OBJECTIVE: Treatment of periodontitis, a chronic inflammatory disease driven by biofilm dysbiosis, remains challenging due to patients\' poor performance and adherence to the necessary oral hygiene procedures. Novel, artificial intelligence-enabled multimodal-sensing toothbrushes (AI-MST) can guide patients\' oral hygiene practices in real-time and transmit valuable data to clinicians, thus enabling effective remote monitoring and guidance. The aim of this trial was to assess the effect of such a system as an adjunct to clinical practice guideline-conform treatment.
METHODS: This was a single-centre, double-blind, standard-of-care controlled, randomized, parallel-group, superiority trial. Male and female adults with generalized Stage II/III periodontitis were recruited at the Shanghai Ninth People\'s Hospital, China. Subjects received a standard-of-care oral hygiene regimen or a technology-enabled, theory-based digital intervention consisting of an AI-MST and targeted doctor\'s guidance by remote micromessaging. Additionally, both groups received guideline-conform periodontal treatment. The primary outcome was the resolution of inflamed periodontal pockets (≥4 mm with bleeding on probing) at 6 months. The intention-to-treat (ITT) analysis included all subjects who received the allocated treatment and at least one follow-up.
RESULTS: One hundred patients were randomized and treated (50 tests/controls) between 1 February and 30 November 2022. Forty-eight tests (19 females) and 47 controls (16 females) were analysed in the ITT population. At 6 months, the proportion of inflamed periodontal pockets decreased from 80.7% (95% confidence interval [CI] 76.5-84.8) to 52.3% (47.7-57.0) in the control group, and from 81.4% (77.1-85.6) to 44.4% (39.9-48.9) in the test group. The inter-group difference was 7.9% (1.6-14.6, p < .05). Test subjects achieved better levels of oral hygiene (p < .001). No significant adverse events were observed.
CONCLUSIONS: The tested digital health intervention significantly improved the outcome of periodontal therapy by enhancing the adherence and performance of self-performed oral hygiene. The model breaks the traditional model of oral health care and has the potential to improve efficiency and reduce costs (NCT05137392).

Deep learning (DL) prediction models hold great promise in the triage of COVID-19.
We aimed to evaluate the diagnostic test accuracy of DL prediction models for assessing and predicting the severity of COVID-19.
We searched PubMed, Scopus, LitCovid, Embase, Ovid, and the Cochrane Library for studies published from December 1, 2019, to April 30, 2022. Studies that used DL prediction models to assess or predict COVID-19 severity were included, while those without diagnostic test accuracy analysis or severity dichotomies were excluded. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2), PROBAST (Prediction Model Risk of Bias Assessment Tool), and funnel plots were used to estimate the bias and applicability.
A total of 12 retrospective studies involving 2006 patients reported the cross-sectionally assessed value of DL on COVID-19 severity. The pooled sensitivity and area under the curve were 0.92 (95% CI 0.89-0.94; I2=0.00%) and 0.95 (95% CI 0.92-0.96), respectively. A total of 13 retrospective studies involving 3951 patients reported the longitudinal predictive value of DL for disease severity. The pooled sensitivity and area under the curve were 0.76 (95% CI 0.74-0.79; I2=0.00%) and 0.80 (95% CI 0.76-0.83), respectively.
DL prediction models can help clinicians identify potentially severe cases for early triage. However, high-quality research is lacking.
PROSPERO CRD42022329252; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD 42022329252.

UNASSIGNED: Chronic diseases are the leading causes of death and disability in the U.S., and disease management largely falls onto patients\' family caregivers. The long-term burden and stress of caregiving negatively impact caregivers\' well-being and ability to provide care. Digital health interventions have the potential to support caregivers. This article aims to provide an updated review of interventions using digital health tools to support family caregivers and the scope of the Human-Centered Design (HCD) approaches.
UNASSIGNED: We conducted a systematic search on July 2019 and January 2021 in PubMed, CINAHL, Embase, Cochrane Library, PsycINFO, ERIC, and ACM Digital Library, limiting to 2014-2021 to identify family caregiver interventions assisted by modern technologies. The Mixed Methods Appraisal Tool and the Grading of Recommendations Assessment, Development and Evaluation were used to evaluate the articles. Data were abstracted and evaluated using Rayyan and Research Electronic Data Capture.
UNASSIGNED: We identified and reviewed 40 studies from 34 journals, 10 fields, and 19 countries. Findings included patients\' conditions and relationships with family caregivers, how the technology is used to deliver the intervention, HCD methods, theoretical frameworks, components of the interventions, and family caregiver health outcomes.
UNASSIGNED: This updated and expanded review revealed that digitally enhanced health interventions were robust at providing high-quality assistance and support to caregivers by improving caregiver psychological health, self-efficacy, caregiving skills, quality of life, social support, and problem-coping abilities. Health professionals need to include informal caregivers as an essential component when providing care to patients. Future research should include more marginalized caregivers from diverse backgrounds, improve the accessibility and usability of the technology tools, and tailor the intervention to be more culturally and linguistically sensitive.

