica Scandinavica, vol. 67, no. 6, pp. 361–370, 1983. View at Google Scholar · View at Scopus
  31. P. T. Costa and R. R. McCrae, NEO PI-R Professional Manual, Psychological Assessment Resources, Odessa, Fla, USA, 1992.
  32. C. D. Ryff and C. L. M. Keyes, “The structure of psychological well-being revisited,” Journal of Personality and Social Psychology, vol. 69, no. 4, pp. 719–727, 1995. View at Google Scholar · View at Scopus
  33. D. van Dierendonck, D. Díaz, R. Rodríguez-Carvajal, A. Blanco, and B. Moreno-Jiménez, “Ryff's six-factor model of psychological well-being, a Spanish exploration,” Social Indicators Research, vol. 87, no. 3, pp. 473–479, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. M. Hunter, “The Women’s Health Questionnaire: a measure of mid-aged women’s perceptions of their emotional and physical health,” Psychology & Health, vol. 7, pp. 45–54, 1992. View at Google Scholar
  35. J. P. Raynaud, J. Tichet, C. Born et al., “Aging male questionnaire in normal and complaining men,” Journal of Sexual Medicine, vol. 5, no. 11, pp. 2703–2712, 2008. View at Publisher · View at Google Scholar · View at Scopus
  36. M. J. Herrero, J. Blanch, J. M. Peri, J. De Pablo, L. Pintor, and A. Bulbena, “A validation study of the hospital anxiety and depression scale (HADS) in a Spanish population,” General Hospital Psychiatry, vol. 25, no. 4, pp. 277–283, 2003. View at Publisher · View at Google Scholar · View at Scopus
  37. T. H. Holmes and R. H. Rahe, “The social readjustment rating scale,” Journal of Psychosomatic Research, vol. 11, pp. 213–218, 1967. View at Google Scholar
  38. C. Rodríguez-Sutil, P. Gil-Corbacho, and R. Martínez, “Presentación de la escala retiro de patrón de conducta TIPO A (ERCTA),” Psicothema, vol. 8, no. 1, pp. 207–213, 1996. View at Google Scholar
  39. M. Zuckerman, “Zuckerman-Kuhlman Personality Questionnaire (ZKPQ): an alternative five-factorial model,” in Big Five Assessment, B. De Raad and M. Perugini, Eds., pp. 377–396, Hogrefe & Huber, Seattle, Wash, USA, 2002. View at Google Scholar
  40. A. Lobo, R. Campos, M. J. Perez-Echeverria et al., “A new interview for the multiaxial assessment of psychiatric morbidity in medical settings,” Psychological Medicine, vol. 23, no. 2, pp. 505–510, 1993. View at Google Scholar · View at Scopus
  41. National Cancer Institute, “Breast cancer risk assessment tool: an interactive tool for measuring the risk of invasive breast cancer,” In: Health, U.S.N.I.o., (Ed.).
  42. M. W. Johns, “A new method for measuring daytime sleepiness: the Epworth sleepiness scale,” Sleep, vol. 14, no. 6, pp. 540–545, 1991. View at Google Scholar · View at Scopus
  43. M. Ferrer, G. Vilagut, C. Monasterio, J. M. Montserrat, M. Mayos, and J. Alonso, “Measurement of the perceived impact of sleep problems: the Spanish version of the functional outcomes sleep questionnaire and the Epworth sleepiness scale,” Medicina Clínica, vol. 113, no. 7, pp. 250–255, 1999. View at Google Scholar · View at Scopus
  44. E. Chiner, J. M. Arriero, J. Signes-Costa, J. Marco, and I. Fuentes, “Validation of the Spanish version of the Epworth Sleepiness Scale in patients with sleep apnea syndrome,” Archivos de Bronconeumología, vol. 35, no. 9, pp. 422–427, 1999. View at Google Scholar · View at Scopus
  45. Canadian Society for Exercise Physiology, “Physical Activity Readiness Questionnaire (PAR-Q),” 2002,
  46. I. Trinidad Rodríguez, J. Fernández Ballart, G. Cucó Pastor, E. Biarnés Jordà, and V. Arija Val, “Validation of a short questionnaire on frequency of dietary intake: reproducibility and validity,” Nutricion Hospitalaria, vol. 23, no. 3, pp. 242–252, 2008. View at Google Scholar · View at Scopus
  47. G. Bray, C. Bouchard, and W. P. T. James, Handbook Definitions and proponed current classifications of obesity, Marcel Dekker, New York, NY, USA, 1998.
