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BACKGROUND: Digitalization with minimal human resources could support self-management among women with gestational diabetes and improve maternal and neonatal outcomes. OBJECTIVE: This study aimed to investigate if a periodic mobile application (eMOM) with wearable sensors improves maternal and neonatal outcomes among women with diet-controlled gestational diabetes without additional guidance from healthcare personnel. STUDY DESIGN: Women with gestational diabetes were randomly assigned in a 1:1 ratio at 24 to 28 weeks' gestation to the intervention or the control arm. The intervention arm received standard care in combination with use of the periodic eMOM, whereas the control arm received only standard care. The intervention arm used eMOM with a continuous glucose monitor, an activity tracker, and a food diary 1 week/month until delivery. The primary outcome was the change in fasting plasma glucose from baseline to 35 to 37 weeks' gestation. Secondary outcomes included capillary glucose, weight gain, nutrition, physical activity, pregnancy complications, and neonatal outcomes, such as macrosomia. RESULTS: In total, 148 women (76 in the intervention arm, 72 in the control arm; average age, 34.1±4.0 years; body mass index, 27.1±5.0 kg/m2) were randomized. The intervention arm showed a lower mean change in fasting plasma glucose than the control arm (difference, -0.15 mmol/L vs -2.7 mg/mL; P=.022) and lower capillary fasting glucose levels (difference, -0.04 mmol/L vs -0.7 mg/mL; P=.002). The intervention arm also increased their intake of vegetables (difference, 11.8 g/MJ; P=.043), decreased their sedentary behavior (difference, -27.3 min/d; P=.043), and increased light physical activity (difference, 22.8 min/d; P=.009) when compared with the control arm. In addition, gestational weight gain was lower (difference, -1.3 kg; P=.015), and there were less newborns with macrosomia in the intervention arm (difference, -13.1 %; P=.036). Adherence to eMOM was high (daily use >90%), and the usage correlated with lower maternal fasting (P=.0006) and postprandial glucose levels (P=.017), weight gain (P=.028), intake of energy (P=.021) and carbohydrates (P=.003), and longer duration of the daily physical activity (P=.0006). There were no significant between-arm differences in terms of pregnancy complications. CONCLUSION: Self-tracking of lifestyle factors and glucose levels without additional guidance improves self-management and the treatment of gestational diabetes, which also benefits newborns. The results of this study support the use of digital self-management and education tools in maternity care.
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BACKGROUND: Technical applications can promote home-based exercise and physical activity of community-dwelling stroke survivors. Caregivers are often able and willing to assist with home-based exercise and physical activity but lack the knowledge and resources to do so. ActivABLES was established to promote home-based exercise and physical activity among community-dwelling stroke survivors, with support from their caregivers. The aim of our study is to investigate the feasibility of ActivABLES in terms of acceptability, demand, implementation and practicality. METHODS: A convergent design of mixed methods research in which quantitative results were combined with personal experiences of a four-week use of ActivABLES by community-dwelling stroke survivors with support from their caregivers. Data collection before, during and after the four-week period included the Berg Balance Scale (BBS), Activities-Specific Balance Confidence Scale (ABC), Timed-Up-and-Go (TUG) and Five Times Sit to Stand Test (5xSST) and data from motion detectors. Semi-structured interviews were conducted with stroke survivors and caregivers after the four-week period. Descriptive statistics were used for quantitative data. Qualitative data was analysed with direct content analysis. Themes were identified related to the domains of feasibility: acceptability, demand, implementation and practicality. Data was integrated by examining any (dis)congruence in the quantitative and qualitative findings. RESULTS: Ten stroke survivors aged 55-79 years participated with their informal caregivers. Functional improvements were shown in BBS (+ 2.5), ABC (+ 0.9), TUG (- 4.2) and 5xSST (- 2.7). More physical activity was detected with motion detectors (stand up/sit down + 2, number of steps + 227, standing + 0.3 h, hours sitting/lying - 0.3 h). The qualitative interviews identified themes for each feasibility domain: (i) acceptability: appreciation, functional improvements, self-initiated activities and expressed potential for future stroke survivors; (2) demand: reported use, interest in further use and need for follow-up; (3) implementation: importance of feedback, variety of exercises and progression of exercises and (4) practicality: need for support and technical problems. The quantitative and qualitative findings converged well with each other and supported the feasibility of ActivABLES. CONCLUSIONS: ActivABLES is feasible and can be a good asset for stroke survivors with slight or moderate disability to use in their homes. Further studies are needed with larger samples.
