Use of mobile health applications for health-promoting behavior among individuals with chronic medical conditions

Mahmood, Asos; Kedia, Satish; Wyant, David K.; Ahn, SangNam; Bhuyan, Soumitra S.

doi:10.1177/2055207619882181

Cited by 95 publications

(90 citation statements)

References 37 publications

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“…Notably, the more contemporary term digital health includes categories such as mHealth, health information technology, wearable devices, telehealth and telemedicine, and personalized medicine [ 20 ]. Our findings complement prior studies exploring various aspects of mHealth and eHealth to support patients’ behavior change and self-management across health conditions—often done via telemedicine, SMS text messaging, or smartphone apps [ 21 - 26 ]—as well as studies showing that tablets and smartphones can help support chronic disease management [ 18 ]. Novel uses of mobile devices are being explored to support shared decision via digital phenotyping [ 27 ] or “moment-by-moment quantification of the individual-level human phenotype in-situ using data from smartphones and other personal digital devices” [ 28 ].…”

Section: Discussionsupporting

confidence: 82%

Use of Tablets and Smartphones to Support Medical Decision Making in US Adults: Cross-Sectional Study

Langford¹,

Orellana²,

Kalinowski³

et al. 2020

JMIR Mhealth Uhealth

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Background Tablet and smartphone ownership have increased among US adults over the past decade. However, the degree to which people use mobile devices to help them make medical decisions remains unclear. Objective The objective of this study is to explore factors associated with self-reported use of tablets or smartphones to support medical decision making in a nationally representative sample of US adults. Methods Cross-sectional data from participants in the 2018 Health Information National Trends Survey (HINTS 5, Cycle 2) were evaluated. There were 3504 responses in the full HINTS 5 Cycle 2 data set; 2321 remained after eliminating respondents who did not have complete data for all the variables of interest. The primary outcome was use of a tablet or smartphone to help make a decision about how to treat an illness or condition. Sociodemographic factors including gender, race/ethnicity, and education were evaluated. Additionally, mobile health (mHealth)- and electronic health (eHealth)-related factors were evaluated including (1) the presence of health and wellness apps on a tablet or smartphone, (2) use of electronic devices other than tablets and smartphones to monitor health (eg, Fitbit, blood glucose monitor, and blood pressure monitor), and (3) whether people shared health information from an electronic monitoring device or smartphone with a health professional within the last 12 months. Descriptive and inferential statistics were conducted using SAS version 9.4. Weighted population estimates and standard errors, univariate odds ratios, and 95% CIs were calculated, comparing respondents who used tablets or smartphones to help make medical decisions (n=944) with those who did not (n=1377), separately for each factor. Factors of interest with a P value of <.10 were included in a subsequent multivariable logistic regression model. Results Compared with women, men had lower odds of reporting that a tablet or smartphone helped them make a medical decision. Respondents aged 75 and older also had lower odds of using a tablet or smartphone compared with younger respondents aged 18-34. By contrast, those who had health and wellness apps on tablets or smartphones, used other electronic devices to monitor health, and shared information from devices or smartphones with health care professionals had higher odds of reporting that tablets or smartphones helped them make a medical decision, compared with those who did not. Conclusions A limitation of this research is that information was not available regarding the specific health condition for which a tablet or smartphone helped people make a decision or the type of decision made (eg, surgery, medication changes). In US adults, mHealth and eHealth use, and also certain sociodemographic factors are associated with using tablets or smartphones to support medical decision making. Findings from this study may inform future mHealth and other digital health interventions designed to support medical decision making.

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Section: Discussionsupporting

confidence: 82%

Use of Tablets and Smartphones to Support Medical Decision Making in US Adults: Cross-Sectional Study

Langford¹,

Orellana²,

Kalinowski³

et al. 2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…Considering previous nursing intervention studies [ 60 , 61 , 62 ], patients’ continuous disease management would require combined interventions including web-based or mobile-based interventions, not just face-to-face nursing education and nursing interventions. Electronic health (eHealth) or mobile health (mHealth) interventions can induce positive behavior changes and promote healthy lifestyles [ 63 ], allowing easy access. Moreover, effective nursing strategies including medication education, post-discharge programs, and nurse provider healthcare services for CVD patients are necessary to increase health-promoting behaviors.…”

