Investigating the effect of an mHealth coaching intervention on health beliefs, adherence and blood pressure of patients with hypertension: A longitudinal single group pilot study.

Background: Mobile health technologies have shown promise as delivery platforms for digital health coaching for chronic conditions. However, the impacts of such strategies on users' health beliefs, intentions and ultimately clinical outcomes are understudied. Objective: This study sought (1) to evaluate the effects of a digital health coaching intervention on participants' belief constructs; and (2) to assess relationships between these belief constructs and intentions to utilize the technological intervention, actual adherence metrics and clinical outcomes related to hypertension. Methods: Thirty-four participants with hypertension were recruited from a university community from January to May 2021. They self-measured weight and blood pressure (BP) for 30 days followed by digital coaching delivered via a mobile application for 30 days. Surveys assessed constructs from the Health Belief Model and Technology Acceptance Model, compared to intention, health belief, BP self-monitoring adherence and BP outcomes. A path analysis model was used to assess the relationships between constructs and intention, adherence metrics and clinical outcomes. A Kruskal-Wallis test was used to identify changes in beliefs. Results: Participant health beliefs significantly improved after coaching, including self-efficacy (H(1) = 15.12, p < 0.001), cues to action (H(1) = 5.33, p = 0.02), attitude (H(1) = 10.35, p = 0.002), perceived usefulness (H(1) = 15.02, p < 0.001) and decreased resistance to change (H(1) = 4.05, p = 0.04). Adherence to BP measurements positively correlated with perceived health threat (ß = .033, p = 0.007) and perceived ease of use (ß = .0277, p < 0.001). Self-efficacy (ß = -2.92, p = 0.02) and perceived usefulness (ß = -3.75, p = 0.01) were linked with a decrease in diastolic BP. Conclusions: A mobile health coaching intervention may help participants improve beliefs regarding hypertension self-management.

Understanding Patient Beliefs in Using Technology to Manage Diabetes: Path Analysis Model From a National Web-Based Sample.

BACKGROUND:  With 425 million individuals globally living with diabetes, it is critical to support the self-management of this life-threatening condition. However, adherence and engagement with existing technologies are inadequate and need further research. OBJECTIVE:  The objective of our study was to develop an integrated belief model that helps identify the significant constructs in predicting intention to use a diabetes self-management device for the detection of hypoglycemia. METHODS:  Adults with type 1 diabetes living in the United States were recruited through Qualtrics to take a web-based questionnaire that assessed their preferences for a device that monitors their tremors and alerts them of the onset of hypoglycemia. As part of this questionnaire, a section focused on eliciting their response to behavioral constructs from the Health Belief Model, Technology Acceptance Model, and others. RESULTS:  A total of 212 eligible participants responded to the Qualtrics survey. Intention to use a device for the self-management of diabetes was well predicted (R2=0.65; F12,199=27.19; P<.001) by 4 main constructs. The most significant constructs were perceived usefulness (ß=.33; P<.001) and perceived health threat (ß=.55; P<.001) followed by cues to action (ß=.17; P<.001) and a negative effect from resistance to change (ß=-.19; P<.001). Older age (ß=.025; P<.001) led to an increase in their perceived health threat. CONCLUSIONS: For individuals to use such a device, they need to perceive it as useful, perceive diabetes as life-threatening, regularly remember to perform actions to manage their condition, and exhibit less resistance to change. The model predicted the intention to use a diabetes self-management device as well, with several constructs found to be significant. This mental modeling approach can be complemented in future work by field-testing with physical prototype devices and assessing their interaction with the device longitudinally.

Hypoglycemia Detection Using Hand Tremors: Home Study of Patients With Type 1 Diabetes.

