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

Amy M. Bauer, MD, MS; Tessa Rue, PhD; Gina A. Keppel, MPH; Allison M. Cole, MD, MPH; Laura Mae Baldwin, MD; Wayne Katon, MD

Disclosures

J Am Board Fam Med. 2014;27(6):780-788.

Abstract and Introduction

Abstract

Purpose The purpose of this study was to determine the prevalence of mobile health (mHealth) use among primary care patients and examine demographic and clinical correlates.

Methods Adult patients who presented to 1 of 6 primary care clinics in a practice-based research network in the northwest United States during a 2-week period received a survey that assessed smartphone ownership; mHealth use; sociodemographic characteristics (age, sex, race/ethnicity, health literacy); chronic conditions; and depressive symptoms (2-item Patient Health Questionnaire). Data analysis used descriptive statistics and mixed logistic regression.

Results Of 918 respondents (estimated response rate, 67.4%), 55% owned a smartphone, among whom 70% were mHealth users. In multivariate analyses, smartphone ownership and mHealth use were not associated with health literacy, chronic conditions, or depression but were less common among adults >45 years old (adjusted odds ratio, 0.07–0.39; P < .001). Only 10% of patients learned about mHealth tools from their physician, and few (31%) prioritized their provider's involvement.

Conclusions Use of mHealth technologies is lower among older adults but otherwise is common among primary care patients, including those with limited health literacy and those with chronic conditions. Findings support the potential role of mHealth in improving disease management among certain groups in need; however, greater involvement of health care providers may be important for realizing this potential.

Introduction

Mobile health (mHealth), which refers to the application of mobile or wireless communication technologies to health and health care,^[1] barely existed 5 years ago; it has since emerged as a multibillion-dollar industry.^[2] Recent data suggest that >90,000 consumer smartphone health applications ("apps") are now available for download.^[3] Few of these have been the subject of scientific study, including their potential risks,^[4] or to US Food and Drug Administration review or approval.^[1]

According to estimates from the Pew Internet & American Life Project, in 2012 nearly one third of mobile phone owners, and more than half of smartphone owners, have used their phone to look up health information—a rate that has nearly doubled since 2010.^[5] In Pew's general population survey, mHealth use was more common among individuals <50 years old, Latinos, African Americans, and those with higher socioeconomic status.

The potential for mHealth tools to represent an important advancement in health care, in particular for chronic disease care, has been recognized.^[1]Whereas an estimated 69% of the US adult population track at least 1 health indicator, such as activity, weight, or symptoms, only about 20% of those use technology to facilitate their tracking.^[6] Although health tracking is significantly more common among people living with chronic conditions, mHealth use has been reported as significantly less common among these individuals.^[5,6] Primary care is the setting in which most chronic disease care occurs, and although there some literature describes mHealth tools available to facilitate chronic disease management, the actual patterns of mHealth use among patients in primary care settings have not been documented.^[7–9] To address this gap in knowledge, this study aimed to determine the prevalence of mobile phone ownership and mHealth use among primary care patients in community-based clinics in the WWAMI region Practice and Research Network (WPRN) and to examine demographic and clinical correlates of use.

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Next Section

Abstract and Introduction
Methods
Results
Discussion
References

Table 1. Demographic and Clinical Characteristics of Patients Sampled from 6 Primary Care Practices in Washington, Wyoming, Alaska, and Idaho (June 2013)
Characteristics	Patients, n (%)
Age, years (n = 858)
18–24	130 (15.2)
25–34	183 (21.3)
35–44	164 (19.1)
45–54	163 (19.0)
55–64	140 (16.3)
≥65	78 (9.1)
Female sex (n = 859)	643 (74.9)
Race/ethnicity (n = 867)
White	694 (80.1)
Native American	18 (2.1)
Asian/Pacific Islander	31 (3.6)
African American	21 (2.4)
Latino	42 (4.8)
Other/multiracial	61 (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.

