This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
Mobile apps hold promise for serving as a lifestyle intervention in public health to promote wellness and attenuate chronic conditions, yet little is known about how individuals with chronic illness use or perceive mobile apps.
The objective of this study was to explore behaviors and perceptions about mobile phone–based apps for health among individuals with chronic conditions.
Data were collected from a national cross-sectional survey of 1604 mobile phone users in the United States that assessed mHealth use, beliefs, and preferences. This study examined health app use, reason for download, and perceived efficacy by chronic condition.
Among participants, having between 1 and 5 apps was reported by 38.9% (314/807) of respondents without a condition and by 6.6% (24/364) of respondents with hypertension. Use of health apps was reported 2 times or more per day by 21.3% (172/807) of respondents without a condition, 2.7% (10/364) with hypertension, 13.1% (26/198) with obesity, 12.3% (20/163) with diabetes, 12.0% (32/267) with depression, and 16.6% (53/319) with high cholesterol. Results of the logistic regression did not indicate a significant difference in health app download between individuals with and without chronic conditions (
Results from this study suggest that individuals with poor self-reported health and low rates of physical activity, arguably those who stand to benefit most from health apps, were least likely to report download and use these health tools.
Health conditions, such as hypertension and obesity, are associated with lower quality of life and increased health care costs [
These behavioral factors pose a significant challenge for effective chronic disease management. For instance, medication nonadherence is prevalent in populations with chronic conditions and is associated with increased risk for hospitalization and mortality [
Overall, mobile technology is increasingly prolific across the populations. Approximately two-thirds of adults in the United States own a mobile phone [
Health apps are a promising future direction for chronic disease treatment and care [
The aim of this study was to examine beliefs related to perceived efficacy of health apps and current health app behaviors among individuals with and without chronic conditions in a national sample of mobile phone users. We utilized data collected in a previous study [
This study utilizes data from a national cross-sectional sample of mobile phone users in the United States [
Items on health apps were developed for this study following standard item design techniques [
The survey was pilot-tested with a sample of nonresearch team members using cognitive interviewing techniques to ensure the survey was clear and the items were easy to understand. Before taking the online questionnaire, participants provided their consent to participate in the study. The survey took on average 9 min to complete. This study was approved by the New York University School of Medicine Institutional Review Board (IRB #i14-02046). As this research included surveys with human subjects, participants’ consent for participation was obtained before any data capture activities. A copy of the consent form may be provided upon request. Data are retained by the corresponding author. Any individuals interested in obtaining a copy of the dataset will be addressed promptly.
The survey consisted of 36 questions, assessing demographics (age, gender, race, income, and education), health (chronic condition diagnoses, self-rated health, and physical activity), reasons for downloading and not downloading health apps, frequency of using health apps, and perceived efficacy of health apps.
Participants were first asked “Have you ever downloaded an ‘app’ to track anything relating to your health?” Participants who reported health app download were prompted with several follow-up questions about reasons for download (eg, “To track what I eat” and “Help with weight loss”). Overall use of health app use was measured by asking participants how frequently they use health apps, both frequency of each session (response options ranged from “less than once a month” to “2 or more times per day”) and duration of each session (response options ranged from “1-10 minutes” to “more than 30 minutes”). Participants who reported using a health app were asked to report perceived efficacy of health apps (on a scale from “made my health worse” to “very much improved my health”). Chronic condition diagnoses were collected via self-report. Chronic illnesses were selected if prevalence was at least 5% in this sample (eg, hypertension, obesity, diabetes, depression, and high blood cholesterol). Chronic conditions comprising less than 5% on the sample included cancer (n=64, 4%), chronic obstructive pulmonary disease (n=62, 4%), heart attack (n=51, 3%), stroke (n=51, 3%), substance abuse (n=45, 3%), ulcers (n=38, 3%), liver disease (n=17, 1%), and human immunodeficiency virus (n=10, 1%).
Differences in response between the conditions (no chronic condition, hypertension, obesity, diabetes, depression, and high blood cholesterol) were examined by demographic factors (age, race, education, sex, self-rated health, and physical activity). As discussed, chronic illnesses were selected if prevalence was at least 5% in the sample (eg, hypertension, obesity, diabetes, depression, and high blood cholesterol).