BACKGROUND: Diabetes affects millions of people worldwide and is steadily increasing. A serious condition associated with diabetes is low glucose levels (hypoglycemia). Monitoring blood glucose is usually performed by invasive methods or intrusive devices, and these devices are currently not available to all patients with diabetes. Hand tremor is a significant symptom of hypoglycemia, as nerves and muscles are powered by blood sugar. However, to our knowledge, no validated tools or algorithms exist to monitor and detect hypoglycemic events via hand tremors. OBJECTIVE: In this paper, we propose a noninvasive method to detect hypoglycemic events based on hand tremors using accelerometer data. METHODS: We analyzed triaxial accelerometer data from a smart watch recorded from 33 patients with type 1 diabetes for 1 month. Time and frequency domain features were extracted from acceleration signals to explore different machine learning models to classify and differentiate between hypoglycemic and nonhypoglycemic states. RESULTS: The mean duration of the hypoglycemic state was 27.31 (SD 5.15) minutes per day for each patient. On average, patients had 1.06 (SD 0.77) hypoglycemic events per day. The ensemble learning model based on random forest, support vector machines, and k-nearest neighbors had the best performance, with a precision of 81.5% and a recall of 78.6%. The results were validated using continuous glucose monitor readings as ground truth. CONCLUSIONS: Our results indicate that the proposed approach can be a potential tool to detect hypoglycemia and can serve as a proactive, nonintrusive alert mechanism for hypoglycemic events.

Use of a Mobile Biofeedback App to Provide Health Coaching for Stress Self-management: Pilot Quasi-Experiment.

BACKGROUND: Mental health is an increasing concern among vulnerable populations, including college students and veterans. OBJECTIVE: The purpose of this study was to determine if mobile health technology combined with health coaching can better enable a user to self-manage their mental health. METHODS: This study evaluated the mobile app "Biofeedback" that provided health coaching on stress self-management for college student veterans' mental health concerns. Twenty-four college student veterans were recruited from a large public university in Texas during the spring 2020 semester, impacted by COVID-19. Ten participants were assigned to the intervention group where they used the mobile Biofeedback app on their smartphones and smartwatches, and 14 were assigned to the control group without the app; assignment was based on mobile phone compatibility. Both groups participated in one initial lab session where they learned a deep-breathing exercise technique. The intervention group was then asked to use the mobile Biofeedback app during their daily lives and a smartwatch, and the control group was asked to perform the breathing exercises on their own. Both groups filled out Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) self-assessments at 2-week intervals. At the end of the semester, both groups were given an exit interview to provide user experience and perceived benefits of health coaching via the mobile biofeedback app. RESULTS: The deep-breathing exercise in the initial lab session reduced stress in both groups. Over the course of the study, the app recorded 565 coached breathing exercises with a significant decrease (approximately 3 beats per minute) in participants' heart rate during the 6-minute time period immediately after conducting the breathing exercises (Spearman rank correlation coefficient -0.61, P<.001; S=9,816,176). There was no significant difference between the two groups for PHQ-9 and GAD-7 scores over the course of the semester. Exit interview responses indicated that participants perceived that the mobile Biofeedback app improved their health and helped them address stress challenges. All participants reported that the intervention helped them manage their stress better and expressed that health coaching via a mobile device would improve their overall health. CONCLUSIONS: Participants reported a positive perception of the app for their mental health self-management during a stressful semester. Future work should examine long-term effects of the app with a larger sample size balanced between male and female participants, randomized participant allocation, real-time detection of mental health symptoms, and additional features of the app.

The Effects of Drowsiness Detection Technology and Education on Nurses' Beliefs and Attitudes toward Drowsy Driving.

Occupational ApplicationsNurses' perceived health threat from driving drowsy along with their attitude toward an intervention can be targeted to improve nurses' intentions to avoid this dangerous behavior. The evidence presented in this paper suggests that educational interventions that raise awareness of the risks of drowsy driving and its consequences (e.g., fatalities or injuries), as well as peer stories about their experiences, may positively affect nurses' perceived health threat and attitudes toward drowsy driving interventions.