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Cuidadores/psicologia , Terapia por Exercício/organização & administração , Serviços de Assistência Domiciliar/organização & administração , Reabilitação do Acidente Vascular Cerebral/métodos , Sobreviventes/psicologia , Idoso , Pessoas com Deficiência , Estudos de Viabilidade , Feminino , Humanos , Vida Independente , Masculino , Pessoa de Meia-Idade , Pesquisa Qualitativa , Apoio Social , Sobreviventes/estatística & dados numéricosRESUMO
BACKGROUND: Novel technical solutions are called for to promote home-based exercise among community-dwelling stroke survivors supported by their caregivers. Lack of resources and knowledge about how to accomplish it, has been demonstrated. The objective of this study is to describe in detail the development of ActivABLES, a technical intervention to promote home-based exercise and physical activity engagement of community-dwelling stroke survivors with support from their caregivers. METHODS: The technical development process of ActivABLES was guided by the Medical Research Council (MRC) framework for development and evaluation of complex interventions as well as by principles of human-centred design and co-design. The main steps included: (1) Synthesis of evidence supporting the inclusion of balance exercises, mobility and walking exercises and exercises for the upper arm; (2) Implementation of initial user studies with qualitative data collection from individual interviews with stroke survivors, and focus group interviews with caregivers and health professionals; (3) Preliminary testing of eight prototypes with seven stroke survivors and their caregivers. RESULTS: After the preliminary testing of eight prototypes, four prototypes were not further developed whereas four prototypes were modified further. In addition, two new prototypes were developed, leaving six prototypes for further modification: 1) ActivFOAM for balance exercises, 2) WalkingSTARR to facilitate walking, 3) ActivBALL for hand exercises, 4) ActivSTICKS for upper arm exercises, and 5) ActivLAMP and 6) ActivTREE which both give visual feedback on progress of daily exercise and physical activities. ActivFOAM, ActivBALL and ActivSTICKS are all connected to a tablet where exercise instructions are given. All the exercise prototypes can be connected to ActivLAMP and ActivTREE to give feedback on how much exercise the user has done. Settings can be individualised and recommended daily time and/or repetition can easily be changed as the user progresses to higher activity levels. CONCLUSIONS: The development process of ActivABLES was guided by the principles of human-centred design, with iterative testing of future users, and by the MRC framework of complex intervention, with a repeated process of development and testing. This process resulted in six prototypes which are available for feasibility testing among a small group of community-dwelling stroke survivors.