Table 2. Description of mHealth Use Among Primary Care Patients Who Report Such Use (n = 353)
mHealth Use	Primary Care Patients Reporting Use
Mobile health use and type
Find health information	324 (91.8)
Use health apps	202 (57.2)
Track or manage health condition	191 (54.1)
Frequency of mHealth use (n = 349)
Once a month or less	130 (37.3)
2–3 times a month	111 (31.8)
1–6 times a week	70 (20.1)
Once a day or more	38 (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 app	18 (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 time	47 (48.0)
Didn't do what you wanted	32 (32.7)
Problem with login/password	6 (6.1)
Other	13 (13.3)
How mHealth users learned about apps (n = 277)
Doctor/clinic	27 (9.8)
Family/friend	37 (13.4)
Website/Internet	150 (54.2)
Flier/mail/other ad	7 (2.5)
Other (eg, "app store")	56 (20.2)
Importance for PCPs to know about mHealth use (n = 311)
Very important	37 (11.9)
Important	59 (19.0)
A little important	93 (29.9)
Not at all important	122 (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

Table 3. Correlates of Smartphone Ownership Among All Patients
	Unadjusted Models			Multivariate Model* (n = 866)
	Patients (n)	OR (95% CI)	PValue	Adjusted OR (95% CI)	PValue
Age (years)	809
18–24		Reference		Reference
25–34		1.13 (0.65–1.95)	.68	1.13 (0.65–1.98)	.66
35–44		0.72 (0.41–1.25)	.24	0.77 (0.44–1.35)	.36
45–54		0.36 (0.21–0.60)	<.001	0.39 (0.22–0.68)	.001
55–64		0.18 (0.10–0.30)	<.001	0.21 (0.12–0.37)	<.001
≥65		0.06 (0.03–0.13)	<.001	0.08 (0.04–0.17)	<.001
Sex
Male		Reference		Reference
Female	811	0.87 (0.63–1.21)	.41	0.75 (0.51–1.09)	.13
Race/ethnicity	818
White		Reference		Reference
Native American		0.89 (0.33–2.45)	.83	0.78 (0.26–2.39)	.66
Asian/Pacific Islander		1.25 (0.56–2.79)	.58	0.74 (0.3–1.83)	.51
African American		2.61 (0.85–8.07)	.10	1.67 (0.51–5.47)	.39
Latino		2.04 (1.00–4.16)	.05	1.18 (0.55–2.53)	.67
Other/multiracial		1.05 (0.60–1.84)	.86	0.74 (0.41–1.37)	.34
Any health literacy limitation	797	0.87 (0.65–1.17)	.37	0.86 (0.62–1.2)	.38
Any chronic medical condition	857	0.5 (0.37–0.67)	<.001	0.74 (0.53–1.04)	.08
Depression	762	0.82 (0.61–1.10)	.18	0.81 (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.

Table 4. Correlates of mHealth Use Among All Patients
	Unadjusted Models			Multivariate Model* (n = 879)
	Patients (n)	OR (95% CI)	P Value	Adjusted OR (95% CI)	P Value
Age (years)	822
18–24		Reference		Reference
25–34		1.00 (0.62–1.62)	.99	1.00 (0.61–1.62)	.99
35–44		0.64 (0.39–1.05)	.08	0.66 (0.4–1.1)	.11
45–54		0.31 (0.18–0.51)	<.001	0.32 (0.19–0.55)	<.001
55–64		0.14 (0.08–0.25)	<.001	0.16 (0.09–0.3)	<.001
≥65		0.05 (0.02–0.12)	<.001	0.07 (0.03–0.16)	<.001
Sex
Male		Reference		Reference
Female	825	1.28 (0.92–1.79)	.14	1.15 (0.79–1.66)	.47
Race/ethnicity	831
White		Reference		Reference
Native American		1.33 (0.50–3.52)	.57	1.16 (0.4–3.34)	.78
Asian/Pacific Islander		1.11 (0.52–2.37)	.79	0.72 (0.31–1.66)	.44
African American		0.93 (0.36–2.42)	.89	0.63 (0.23–1.74)	.37
Latino		2.29 (1.20–4.37)	.01	1.47 (0.73–2.93)	.28
Other/multiracial		1.30 (0.76–2.25)	.34	0.91 (0.5–1.64)	.75
Any health literacy limitation	810	1.00 (0.74–1.33)	.98	0.98 (0.7–1.36)	.89
Any chronic medical condition	870	0.60 (0.45–0.79)	<.001	0.91 (0.66–1.26)	.58
Depression	774	0.96 (0.71–1.29)	.78	0.95 (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.