Background Drowsy driving is prevalent among night-shift nurses, yet there is a gap in understanding nurses' beliefs and attitudes that may affect their intention to avoid drowsy driving.Objectives The objectives of the study were twofold: 1) investigate how behavioral constructs such as beliefs and attitudes may affect nurses' intention to avoid drowsy driving; and 2) assess changes in such beliefs and attitudes during a study that evaluated the effectiveness of educational and technological interventions.Methods Three-hundred night-shift nurses were recruited from a large hospital in Texas to participate in a randomized controlled trial. Participants were randomly assigned to three groups: 1) control; 2) educational intervention; and 3) combined educational and technological intervention. The study utilized an integrated model drawing from the constructs of the Theory of Planned Behavior and the Health Belief Model to elicit attitudes, beliefs, and intentions to use in-vehicle drowsiness detection technologies. Each group was surveyed pre- intervention and at post-intervention around 3 months later to assess changes in beliefs and attitudes. Structural equation models and path analysis were used to analyze changes in beliefs.Results Seventy-nine participants completed the pre-intervention questionnaire, and 44 nurses completed the pre- and post-intervention surveys. Intention was predicted primarily by attitude and perceived health threat. Perceived health threat also mediated the relationship between behavioral intention and the influence of subjective norms as well as perceived behavioral control. Participants who received education about drowsy driving had positive changes in beliefs.Conclusions Nurses' perceived health threat from driving drowsy and their attitude toward our intervention were important motivators to avoid drowsy driving. Interventions aiming at raising awareness of the risks associated with drowsy driving may be effective at motivating nurses to avoid drowsy driving.

Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study.

BACKGROUND: Hypoglycemia, or low blood sugar levels, in people with diabetes can be a serious life-threatening condition, and serious outcomes can be avoided if low levels of blood sugar are proactively detected. Although technologies exist to detect the onset of hypoglycemia, they are invasive or costly or exhibit a high incidence of false alarms. Tremors are commonly reported symptoms of hypoglycemia and may be used to detect hypoglycemic events, yet their onset is not well researched or understood. OBJECTIVE: This study aimed to understand diabetic patients' perceptions of hypoglycemic tremors, as well as their user experiences with technology to manage diabetes, and expectations from a self-management tool to ultimately inform the design of a noninvasive and cost-effective technology that detects tremors associated with hypoglycemia. METHODS: A cross-sectional internet panel survey was administered to adult patients with type 1 diabetes using the Qualtrics platform in May 2019. The questions focused on 3 main constructs: (1) perceived experiences of hypoglycemia, (2) experiences and expectations about a diabetes management device and mobile app, and (3) beliefs and attitudes regarding intention to use a diabetes management device. The analysis in this paper focuses on the first two constructs. Nonparametric tests were used to analyze the Likert scale data, with a Mann-Whitney U test, Kruskal-Wallis test, and Games-Howell post hoc test as applicable, for subgroup comparisons to highlight differences in perceived frequency, severity, and noticeability of hypoglycemic tremors across age, gender, years living with diabetes, and physical activity. RESULTS: Data from 212 respondents (129 [60.8%] females) revealed statistically significant differences in perceived noticeability of tremors by gender, whereby males noticed their tremors more (P<.001), and age, with the older population reporting lower noticeability than the young and middle age groups (P<.001). Individuals living longer with diabetes noticed their tremors significantly less than those with diabetes for ≤1 year but not in terms of frequency or severity. Additionally, the majority of our participants (150/212, 70.7%) reported experience with diabetes-monitoring devices. CONCLUSIONS: Our findings support the need for cost-efficient and noninvasive continuous monitoring technologies. Although hypoglycemic tremors were perceived to occur frequently, such tremors were not found to be severe compared with other symptoms such as sweating, which was the highest rated symptom in our study. Using a combination of tremor and galvanic skin response sensors may show promise in detecting the onset of hypoglycemic events.