Mining patterns associated with mobility outcomes in home healthcare

Sanjoy Dey, Jacob Cooner, Connie W. Delaney, Joanna Fakhoury, Vipin Kumar, Gyorgy Simon, Michael Steinbach, Jeremy Weed, Bonnie L. Westra

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Background: Mobility is critical for self-management. Understanding factors associated with improvement in mobility during home healthcare can help nurses tailor interventions to improve mobility outcomes and keep patients safely at home. Objectives: The aims were to (a) identify patient and support system factors associated with mobility improvement during home care, (b) evaluate consistency of factors across groups defined by mobility status at the start of home care, and (c) identify patterns of factors associated with improvement and no improvement in mobility within each group. Methods: Outcome and Assessment Information Set data extracted from a national convenience sample of 270,634 patient records collected from October 1, 2008 to December 31, 2009 from 581 Medicare-certified, home healthcare agencies were used. Patients were placed into groups based on mobility scores at admission. Odds ratios were used to index associations of factors with improvement at discharge. Discriminative pattern mining was used to discover patterns associated with improvement of mobility. Results: Overall, mobility improved for 49.4% of patients; improvement occurred most frequently (80%) among patients who were able, at admission, to walk only with the supervision or assistance of another person at all times. Numerous factors associated with improvement in mobility outcome were similar across the groups (except for those who were chairfast but were able to wheel themselves independently); however, the number, strength, and direction of associations varied. In most groups, data mining-discovered patterns of factors associated with the mobility outcome were composed of combinations of functional and cognitive status and the type and amount of help required at home. Discussion: This study provides new data mining-based information about how factors associated with improvement inmobility group together and vary by mobility at admission. These approaches have potential to provide new insights for clinicians to tailor interventions for improvement of mobility.

Original languageEnglish (US)
Pages (from-to)235-245
Number of pages11
JournalNursing research
Issue number4
StatePublished - 2015

Bibliographical note

Publisher Copyright:
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.


  • Ambulation
  • Data mining
  • Home care
  • Informatics
  • Outcome and assessment information set (OASIS)


Dive into the research topics of 'Mining patterns associated with mobility outcomes in home healthcare'. Together they form a unique fingerprint.

Cite this