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Research ArticleOriginal Research

Use of Mobile Health (mHealth) Tools by Primary Care Patients in the WWAMI Region Practice and Research Network (WPRN)

Amy M. Bauer, Tessa Rue, Gina A. Keppel, Allison M. Cole, Laura-Mae Baldwin and Wayne Katon
The Journal of the American Board of Family Medicine November 2014, 27 (6) 780-788; DOI: https://doi.org/10.3122/jabfm.2014.06.140108
Amy M. Bauer
From the Department of Psychiatry and Behavioral Sciences (AMB, WK), the Department of Biostatistics (TR), the Institute of Translational Health Sciences (TR, GAK, AMC), and the Department of Family Medicine (GAK, AMC, L-MB), University of Washington, Seattle.
MD, MS
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Tessa Rue
From the Department of Psychiatry and Behavioral Sciences (AMB, WK), the Department of Biostatistics (TR), the Institute of Translational Health Sciences (TR, GAK, AMC), and the Department of Family Medicine (GAK, AMC, L-MB), University of Washington, Seattle.
PhD
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Gina A. Keppel
From the Department of Psychiatry and Behavioral Sciences (AMB, WK), the Department of Biostatistics (TR), the Institute of Translational Health Sciences (TR, GAK, AMC), and the Department of Family Medicine (GAK, AMC, L-MB), University of Washington, Seattle.
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Allison M. Cole
From the Department of Psychiatry and Behavioral Sciences (AMB, WK), the Department of Biostatistics (TR), the Institute of Translational Health Sciences (TR, GAK, AMC), and the Department of Family Medicine (GAK, AMC, L-MB), University of Washington, Seattle.
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Laura-Mae Baldwin
From the Department of Psychiatry and Behavioral Sciences (AMB, WK), the Department of Biostatistics (TR), the Institute of Translational Health Sciences (TR, GAK, AMC), and the Department of Family Medicine (GAK, AMC, L-MB), University of Washington, Seattle.
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Wayne Katon
From the Department of Psychiatry and Behavioral Sciences (AMB, WK), the Department of Biostatistics (TR), the Institute of Translational Health Sciences (TR, GAK, AMC), and the Department of Family Medicine (GAK, AMC, L-MB), University of Washington, Seattle.
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    Figure 1.

    Mobile phone ownership and mHealth use among primary care patients.

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    Table 1. Demographic and Clinical Characteristics of Patients Sampled from 6 Primary Care Practices in Washington, Wyoming, Alaska, and Idaho (June 2013)
    CharacteristicsPatients, n (%)
    Age, years (n = 858)
        18–24130 (15.2)
        25–34183 (21.3)
        35–44164 (19.1)
        45–54163 (19.0)
        55–64140 (16.3)
        ≥6578 (9.1)
    Female sex (n = 859)643 (74.9)
    Race/ethnicity (n = 867)
        White694 (80.1)
        Native American18 (2.1)
        Asian/Pacific Islander31 (3.6)
        African American21 (2.4)
        Latino42 (4.8)
        Other/multiracial61 (7.0)
    Any health literacy limitation (n = 844)526 (62.3)
    Any chronic medical condition (n = 909)575 (63.3)
    Depression (n = 808)*334 (41.3)
    • ↵* Depression is defined as a 2-item Patient Health Questionnaire score ≥3 or a history of depression endorsed as a medical condition.

    • View popup
    Table 2. Description of mHealth Use Among Primary Care Patients Who Report Such Use (n = 353)
    mHealth UsePrimary Care Patients Reporting Use
    Mobile health use and type
        Find health information324 (91.8)
        Use health apps202 (57.2)
        Track or manage health condition191 (54.1)
    Frequency of mHealth use (n = 349)
        Once a month or less130 (37.3)
        2–3 times a month111 (31.8)
        1–6 times a week70 (20.1)
        Once a day or more38 (10.9)
    Favorite app (n = 235)
        General health app (eg, WedMD, iTriage, Mayo Clinic)85 (36.2)
        Activity/fitness (eg, My Fitness Pal)34 (14.5)
        Weight/diet (eg, Weight Watchers, LoseIt)24 (10.2)
        Reproductive (menstrual, pregnancy, or infant trackers)19 (8.1)
        Web search (eg, Google, Bing)18 (7.7)
        More than one favorite app18 (7.7)
        Other (eg, goal trackers, smoking logs)14 (6.0)
        Patient portal (eg, e-care, MyChart)10 (4.3)
        Medication (eg, pharmacy app, pill trackers)7 (3.0)
        Disease specific (eg, glucose monitoring, mood monitoring)6 (2.6)
    Ever stopped using an app after a short time (n = 310)122 (39.4)
    Reasons cited for stopping use of an app after a short time (n = 98)
        Took too much time47 (48.0)
        Didn't do what you wanted32 (32.7)
        Problem with login/password6 (6.1)
        Other13 (13.3)
    How mHealth users learned about apps (n = 277)
        Doctor/clinic27 (9.8)
        Family/friend37 (13.4)
        Website/Internet150 (54.2)
        Flier/mail/other ad7 (2.5)
        Other (eg, “app store”)56 (20.2)
    Importance for PCPs to know about mHealth use (n = 311)
        Very important37 (11.9)
        Important59 (19.0)
        A little important93 (29.9)
        Not at all important122 (39.2)
    My doctor has recommended a health app (n = 322)22 (6.8)
    How useful this feature would be on your phone*
        Appointment reminders (n = 318)4.0 ± 1.4
        Medication reminders (n = 312)3.6 ± 1.7
        General health information (n = 314)3.5 ± 1.4
        Track progress (eg, mood, weight) (n = 310)3.5 ± 1.5
        Help changing a habit (n = 308)3.2 ± 1.7
        Feedback on how I'm doing (n = 312)3.2 ± 1.6
        Tell doctor how I'm doing (n = 311)3.2 ± 1.7
        Stress management/coping (n = 310)3.1 ± 1.7
        Support group/social network (n = 308)2.3 ± 1.8
        Tell friend/family how I'm doing (n = 305)1.9 ± 1.8
    • Data are number (%) or mean ± standard deviation.

