Loading...
Person
Baptista de Lemos Guerra de Oliveira, Raquel Maria
8 results
Search Results
Now showing 1 - 8 of 8
- Cross-cultural adaptation and psychometric evaluation of the perceived ability to cope with trauma scale in portuguese patients with breast cancerPublication . Lemos, Raquel; Costa, Beatriz; Frasquilho, Diana; Almeida, Sílvia; Sousa, Berta; Maia, Albino J. Oliveira
- Cross-cultural adaptation and psychometric evaluation of the Portuguese version of the family resilience questionnaire – short form (FaRE-SF-P) in women with breast cancerPublication . Almeida, Sílvia; Rodrigues da Silva, Daniel; Frasquilho, Diana; Costa, Beatriz; Sousa, Berta; Baptista, Telmo Mourinho; Grácio, Jaime; Lemos, Raquel; Oliveira-Maia, Albino J.Background: A diagnosis of cancer, and the resulting treatment process, can be perceived as a life-threatening event, affecting not only patients but also their social network and, more specifically, their relatives. While the ability to cope and adjust to difficult health situations may be challenging, family resilience may optimize a positive adaptation to adversity and contribute to enhance the patient’s quality of life. The Family Resilience Questionnaire (FaRE) is a self-report measure of family resilience that assesses this construct systematically. We aimed to validate the Portuguese version of a short form of the FaRE (FaRE-SF-P) in a sample of women with breast cancer. Methods: 147 women recently diagnosed with early breast cancer were recruited at the Champalimaud Clinical Centre in Lisbon. Participants completed psychometric assessment including the Portuguese version of the FaRE-SF-P, composed by two subscales of the original version – the FaRE Perceived Family Coping (FaRE-PFC) and the FaRE Communication and Cohesion (FaRE-CC). Confirmatory factor analysis (CFA) was performed to assess the factor structure of the FaRE-SF-P. Construct validity was assessed using the Hospital Anxiety and Depression Scale (HADS) for divergent validity, and the Modified Medical Outcomes Study Social Support Survey (mMOSSS) as well as the social functioning subscale from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) for convergent validity. Results: The CFA results confirmed a correlated two-factor structure model consistent with the Perceived Family Coping and the Communication and Cohesion subscales. Internal consistency reliability indicated good values both for Perceived Family Coping and Communication and Cohesion subscales. The results for construct validity showed acceptable convergent and divergent validity. Discussion: The FaRE-SF-P showed good psychometric properties demonstrating to be a valid and reliable family resilience measure to use in Portuguese women diagnosed with breast cancer. Since FaRE-SF-P is a short instrument it may be a useful screening tool in an oncological clinical practice routine
- Criterion and construct validity of the Beck Depression Inventory (BDI-II) to measure depression in patients with cancer: The contribution of somatic itemsPublication . Almeida, Sílvia; Camacho, Marta; Barahona-Corrêa, J. Bernardo; Oliveira, José; Lemos, Raquel; Da Silva Rodrigues, Daniel; da Silva, Joaquim Alves; Baptista, Telmo Mourinho; Grácio, Jaime; Oliveira-Maia, Albino J.Background/Objective: Screening for depression in patients with cancer can be difficult due to overlap between symptoms of depression and cancer. We assessed validity of the Beck Depression Inventory (BDI-II) in this population. Method: Data was obtained in an outpatient neuropsychiatry unit treating patients with and without cancer. Psychometric properties of the BDI-II Portuguese version were assessed separately in 202 patients with cancer, and 376 outpatients with mental health complaints but without cancer. Results: Confirmatory factor analysis suggested a three-factor structure model (cognitive, affective and somatic) provided best fit to data in both samples. Criterion validity was good for detecting depression in oncological patients, with an area under the ROC curve (AUC) of 0.85 (95% confidence interval [CI], 0.760.91). A cut-off score of 14 had sensitivity of 87% and specificity of 73%. Excluding somatic items did not significantly change the ROC curve for BDI-II (difference AUCs = 0.002, p=0.9). A good criterion validity for BDI-II was also obtained in the non-oncological population (AUC = 0.87; 95% CI 0.810.91), with a cut-off of 18 (sensitivity=84%; specificity=73%). Conclusions: The BDI-II demonstrated good psychometric properties in patients with cancer, comparable to a population without cancer. Exclusion of somatic items did not affect screening accuracy
