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.

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: The number of mHealth apps has increased rapidly during recent years. Literature suggests a number of problems and barriers to the adoption of mHealth apps, including issues such as validity, usability, as well as data privacy and security. Continuous quality assessment and assurance systems might help to overcome these barriers. Aim of this scoping review was to collate literature on quality assessment tools and quality assurance systems for mHealth apps, compile the components of the tools, and derive overarching quality dimensions, which are potentially relevant for the continuous quality assessment of mHealth apps.
UNASSIGNED: Literature searches were performed in Medline, EMBASE and PsycInfo. Articles in English or German language were included if they contained information on development, application, or validation of generic concepts of quality assessment or quality assurance of mHealth apps. Screening and extraction were carried out by two researchers independently. Identified quality criteria and aspects were extracted and clustered into quality dimensions.
UNASSIGNED: A total of 70 publications met inclusion criteria. Included publications contain information on five quality assurance systems and further 24 quality assessment tools for mHealth apps. Of these 29 systems/tools, 8 were developed for the assessment of mHealth apps for specific diseases, 16 for assessing mHealth apps for all fields of health and another five are not restricted to health apps. Identified quality criteria and aspects were extracted and grouped into a total of 14 quality dimensions, namely \"information and transparency\", \"validity and (added) value\", \"(medical) safety\", \"interoperability and compatibility\", \"actuality\", \"engagement\", \"data privacy and data security\", \"usability and design\", \"technology\", \"organizational aspects\", \"social aspects\", \"legal aspects\", \"equity and equality\", and \"cost(-effectiveness)\".
UNASSIGNED: This scoping review provides a broad overview of existing quality assessment and assurance systems. Many of the tools included cover only a few dimensions and aspects and therefore do not allow for a comprehensive quality assessment or quality assurance. Our findings can contribute to the development of continuous quality assessment and assurance systems for mHealth apps.
UNASSIGNED: https://www.researchprotocols.org/2022/7/e36974/, International Registered Report Identifier, IRRID (DERR1-10.2196/36974).

[This corrects the article DOI: 10.3389/frhs.2024.1372522.].

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.

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.

BACKGROUND: Many persons with multiple sclerosis (pwMS) desire to learn how health behaviour changes (e.g., dietary adjustments, physical activity, improvements in stress management) might help them manage their disease. Previous research has shown that certain health behaviour changes can improve quality of life (QoL), fatigue and other MS outcomes. Digital health applications may be well suited to deliver relevant health behavioural interventions because of their accessibility and flexibility. The digital health application \"levidex\" was designed to facilitate health behaviour change by offering evidence-based patient information and cognitive-behavioural therapy techniques to pwMS. By doing so, levidex aims to improve QoL and MS symptoms such as fatigue and mental health.
OBJECTIVE: A previous study reported on the development of levidex; this non-randomised pilot study examined the feasibility (practicability and acceptability) of levidex in pwMS with moderate to severe disability. Furthermore, the intervention\'s impact on empowerment, stress management, and relevant health behaviours (e.g., dietary behaviour, physical activity) was explored.
METHODS: levidex was originally developed for newly diagnosed pwMS in the first year after diagnosis and eventually modified to offer access to pwMS with moderate to severe disability. Participants (n = 43) with an Expanded Disability Status Scale between 3.5 and 7.5 and a disease duration of more than one year were eligible to participate. The intervention was used over a period of six months with measurement time points at baseline, month 3 and month 6.
RESULTS: Out of 38 participants who completed the six-month intervention period, 18 (47.4%) completed all 16 modules and 9 (23.7%) reached modules 13-16, the long-term maintenance part of levidex. Participants rated levidex positively in terms of practicability and acceptability and had only few points of criticism such as to include more physical exercise routine suggestions suitable for participants with severe impairment. Data on secondary endpoints showed no significant changes.
CONCLUSIONS: This pilot study provided evidence for the practicability and acceptability of levidex, a digital health application designed to facilitate health behaviour change in pwMS with moderate to severe disability. Adequately powered randomised controlled studies with longer follow-up periods are needed to clarify the benefit of levidex in pwMS with moderate to severe disability.
BACKGROUND: German Clinical Trials Register (DRKS) DRKS00032667 (14/09/2023); Retrospectively registered.

Conversational user interfaces, or chatbots, are becoming more popular in the realm of digital health and well-being. While many studies focus on measuring the cause or effect of a digital intervention on people\'s health and well-being (outcomes), there is a need to understand how users really engage and use a digital intervention in the real world.
In this study, we examine the user logs of a mental well-being chatbot called ChatPal, which is based on the concept of positive psychology. The aim of this research is to analyze the log data from the chatbot to provide insight into usage patterns, the different types of users using clustering, and associations between the usage of the app\'s features.
Log data from ChatPal was analyzed to explore usage. A number of user characteristics including user tenure, unique days, mood logs recorded, conversations accessed, and total number of interactions were used with k-means clustering to identify user archetypes. Association rule mining was used to explore links between conversations.
ChatPal log data revealed 579 individuals older than 18 years used the app with most users being female (n=387, 67%). User interactions peaked around breakfast, lunchtime, and early evening. Clustering revealed 3 groups including \"abandoning users\" (n=473), \"sporadic users\" (n=93), and \"frequent transient users\" (n=13). Each cluster had distinct usage characteristics, and the features were significantly different (P<.001) across each group. While all conversations within the chatbot were accessed at least once by users, the \"treat yourself like a friend\" conversation was the most popular, which was accessed by 29% (n=168) of users. However, only 11.7% (n=68) of users repeated this exercise more than once. Analysis of transitions between conversations revealed strong links between \"treat yourself like a friend,\" \"soothing touch,\" and \"thoughts diary\" among others. Association rule mining confirmed these 3 conversations as having the strongest linkages and suggested other associations between the co-use of chatbot features.
This study has provided insight into the types of people using the ChatPal chatbot, patterns of use, and associations between the usage of the app\'s features, which can be used to further develop the app by considering the features most accessed by users.