Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Background: Hypertension is a significant global disease burden. Mobile health (mHealth) offers a promising means to provide patients with hypertension with easy access to health care services. Yet, its efficacy needs to be validated, especially in lower-income areas with a high-salt diet. Objective: This study aims to assess the efficacy of an mHealth app–based intervention in supporting patients’ self-management of hypertension. Methods: A 2-arm randomized controlled trial was conducted among 297 patients with hypertension at the General Hospital of Ningxia Medical University, Ningxia Hui Autonomous Region, China. Participants selected via convenience sampling were randomly allocated into intervention and control groups. Intervention group participants were trained and asked to use an mHealth app named Blood Pressure Assistant for 6 months. They could use the app to record and upload vital signs, access educational materials, and receive self-management reminders and feedback from health care providers based on the analysis of the uploaded data. Control group participants received usual care. Blood pressure (BP) and 2 questionnaire surveys about hypertension knowledge and lifestyle behavior were used to assess all participants at baseline and 6 months. Data analysis was performed with SPSS software using 2-tailed t tests and a chi-square test. Results: There were no significant differences in baseline characteristics and medication use between the 2 groups (all P>.05). After 6 months, although both groups show a significant pre-post improvement (P<.001 each), the BP control rate (ie, the proportion of patients with a systolic BP of <140 mm Hg and diastolic BP of <90 mm Hg) in the intervention group was better than that in the control group (100/111, 90.1% vs 75/115, 65.2%; P<.001). The mean systolic and diastolic BP were significantly reduced by 25.83 (SD 8.99) and 14.28 (SD 3.74) mm Hg in the intervention group (P<.001) and by 21.83 (SD 6.86) and 8.87 (SD 4.22) mm Hg in the control group (P<.001), respectively. The differences in systolic and diastolic BP between the 2 groups were significant (P<.001 and P=.01, respectively). Hypertension knowledge significantly improved only in the intervention group in both pre-post and intergroup comparisons (both P<.001). However, only intragroup improvement was observed for lifestyle behaviors in the intervention group (P<.001), including medication adherence (P<.001), healthy diet (P=.02), low salt intake (P<.001), and physical exercises (P=.02), and no significant difference was observed in the control group or on intergroup comparisons. Conclusions: This research shows that the mHealth app–based intervention has the potential to improve patient health knowledge and support self-management among them toward a healthier lifestyle, including medication adherence, low-salt diets, and physical exercises, thereby achieving optimal BP control. Further research is still needed to verify the specific effects of these interventions. Trial Registration: Chinese Clinical Trial Registry ChiCTR1900026437; https://www.chictr.org.cn/showproj.html?proj=38801

Background: Hypertension is a lifestyle-induced chronic disease that threatens the lives of patients. Control of hypertension requires patients to follow self-management regimes; unfortunately, however, patient compliance with hypertension self-management is low, especially in developing countries. Improvement of patient compliance is premised on meeting patient needs. Mobile health apps are becoming increasingly popular for self-management of chronic diseases. However, few mobile apps have been designed to meet patient needs for hypertension self-management. Objective: The goal of this study was to develop a mobile health app to improve patient compliance with hypertension self-management and evaluate the effectiveness of the app in terms of patient compliance. Methods: The goal-directed design method was applied to guide study design. We divided the study into 4 stages. Stages 1 to 3 comprised the development process. To improve the applicability of the goal-directed design method to chronic disease management, we extracted elements of user models concerned with patient compliance and defined a concrete process for user modeling. In stage 1, personas of hypertensive patients were built using qualitative and quantitative methods. Clustering methods based on questionnaire responses were used to group patients. Qualitative interviews were conducted to identify the needs of different groups. In stage 2, several functional modules were designed to meet the needs of different groups based on the results from stage 1. In stage 3, prototypes of functional modules were designed and implemented as a real app. Stage 4 was the deployment process, in which we conducted a pilot study to investigate patient compliance after using the app. Patient compliance was calculated through the frequency with which they took blood pressure measurements. In addition, qualitative interviews were conducted to learn the underlying reasons for the compliance results. Results: In stage 1, patients were divided into 3 groups based on 82 valid questionnaire responses. Eighteen patients from the different groups (7, 5, and 6 patients) were interviewed, and the needs of the groups were summarized as follows: improve self-management ability, enhance self-management motivation, and receive self-management support. In stages 2 and 3, 6 functional modules were designed and implemented based on specified needs, and the usability of the app was improved through usability tests. In stage 4, 143 patients were recruited to use different versions of the app for 2 months. Results show that patient compliance improved as functional modules were added (P<.001) and was maintained at a high level (rate of 0.73). Interview results from 32 patients show that the design of the app met different needs; thus, patients were more compliant with it. Conclusions: This study developed a mobile health app for hypertension self-management using the goal-directed design method. The app proved to be effective for improving patient compliance with hypertension self-management.

