Obesity and Patient Activation: Confidence, Communication, and Information Seeking Behavior

The Association of eHealth Literacy Skills and mHealth Application Use Among US Adults With Obesity: Analysis of Health Information National Trends Survey Data

BACKGROUND

Physical inactivity and a poor diet are modifiable behaviors that contribute to obesity. Obesity is a well-recognized risk factor for chronic diseases, including diabetes. Mobile health (mHealth) apps can play an important adjuvant role in preventing and treating chronic diseases and promoting positive health behavior change among people with obesity, and eHealth literacy skills have the potential to impact mHealth app use.

OBJECTIVE

The purpose of this study was to explore the associations between the 2 dimensions, access and application, of eHealth literacy skills and mHealth app use among US adults (≥18 years of age) with obesity (BMI ≥30 kg/m2).

METHODS

Data were obtained from February to June 2020 using the Health Information National Trends Survey 5. A total of 1079 respondents met the inclusion criteria of adults with obesity and owners of smartphones. Individual associations between mHealth app use and sociodemographic variables were explored using weighted chi-square and 2-tailed t tests. A multivariable weighted logistic regression model was fitted, and adjusted odds ratios (ORs) of using mHealth apps with corresponding 95% CIs were reported across multiple sociodemographic variables. An Ising model-weighted network visualization was produced. A receiver operating characteristic curve was calculated, and the area under the curve was reported with the corresponding Delong 95% CI.

RESULTS

A majority of respondents were female (550/923, 59.6%) or non-Hispanic White (543/923, 58.8%). Individuals in households earning less than US $50,000 comprised 41.4% (382/923) of the sample. All sociodemographic variables were found to be univariately significant at the 5% level, except employment and region. Results from the multivariable weighted logistic regression model showed that the adjusted odds of using an mHealth app are 3.13 (95% CI 1.69-5.80) and 2.99 (95% CI 1.67-5.37) times higher among those with an access eHealth literacy skill of using an electronic device to look for health or medical information for themselves and an application eHealth literacy skill of using electronic communications with a doctor or doctor's office, respectively. Several sociodemographic variables were found to be significant, such as education, where adjusted ORs comparing subgroups to the lowest educational attainment were substantial (ORs ≥7.77). The network visualization demonstrated that all eHealth literacy skills and the mHealth app use variable were positively associated to varying degrees.

CONCLUSIONS

This work provides an initial understanding of mHealth app use and eHealth literacy skills among people with obesity, identifying people with obesity subpopulations who are at risk of a digital health divide. Future studies should identify equitable solutions for people with obesity (as well as other groups) and their use of mHealth apps.

Impact of group medical visits on patient engagement and quality of life

BACKGROUND

Group medical visits (GMV) effectively improve patient care and outcomes through interactive education, increased patient contact, and facilitated social support. This quality improvement research examined if patient activation and quality of life correlate with weight, blood pressure (BP), and hemoglobin A1c (A1C) through GMV interventions.

METHODS

Participants were enrolled in GMV Lighten Up for weight management or GMV Diabetes. At pre- and post-intervention, patients completed the Patient Activation Measure (PAM) and the health-related quality of life measure, the SF-12; and were assessed for weight, blood pressure (BP), and hemoglobin A1c (A1C).

RESULTS

Weight and PAM scores significantly improved regardless of group. For patients in GMV Diabetes, A1C significantly decreased. GMV Lighten Up participants had statistically significant declines in diastolic BP. Both groups improved patient activation, but statistically significantly so only in GMV Diabetes participants. SF-12 scores did not statistically significantly improve. There were no predictors of A1C and PAM score change for the Diabetes GMV. However, age, SBP and SF-12 scores predicted PAM score changes in GMV Lighten up participants.

CONCLUSIONS

Participants in this study showed overall improvement in biomarkers and patient activation. Thus, GMV continue to be a viable method for healthcare delivery.