Credibility of self-reported health parameters in elderly population

AIM: Examining the credibility of self-reported height, weight, and blood pressure by the elderly population using a tablet in a retirement residence, and examining the influence of health beliefs on the self-reporting credibility. BACKGROUND: Obesity is a major problem with rising prevalence in the western world. Hypertension is also a significant risk factor for cardiovascular diseases. Self-report, remotely from the clinic, becomes even more essential when patients are encouraged to avoid visiting the clinic as during the COVID-19 pandemic. Self-reporting of height and weight is suspected of leading to underestimation of obesity prevalence in the population; however, it has not been well studied in the elderly population. The Health Belief Model tries to predict and explain decision making of patients based on the patient’s health beliefs. METHODS: Residents of a retirement home network filled a questionnaire about their health beliefs regarding hypertension and obesity and self-reported their height, weight, and blood pressure. Blood pressure, height, and weight were then measured and compared to the patients’ self-reporting. FINDINGS: Ninety residents, aged 84.90 ± 5.88, filled the questionnaire. From a clinical perspective, the overall gap between the measured and the self-reported BMI (M = 1.43, SD = 2.72), which represents an absolute gap of 0.74 kilograms and 2.95 centimeters, is expected to have only a mild influence on the physician’s clinical evaluation of the patient’s medical condition. This can allow the physician to estimate their patient’s BMI status before the medical consultation and physical examination upon the patient’s self-reporting. Patients’ dichotomous (normal/abnormal) self-report of their blood pressure condition was relatively credible: positive predictive value (PPV) of 77.78% for normal blood pressure (BP) and 78.57% for abnormal BP. The relatively high PPV of BP self-reporting demonstrates an option for the physician to recognize patients at risk. Regression analysis found no correlation between the anthropometric parameters and the Health Belief Model.