- Predicting effective adaptation to breast cancer to help women BOUNCE back: Protocol for a Multicenter Clinical Pilot StudyPublication . Pettini, Greta; Sanchini, Virginia; Pat-Horenczyk, Ruth; Sousa, Berta; Masiero, Marianna; Marzorati, Chiara; Galimberti, Viviana Enrica; Munzone, Elisabetta; Mattson, Johanna; Vehmanen, Leena; Utriainen, Meri; Roziner, Ilan; Lemos, Raquel; Frasquilho, Diana; Cardoso, Fatima; Oliveira-Maia, Albino J; Kolokotroni, Eleni; Stamatakos, Georgios; Leskelä, Riikka-Leena; Haavisto, Ira; Salonen, Juha; Richter, Robert; Karademas, Evangelos; Poikonen-Saksela, Paula; Mazzocco, KettiBackground: Despite the continued progress of medicine, dealing with breast cancer is becoming a major socioeconomic challenge, particularly due to its increasing incidence. The ability to better manage and adapt to the entire care process depends not only on the type of cancer but also on the patient’s sociodemographic and psychological characteristics as well as on the social environment in which a person lives and interacts. Therefore, it is important to understand which factors may contribute to successful adaptation to breast cancer. To our knowledge, no studies have been performed on the combination effect of multiple psychological, biological, and functional variables in predicting the patient’s ability to bounce back from a stressful life event, such as a breast cancer diagnosis. Here we describe the study protocol of a multicenter clinical study entitled “Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back” or, in short, BOUNCE. Objective: The aim of the study is to build a quantitative mathematical model of factors associated with the capacity for optimal adjustment to cancer and to study resilience through the cancer continuum in a population of patients with breast cancer. Methods: A total of 660 women with breast cancer will be recruited from five European cancer centers in Italy, Finland, Israel, and Portugal. Biomedical and psychosocial variables will be collected using the Noona Healthcare platform. Psychosocial, sociodemographic, lifestyle, and clinical variables will be measured every 3 months, starting from presurgery assessment (ie, baseline) to 18 months after surgery. Temporal data mining, time-series prediction, sequence classification methods, clustering time-series data, and temporal association rules will be used to develop the predictive model. Results: The recruitment process stared in January 2019 and ended in November 2021. Preliminary results have been published in a scientific journal and are available for consultation on the BOUNCE project website. Data analysis and dissemination of the study results will be performed in 2022. Conclusions: This study will develop a predictive model that is able to describe individual resilience and identify different resilience trajectories along the care process. The results will allow the implementation of tailored interventions according to patients’ needs, supported by eHealth technologies. Trial Registration: ClinicalTrials.gov NCT05095675; https://clinicaltrials.gov/ct2/show/NCT05095675 International Registered Report Identifier (IRRID): DERR1-10.2196/34564
- A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocolPublication . Lemos, Raquel; Areias-Marques, Sofia; Ferreira, Pedro; O’Brien, Philip; Beltrán-Jaunsarás, María Eugenia; Ribeiro, Gabriela; Martín, Miguel; del Monte-Millán, María; López-Tarruella, Sara; Massarrah, Tatiana; Luís-Ferreira, Fernando; Frau, Giuseppe; Venios, Stefanos; McManus, Gary; Oliveira-Maia, Albino J.Background: Depression is a common condition among cancer patients, across several points in the disease trajec‑ tory. Although presenting higher prevalence rates than the general population, it is often not reported or remains unnoticed. Moreover, somatic symptoms of depression are common in the oncological context and should not be dismissed as a general symptom of cancer. It becomes even more challenging to track psychological distress in the period after the treatment, where connection with the healthcare system typically becomes sporadic. The main goal of the FAITH project is to remotely identify and predict depressive symptoms in cancer survivors, based on a federated machine learning (ML) approach, towards optimization of privacy. Methods: FAITH will remotely analyse depression markers, predicting their negative trends. These markers will be treated in distinct categories, namely nutrition, sleep, activity and voice, assessed in part through wearable technolo‑ gies. The study will include 300 patients who have had a previous diagnosis of breast or lung cancer and will be recruited 1 to 5 years after the end of primary cancer. The study will be organized as a 12-month longitudinal pro‑ spective observational cohort study, with monthly assessments to evaluate depression symptoms and quality of life among cancer survivors. The primary endpoint is the severity of depressive symptoms as measured by the Hamilton Depression Rating Scale (Ham-D) at months 3, 6, 9 and 12. Secondary outcomes include self-reported anxiety and depression symptoms (HADS scale), and perceived quality of life (EORTC questionnaires), at baseline and monthly. Based on the predictive models gathered during the study, FAITH will also aim at further developing a conceptual fed‑ erated learning framework, enabling to build machine learning models for the prediction and monitoring of depres‑ sion without direct access to user’s personal data. Discussion: Improvements in the objectivity of psychiatric assessment are necessary. Wearable technologies can provide potential indicators of depression and anxiety and be used for biofeedback. If the FAITH application is
