The use of smartphone-derived location data to evaluate participation following critical illness: A pilot observational cohort study
Disability is common following critical illness, impacting the quality of life of survivors, and is difficult to measure. ‘Participation’ can be quantified as involvement in life outside of their home requiring movement from their home to other locations. Participation restriction is a key element o...
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Published in | Australian critical care Vol. 35; no. 3; pp. 225 - 232 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
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Elsevier Ltd
01.05.2022
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Abstract | Disability is common following critical illness, impacting the quality of life of survivors, and is difficult to measure. ‘Participation’ can be quantified as involvement in life outside of their home requiring movement from their home to other locations. Participation restriction is a key element of disability, and following critical illness, participation may be diminished. It may be possible to quantify this change using pre-existing smartphone data.
The feasibility of extracting location data from smartphones of survivors of intensive care unit (ICU) admission and assessing participation, using location-based outcomes, during recovery from critical illness was evaluated.
Fifty consecutively admitted, consenting adult survivors of non-elective admission to ICU of greater than 48-h duration were recruited to a prospective observational cohort study where they were followed up at 3 and 6 months following discharge. The feasibility of extracting location data from survivors' smartphones and creating location-derived outcomes assessing participation was investigated over three 28-d study periods: pre-ICU admission and at 3 and 6 months following discharge. The following were calculated: time spent at home; the number of destinations visited; linear distance travelled; and two ‘activity spaces’, a minimum convex polygon and standard deviation ellipse.
Results are median [interquartile range] or n (%). The number of successful extractions was 9/50 (18%), 12/39 (31%), and 13/33 (39%); the percentage of time spent at home was 61 [56–68]%, 77 [66–87]%, and 67 [58–77]% (P = 0.16); the number of destinations visited was 34 [18–64], 38 [22–63], and 65 [46–88] (P = 0.02); linear distance travelled was 367 [56–788], 251 [114–323], and 747 [326–933] km over 28 d (P = 0.02), pre-ICU admission and at 3 and 6 months following ICU discharge, respectively. Activity spaces were successfully created.
Limited smartphone ownership, missing data, and time-consuming data extraction limit current implementation of mass extraction of location data from patients’ smartphones to aid prognostication or measure outcomes. The number of journeys taken and the linear distance travelled increased between 3 and 6 months, suggesting participation may improve over time. |
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AbstractList | BACKGROUNDDisability is common following critical illness, impacting the quality of life of survivors, and is difficult to measure. 'Participation' can be quantified as involvement in life outside of their home requiring movement from their home to other locations. Participation restriction is a key element of disability, and following critical illness, participation may be diminished. It may be possible to quantify this change using pre-existing smartphone data. OBJECTIVESThe feasibility of extracting location data from smartphones of survivors of intensive care unit (ICU) admission and assessing participation, using location-based outcomes, during recovery from critical illness was evaluated. METHODSFifty consecutively admitted, consenting adult survivors of non-elective admission to ICU of greater than 48-h duration were recruited to a prospective observational cohort study where they were followed up at 3 and 6 months following discharge. The feasibility of extracting location data from survivors' smartphones and creating location-derived outcomes assessing participation was investigated over three 28-d study periods: pre-ICU admission and at 3 and 6 months following discharge. The following were calculated: time spent at home; the number of destinations visited; linear distance travelled; and two 'activity spaces', a minimum convex polygon and standard deviation ellipse. RESULTSResults are median [interquartile range] or n (%). The number of successful extractions was 9/50 (18%), 12/39 (31%), and 13/33 (39%); the percentage of time spent at home was 61 [56-68]%, 77 [66-87]%, and 67 [58-77]% (P = 0.16); the number of destinations visited was 34 [18-64], 38 [22-63], and 65 [46-88] (P = 0.02); linear distance travelled was 367 [56-788], 251 [114-323], and 747 [326-933] km over 28 d (P = 0.02), pre-ICU admission and at 3 and 6 months following ICU discharge, respectively. Activity spaces were successfully created. CONCLUSIONLimited smartphone ownership, missing data, and time-consuming data extraction limit current implementation of mass extraction of location data from patients' smartphones to aid prognostication or measure outcomes. The number of journeys taken and the linear distance travelled increased between 3 and 6 months, suggesting participation may improve over time. Disability is common following critical illness, impacting the quality of life of survivors, and is difficult to measure. 