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Exploring Factors Associated with Functional Change and Predictors of Participation Improvement-A Two Years Follow-Up on People with Depression.

Wen-Chou ChiChia-Feng YenTsan-Hon LiouKwang-Hwa ChangHua-Fang LiaoYa-Li Chang
Published in: International journal of environmental research and public health (2021)
The purpose of this study is to understand the functional status distribution and to explore the factors associated with changes in functional status and social participation in people with depression using two-year follow-up data. Subjects were selected from the Taiwan Databank of Persons with Disabilities (TDPD) if they had an evaluation date between July 2012 and 31 December 2017. We used data for 1138 individuals with multiple evaluation records and who were diagnosed with depression. The WHO Disability Assessment Schedule 2.0 (WHODAS 2.0) was the primary functional status measure. Other factors selected from the TDPD included social demographic data, living situation, employment status, economic status, and educational level. The results show scores in all dimensions of the WHODAS 2.0 declined over two years, especially in the domains of cognition, household activities, social participation, and total WHODAS 2.0 score. Aging groups showed poor recovery in cognition, getting along with others, and household activities. People living in suburban areas showed poorer recovery than people living in rural and urban areas in cognition, self-care, and general function (total score of WHODAS 2.0). Employment was also strongly associated with functional recovery in household activities, social participation, and general function. The original scores for cognition and getting along with others showed a significant negative relationship with social participation improvement. Our results can be used by policy makers to provide resources and conduct investigations, and by clinicians when making rehabilitation plans.
Keyphrases
  • healthcare
  • mental health
  • physical activity
  • depressive symptoms
  • mild cognitive impairment
  • electronic health record
  • white matter
  • public health
  • sleep quality
  • palliative care
  • multiple sclerosis
  • machine learning