- Effects of an individual cognitive stimulation intervention on global cognition, memory, and executive function in older adults with mild to moderate Alzheimer’s diseasePublication . Justo-Henriques, Susana I; Pérez Sáez, Enrique; Carvalho, Janessa O.; Lemos, Raquel; Ribeiro, OscarObjective: To determine the efficacy of a 12-week individual cogni-tive stimulation (iCS) intervention on global cognition, memory,and executive function of older adults with mild to moderateAlzheimer’s disease (AD). Method: Protocolized analysis using datafrom a multicenter, single-blind, randomized, parallel two-arm RCTof iCS for older adults with probable AD. A sample of 142 peoplewith probable Alzheimer’s disease attending 13 Portuguese institu-tions providing care and support services for older adults wereselected. Intervention group (n = 72) received 24 iCS sessions, twicea week for 12 weeks. Control group (n = 70) maintained their activ-ities as usual. Outcomes included global cognitive function(Mini-Mental State Examination, and Alzheimer’s Disease AssessmentScale—Cognitive Subscale), memory (Memory Alteration Test, andFree and Cued Selective Reminding Test), and executive function-ing (Frontal Assessment Battery). All participants were assessed atbaseline (T0), after the intervention (T1), and 12 weeks follow-up(T2). Results: The results showed significant improvements in mem-ory performance at follow-up for the intervention group andgreater stability in global cognition in the intervention relative tothe control group. Conclusion: The current iCS protocol showseffectiveness in cognitive functioning in older adults with probableAD, particularly for memory upon completion of the interventionand at follow-up, adding further support to previous iCS studiesshowing similar results and to the effectiveness of the currentintervention.
- Well‐being trajectories in breast cancer and their predictors: A machine‐learning approachPublication . Karademas, Evangelos; Mylona, Eugenia; Mazzocco, Ketti; Pat‐Horenczyk, Ruth; Sousa, Berta; Oliveira‐Maia, Albino J.; Oliveira, Jose; Roziner, Ilan; Stamatakos, Georgios; Cardoso, Fatima; Kondylakis, Haridimos; Kolokotroni, Eleni; Kourou, Konstantina; Lemos, Raquel; Manica, Isabel; Manikis, George; Marzorati, Chiara; Mattson, Johanna; Travado, Luzia; Tziraki‐Segal, Chariklia; Fotiadis, Dimitris; Poikonen‐Saksela, Paula; Simos, PanagiotisObjective:This study aimed to described istinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months followinga breast cancer diagnosis,and identify the medical, socio-demographic,lifestyle, and psychologica lfactors that predict these trajectories.Methods:474 females (mean age=55.79 years) were enrolled in the first weeksafter surgery or biopsy. Data from seven assessmentpoints over 18 months, at 3-month intervals,were used. The two outcomeswere assessedat all points. Potential predictors were assessed at baseline and the first follow‐up. Machine‐ Learning techniques were used to detect latent patterns of change and identify the most important predictors. Results: Five trajectories were identified for each outcome: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well‐being (chronic distress). Psychological factors (i.e., negative affect, coping, sense of control, social support), age, and a few medical variables (e.g., symptoms, immune‐ related inflammation) predicted patients' participation in the delayed response and the chronic distress trajectories versus all other trajectories. Conclusions: There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine‐learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well‐being
- Free and Cued Selective Reminding Test is superior to the Wechsler Memory Scale in discriminating mild cognitive impairment from alzheimer's diseasePublication . Lemos, Raquel; Cunha, Catarina; Marôco, João Paulo; Afonso, Ana; Simões, Mário R.; Santana, IsabelAim: The Logical Memory (LM) and the Verbal Paired Associative Learning (VPAL) are subtests from the Wechsler Memory Scale commonly used to characterize the memory deficit of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). The Free and Cued Selective Reminding Test (FCSRT) was suggested to assess the memory impairment of AD spectrum patients by the International Working Group on AD. In the present study, we compared the properties of the tests and their accuracy in classifying aMCI and AD. Methods: A group of aMCI patients (n=85) and AD patients (n=43) were included. The reliability and the validity of the three tests were analyzed. Results: AD patients showed a significant pattern of worse impairment on all tests than aMCI. The FCSRT was able to classify more patients as having memory impairment in the aMCI group rather than the WMS subtests. The FCSRT proved to be good in discriminating the two groups in both lower and higher educational levels, whereas the LM was more useful in higher educated patients. Conclusions: Although the instruments had good results, the FCSRT was more accurate in discriminating MCI from AD, and less influenced by the educational level.