UNASSIGNED: We present a digital therapeutic (DTx) using continuous glucose monitoring (CGM) and an advanced artificial intelligence (AI) algorithm to digitally personalize lifestyle interventions for people with type 2 diabetes (T2D).
UNASSIGNED: A study of 118 participants with non-insulin-treated T2D (HbA1c ≥ 6.5%) who were already receiving standard care and had a mean baseline (BL) HbA1c of 7.46% (0.93) used the DTx for three months to evaluate clinical endpoints, such as HbA1c, body weight, quality of life and app usage, for a pre-post comparison. The study also included an assessment of initial long-term data from a second use of the DTx.
UNASSIGNED: After three months of using the DTx, there was an improvement of 0.67% HbA1c in the complete cohort and -1.08% HbA1c in patients with poorly controlled diabetes (BL-HbA1c ≥ 7.0%) compared with standard of care (P < .001). The number of patients within the therapeutic target range (< 7.0%) increased from 38% to 60%, and 33% were on the way to remission (< 6.5%). Patients who used the DTx a second time experienced a reduction of -0.76% in their HbA1c levels and a mean weight loss of -6.84 kg after six months (P < .001) compared with BL.
UNASSIGNED: These results indicate that the DTx has clinically relevant effects on glycemic control and weight reduction for patients with both well and poorly controlled diabetes, whether through single or repeated usage. It is a noteworthy improvement in T2D management, offering a non-pharmacological, fully digital solution that integrates biofeedback through CGM and an advanced AI algorithm.

Chronic non-specific neck pain (CNNP) poses a substantial health and economic burden in China. This study introduces a gamified motion-sensing health application framework to address the limitations of existing health applications. The gamified cervical spine somatic exercise application employs motion capture technology alongside the smartphone\'s built-in sensors to simulate accurate somatic interactions. Controlled experiments and data analyses demonstrated that the application significantly outperformed traditional text and video interventions in relieving participants\' neck pain by increasing their average daily activity and compliance with the cervical spine exercise routine. The neck pain level of the participants is quantified by the Neck Disability Index (NDI). The results from the controlled experiments demonstrate that this gamified approach significantly decreases the Neck Disability Index (NDI) score from 1.54 to 1.24, highlighting its ability to alleviate neck pain and increase user compliance.

The purpose of this study was to explore Chinese older adults\' experiences of using digital health interventions and their perspectives of a digital falls prevention program (KOKU), to explore acceptability for its use in their daily life. Participants\' needs and preferences will be incorporated into the existing KOKU digital program to improve user-experience, engagement and digital inclusion of Chinese older adults living in the UK. Conducting interviews with Chinese older adults in community settings provide a better understanding of cultural factors when co-designing digital health applications.

In this pilot study, the authors investigated the preliminary effectiveness of the digital lifestyle intervention, actensio (mementor DE GmbH), in treating arterial hypertension. Adults with arterial hypertension were randomly assigned to an intervention group (actensio + standard care) or a control group (waiting list + standard care) in a 1:1 ratio. Primary and secondary endpoints were assessed at baseline (t0) and 3 months post-randomization (t1). The primary endpoint was average systolic blood pressure, measured at home for 1 week. Secondary endpoints included patient engagement (measured using the \"patient activation measure\"; PAM-13), average diastolic blood pressure, and heart rate. All endpoints were analyzed using ANCOVA models, following an intention-to-treat approach, while adjusting for baseline values. Missing data were estimated using multiple imputation models. A total of N = 102 participants (f = 59, age = 52.94 ± 9.01) were randomized to either the intervention (IG; N = 52) or the control group (CG; N = 50), of which N = 80 completed the blood pressure diary, and N = 81 the PAM-13 at t1. Between-group comparisons showed an average group difference in systolic blood pressure of -5.06 mm Hg (95% CI = -8.71 to -1.41, p = .013) between the intervention group (M = 137.37 ± 10.13) and the control group (M = 142.35 ± 11.23). Average group difference for patient engagement was 3.35 points with a trend towards statistical significance (95% CI = -018 to 6.89, p = .064), favoring the intervention group (MIG = 79.38 ± 9.44 vs. MCG = 75.45 ± 10.62). There were no group differences in diastolic blood pressure (-1.78 mm Hg; 95% CI = -4.50 to 0.95, p = .402) and heart rate (-0.684; 95% CI = -3.73 to 2.36, p = 0.683). The results of the present pilot study confirm the preliminary effectiveness of the digital lifestyle intervention, actensio, in reducing high blood pressure in patients with hypertension.