    • ↵* Usefulness of the features was rated on a scale of 0 (least useful) to 5 (most useful).

    • PCPs, primary care providers.

    • View popup
    Table 3. Correlates of Smartphone Ownership Among All Patients
    Unadjusted ModelsMultivariate Model* (n = 866)
    Patients (n)OR (95% CI)P ValueAdjusted OR (95% CI)P Value
    Age (years)809
        18–24ReferenceReference
        25–341.13 (0.65–1.95).681.13 (0.65–1.98).66
        35–440.72 (0.41–1.25).240.77 (0.44–1.35).36
        45–540.36 (0.21–0.60)<.0010.39 (0.22–0.68).001
        55–640.18 (0.10–0.30)<.0010.21 (0.12–0.37)<.001
        ≥650.06 (0.03–0.13)<.0010.08 (0.04–0.17)<.001
    Sex
        MaleReferenceReference
        Female8110.87 (0.63–1.21).410.75 (0.51–1.09).13
    Race/ethnicity818
        WhiteReferenceReference
        Native American0.89 (0.33–2.45).830.78 (0.26–2.39).66
        Asian/Pacific Islander1.25 (0.56–2.79).580.74 (0.3–1.83).51
        African American2.61 (0.85–8.07).101.67 (0.51–5.47).39
        Latino2.04 (1.00–4.16).051.18 (0.55–2.53).67
        Other/multiracial1.05 (0.60–1.84).860.74 (0.41–1.37).34
    Any health literacy limitation7970.87 (0.65–1.17).370.86 (0.62–1.2).38
    Any chronic medical condition8570.5 (0.37–0.67)<.0010.74 (0.53–1.04).08
    Depression7620.82 (0.61–1.10).180.81 (0.58–1.14).23
    • All models were adjusted for clustering within clinics.

    • ↵* The multivariate model reports results from a single model that includes all of the independent variables in the table. Multiple imputation was used for missing variables in the multivariate model.

    • CI, confidence interval; OR, odds ratio.

    • View popup
    Table 4. Correlates of mHealth Use Among All Patients
    Unadjusted ModelsMultivariate Model* (n = 879)
    Patients (n)OR (95% CI)P ValueAdjusted OR (95% CI)P Value
    Age (years)822
        18–24ReferenceReference
        25–341.00 (0.62–1.62).991.00 (0.61–1.62).99
        35–440.64 (0.39–1.05).080.66 (0.4–1.1).11
        45–540.31 (0.18–0.51)<.0010.32 (0.19–0.55)<.001
        55–640.14 (0.08–0.25)<.0010.16 (0.09–0.3)<.001
        ≥650.05 (0.02–0.12)<.0010.07 (0.03–0.16)<.001
    Sex
        MaleReferenceReference
        Female8251.28 (0.92–1.79).141.15 (0.79–1.66).47
    Race/ethnicity831
        WhiteReferenceReference
        Native American1.33 (0.50–3.52).571.16 (0.4–3.34).78
        Asian/Pacific Islander1.11 (0.52–2.37).790.72 (0.31–1.66).44
        African American0.93 (0.36–2.42).890.63 (0.23–1.74).37
        Latino2.29 (1.20–4.37).011.47 (0.73–2.93).28
        Other/multiracial1.30 (0.76–2.25).340.91 (0.5–1.64).75
    Any health literacy limitation8101.00 (0.74–1.33).980.98 (0.7–1.36).89
    Any chronic medical condition8700.60 (0.45–0.79)<.0010.91 (0.66–1.26).58
    Depression7740.96 (0.71–1.29).780.95 (0.68–1.32).75
    • All models are adjusted for clustering within clinics.

