Gender difference and correlation between sexuality, thyroid hormones, cognitive, and physical functions in elderly fit

PURPOSE

Sexuality in the elderly is related to psycho-physical well-being. Aim of this study was to analyze the correlation between sexual health, thyroid hormones, cognitive functions, and physical fitness in elderly population.

METHODS

Fifty-one fit adults were recruited (age: 71.9 ± 5.3 years, 26 females and 25 males). Sexuality was evaluated using the Changes in Sexual Functioning Questionnaire-short form (CSFQ-14) and the Sexual Attitude Scale (SAS). Thyroid function was assessed by measuring serum TSH, FT3, and FT4. Cognitive functions and depressive symptoms were evaluated by the Mini Mental State Examination (MMSE) test and Geriatric Depression Scale (GDS) scores. Subjects' physical fitness was evaluated using the following tests: Short Physical Performance Battery (SPPB), Handgrip test (HG), Timed Up and Go test (TUG), and 2-Minute step test (ST).

RESULTS

CSFQ-14 positively correlated with MMSE (p < 0.05) and negatively with GDS (p < 0.05), while thyroid function was not correlated with sexuality, in both genders. A negative relationship between FT4 vs. weight, FT3 vs. HG and FT3/FT4 ratio vs. ST were found (p = 0.05) in females, while in males, it occurred for TSH vs. TUG (p < 0.05); a positive relationship existed in females between FT4 vs. ST (p < 0.05). Finally, CSFQ-14 was significantly correlated with SPPB (p < 0.05), CST, TUG, and ST (p < 0.01), in both genders.

CONCLUSION

We demonstrated a strict relationship between active sexuality, preserved cognitive function and appropriate physical fitness in elderly subjects, independently from gender. Our preliminary data suggest that in elderly fit population, peripheral thyroxin deiodination may be a useful predictor of better physical performance and more successful aging.

The Detection of Malingering: A New Tool to Identify Made-Up Depression

Major depression is a high-prevalence mental disease with major socio-economic impact, for both the direct and the indirect costs. Major depression symptoms can be faked or exaggerated in order to obtain economic compensation from insurance companies. Critically, depression is potentially easily malingered, as the symptoms that characterize this psychiatric disorder are not difficult to emulate. Although some tools to assess malingering of psychiatric conditions are already available, they are principally based on self-reporting and are thus easily faked. In this paper, we propose a new method to automatically detect the simulation of depression, which is based on the analysis of mouse movements while the patient is engaged in a double-choice computerized task, responding to simple and complex questions about depressive symptoms. This tool clearly has a key advantage over the other tools: the kinematic movement is not consciously controllable by the subjects, and thus it is almost impossible to deceive. Two groups of subjects were recruited for the study. The first one, which was used to train different machine-learning algorithms, comprises 60 subjects (20 depressed patients and 40 healthy volunteers); the second one, which was used to test the machine-learning models, comprises 27 subjects (9 depressed patients and 18 healthy volunteers). In both groups, the healthy volunteers were randomly assigned to the liars and truth-tellers group. Machine-learning models were trained on mouse dynamics features, which were collected during the subject response, and on the number of symptoms reported by participants. Statistical results demonstrated that individuals that malingered depression reported a higher number of depressive and non-depressive symptoms than depressed participants, whereas individuals suffering from depression took more time to perform the mouse-based tasks compared to both truth-tellers and liars. Machine-learning models reached a classification accuracy up to 96% in distinguishing liars from depressed patients and truth-tellers. Despite this, the data are not conclusive, as the accuracy of the algorithm has not been compared with the accuracy of the clinicians; this study presents a possible useful method that is worth further investigation.