Emerging digital technologies promise to improve breast cancer care, however lack of awareness among clinicians often prevents timely adoption. This study aims to investigate current awareness and intention-to-use of three technologies among breast cancer healthcare professionals (HCP): (1) digital health applications (DHA), (2) artificial intelligence (AI), and (3) blockchain technology (BC). A 22-item questionnaire was designed and administered before and after a 30 min educational presentation highlighting technology implementation examples. Technology awareness and intention-to-use were measured using 7-point Likert scales. Correlations between demographics, technology awareness, intention-to-use, and eHealth literacy (GR-eHEALS scale) were analyzed. 45 HCP completed the questionnaire, of whom 26 (57.8%) were female. Age ranged from 24 to 67 {mean age (SD): 44.93 ± 12.62}. Awareness was highest for DHA (68.9%) followed by AI (66.7%) and BC (24.4%). The presentation led to a non-significant increase of intention-to-use AI {5.37 (±1.81) to 5.83 (±1.64)}. HCPs´ intention-to-use BC after the presentation increased significantly {4.30 (±2.04) to 5.90 (±1.67), p < 0.01}. Mean accumulated score for GR-eHEALS averaged 33.04 (± 6.61). HCPs´ intended use of AI significantly correlated with eHealth literacy (ρ = 0.383; p < 0.01), intention-to-use BC (ρ = 0.591; p < 0.01) and participants´ age (ρ = -0.438; p < 0.01). This study demonstrates the effect that even a short practical presentation can have on HCPs´ intention-to-use emerging digital technologies. Training potential professional users should be addressed alongside the development of new information technologies and is crucial to increase HCPs´ corresponding awareness and intended use.

BACKGROUND: The accuracy of movement determination software in current activity trackers is insufficient for scientific applications, which are also not open-source.
OBJECTIVE: To address this issue, we developed an accurate, trainable, and open-source smartphone-based activity-tracking toolbox that consists of an Android app (HumanActivityRecorder) and 2 different deep learning algorithms that can be adapted to new behaviors.
METHODS: We employed a semisupervised deep learning approach to identify the different classes of activity based on accelerometry and gyroscope data, using both our own data and open competition data.
RESULTS: Our approach is robust against variation in sampling rate and sensor dimensional input and achieved an accuracy of around 87% in classifying 6 different behaviors on both our own recorded data and the MotionSense data. However, if the dimension-adaptive neural architecture model is tested on our own data, the accuracy drops to 26%, which demonstrates the superiority of our algorithm, which performs at 63% on the MotionSense data used to train the dimension-adaptive neural architecture model.
CONCLUSIONS: HumanActivityRecorder is a versatile, retrainable, open-source, and accurate toolbox that is continually tested on new data. This enables researchers to adapt to the behavior being measured and achieve repeatability in scientific studies.

UNASSIGNED: Since 2019 people who have insured in the German statutory health insurance are entitled to use certified apps called the Digitale Gesundheitsanwendungen [Digital Health Applications (DiGAs)]. The prerequisite for this is that an app certified as DiGA and suitable for their diagnosis exists. The DiGA can then either be prescribed by a physician or psychotherapist or requested by the patient from the statutory health insurance fund. Given the novelty of this type of healthcare, the implementation of a DiGA should be closely monitored to identify potential weaknesses and achieve quality improvements. To enable an analysis of the supply of DiGAs step-by-step, we aimed to create the DiGA-Care Path.
UNASSIGNED: We conducted three steps to create the DiGA-Care Path. First, a knowledge base was created based on a structured literature research matched with knowledge gathered from the superordinate research project \"QuaSiApps\" funded by the German Federal Joint Committee. Second, we aimed to create an \"ideal-typical\" DiGA-Care Path using a flowchart. Third, based on the first path, a final path was developed using the graphical modeling language \"Event-Driven Process Chain.\"
UNASSIGNED: The DiGA-Care Path was developed to depict the supply of DiGAs in Germany. The final path is constituted by a \"main path\" as well as a corresponding \"sub-path\". While the \"main path\" focuses more on the supply environment in which a DiGA is used, the \"sub-path\" depicts the supply delivered by the DiGA itself. Besides the process itself, the paths include relevant actors to indicate responsibilities for individual process steps.
UNASSIGNED: The DiGA-Care Path helps to analyze the current supply of DiGAs step-by-step. Thereby, each step can be investigated in detail to identify problems and to detect further steps where quality improvements can be enabled. Depending on the perspective, focused either on the supply environment, or the supply delivered by the DiGA itself, the \"main path\" or the \"sub-path\" can be used, respectively. Besides the potential of the DiGA-Care Path to improve the current supply of DiGAs, it can help as an orientation for international policymakers or further stakeholders either to develop their own integration of apps into healthcare systems or for international manufacturers to consider entering the German market.

