Introduction/objectives <p>This study aims to develop a new scale to measure treatment adherence in rheumatic diseases and to measure participants’ attitudes towards treatment adherence.</p> Method <p>The data were obtained using the “treatment adherence scale for rheumatic diseases,” which was developed to measure treatment adherence in rheumatic conditions. The research sample comprised 402 patients, and demographic variables were collected. During the scale development process, the Pearson correlation coefficient, Bartlett’s test, the Kaiser–Meyer–Olkin value, and exploratory and confirmatory factor analysis methods were used. In confirmatory factor analysis, the validity of the model was evaluated using root mean square error of approximation, non-normed fit index/Tucker–Lewis indices, comparative fit index, normed fit index, and chi-square goodness-of-fit test. In reliability analyses, composite reliability and Cronbach’s alpha coefficient were calculated. For descriptive analyses, frequency distributions, measures of dispersion and central tendency, and multiple linear regression analyses were employed.</p> Results <p>Findings related to the scale development phase: The developed “treatment adherence scale for rheumatic diseases” consists of four factors: behavioral adherence to the treatment process, collaboration and communication with healthcare professionals, medication awareness and consciousness, and medication treatment commitment. The scale comprises 20 items and is scored on a Likert scale. The scale’s reliability was 0.92, explaining 55.7% of the total variance. According to confirmatory factor analysis results, the fit indices were found to be χ<sup>2</sup>/df ratio of 1.99; root mean square error of approximation = 0.07; comparative fit index = 0.99; normed fit index = 0.99; non-normed fit index = 0.99.</p> <p>The multiple linear regression analysis indicated that the model was significant (<i>F</i> = 5.930; <i>p</i> = 0.001) and that approximately 8% of the variation in treatment adherence scores could be explained (<i>R</i><sup>2</sup> = 0.083).</p> Conclusions <p>The findings reveal that the researchers’ treatment adherence scale for rheumatic diseases is a reliable and valid assessment instrument. Using this scale may enhance patient care and treatment management in rheumatic disorders. Furthermore, understanding characteristics that influence treatment adherence might improve treatment efficacy and help lower the total societal burden.</p> <p><Table Float="No" ID="Taba"> <tgroup cols="2"> <colspec align="left" colname="c1" colnum="1" /> <colspec align="left" colname="c2" colnum="2" /> <tbody> <row> <entry align="left" nameend="c2" namest="c1"> <p><b>Key Points</b></p> <p>• <i>This study reveals that a newly developed scale for assessing treatment adherence in rheumatic patients is valid and reliable</i>.</p> <p>• <i>The scale offers a thorough assessment by examining patients’' treatment approaches from multiple perspectives, including behavioral, communicative, and drug awareness</i>.</p> <p>• <i>Age and particular individual factors can influence treatment adherence; therefore, customized encouragement and interventions are important.</i>.</p> </entry> </row> </tbody> </tgroup> </Table></p>

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Development of the treatment adherence scale for rheumatic diseases

  • Ibrahim Bashan,
  • Gulsah Yasa Ozturk,
  • Huseyin Selvi,
  • Burhan Fatih Kocyigit,
  • Burak Okyar

摘要

Introduction/objectives

This study aims to develop a new scale to measure treatment adherence in rheumatic diseases and to measure participants’ attitudes towards treatment adherence.

Method

The data were obtained using the “treatment adherence scale for rheumatic diseases,” which was developed to measure treatment adherence in rheumatic conditions. The research sample comprised 402 patients, and demographic variables were collected. During the scale development process, the Pearson correlation coefficient, Bartlett’s test, the Kaiser–Meyer–Olkin value, and exploratory and confirmatory factor analysis methods were used. In confirmatory factor analysis, the validity of the model was evaluated using root mean square error of approximation, non-normed fit index/Tucker–Lewis indices, comparative fit index, normed fit index, and chi-square goodness-of-fit test. In reliability analyses, composite reliability and Cronbach’s alpha coefficient were calculated. For descriptive analyses, frequency distributions, measures of dispersion and central tendency, and multiple linear regression analyses were employed.

Results

Findings related to the scale development phase: The developed “treatment adherence scale for rheumatic diseases” consists of four factors: behavioral adherence to the treatment process, collaboration and communication with healthcare professionals, medication awareness and consciousness, and medication treatment commitment. The scale comprises 20 items and is scored on a Likert scale. The scale’s reliability was 0.92, explaining 55.7% of the total variance. According to confirmatory factor analysis results, the fit indices were found to be χ2/df ratio of 1.99; root mean square error of approximation = 0.07; comparative fit index = 0.99; normed fit index = 0.99; non-normed fit index = 0.99.

The multiple linear regression analysis indicated that the model was significant (F = 5.930; p = 0.001) and that approximately 8% of the variation in treatment adherence scores could be explained (R2 = 0.083).

Conclusions

The findings reveal that the researchers’ treatment adherence scale for rheumatic diseases is a reliable and valid assessment instrument. Using this scale may enhance patient care and treatment management in rheumatic disorders. Furthermore, understanding characteristics that influence treatment adherence might improve treatment efficacy and help lower the total societal burden.

Key Points

This study reveals that a newly developed scale for assessing treatment adherence in rheumatic patients is valid and reliable.

The scale offers a thorough assessment by examining patients’' treatment approaches from multiple perspectives, including behavioral, communicative, and drug awareness.

Age and particular individual factors can influence treatment adherence; therefore, customized encouragement and interventions are important..