Kön, kroppsbild och dysfunktionella tankar : en sambandsstudie

Body esteem is the affective aspect of body image, which is shaped by social experience. Compared with men, women have a more negative body image, which is more frequently correlated with depression and dysfunctional thoughts, especially in the case of eating disorders. The purpose of the present study was to examine gender differences in body esteem and its subcategories, and to find out whether there exists a stronger link between negative body esteem and higher levels of dysfunctional thoughts in women. The relationship between body esteem, dysfunctional thoughts and mental illness was examined. Participants were 73 college students doing social sciences and sports training educations. Body esteem questionnaire and dysfunctional thoughts questionnaire as well as a self made questionnaire on mental illness were used. Results showed that women had a more negative body esteem compared to men, especially considering weight. In women, there was a medium-strong negative relation between body esteem and dysfunctional thoughts. The results indicate that the norms for female body ideal that abound in the Western society have a negative affect on women's thinking and body image.Key words: Gender, body esteem, dysfunctional thoughts, mental illness.

Effekter av icke-farmakologiska metoder vid beteendemässiga och psykiska symtom hos personer som lever med Alzheimers sjukdom : En litteraturöversikt

Bakgrund: Alzheimers sjukdom (AD) är den vanligaste formen av demenssjukdomar och antalet människor som insjuknar i AD förväntas öka kraftigt med tiden. Dessutom kännetecknas personer med AD ofta av beteendemässiga och psykiska symtom (BPSD) som kan innefatta agitation, depression, vanföreställningar, oro, ångest, hallucinationer, sömnrubbningar, rastlöshet och apati. Dessa symtom kan orsaka lidande hos patienten och är svåra att hantera för både vårdgivaren och anhöriga, samt försvårar omvårdnadsarbetet. Syftet var att beskriva icke-farmakologiska metoder och effekter av dessa metoder vid omvårdnad av personer med Alzheimers sjukdom som har beteendemässiga och psykiska symtom. Metod: En litteraturöversikt bestående av 16 utvalda kvantitativa forskningsartiklar har genomförts. Artiklarna publicerades mellan år 2006-2016. Resultat. De studerade icke-farmakologiska metoderna var musikterapi, vissa typer av massage, reminiscence-terapi, vårdhundterapi och ljusterapi. Resultaten visade att icke-farmakologiska metoder kan ha en varierande effekt på BPSD. Litteraturöversikten visade att musikintervention var mest effektiv för att minska agitationsbeteende. Individualiserad musik i samband med speciella minnen minskade stress, fobier hos personer med svår demens. Intervention av handmassage, aromaterapi, taktil massage och terapeutisk beröring minskade aggression och agitationsbeteende. Vissa studier visade dock att fotmassageintervention och vårdhundterapi kunde öka verbal aggressivitet hos personer med demens, medan en annan studie visade att djurassisterade aktiviteter kunde minska nedstämdhet medan glädje och generell uppmärksamhet ökade. Effekten av ljusbehandling var förbättrad sömn, minskad depression, agitation och ätstörningar. Slutsats. Icke-farmakologiska metoder kan minska beteendemässiga och psykiska symtom hos personer med Alzheimers sjukdom, dock med varierande effekt. De varierande resultaten kan tolkas som att icke-farmakologiska metoder bör individanpassas och att det behövs vidare forskning inom området.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.