UNASSIGNED: In a randomized controlled trial, the efficacy of a digital diabetes diary regarding a reduction of diabetes distress was evaluated.
UNASSIGNED: A randomized controlled trial with a 12-week follow-up was conducted in 41 study sites across Germany. Key eligibility criteria were a diagnosis of type 1, type 2, or gestational diabetes and regular self-monitoring of blood glucose. Participants were randomly assigned (2:1 ratio) to either use the digital diabetes logbook (mySugr PRO), or to the control group without app use. The primary outcome was the reduction in diabetes distress at the 12-week follow-up. All analyses were based on the intention-to-treat population with all randomized participants. The trial was registered at the German Register for Clinical Studies (DRKS00022923).
UNASSIGNED: Between February 11, 2021, and June 24, 2022, 424 participants (50% female, 50% male) were included, with 282 being randomized to the intervention group (66.5%) and 142 to the control group (33.5%). A total of 397 participants completed the trial (drop-out rate: 6.4%). The median reduction in diabetes distress was 2.41 (interquartile range [IQR]: -2.50 to 8.11) in the intervention group and 1.25 (IQR: -5.00 to 7.50) in the control group. The model-based adjusted between-group difference was significant (-2.20, IQR: -4.02 to -0.38, P = .0182) favoring the intervention group. There were 27 adverse events, 17 (6.0%) in the intervention group, and 10 (7.0%) in the control group.
UNASSIGNED: The efficacy of the digital diabetes logbook was demonstrated regarding improvements in mental health in people with type 1, type 2, and gestational diabetes.

Epidemics of seasonal influenza is a major public health concern in china. Historical percentage of influenza-like illness (ILI%) from CDC and health enquiry data from a health-related application were collected, when combining the real-time ILI-related search queries with one-week ago\'s ILI%, it was able to predict the trend of ILI correctly and timely. Digital health application is potentializing a supplement to the traditional influenza surveillance systems in China.

BACKGROUND: Mobile eHealth apps have been used as a complementary treatment to increase the quality of life of patients and provide new opportunities for the management of rheumatic diseases. Telemedicine, particularly in the areas of prevention, diagnostics, and therapy, has become an essential cornerstone in the care of patients with rheumatic diseases.
OBJECTIVE: This study aims to improve the design and technology of YogiTherapy and evaluate its usability and quality.
METHODS: We newly implemented the mobile eHealth app YogiTherapy with a modern design, the option to change language, and easy navigation to improve the app\'s usability and quality for patients. After refinement, we evaluated the app by conducting a study with 16 patients with AS (4 female and 12 male; mean age 48.1, SD 16.8 y). We assessed the usability of YogiTherapy with a task performance test (TPT) with a think-aloud protocol and the quality with the German version of the Mobile App Rating Scale (MARS).
RESULTS: In the TPT, the participants had to solve 6 tasks that should be performed on the app. The overall task completion rate in the TPT was high (84/96, 88% completed tasks). Filtering for videos and navigating to perform an assessment test caused the largest issues during the TPT, while registering in the app and watching a yoga video were highly intuitive. Additionally, 12 (75%) of the 16 participants completed the German version of MARS. The quality of YogiTherapy was rated with an average MARS score of 3.79 (SD 0.51) from a maximum score of 5. Furthermore, results from the MARS questionnaire demonstrated a positive evaluation regarding functionality and aesthetics.
CONCLUSIONS: The refined and tested YogiTherapy app showed promising results among most participants. In the future, the app could serve its function as a complementary treatment for patients with AS. For this purpose, surveys with a larger number of patients should still be conducted. As a substantial advancement, we made the app free and openly available on the iOS App and Google Play stores.