    • ↵* The multivariate model reports results from a single model that includes all of the independent variables in the table. Multiple imputation was used for missing variables in the multivariate model.

    • CI, confidence interval; OR, odds ratio.

    • View popup
    Appendix Table 1. Correlates of Smartphone Ownership Among Mobile Phone Owners
    Unadjusted ModelsMultivariate Model* (n = 783)
    Patients (n)OR (95% CI)P ValueAdjusted OR (95% CI)P Value
    Age (years)738
        18–24ReferenceReference
        25–341.15 (0.60–2.20).671.14 (0.6–2.17).69
        35–440.64 (0.34–1.21).170.69 (0.36–1.31).25
        45–540.27 (0.15–0.49)<.0010.29 (0.16–0.54)<.001
        55–640.14 (0.08–0.27)<.0010.16 (0.09–0.31)<.001
        ≥650.05 (0.02–0.11)<.0010.06 (0.03–0.14)<.001
    Sex
        MaleReferenceReference
        Female7360.81 (0.56–1.17).260.69 (0.46–1.05).09
    Race/ethnicity743
        WhiteReferenceReference
        Native American0.93 (0.30–2.83).890.71 (0.21–2.46).59
        Asian/Pacific Islander1.45 (0.58–3.58).420.87 (0.3–2.51).80
        African American2.07 (0.67–6.41).211.27 (0.38–4.26).70
        Latino1.95 (0.90–4.20).091.05 (0.46–2.43).91
        Other/multiracial1.26 (0.66–2.37).480.86 (0.43–1.73).67
    Any health literacy limitation7250.92 (0.67–1.27).620.92 (0.63–1.32).64
    Any chronic medical condition7760.56 (0.41–0.77)<.0010.86 (0.6–1.25).43
    Depression6900.84 (0.61–1.15).280.79 (0.55–1.16).23
    • All models are adjusted for clustering within clinics.

    • ↵* The multivariate model reports results from a single model that includes all of the independent variables in the table. Multiple imputation was used for missing variables in the multivariate model.

    • CI, confidence interval; OR, odds ratio.

    • View popup
    Appendix Table 2. Correlates of mHealth Use Among Smartphone Owners
    Unadjusted ModelsMultivariate Model* (n = 498)
    Patients (n)OR (95% CI)P ValueAdjusted OR (95% CI)P Value
    Age (years)473
        18–24ReferenceReference
        25–340.83 (0.43–1.59).580.83 (0.42–1.61).58
        35–440.72 (0.37–1.42).350.71 (0.35–1.43).33
        45–540.41 (0.21–0.83).010.43 (0.21–0.89).02
        55–640.26 (0.12–0.56).0010.26 (0.12–0.6).001
        ≥650.17 (0.05–0.57).0040.19 (0.05–0.68).011
    Sex
        MaleReferenceReference
        Female4711.77 (1.14–2.74).011.59 (0.99–2.55).06
    Race/ethnicity476
        WhiteReferenceReference
        Native American3.51 (0.43–28.75).243.02 (0.38–24.18).30
        Asian/Pacific Islander0.95 (0.35–2.59).930.88 (0.3–2.57).81
        African American0.44 (0.15–1.29).130.36 (0.11–1.1).07
        Latino1.77 (0.70–4.44).231.43 (0.55–3.73).47
        Other/multiracial1.49 (0.65–3.38).351.21 (0.51–2.85).67
    Any health literacy limitation4641.2 (0.79–1.81).391.2 (0.77–1.88).43
    Any chronic medical condition4930.95 (0.64–1.41).811.2 (0.77–1.88).41
    Depression4361.28 (0.83–1.99).271.2 (0.75–1.94).44
    • All models are adjusted for clustering within clinics.

    • ↵* The multivariate model reports results from a single model that includes all of the independent variables in the table. Multiple imputation was used for missing variables in the multivariate model.

    • CI, confidence interval; OR, odds ratio.

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The Journal of the American Board of Family     Medicine: 27 (6)
The Journal of the American Board of Family Medicine
Vol. 27, Issue 6
November-December 2014
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Use of Mobile Health (mHealth) Tools by Primary Care Patients in the WWAMI Region Practice and Research Network (WPRN)
Amy M. Bauer, Tessa Rue, Gina A. Keppel, Allison M. Cole, Laura-Mae Baldwin, Wayne Katon
The Journal of the American Board of Family Medicine Nov 2014, 27 (6) 780-788; DOI: 10.3122/jabfm.2014.06.140108

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Use of Mobile Health (mHealth) Tools by Primary Care Patients in the WWAMI Region Practice and Research Network (WPRN)
Amy M. Bauer, Tessa Rue, Gina A. Keppel, Allison M. Cole, Laura-Mae Baldwin, Wayne Katon
The Journal of the American Board of Family Medicine Nov 2014, 27 (6) 780-788; DOI: 10.3122/jabfm.2014.06.140108
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