Section: Discussionmentioning

confidence: 99%

Factors Affecting Health-Promoting Behaviors in Patients with Cardiovascular Disease

Park

2021

Healthcare

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Cardiovascular disease is the leading cause of death globally and the second most common cause of death in South Korea. Health-promoting behaviors recommended for patients with cardiovascular disease include control of diet, physical activity, cessation of smoking, medication adherence, and adherence to medical recommendations. This study aimed to determine the relationship between depression, anxiety, perception of health status, and health-promoting behavior in patients from South Korea who have suffered from cardiovascular disease. The study population comprised 161 patients at the cardiovascular center at H Hospital who were diagnosed with cardiovascular disease. Descriptive statistics and stepwise multiple regression were employed to analyze the data. Negative correlations existed between depression, perception of health status, and health-promoting behavior. By contrast, a positive correlation existed between the perception of health status and health-promoting behavior. The main factors affecting health-promoting behaviors were alcohol consumption, duration of diagnosis, perception of health status, and depression. These variables explained 15.8% of the variance. To prevent adverse cardiac events, patients who suffer from cardiovascular disease should be assessed as soon as possible to identify psychiatric symptoms, thereby developing a potential intervention aimed at decreasing negative illness consequences.

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“…Roshanov et al [36] argued that this probably is due to the increased empowerment of the patients when they become actively involved in their care. Mahmood et al [37] found that patients using mHealth applications to track progress on a health-related goal performed health-related decisions and engaged in health-related discussions with the healthcare providers to a larger extent than patients not using such mHealth applications.…”

Section: Patient Empowermentmentioning

confidence: 99%

Process evaluation of the implementation of a decision support system to prevent and treat disease-related malnutrition in a hospital setting

Paulsen

Varsi

Andersen

2021

BMC Health Serv Res

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Background Malnutrition is present in 30% of hospitalized patients and has adverse outcomes for the patient and the healthcare system. The current practice for nutritional care is associated with many barriers. The MyFood decision support system was developed to prevent and treat malnutrition. Methods This paper reports on a process evaluation that was completed within an effectiveness trial. MyFood is a digital tool with an interface consisting of an app and a website. MyFood includes functions to record and evaluate dietary intake. It also provides reports to nurses, including tailored recommendations for nutritional treatment. We used an effectiveness-implementation hybrid design in a randomized controlled trial. The RE-AIM (Reach, Efficiency, Adoption, Implementation, Maintenance) framework was used to perform a process evaluation alongside the randomized controlled trial, using a combination of quantitative and qualitative methods. An implementation plan, including implementation strategies, was developed to plan and guide the study. Results Reach: In total, 88% of eligible patients consented to participate (n = 100). Adoption: Approximately 75% of the nurses signed up to use MyFood and 50% used the reports. Implementation: MyFood empowered the patients in their nutritional situation and acted as a motivation to eat to reach their nutritional target. The compliance of using MyFood was higher among the patients than the nurses. A barrier for use of MyFood among the nurses was different digital systems which were not integrated and the log-in procedure to the MyFood website. Despite limited use by some nurses, the majority of the nurses claimed that MyFood was useful, better than the current practice, and should be implemented in the healthcare system. Conclusions This study used a process evaluation to interpret the results of a randomized controlled trial more in-depth. The patients were highly compliant, however, the compliance was lower among the nurses. MyFood empowered the patients in their nutritional situation, the usability was considered as high, and the experiences and attitudes towards MyFood were primarily positive. Focus on strategies to improve the nurses’ compliance may in the future improve the MyFood system’s potential. Trial registration The trial was registered in ClinicalTrials.gov 26/01/2018 (NCT03412695).

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Use of mobile health applications for health-promoting behavior among individuals with chronic medical conditions

Cited by 95 publications

References 37 publications

Use of Tablets and Smartphones to Support Medical Decision Making in US Adults: Cross-Sectional Study

Use of Tablets and Smartphones to Support Medical Decision Making in US Adults: Cross-Sectional Study

Factors Affecting Health-Promoting Behaviors in Patients with Cardiovascular Disease

Process evaluation of the implementation of a decision support system to prevent and treat disease-related malnutrition in a hospital setting

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