Differences in responses to frequency of health app use, perceived app efficacy, and reasons for download were also examined by condition. Finally, logistic regression was performed utilizing the generalized linear model technique. Health app download was examined with chronic condition, self-reported health, and physical activity. In the case of health conditions, a variable was created that was coded to indicate condition (eg, no condition, hypertension, obesity, diabetes, depression, and high cholesterol) to allow for analyses between conditions. Consistent with previous literature [
A total of 7189 people visited the survey page, 6871 (95.61%) agreed to participate in the survey, 2089 (29.04%) completed the survey, and 485 (6.75%) were randomly removed because of overfilling of sociodemographic quotas.
Of the 1604 individuals in the study, the most prevalent chronic conditions included hypertension (n=364, 22.69%), obesity (n=198, 12.34%), diabetes (n=163, 10.16%), depression (n=267, 16.64%), and high cholesterol (n=319, 19.89%).
Among individuals with no chronic conditions, 66.0% (533/807) reported health app download. Of the individuals with one chronic condition, 53.4% (189/352) reported health app download, whereas just less than half (47.0%, 211/449) of individuals with a chronic condition reported health app download. Significant differences in app download were found by condition (χ22=44.3,
Demographic characteristics of mobile phone users in the United States by chronic condition (N=1604).
Variable | No condition (n=807) | Hypertension (n=364) | Obesity (n=198) | Diabetes (n=163) | Depression (n=267) | High cholesterol (n=319) | ||
n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | |||
Agea | 33.8 (12.8) | 40.1 (15.8) | 33.8 (16.5) | 41.4 (16.5) | 50.6 (16.1) | 38.6 (15.9) | <.001 | |
<.001 | ||||||||
Male | 399 (49.4) | 193 (53.0) | 71 (35.8) | 84 (51.5) | 91 (34.1) | 203 (63.6) | ||
Female | 408 (50.6) | 170 (46.7) | 125 (63.1) | 79 (48.5) | 176 (65.9) | 116 (36.4) | ||
<.001 | ||||||||
Less than 12th grade | 43 (5.3) | 17 (4.7) | 9 (4.5) | 7 (4.3) | 20 (7.5) | 12 (3.8) | ||
High school or General Equivalency Degree | 388 (48.1) | 142 (39.0) | 73 (36.8) | 61 (37.4) | 120 (44.9) | 115 (36.1) | ||
Some college | 176 (21.8) | 110 (30.2) | 66 (33.3) | 41 (25.2) | 75 (28.1) | 91 (28.5) | ||
Bachelor’s degree | 148 (18.3) | 61 (16.7) | 35 (17.6) | 40 (24.5) | 38 (14.2) | 66 (20.7) | ||
Graduate degree | 52 (6.4) | 34 (9.3) | 15 (7.6) | 14 (8.6) | 14 (5.2) | 35 (11.0) | ||
<.001 | ||||||||
African American/black | 219 (27.1) | 111 (30.5) | 58 (29.3) | 43 (26.4) | 61 (22.8) | 54 (16.9) | ||
Asian | 70 (8.7) | 15 (4.12) | 10 (5.1) | 10 (6.1) | 9 (3.3) | 15 (4.7) | ||
White | 199 (24.6) | 175 (48.1) | 77 (38.9) | 65 (39.8) | 125 (46.8) | 173 (54.2) | ||
Native American | 6 (0.7) | 2 (0.5) | 2 (1.0) | 1 (0.6) | 4 (1.5) | 3 (0.94) | ||
Latino/Hispanic | 279 (34.6) | 56 (15.4) | 48 (24.2) | 44 (26.9) | 62 (23.2) | 65 (20.4) | ||
Other | 34 (4.2) | 5 (1.4) | 3 (1.5) | 0 (0.0) | 6 (2.3) | 9 (2.8) | ||
<.001 | ||||||||
Poor | 9 (1.1) | 5 (1.4) | 1 (0.5) | 7 (4.3) | 21 (7.8) | 5 (1.6) | ||
Fair | 44 (5.5) | 17 (4.7) | 7 (3.5) | 46 (28.2) | 79 (29.6) | 17 (5.3) | ||
Average | 254 (31.5) | 26 (7.1) | 20 (10.1) | 80 (49.1) | 118 (44.2) | 26 (8.2) | ||
Very good | 417 (51.7) | 24 (6.6) | 23 (11.6) | 33 (20.3) | 73 (27.3) | 24 (7.5) | ||
Excellent | 155 (19.2) | 7 (1.9) | 6 (3.0) | 17 (10.4) | 28 (10.5) | 7 (2.19) | ||
<.001 | ||||||||
Never | 115 (14.3) | 21 (5.8) | 14 (7.1) | 5 (3.1) | 38 (14.2) | 70 (21.9) | ||
1 day | 86 (10.6) | 7 (1.9) | 10 (5.1) | 1 (0.) | 26 (9.74) | 38 (11.9) | ||
2 days | 198 (24.5) | 17 (4.7) | 22 (11. 1) | 13 (7.9) | 40 (15.0) | 58 (18.1) | ||
3-4 days | 340 (42.1) | 28 (7.7) | 25 (12.6) | 24 (14.7) | 48 (18.0) | 98 (30.7) | ||
5-7 days | 140 (17.3) | 14 (3.8) | 8 (4.0) | 14 (8.6) | 31 (11.6) | 55 (17.2) |
aRepresents mean and standard deviation.
