Psychosocial Factors as Predictors of Adjustment to

Psychosocial Factors as Predictors of Adjustment to Life in Chronic Portuguese Patients Estela Vilhena1;8, José Luís Pais Ribeiro2;7, Isabel Silva3, Luísa Pedro4;7, Rute Meneses3, Helena Cardoso5;6 António Martins da Silva6;9, Denisa Mendonça5;8 1Polytechnic Presented by: Name goes hereof Institute

Cávado and Ave, Barcelos; 2FPCE University of Porto; 3University of Fernando Pessoa; 4ESTeSL Polytechnic Institute of Lisbon; 5ICBAS University of Porto; 6HGSA/CHP Hospital Center of Porto; 7UIPES Portugal; 8EPIUnit, ISPUP University of Porto; 9UMIB/ICBAS University of Porto

BACKGROUND

Living with a chronic disease is a demanding experience that may affect multiple aspects of an individual’s life. Patients are responsible for the management of a wide range of psychosocial factors which contribute to their Quality of Life (QoL).

QoL has become an important concept for health care and to evaluate QoL in chronic patients is an increasingly important issue.

AIM

To test a hypothetical model to evaluate the simultaneous impact of psychosocial predictors (dispositional

optimism, positive and negative affect, spirituality, social support and treatment adherence) of QoL and of Subjective Well-being (SWB) in chronic Portuguese patients. Background Variables ontrol for…

QoL and SWB Psychosocial Variables (Predictors)

METHODS Sample  774 volunteer patients: mean age (age) 48.8 years (sd=10.1), mean education level (school) was 9.4 years (sd=4.8), mean severity of disease perception (SDP) was 7.3 (sd=2.7);  approached directly by their physicians during the consultation in outpatient departments of central Portuguese Hospitals;  all complete a self – report questionnaires to assess sociodemographic and clinical, psychosocial and quality of life variables.

METHODS Inclusion Criteria  diagnosis according chronic disease (cancer, obesity, diabetes, epilepsy, myasthenia gravis or multiple sclerosis);

 age ≥18 years at the time of the interview;  educational level higher than 6 years;  diagnosed at least 3 years prior to the study;

 life stability with disease under control;  no psychiatric disturbances.

METHODS Measures  Socio-demographic and clinical variables age, sex, education level, time since diagnosis and severity of disease perception (“how do you classify generally your illness?” )  Psychosocial Variables Dispositional Optimism (Life Orientation Test-Revised (LOT-R))1 Higher scores indicate greater optimism

Social Support (Social Support Survey (MOS))2 Higher scores reflect a greater social support

Spirituality (Portuguese Scale of Spirituality)3 Higher scores reflect higher perception of spirituality J. Pais Ribeiro and L. Pedro, Contribuição para a análise psicométrica e estrutural da escala revista de avaliação do optimismo (escala de orientação para a vida revista-EOR-R) em doentes com esclerose múltipla. In Actas do 6º Congresso Nacional de Psicologia da Saúde, I. Leal, J. Pais Ribeiro and S. Neves (eds.), pp. 133-139, 2006. 2 A.C. Ponte and J. Pais Ribeiro, Estudo preliminar das propriedades métricas do mos social support survey. In Actas do 7º congresso nacional de psicologia da saúde. J.P.R. Lisboa: ISPA In: I. Leal, I. Silva & Marques (ed.), pp. 53-56., 2008. J. Pais-Ribeiro and A.C. Ponte,Propriedades métricas da versão portuguesa da escala de suporte social do MOS (MOS Social Support Survey) com idosos. Psicologia, Saúde & Doenças Vol 10(2), pp. 163-174 2009. 3 Pinto C, Pais Ribeiro J. Construção de uma escala de avaliação da espiritualidade em contextos de saúde Arquivos de Medicina. 2007; 21 (2): 47-53. 1

METHODS

Measures  Psychosocial Variables Positive and Negative Affect (PANAS)4 Higher scores indicate greater positive or negative affect

Treatment Adherence (Medida de Adesão aos tratamentos)5 Higher scores reflect a greater treatment adherence 4Galinha

IC, Pais Ribeiro JL. Contribuição para o estudo da versão portuguesa da

Positive and Negative Affect Schedule (PANAS): II – Estudo psicométrico. Análise Psicológica 5Delgado

2005; 2(XXIII): 219-29.

A, Lima M. Contributo para a avalição concorrente de uma medida de adesão

aos tratamentos. . Psicologia, Saúde & Doenças. 2001; 2 (2): 81-100.