'Participation' can be quantified as involvement in life outside of their home requiring movement from their home to other locations. Participation restriction is a key element of disability, and following critical illness, participation may be diminished. It may be possible to quantify this change using pre-existing smartphone data. The feasibility of extracting location data from smartphones of survivors of intensive care unit (ICU) admission and assessing participation, using location-based outcomes, during recovery from critical illness was evaluated. Fifty consecutively admitted, consenting adult survivors of non-elective admission to ICU of greater than 48-h duration were recruited to a prospective observational cohort study where they were followed up at 3 and 6 months following discharge. The feasibility of extracting location data from survivors' smartphones and creating location-derived outcomes assessing participation was investigated over three 28-d study periods: pre-ICU admission and at 3 and 6 months following discharge. The following were calculated: time spent at home; the number of destinations visited; linear distance travelled; and two 'activity spaces', a minimum convex polygon and standard deviation ellipse. Results are median [interquartile range] or n (%). The number of successful extractions was 9/50 (18%), 12/39 (31%), and 13/33 (39%); the percentage of time spent at home was 61 [56-68]%, 77 [66-87]%, and 67 [58-77]% (P = 0.16); the number of destinations visited was 34 [18-64], 38 [22-63], and 65 [46-88] (P = 0.02); linear distance travelled was 367 [56-788], 251 [114-323], and 747 [326-933] km over 28 d (P = 0.02), pre-ICU admission and at 3 and 6 months following ICU discharge, respectively. Activity spaces were successfully created. Limited smartphone ownership, missing data, and time-consuming data extraction limit current implementation of mass extraction of location data from patients' smartphones to aid prognostication or measure outcomes. The number of journeys taken and the linear distance travelled increased between 3 and 6 months, suggesting participation may improve over time. |
Author | Summers, Matthew J. Finnis, Mark E. Lange, Jarrod Deane, Adam M. Andrawos, Alice Chapman, Marianne J. Gluck, Samuel |
Author_xml | – sequence: 1 givenname: Samuel surname: Gluck fullname: Gluck, Samuel email: gluck.sam@gmail.com organization: Discipline of Acute Care Medicine, The University of Adelaide, AHMS, North Terrace, Adelaide, SA 5000, Australia – sequence: 2 givenname: Alice surname: Andrawos fullname: Andrawos, Alice email: aliceandrawos@gmail.com organization: Discipline of Acute Care Medicine, The University of Adelaide, AHMS, North Terrace, Adelaide, SA 5000, Australia – sequence: 3 givenname: Matthew J. surname: Summers fullname: Summers, Matthew J. email: Matthew.Summers@sa.gov.au organization: Discipline of Acute Care Medicine, The University of Adelaide, AHMS, North Terrace, Adelaide, SA 5000, Australia – sequence: 4 givenname: Jarrod surname: Lange fullname: Lange, Jarrod email: jarrod.lange@adelaide.edu.au organization: Hugo Centre for Population and Housing, University of Adelaide, Napier Building, North Terrace, Adelaide, SA 5000, Australia – sequence: 5 givenname: Marianne J. surname: Chapman fullname: Chapman, Marianne J. email: marianne.chapman@sa.gov.au organization: Discipline of Acute Care Medicine, The University of Adelaide, AHMS, North Terrace, Adelaide, SA 5000, Australia – sequence: 6 givenname: Mark E. surname: Finnis fullname: Finnis, Mark E. email: mark.finnis@adelaide.edu.au organization: Discipline of Acute Care Medicine, The University of Adelaide, AHMS, North Terrace, Adelaide, SA 5000, Australia – sequence: 7 givenname: Adam M. surname: Deane fullname: Deane, Adam M. email: adam.m.deane@gmail.com organization: Intensive Care Unit, The Royal Melbourne Hospital, 300 Grattan St, Parkville, Melbourne, VIC 3010, Australia |
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Snippet | Disability is common following critical illness, impacting the quality of life of survivors, and is difficult to measure. ‘Participation’ can be quantified as... Disability is common following critical illness, impacting the quality of life of survivors, and is difficult to measure. 'Participation' can be quantified as... BACKGROUNDDisability is common following critical illness, impacting the quality of life of survivors, and is difficult to measure. 'Participation' can be... |
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SubjectTerms | Adult Cohort Studies Critical Illness Global position system Humans Intensive Care Units Outcomes Patient Discharge Prospective Studies Quality of Life Smartphone Technology |
Title | The use of smartphone-derived location data to evaluate participation following critical illness: A pilot observational cohort study |
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