ABSTRACT
BACKGROUND: Gestational diabetes mellitus (GDM) is an increasing health risk for pregnant women as well as their children. Telehealth interventions targeted at the management of GDM have been shown to be effective, but they still require health care professionals for providing guidance and feedback. Feedback from wearable sensors has been suggested to support the self-management of GDM, but it is unknown how self-tracking should be designed in clinical care. OBJECTIVE: This study aimed to investigate how to support the self-management of GDM with self-tracking of continuous blood glucose and lifestyle factors without help from health care personnel. We examined comprehensive self-tracking from self-discovery (ie, learning associations between glucose levels and lifestyle) and user experience perspectives. METHODS: We conducted a mixed methods study where women with GDM (N=10) used a continuous glucose monitor (CGM; Medtronic Guardian) and 3 physical activity sensors: activity bracelet (Garmin Vivosmart 3), hip-worn sensor (UKK Exsed), and electrocardiography sensor (Firstbeat 2) for a week. We collected data from the sensors, and after use, participants took part in semistructured interviews about the wearable sensors. Acceptability of the wearable sensors was evaluated with the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. Moreover, maternal nutrition data were collected with a 3-day food diary, and self-reported physical activity data were collected with a logbook. RESULTS: We found that the CGM was the most useful sensor for the self-discovery process, especially when learning associations between glucose and nutrition intake. We identified new challenges for using data from the CGM and physical activity sensors in supporting self-discovery in GDM. These challenges included (1) dispersion of glucose and physical activity data in separate applications, (2) absence of important trackable features like amount of light physical activity and physical activities other than walking, (3) discrepancy in the data between different wearable physical activity sensors and between CGMs and capillary glucose meters, and (4) discrepancy in perceived and measured quantification of physical activity. We found the body placement of sensors to be a key factor in measurement quality and preference, and ultimately a challenge for collecting data. For example, a wrist-worn sensor was used for longer compared with a hip-worn sensor. In general, there was a high acceptance for wearable sensors. CONCLUSIONS: A mobile app that combines glucose, nutrition, and physical activity data in a single view is needed to support self-discovery. The design should support tracking features that are important for women with GDM (such as light physical activity), and data for each feature should originate from a single sensor to avoid discrepancy and redundancy. Future work with a larger sample should involve evaluation of the effects of such a mobile app on clinical outcomes. TRIAL REGISTRATION: Clinicaltrials.gov NCT03941652; https://clinicaltrials.gov/study/NCT03941652.
ABSTRACT
BACKGROUND: Gestational diabetes (GDM) has considerable and increasing health effects as it raises both the mother's and the offspring's risk for short- and long-term health problems. GDM can usually be treated with a healthier lifestyle, such as appropriate dietary modifications and sufficient physical activity. Although telemedicine interventions providing weekly or more frequent feedback from health care professionals have shown the potential to improve glycemic control among women with GDM, apps without extensive input from health care professionals are limited and have not been shown to be effective. Different features in personalization and support have been proposed to increase the efficacy of GDM apps, but the knowledge of how these features should be designed is lacking. OBJECTIVE: The aim of this study is to investigate how GDM apps should be designed, considering the desirable features based on the previous literature. METHODS: We designed an interactive GDM prototype app that provided example implementations of desirable features, such as providing automatic and personalized suggestions and social support through the app. Women with GDM explored the prototype and provided feedback in semistructured interviews. RESULTS: We identified that (1) self-tracking data in GDM apps should be extended with written feedback, (2) habits and goals should be highly customizable to be useful, (3) the app should have different functions to provide social support, and (4) health care professionals should be notified through the app if something unusual occurs. In addition, we found 2 additional themes. First, basic functionalities that are fast to learn by women with GDM who have recently received the diagnosis should be provided, but there should also be deeper features to maintain interest for women with GDM at a later stage of pregnancy. Second, as women with GDM may have feelings of guilt, the app should have a tolerance for and a supporting approach to unfavorable behavior. CONCLUSIONS: The feedback on the GDM prototype app supported the need for desirable features and provided new insights into how these features should be incorporated into GDM apps. We expect that following the proposed designs and feedback will increase the efficacy of GDM self-management apps. TRIAL REGISTRATION: ClinicalTrials.gov NCT03941652; https://clinicaltrials.gov/ct2/show/NCT03941652.
ABSTRACT
The tendency to simulate the pain of others within our own sensorimotor systems is a vital component of empathy. However, this sensorimotor resonance is modulated by a multitude of social factors including similarity in bodily appearance, e.g. skin colour. The current study investigated whether increasing self-other similarity via virtual transfer to another colour body reduced ingroup bias in sensorimotor resonance. A sample of 58 white participants was momentarily transferred to either a black or a white body using virtual reality technology. We then employed electroencephalography to examine event-related desynchronization (ERD) in the sensorimotor beta (13-23 Hz) oscillations while they viewed black, white and violet photorealistic virtual agents being touched with a noxious or soft object. While the noxious treatment of a violet agent did not increase beta ERD, amplified beta ERD in response to black agent's noxious vs soft treatment was found in perceivers transferred to a black body. Transfer to the white body dismissed the effect. Further exploratory analysis implied that the pain-related beta ERD occurred only when the agent and the participant were of the same colour. The results suggest that even short-lasting changes in bodily resemblance can modulate sensorimotor resonance to others' perceived pain.