Responses to health app use, frequency, and perceived efficacy by chronic condition (N=1604).
Variable | No condition | Hypertension | Obesity | Diabetes | Depression | High cholesterol | ||||||||
n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | |||||||||
<.001 | ||||||||||||||
1-5 apps | 314 (38.9) | 24 (6.6) | 34 (17.2) | 16 (9.8) | 69 (25.8) | 88 (27.6) | ||||||||
6-10 apps | 55(6.8) | 2(0.5) | 12 (6.1) | 2 (1.0) | 15 (5.6) | 18 (5.6) | ||||||||
11-15 apps | 52 (6.4) | 2 (0.5) | 1 (0.5) | 2 (1.2) | 6 (2.2) | 4 (1.3) | ||||||||
16-20 apps | 77 (9.5) | 4 (1.1) | 1 (0.5) | 2 (1.2) | 4 (1.5) | 5 (1.6) | ||||||||
.001 | ||||||||||||||
Less than once a month | 32 (4.0) | 0 (0.0) | 3 (1.5) | 0 (0.0) | 15 (5.6) | 9 (2.8) | ||||||||
A few times a month | 34 (4.2) | 6 (1.60) | 4 (2.0) | 0 (0.0) | 8 (3.0) | 16 (5.0) | ||||||||
A few times each week | 119 (14.70) | 7 (1.9) | 10 (5.1) | 5 (3.1) | 21 (7.9) | 33 (10.3) | ||||||||
About 1 time each day | 211 (26.1) | 10 (2.7) | 11 (5.6) | 5 (3.1) | 28 (10.5) | 34 (10.7) | ||||||||
2 or more times a day | 172 (21.3) | 10 (2.7) | 26 (13.1) | 20 (12.3) | 32 (12.0) | 53 (16.6) | ||||||||
.17 | ||||||||||||||
1-10 min | 312 (38.7) | 78 (21.4) | 52 (26.3) | 33 (20.2) | 77 (28.8) | 66 (20.7) | ||||||||
11-30 min | 339 (42.0) | 50 (13.7) | 47 (23.7) | 32 (19.6) | 48 (18.0) | 55 (17.2) | ||||||||
More than 30 min | 72 (8.9) | 27 (7.4) | 20 (10.1) | 20 (12.3) | 25 (9.4) | 24 (7.5) | ||||||||
.13 | ||||||||||||||
Made my health worse | 20 (2.5) | 2 (0.5) | 2 (1.0) | 2 (1.2) | 1 (0.4) | 3 (0.9) | ||||||||
Did not help at all | 51 (6.3) | 15 (4.1) | 15 (7.6) | 14 (8.6) | 9 (3.4) | 6 (1.9) | ||||||||
Just a little improved | 164 (20.3) | 47 (12.9) | 43 (21.7) | 43 (26.4) | 25 (9.4) | 21 (6.6) | ||||||||
Somewhat | 237 (29.4) | 47 (12.9) | 38 (19.2) | 45 (27.6) | 43 (16.1) | 24 (7.5) | ||||||||
Very much improved | 215 (26.6) | 44(12.1) | 47 (23.7) | 46 (28.2) | 41 (15.4) | 31 (9.7) | ||||||||
Track activity or exercise I get | 370 (45.8) | 97 (26.6) | 77 (38.9) | 53 (32.5) | 82 (30.7) | 92 (28.8) | .06 | |||||||
Help me watch/improve what I eat | 335 (41.5) | 85 (23.4) | 76 (38.4) | 52 (31.9) | 90 (33.7) | 72 (22.6) | .00 | |||||||
Weight loss | 333 (41.3) | 77 (21.2) | 80 (40.4) | 49 (30.1) | 86 (32.2) | 66 (20.7) | .01 | |||||||
Track a health measure | 189 (23.4) | 60 (16.5) | 38 (19.2) | 42 (25.8) | 50 (18.7) | 54 (16.9) | .04 | |||||||
Help me relax | 143 (17.7) | 3 (0.8) | 8 (4.0) | 6 (3.7) | 17 (6.4) | 33 (10.3) | .01 |
Health app download, self-reported health, and physical activity by chronic condition (N=1604).
