Search

BACKGROUND Student mental health is an increasing concern among college student populations. OBJECTIVE To determine if college students could better self-manage their anxiety, stress, and depression symptomology by using automated mHealth to support health coaching for managing mental health. METHODS Students were provided with a mobile health self-management app called mHELP. The intervention group students had on-demand access to interactive mental health management tools on the app and completed weekly self-assessments of anxiety (GAD-7), perceived stress (PSS-10), and depression (PHQ-8) with immediate feedback of self-reported measurements during the first phase (five weeks) of the study. During the second five-week phase, students were additionally provided daily educational material in the form of videos, brochures, and articles related to the self-management of mental health issues. The educational material was developed in collaboration with the university’s Counseling and Psychological Services Department. Weekly self-reported measures of anxiety, perceived stress, and depression were compared in a pre-post analysis for each of the two phases as well as an analysis of variance of the changes in the outcome values for each phase to check for significance (within- and between-group). The control group did not receive or have access to any digital health coaching information from the mHELP app, but they did complete weekly assessments of self-reported measures of anxiety, perceived stress, and depression with immediate feedback on the results. RESULTS A total of 87 participants, 72.4% assigned randomly to the intervention group, completed the study with a mean age of 22.1 years (SD = 4.35, Range: 18-37). No significant improvements in outcomes over time were noted for the intervention group during the first phase of the study; however, during the second phase of the study, significant improvements were noted for anxiety (p = .002, d = .428) and perceived stress (p = .033, d = .306) over time. Over the duration of both phases of the study, the control and intervention groups both showed improvements in mean GAD-7, PSS-10 and PHQ-8 scores. The intervention group exhibited a statistically significant improvement in mean GAD-7 (M = 3.143, SE = 0.638, p < .001, d = .621), PSS-10 (M = 3.476, SE = 0.968, p = .001, d = .452) and PHQ-8 (M = 1.619, SE = 0.648, p = .041, d = .315). The control group only exhibited a statistically significant improvement in their GAD-7 score (M = 3.167, SE = 1.251, p = .019, d = .517). An ANOVA of the differences in improvements between groups did not find a significant effect of the mHELP intervention for GAD-7, PSS-10 or PHQ-8 scores. CONCLUSIONS Our study demonstrated that using mHealth to support health coaching may assist in the self-management of anxiety and stress among college students, while a self-measurement routine may also provide benefits for improving anxiety over time, perhaps due to improved self-awareness of mental health conditions.

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; Pperceived usefulness (β=.33; Pperceived health threat (β=.55; Pcues to action (β=.17; Presistance to change (β=−.19; PPperceived 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.

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.

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 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.

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.

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 (PP 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.

BACKGROUND Advances in technology engender the investigation of technological solutions to opioid use disorder (OUD). However, in comparison to chronic disease management, the application of mobile health (mHealth) to OUD has been limited. OBJECTIVE The overarching aim of our research was to design OUD management technologies that utilize wearable sensors to provide continuous monitoring capabilities. The objectives of this study were to (1) document the currently available opioid-related mHealth apps, (2) review past and existing technology solutions that address OUD, and (3) discuss opportunities for technological withdrawal management solutions. METHODS We used a two-phase parallel search approach: (1) an app search to determine the availability of opioid-related mHealth apps and (2) a scoping review of relevant literature to identify relevant technologies and mHealth apps used to address OUD. RESULTS The app search revealed a steady rise in app development, with most apps being clinician-facing. Most of the apps were designed to aid in opioid dose conversion. Despite the availability of these apps, the scoping review found no study that investigated the efficacy of mHealth apps to address OUD. CONCLUSIONS Our findings highlight a general gap in technological solutions of OUD management and the potential for mHealth apps and wearable sensors to address OUD.