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Terapia por Exercício/métodos , Serviços de Assistência Domiciliar/organização & administração , Reabilitação do Acidente Vascular Cerebral/métodos , Sobreviventes/psicologia , Cuidadores/psicologia , Grupos Focais , Pesquisa sobre Serviços de Saúde/organização & administração , Humanos , Vida Independente , Apoio SocialRESUMO
BACKGROUND: In recent years, numerous methods have been introduced to predict glucose levels using machine-learning techniques on patients' daily behavioral and continuous glucose data. Nevertheless, a definitive consensus remains elusive regarding modeling the combined effects of diet and exercise for optimal glucose prediction. A notable challenge is the propensity for observational patient datasets from uncontrolled environments to overfit due to skewed feature distributions of target behaviors; for instance, diabetic patients seldom engage in high-intensity exercise post-meal. METHODS: In this study, we introduce a unique application of Bayesian transfer learning for postprandial glucose prediction using randomized controlled trial (RCT) data. The data comprises a time series of three key variables: continuous glucose levels, exercise expenditure, and carbohydrate intake. For building the optimal model to predict postprandial glucose levels we initially gathered balanced training data from RCTs on healthy participants by randomizing behavioral conditions. Subsequently, we pretrained the model's parameter distribution using RCT data from the healthy cohort. This pretrained distribution was then adjusted, transferred, and utilized to determine the model parameters for each patient. RESULTS: The efficacy of the proposed method was appraised using data from 68 gestational diabetes mellitus (GDM) patients in uncontrolled settings. The evaluation underscored the enhanced performance attained through our method. Furthermore, when modeling the joint impact of diet and exercise, the synergetic model proved more precise than its additive counterpart. CONCLUSION: An innovative application of the transfer-learning utilizing randomized controlled trial data can improve the challenging modeling task of postprandial glucose prediction for GDM patients, integrating both dietary and exercise behaviors. For more accurate prediction, future research should focus on incorporating the long-term effects of exercise and other glycemic-related factors such as stress, sleep.
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Teorema de Bayes , Glicemia , Exercício Físico , Aprendizado de Máquina , Período Pós-Prandial , Humanos , Glicemia/metabolismo , Glicemia/análise , Feminino , Período Pós-Prandial/fisiologia , Exercício Físico/fisiologia , Dieta , Gravidez , Diabetes Gestacional/sangue , Diabetes Gestacional/metabolismo , AdultoRESUMO
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.
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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.
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INTRODUCTION: Gestational diabetes (GDM) causes various adverse short-term and long-term consequences for the mother and child, and its incidence is increasing globally. So far, the most promising digital health interventions for GDM management have involved healthcare professionals to provide guidance and feedback. The principal aim of this study is to evaluate the effects of comprehensive and real-time self-tracking with eMOM GDM mobile application (app) on glucose levels in women with GDM, and more broadly, on different other maternal and neonatal outcomes. METHODS AND ANALYSIS: This randomised controlled trial is carried out in Helsinki metropolitan area. We randomise 200 pregnant women with GDM into the intervention and the control group at gestational week (GW) 24-28 (baseline, BL). The intervention group receives standard antenatal care and the eMOM GDM app, while the control group will receive only standard care. Participants in the intervention group use the eMOM GDM app with continuous glucose metre (CGM) and activity bracelet for 1 week every month until delivery and an electronic 3-day food record every month until delivery. The follow-up visit after intervention takes place 3 months post partum for both groups. Data are collected by laboratory blood tests, clinical measurements, capillary glucose measures, wearable sensors, air displacement plethysmography and digital questionnaires. The primary outcome is fasting plasma glucose change from BL to GW 35-37. Secondary outcomes include, for example, self-tracked capillary fasting and postprandial glucose measures, change in gestational weight gain, change in nutrition quality, change in physical activity, medication use due to GDM, birth weight and fat percentage of the child. ETHICS AND DISSEMINATION: The study has been approved by Ethics Committee of the Helsinki and Uusimaa Hospital District. The results will be presented in peer-reviewed journals and at conferences. TRIAL REGISTRATION NUMBER: NCT04714762.
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Diabetes Gestacional , Aplicativos Móveis , Recém-Nascido , Criança , Feminino , Gravidez , Humanos , Diabetes Gestacional/epidemiologia , Glicemia , Estilo de Vida , Peso ao Nascer , Ensaios Clínicos Controlados Aleatórios como AssuntoRESUMO
The most time-consuming part of character animation is 3D character posing. Posing using a mouse is a slow and tedious task that involves sequences of selecting on-screen control handles and manipulating the handles to adjust character parameters, such as joint rotations and end effector positions. Thus, various 3D user interfaces have been proposed to make animating easier, but they typically provide less accuracy. The proposed interface combines a mouse with the Leap Motion device to provide 3D input. A usability study showed that users preferred the Leap Motion over a mouse as a 3D gestural input device. The Leap Motion drastically decreased the number of required operations and the task completion time, especially for novice users.