Hearing loss is associated with important adverse effects on the quality of life of elderly persons, effects which are reversible with hearing aids.

Hearing impairment is one of the most common chronic health problems of elderly Americans. Although adverse effects on quality of life are thought to be considerable, they have not been rigorously evaluated. This study was designed to identify the types and extent of dysfunction experienced by elderly individuals with hearing loss, and to define the most appropriate measures for assessing this dysfunction. Elderly male veterans attending a primary care clinic were screened for hearing loss and had their quality of life assessed with a comprehensive battery of disease-specific and generic measures. Of 472 people who had their hearing tested, 106 had hearing loss. Hearing loss was associated with significant emotional (P = .0001), social (P = .0001), and communication (P = .02) dysfunction. Most individuals (66%) perceived these dysfunctions as severe handicaps even though audiologic loss revealed only mild to moderate impairment (pure tone average loss, 27-55 dB). Adverse effects were best detected with disease-specific rather than generic functional status measures. We conclude that hearing impairment is associated with important adverse effects on the quality of life of elderly individuals, and that these effects are perceived as severe handicaps even by individuals with only mild to moderate degrees of hearing loss.

BackgroundAdequate health literacy is important for people to maintain good health and manage diseases and injuries. Educational text, either retrieved from the Internet or provided by a doctor’s office, is a popular method to communicate health-related information. Unfortunately, it is difficult to write text that is easy to understand, and existing approaches, mostly the application of readability formulas, have not convincingly been shown to reduce the difficulty of text.ObjectiveTo develop an evidence-based writer support tool to improve perceived and actual text difficulty. To this end, we are developing and testing algorithms that automatically identify difficult sections in text and provide appropriate, easier alternatives; algorithms that effectively reduce text difficulty will be included in the support tool. This work describes the user evaluation with an independent writer of an automated simplification algorithm using term familiarity.MethodsTerm familiarity indicates how easy words are for readers and is estimated using term frequencies in the Google Web Corpus. Unfamiliar words are algorithmically identified and tagged for potential replacement. Easier alternatives consisting of synonyms, hypernyms, definitions, and semantic types are extracted from WordNet, the Unified Medical Language System (UMLS), and Wiktionary and ranked for a writer to choose from to simplify the text. We conducted a controlled user study with a representative writer who used our simplification algorithm to simplify texts. We tested the impact with representative consumers. The key independent variable of our study is lexical simplification, and we measured its effect on both perceived and actual text difficulty. Participants were recruited from Amazon’s Mechanical Turk website. Perceived difficulty was measured with 1 metric, a 5-point Likert scale. Actual difficulty was measured with 3 metrics: 5 multiple-choice questions alongside each text to measure understanding, 7 multiple-choice questions without the text for learning, and 2 free recall questions for information retention.ResultsNinety-nine participants completed the study. We found strong beneficial effects on both perceived and actual difficulty. After simplification, the text was perceived as simpler (P<.001) with simplified text scoring 2.3 and original text 3.2 on the 5-point Likert scale (score 1: easiest). It also led to better understanding of the text (P<.001) with 11% more correct answers with simplified text (63% correct) compared to the original (52% correct). There was more learning with 18% more correct answers after reading simplified text compared to 9% more correct answers after reading the original text (P=.003). There was no significant effect on free recall.ConclusionsTerm familiarity is a valuable feature in simplifying text. Although the topic of the text influences the effect size, the results were convincing and consistent.

In this guide, we introduce researchers in the behavioral sciences in general and MIS in particular to text analysis as done with latent semantic analysis (LSA). The guide contains hands-on annotated code samples in R that walk the reader through a typical process of acquiring relevant texts, creating a semantic space out of them, and then projecting words, phrase, or documents onto that semantic space to calculate their lexical similarities. R is an open source, popular programming language with extensive statistical libraries. We introduce LSA as a concept, discuss the process of preparing the data, and note its potential and limitations. We demonstrate this process through a sequence of annotated code examples: we start with a study of online reviews that extracts lexical insight about trust. That R code applies singular value decomposition (SVD). The guide next demonstrates a realistically large data analysis of Stack Exchange, a popular Q&A site for programmers. That R code applies an alternative sparse SVD method. All the code and data are available on github.com.