METHODS

 Outcome Variables Quality of Life (SF-36)6 (General Well-Being, Physical Health and Mental Health) Higher scores indicate a better quality of life

Subjective Well-being (Portuguese Version of Well-being Scale)7 Higher scores indicate a better quality of life

6- FerreiraP.CriaçãodaversãoportuguesadoMOSSF-36:ParteI-Adaptação Cultural e linguística. Acta Médica Portuguesa. 2000a; 13:55-66. Ferreira P. Criação da versão portuguesa do MOSSF-36: ParteII- Testes de validade. Acta Médica Portuguesa.2000b;13:55-66. 7- Pais Ribeiro J, Cummins R. O bem-estar pessoal: estudo de validação da versão portuguesa da escala. In: I.Leal, J.Pais-Ribeiro, (Edts.) ISSM, editors. Actas do 7º congresso nacional de psicologia da saúde. Lisboa: ISPA; 2008. p. 505-8

Statistical Analysis  Structural Equation Modeling (SEM) is considered to be a

major component of applied multivariate statistical analysis for addressing complex scientific questions in a most variety investigations areas;  Is a multivariate technique that allows for representing, estimating and testing theoretical models that involve several relationships between variables (observed and latent), in order to understand the patterns of correlation/ covariance between them.

METHODS Statistical Analysis  SEM is a combination of factor and path analyses, corresponding to the measurement and structural models, respectively;  First, we applied confirmatory factor analysis (CFA) (measurement model) in order to assess whether all the

latent variables were represented by their respective indicators (observed variables).

METHODS

Mesureament Model (CFA) Modelo de Medida (AFC)

δ1

X1

Y1

δ2

X2

Y2

δ3

X3

D2

Structural Model Modelo Estrutural

METHODS Statistical Analysis  SEM is a combination of factor and path analyses,

corresponding to the measurement and structural models, respectively;

 First, we applied confirmatory factor analysis (CFA) (measurement model) in order to assess whether all the latent variables were represented by their respective indicators (observed variables).

METHODS

Statistical Analysis

 The structural model indicates the direct and indirect effects of latent and observed variables (which are not indicators of latent variables).

METHODS Statistical Analysis

 The structural model indicates the direct and indirect effects of latent and observed variables (which are not indicators of latent variables).

METHODS

Fundamental Equations for SEM

Measurement Model

Model Specification Structural Model

In SEM a model is specified and parameters for the model are estimated using sample data and parameters are combined to produce the estimated population covariance matrix.

METHODS Latent Variables

 Maximum Likelihood

(ML) estimation were

Psychosocial Variables Dispositional Optimism (DO) Social Support (SS)

used with Satorra-Bentler Spirituality (S)

Scaled correction;

corrected to adjust for the

extent of nonnormality.

Qi Life Orientation i=1, …, 6 Affective (SSA) Tangible (SST) Positive Social Interaction (SSPSI) Emotional/Informational (SSEI) Qi Scale of Spirituality i=1, …, 5

Negative Affect (NA)

Qi Scale of Feelings i=1, …, 10

Positive Affect (PA)

Qi Scale of Feelings i=1, …, 10 Qi Scale of Medida de Adesão aos Tratamentos i=1, …, 7

Treatment Adherence (TA)

 Standard errors were

Observed Variables

QoL Components General Well-being (GWB) Physical Health (PH)

Mental Health (MH)

Subjective Well-being (SWB)

General Health (GH) Vital Health (VH) Physical Function (PF) Physical Pain (PP) Corporal Pain (CP) Emotion Pain (EP) Social Function (SF) Mental Function (MF) Qi Scale of Personal Well-being i=1, …, 8

METHODS

 To test the adequacy of the model two other goodness-of-

fit indices were used: Comparative Fit Index (CFI) (reference values [0,9; 1[: good fit)

Root

Mean

Square

Error

of

Approximation

(RMSEA) (reference values ]0.05; 0.1]: reazonable fit; [0.01; 0.05[: good fit);  Analysis were performed using EQS 6.1.

RESULTS

Good Fit

Good Fit

RESULTS

All factors had a simultaneous independent impact on general well-being, physical, mental health and subjective well being. Patients more optimistic, more active and with a better treatment

adherence had a better general well-being; A better treatment adherence contributes to a better physical health; Optimistic patients, more active, with a better treatment adherence, and more social support had a better mental health; An attitude more optimistic, a better positive affect, a better treatment adherence and more social support contributes to a better subjective well-being.

RESULTS

CONCLUSIONS

The principal goal of the study was to simultaneously analyze psychosocial variables to clarify their simultaneous impact on Quality of Life and Subjective Well-being.

CONCLUSIONS

The use of SEM allows us to understand the complexity of the simultaneous relationships between these variables and their impact and contribution to a better

quality of life. These findings are relevant for health care providers and contribute to the understanding of the processes associated with Quality of Life.

Thanks for your attention!

There is no conflict of interest!