  48. D. R. Matthews, METODO HOMA-IR, Homeostasis Model Assessment, 1985.
  49. B. E. Ainsworth, W. L. Haskell, M. C. Whitt et al., “Compendium of physical activities: an update of activity codes and MET intensities,” Medicine and Science in Sports and Exercise, vol. 32, no. 9, pp. S498–S504, 2000. View at Google Scholar · View at Scopus
  50. J. A. Ewing, “Detecting alcoholism. The CAGE questionnaire,” Journal of the American Medical Association, vol. 252, no. 14, pp. 1905–1907, 1984. View at Publisher · View at Google Scholar · View at Scopus
  51. A. Rodríguez-Martos, R. Navarro, C. Vecino, and R. Pérez, “Validación de los cuestionarios KFA (CBA) y CAGE para diagnóstico del alcoholismo,” Drogalcohol, vol. 11, pp. 132–139, 1986. View at Google Scholar
  52. T. F. Heatherton, L. T. Kozlowski, R. C. Frecker, and K. O. Fagerstrom, “The Fagerstrom test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire,” British Journal of Addiction, vol. 86, no. 9, pp. 1119–1127, 1991. View at Google Scholar · View at Scopus
  53. E. Becoña and F. L. Vázquez, “The Fagerström Test for Nicotine Dependence in a Spanish sample,” Psychological Reports, vol. 83, no. 3, pp. 1455–1458, 1998. View at Google Scholar · View at Scopus
  54. G. Andrews, L. Perters, and M. Tesson, Measurement of Consumer Outcome in Mental Health: A Report to the National Mental Health Information Strategy Committee, Clinical Research Unit for Anxiety Disorders, Sydney, Australia, 1994.
  55. T. E. Seeman, B. H. Singer, C. D. Ryff, G. Dienberg Love, and L. Levy-Storms, “Social relationships, gender, and allostatic load across two age cohorts,” Psychosomatic Medicine, vol. 64, no. 3, pp. 395–406, 2002. View at Google Scholar · View at Scopus
  56. World Health Organization, International Classification of Diseases (ICD), World Health Organization, Geneva, Switzerland, 1994.
  57. J. Beddington, C. L. Cooper, J. Field et al., “The mental wealth of nations,” Nature, vol. 455, no. 7216, pp. 1057–1060, 2008. View at Publisher · View at Google Scholar · View at Scopus
  58. L. Salvador-Carulla and V. Gasca, “Defining disability, functioning, autonomy and dependency in person-centered medicine and integrated care,” International Journal of Integrated Care, vol. 10, pp. 69–72, 2010. View at Google Scholar
  59. C. Bridle, R. P. Riemsma, J. Pattenden et al., “Systematic review of the effectiveness of health behavior interventions based on the transtheoretical model,” Psychology and Health, vol. 20, no. 3, pp. 283–301, 2005. View at Publisher · View at Google Scholar · View at Scopus
  60. J. J. Prochaska, W. F. Velicer, C. R. Nigg, and J. O. Prochaska, “Methods of quantifying change in multiple risk factor interventions,” Preventive Medicine, vol. 46, no. 3, pp. 260–265, 2008. View at Publisher · View at Google Scholar · View at Scopus
  61. M. Berk, F. Ng, S. Dodd et al., “The validity of the CGI severity and improvement scales as measures of clinical effectiveness suitable for routine clinical use,” Journal of Evaluation in Clinical Practice, vol. 14, no. 6, pp. 979–983, 2008. View at Publisher · View at Google Scholar · View at Scopus
  62. A. J. Rotondi, J. Sinkule, G. L. Haas et al., “Designing websites for persons with cognitive deficits: design and usability of a psychoeducational intervention for persons with severe mental illness,” Psychological Services, vol. 4, no. 3, pp. 202–224, 2007. View at Publisher · View at Google Scholar · View at Scopus
  63. G. Grandes, A. Sanchez, J. M. Cortada et al., “Is integration of healthy lifestyle promotion into primary care feasible? Discussion and consensus sessions between clinicians and researchers,” BMC Health Services Research, vol. 8, article 213, 2008. View at Publisher · View at Google Scholar · View at Scopus
  64. J. E. Mezzich, I. M. Salloum, C. R. Cloninger et al., “Person-centred integrative diagnosis: conceptual bases and structural model,” Canadian Journal of Psychiatry, vol. 55, no. 11, pp. 701–708, 2010. View at Google Scholar · View at Scopus