Background: Mobile health services are gradually being introduced to support patients’ self-management of chronic conditions. The success of these services is contingent upon patients’ continuous use of them. Objective: This study aims to develop a model to measure the success of patients’ continuous use of mobile health services for the self-management of chronic conditions. Methods: The proposed model was derived from the information systems continuance model and the information systems success model. This model contains 7 theoretical constructs: information quality, system quality, service quality, perceived usefulness, user satisfaction, perceived health status, and continuous use intention. A web-based questionnaire survey instrument was developed to test the model. The survey was conducted to collect data from 129 patients who used a mobile health app for hypertension management from 2017 to 2019. The questionnaire items were derived from validated instruments and were measured using a 5-point Likert scale. The partial least squares modelling method was used to test the theoretical model. Results: The model accounted for 58.5% of the variance in perceived usefulness (R2=0.585), 52.3% of the variance in user satisfaction (R2=0.523), and 41.4% of the variance in patients’ intention to make continuous use of mobile health services (R2=0.414). The continuous use intention was significantly influenced by their perceived health status (β=.195, P=.03), perceived usefulness (β=.307, P=.004), and user satisfaction (β=.254, P=.04) with the mobile health service. Information quality (β=.235, P=.005), system quality (β=.192, P=.02), and service quality (β=.494, P<.001) had a significantly positive influence on perceived usefulness but not on user satisfaction. Perceived usefulness had a significantly positive influence on user satisfaction (β=.664, P<.001). In a result opposite to the original hypothesis, perceived health status did not negatively influence patients’ intention to continue using the mobile health service but showed a significantly positive correlation. Conclusions: This study developed a theoretical model to predict and explain patients’ continuous use of mobile health services for self-management of chronic conditions and empirically tested the model. Perceived usefulness, user satisfaction, and health status contributed to patients’ intention to make continuous use of mobile health services for self-managing their chronic conditions.

Background: Health education emerged as an important intervention for improving the awareness and self-management abilities of chronic disease patients. The development of information technologies has changed the form of patient educational materials from traditional paper materials to electronic materials. To date, the amount of patient educational materials on the internet is tremendous, with variable quality, which makes it hard to identify the most valuable materials by individuals lacking medical backgrounds. Objective: The aim of this study was to develop a health recommender system to provide appropriate educational materials for chronic disease patients in China and evaluate the effect of this system. Methods: A knowledge-based recommender system was implemented using ontology and several natural language processing (NLP) techniques. The development process was divided into 3 stages. In stage 1, an ontology was constructed to describe patient characteristics contained in the data. In stage 2, an algorithm was designed and implemented to generate recommendations based on the ontology. Patient data and educational materials were mapped to the ontology and converted into vectors of the same length, and then recommendations were generated according to similarity between these vectors. In stage 3, the ontology and algorithm were incorporated into an mHealth system for practical use. Keyword extraction algorithms and pretrained word embeddings were used to preprocess educational materials. Three strategies were proposed to improve the performance of keyword extraction. System evaluation was based on a manually assembled test collection for 50 patients and 100 educational documents. Recommendation performance was assessed using the macro precision of top-ranked documents and the overall mean average precision (MAP). Results: The constructed ontology contained 40 classes, 31 object properties, 67 data properties, and 32 individuals. A total of 80 SWRL rules were defined to implement the semantic logic of mapping patient original data to the ontology vector space. The recommender system was implemented as a separate Web service connected with patients' smartphones. According to the evaluation results, our system can achieve a macro precision up to 0.970 for the top 1 recommendation and an overall MAP score up to 0.628. Conclusions: This study demonstrated that a knowledge-based health recommender system has the potential to accurately recommend educational materials to chronic disease patients. Traditional NLP techniques combined with improvement strategies for specific language and domain proved to be effective for improving system performance. One direction for future work is to explore the effect of such systems from the perspective of patients in a practical setting.

Objectives Action planning is a brief and effective behaviour change technique (BCT) to improve physical activity (PA) and diet behaviour (DB). This study aimed to identify critical BCTs and mechanisms of action (MoAs) to interpret the effectiveness of planning interventions based on the Health Action Process Approach (HAPA) model. Design Systematic review. Data sources PubMed, Web of Science, CINAHL (EBSCO), PsycINFO (EBSCO), Psychology and Behavioural Sciences Collection (EBSCO), psyARTICLES and Medline were searched for studies from January 1990 to September 2021 published in English. Eligibility criteria Experiment involving action planning intervention to improve PA or DB in community-dwelling adult patients with chronic conditions. Data extraction and synthesis Two reviewers independently coded the planning interventions into BCT combinations and MoA assemblies. Outcome was dichotomised according to the statistical power and Cohen’s d. The Cochrane risk of bias assessment tool and the Risk of Bias in Nonrandomized Studies–of Interventions assessment tool were used to assess the quality of randomised controlled trials (RCTs) and non-RCTs, respectively. Results From the 52 included studies, 46 BCTs were identified and linked to 21 MoAs. Long-term facilitators for planning intervention included ‘self-monitoring of behaviour’, ‘problem solving’, ‘instruction on how to perform the behaviour’ and ‘adding objects to the environments’. The three most frequently occurring MoAs were ‘intention’, ‘behavioural regulation’, ‘beliefs about capabilities’. The effective intervention groups had higher MoA scores that corresponded to the HAPA model constructs than the ineffective groups. Conclusions The findings from this review may inform scientific and effective planning intervention designs for community-dwelling people with chronic conditions in the future. PROSPERO registration number CRD42021241227.