Adjusted modelsa | |||||
Odds ratio (95% CI) | Odds ratio (95% CI) | ||||
No chronic condition | Reference | Reference | |||
Hypertension | 0.34 (0.21-0.53) | <.001 | 0.74 (0.45-1.22) | .24 | |
Obesity | 1.18 (0.72-1.94) | .51 | 1.63 (0.96-2.77) | .07 | |
Diabetes | 0.61 (0.36-1.04) | .07 | 1.24 (0.69-2.24) | .47 | |
Depression | 0.72 (0.52-1.00) | .05 | 0.91 (0.64-1.28) | .58 | |
High cholesterol | 0.46 (0.35-0.59) | <.001 | 1.00 (0.73-1.37) | .99 | |
Poor health | Reference | Reference | |||
Fair health | 1.07 (0.69-1.66) | .76 | 1.30 (0.82-2.07) | .27 | |
Good health | 1.29 (0.86-1.94) | .23 | 1.55 (1.00-2.40) | .05 | |
Very good health | 3.28 (2.12-5.06) | .000 | 3.80 (2.38-6.09) | .000 | |
Excellent health | 5.36 (3.14-9.14) | .000 | 4.77 (2.70-8.42) | .000 | |
Never | Reference | Reference | |||
1 day per week | 3.08 (2.05-4.64) | .000 | 2.47 (1.60-3.83) | .000 | |
2 days per week | 5.38 (3.78-7.66) | .000 | 4.77 (3.27-6.94) | .000 | |
3-4 days per week | 6.15 (4.43-8.54) | .000 | 5.00 (3.52-7.10) | .000 | |
5-7 days per week | 5.13 (3.53-7.45) | .000 | 4.64 (3.11-6.92) | .000 |
aModel adjusted for age, sex, race or ethnicity.
In unadjusted models, individuals who were less likely to report health app download included those with hypertension (
Health apps and other mobile technologies hold promise as tools for health promotion among healthy individuals as well as those with chronic illness [
Previous research has examined motivation to download health apps among healthy populations, including college students and adults. In the study conducted by Kwon and colleagues, mobile health app use was associated with perceived efficacy of apps [
Our study provides a meaningful contribution to the literature, in examining beliefs about health apps among those with good health indicators as well as poor health indicators. Our study found that approximately one-third of individuals across each chronic illness agreed that health apps have the ability to dramatically improve health. Although it is promising that one-third of people with chronic conditions report belief in health app efficacy, it remains that only a minority of at-risk populations would be likely to use health apps to improve their conditions and that most either do not know they exist or believe that apps could be helpful. Interest in and use of these apps will likely remain low and that motivating download of these resources among high-risk populations remains a critical challenge for the field.