Appendix Table 1. Correlates of Smartphone Ownership Among Mobile Phone Owners
	Unadjusted Models			Multivariate Model* (n = 783)
	Patients (n)	OR (95% CI)	P Value	Adjusted OR (95% CI)	P Value
Age (years)	738
18–24		Reference		Reference
25–34		1.15 (0.60–2.20)	.67	1.14 (0.6–2.17)	.69
35–44		0.64 (0.34–1.21)	.17	0.69 (0.36–1.31)	.25
45–54		0.27 (0.15–0.49)	<.001	0.29 (0.16–0.54)	<.001
55–64		0.14 (0.08–0.27)	<.001	0.16 (0.09–0.31)	<.001
≥65		0.05 (0.02–0.11)	<.001	0.06 (0.03–0.14)	<.001
Sex
Male		Reference		Reference
Female	736	0.81 (0.56–1.17)	.26	0.69 (0.46–1.05)	.09
Race/ethnicity	743
White		Reference		Reference
Native American		0.93 (0.30–2.83)	.89	0.71 (0.21–2.46)	.59
Asian/Pacific Islander		1.45 (0.58–3.58)	.42	0.87 (0.3–2.51)	.80
African American		2.07 (0.67–6.41)	.21	1.27 (0.38–4.26)	.70
Latino		1.95 (0.90–4.20)	.09	1.05 (0.46–2.43)	.91
Other/multiracial		1.26 (0.66–2.37)	.48	0.86 (0.43–1.73)	.67
Any health literacy limitation	725	0.92 (0.67–1.27)	.62	0.92 (0.63–1.32)	.64
Any chronic medical condition	776	0.56 (0.41–0.77)	<.001	0.86 (0.6–1.25)	.43
Depression	690	0.84 (0.61–1.15)	.28	0.79 (0.55–1.16)	.23

Appendix Table 2. Correlates of mHealth Use Among Smartphone Owners
	Unadjusted Models			Multivariate Model* (n = 498)
	Patients (n)	OR (95% CI)	P Value	Adjusted OR (95% CI)	P Value
Age (years)	473
18–24		Reference		Reference
25–34		0.83 (0.43–1.59)	.58	0.83 (0.42–1.61)	.58
35–44		0.72 (0.37–1.42)	.35	0.71 (0.35–1.43)	.33
45–54		0.41 (0.21–0.83)	.01	0.43 (0.21–0.89)	.02
55–64		0.26 (0.12–0.56)	.001	0.26 (0.12–0.6)	.001
≥65		0.17 (0.05–0.57)	.004	0.19 (0.05–0.68)	.011
Sex
Male		Reference		Reference
Female	471	1.77 (1.14–2.74)	.01	1.59 (0.99–2.55)	.06
Race/ethnicity	476
White		Reference		Reference
Native American		3.51 (0.43–28.75)	.24	3.02 (0.38–24.18)	.30
Asian/Pacific Islander		0.95 (0.35–2.59)	.93	0.88 (0.3–2.57)	.81
African American		0.44 (0.15–1.29)	.13	0.36 (0.11–1.1)	.07
Latino		1.77 (0.70–4.44)	.23	1.43 (0.55–3.73)	.47
Other/multiracial		1.49 (0.65–3.38)	.35	1.21 (0.51–2.85)	.67
Any health literacy limitation	464	1.2 (0.79–1.81)	.39	1.2 (0.77–1.88)	.43
Any chronic medical condition	493	0.95 (0.64–1.41)	.81	1.2 (0.77–1.88)	.41
Depression	436	1.28 (0.83–1.99)	.27	1.2 (0.75–1.94)	.44