ABSTRACT
Nocebo effects, denoting unfavourable outcomes after a medical intervention because of negative expectations rather than a direct pharmacologic action, are an important cause of dropout from clinical trials and non-adherence to medication, and may be especially pertinent for older adults. Several characteristics of aging individuals and their medical care have a potential to augment nocebo susceptibility, such as depression and anxiety, neurodegenerative diseases and chronic pain states, adverse healthcare experiences, generic drug use, age-related stereotypes, and strained patient-physician communication. Nocebo-related research in older adults is hindered by under-representation in clinical trials, medical complexity of geriatric patients, and lack of validated tools to accurately assess susceptibility and efficacy of preventive efforts.
Subject(s)
Aging/psychology , Nocebo Effect , Humans , Risk FactorsABSTRACT
OBJECTIVES: To propose a simple frailty screening tool able to identify frailty profiles. DESIGN: Cross-sectional observational study. SETTING: Participants were recruited in 3 different clinical settings: a primary care outpatient clinic (RURAL population, N=591), a geriatric day clinic (DAY-CLINIC population, N=76) and healthy volunteers (URBAN population, N=147). PARTICIPANTS: A total of 817 older adults (>70 years old) living at home were included. INTERVENTION: A 9-item questionnaire (Lorraine Frailty Profiling Screening Scale, LoFProSS), constructed by an experts' working group, was administered to participants by health professionals. MEASUREMENTS: A Multiple Correspondence Analysis (MCA) followed by a hierarchical clustering of the results of the MCA performed in each population was conducted to identify participant profiles based on their answers to LoFProSS. A response pattern algorithm was resultantly identified in the RURAL (main) population and subsequently applied to the URBAN and DAY-CLINIC populations and, in these populations, the two classification methods were compared. Finally, clinically-relevant profiles were generated and compared for their ability to similarly classify subjects. RESULTS: The response pattern differed between the 3 sub-populations for all 9 items, revealing significant intergroup differences (1.2±1.4 positive responses for URBAN vs. 2.1±1.3 for RURAL vs. 3.1±2.1 for DAY-CLINIC, all p<0.05). Five clusters were highlighted in the main RURAL population: "non-frail", "hospitalizations", "physical problems", "social isolation" and "behavioral", with similar clusters highlighted in the remaining two populations. Identification of the response pattern algorithm in the RURAL population yielded a second classification approach, with 83% of tested participants classified in the same cluster using the 2 different approaches. Three clinically-relevant profiles ("non-frail" profile, "physical frailty and diseases" profile and "cognitive-psychological frailty" profile) were subsequently generated from the 5 clusters. A similar double classification approach as above was applied to these 3 profiles revealing a very high percentage (95.6%) of similar profile classifications using both methods. CONCLUSION: The present results demonstrate the ability of LoFProSS to highlight 3 frailty-related profiles, in a consistent manner, among different older populations living at home. Such scale could represent an added value as a simple frailty screening tool for accelerated and better-targeted investigations and interventions.