Among research on health apps with chronically ill populations, another area of emphasis has been designing apps tailored to chronically ill patients. For instance, research has developed apps for assisting with specific disease management functions, such as improving medication adherence [
Our results meaningfully extend the literature on health apps in several ways. Interestingly, our results found no significant difference in likelihood of health app download between individuals with and without chronic illness. That is, individuals with health apps were not more likely to have chronic health conditions than those without health apps. This could be due to the fact that use of health apps reported by participants in this study was actually quite high among those with and without chronic illness. In addition, individuals with chronic illness represented less than half of our sample. Nevertheless, we found individuals with
Our study extends the literature and our understanding of health app use and beliefs about these tools by comparing responses from individuals with markers of good health and those with markers of poor general health. Taken together, our findings suggest preliminary evidence that individuals who are using health apps may be those already engaging in healthy lifestyle behaviors. There may be an opportunity to better market health apps toward chronically ill populations, or design tailored apps specifically for these groups.
Despite strengths, this study was not without limitations. The primary limitation is the cross-sectional survey. In addition, our sample was skewed toward younger populations, and a more generalized sample across age would likely have yielded different results as patterns of use and preference are likely to be different in older populations. One example of sampling bias is the low prevalence of participants with a history of cancer (<5%), whereas the lifetime risk of developing cancer is about 40% for men and women. Sampling a more diverse or broad sample may have achieved different findings. It should also be noted that individuals without chronic illness represented a large portion of the sample (n=807). Additionally, the study surveyed a general population rather than those known to be medically ill. Hospital- or clinic-based populations that regularly receive health care may differ in their behaviors and current uses of health apps. Furthermore, the groups of comorbidity were heterogeneous, presenting a limitation in the ability of the findings identified here to apply to all individuals with comorbidity. Opinions of health app use could change over time, and although we used a validated instrument to assess chronic medical conditions, it nevertheless relied on self-reported data. Furthermore, the potential uses of mobile health and medical apps are nuanced and varied in nature. It would be challenging to capture the numerous and varied uses and types of apps. The results of this study are limited and may not capture all potential uses of apps or types of apps for health or disease management. It should be noted that the authors measured health app
Our study extends the literature and our understanding of health app use and beliefs about these tools by comparing responses from individuals with markers of good health and those with markers of poor general health. Our findings illuminate not only behavioral patterns of healthy individuals but also those of individuals with poor general health indicators (eg, low self-rated health). We also found lower download among individuals who may need these interventions the most. The results of this study suggest high use of health apps among individuals with high self-rated health and physical activity.
The study illuminates future research on public health interventions to promote mobile health uptake among individuals with chronic conditions. Future interventions may consider how best to tailor health apps toward individuals with specific conditions and the needs of those conditions (eg, weight management among individuals with obesity) or identify ways to better communicate health apps and their benefits to those with chronic illness. More trials and well-designed studies can help provide data regarding efficacy of specific health apps to change the cost-value perception among both patients with chronic conditions and health care providers. Furthermore, designing targeted interventions may be a strategy for easing the burden of complex treatment regimens and promoting health in populations with chronic conditions.
Mobile technology is increasingly low cost and well suited for population health. Although there is interest in applying mobile technology to health and particularly to disease management, little attention has been paid to current use in populations with chronic health conditions. Our study found no difference in health app use between healthy and chronically ill populations, but we did find self-reported health and physical activity to be the strongest predictors of health app use. Our study also found approximately one-third of individuals with chronic illness reported beliefs that health apps have potential to improve health, suggesting these tools could be better marketed toward individuals with chronic illness. Results have direct application for health communication and intervention to promote population health and assist individuals with chronic disease management.
odds ratio
The authors thank Mark Butler, Jonathan Varghese, Jermaine Blakely, and Jeff Blossom for their assistance with the preparation of this manuscript, and Jackson Forse, Colleen Dunn, and the team at Toluna Inc for their assistance in conducting the survey. The authors also wish to thank Hayden D Mountcastle for reviewing the manuscript. RR was supported by a postdoctoral fellowship at the NYU School of Medicine on a grant from the National Institutes of Health (R25HL116378). RJ was supported by a postdoctoral fellowship at the NYU School of Medicine on two grants from the National Institutes of Health (R01DK100492 and K24‐NR012226). This research was supported by a grant from the Verizon Foundation to DD.
RR analyzed data and drafted the Introduction, Methods, and Discussion. RJ analyzed data and drafted the manuscript. PK developed the analysis plan and collected the data. GJL drafted the Conclusions. DD developed the analysis plan and collected the data. All authors read and approved the final manuscript.
None declared.