While questionnaires for assessing digital literacy exist, there is still a need for an easy-to-use and implementable questionnaire for assessing digital readiness in a broader sense. Additionally, learnability should be assessed to identify those patients who need additional training to use digital tools in a health care setting.
The aim of the development of the Digital Health Readiness Questionnaire (DHRQ) was to create a short, usable, and freely accessible questionnaire that was designed from a clinical practice perspective.
It was a prospective single-center survey study conducted in Jessa Hospital Hasselt in Belgium. The questionnaire was developed with a panel of field experts with questions in following 5 categories: digital usage, digital skills, digital literacy, digital health literacy, and digital learnability. All participants who were visiting the cardiology department as patients between February 1, 2022, and June 1, 2022, were eligible for participation. Cronbach α and confirmatory factor analysis were performed.
A total number of 315 participants were included in this survey study, of which 118 (37.5%) were female. The mean age of the participants was 62.6 (SD 15.1) years. Cronbach α analysis yielded a score of >.7 in all domains of the DHRQ, which indicates acceptable internal consistency. The fit indices of the confirmatory factor analysis showed a reasonably good fit: standardized root-mean-square residual=0.065, root-mean-square error of approximation=0.098 (95% CI 0.09-0.106), Tucker-Lewis fit index=0.895, and comparative fit index=0.912.
The DHRQ was developed as an easy-to-use, short questionnaire to assess the digital readiness of patients in a routine clinical setting. Initial validation demonstrates good internal consistency, and future research will be needed to externally validate the questionnaire. The DHRQ has the potential to be implemented as a useful tool to gain insight into the patients who are treated in a care pathway, tailor digital care pathways to different patient populations, and offer those with low digital readiness but high learnability appropriate education programs in order to let them take part in the digital pathways.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic condition that is associated with multiple comorbidities. Apart from pharmacological approaches, patient self-management remains the gold standard of care for diabetes. Improving patients\' self-management among the elderly with mobile health (mHealth) interventions is critical, especially in times of the COVID-19 pandemic. However, the extent of mHealth efficacy in managing T2DM in the older population remains unknown. Hence, the present review examined the effectiveness of mHealth interventions on cardiometabolic outcomes in older adults with T2DM.
A systematic search from the inception till May 31, 2021, in the MEDLINE, Embase, and PubMed databases was conducted, and 16 randomized controlled trials were included in the analysis.
The results showed significant benefits on glycosylated hemoglobin (HbA1c) (mean difference -0.24%; 95% confidence interval [CI]: -0.44, -0.05; p = 0.01), postprandial blood glucose (-2.91 mmol/L; 95% CI: -4.78, -1.03; p = 0.002), and triglycerides (-0.09 mmol/L; 95% CI: -0.17, -0.02; p = 0.010), but not on low-density lipoprotein cholesterol (-0.06 mmol/L; 95% CI: -0.14, 0.02; p = 0.170), high-density lipoprotein cholesterol (0.05 mmol/L; 95% CI: -0.03, 0.13; p = 0.220), and blood pressure (systolic blood pressure -0.82 mm Hg; 95% CI: -4.65, 3.00; p = 0.670; diastolic blood pressure -1.71 mmHg; 95% CI: -3.71, 0.29; p = 0.090).
Among older adults with T2DM, mHealth interventions were associated with improved cardiometabolic outcomes versus usual care. Its efficacy can be improved in the future as the current stage of mHealth development is at its infancy. Addressing barriers such as technological frustrations may help strategize approaches to further increase the uptake and efficacy of mHealth interventions among older adults with T2DM.
概述: 2型糖尿病(T2DM)是一种与多种疾病相关的慢性代谢疾病。除药物治疗外, 患者自我管理仍然是糖尿病治疗的金标准。在COVID-19大流行期间, 通过移动医疗(mHealth)干预措施改善老年人的自我管理至关重要。然而, 移动医疗在老年人群中治疗2型糖尿病的疗效仍不清楚。因此, 本综述研究了移动健康干预对老年2型糖尿病患者心脏代谢结局的有效性。 方法: 从研究开始到2021年5月31日, 对MEDLINE、Embase和PubMed数据库进行了系统检索, 并将16项随机对照试验纳入分析。 结果: HbA1c (平均差-0.24%;95% ci: -0.44, -0.05;p=0.01)、餐后血糖(-2.91 mmol/L;95%ci:-4.78, -1.03;p=0.002)和甘油三酯(-0.09mmol/L;95%ci:-0.17, -0.02;p=0.010)明显改善, 而低密度脂蛋白胆固醇(-0.06 mmol/L;95% ci: -0.14, 0.02;p=0.170)、高密度脂蛋白胆固醇(0.05 mmol/L;95% ci: -0.03, 0.13;p=0.220)和血压(SBP -0.82 mmHg;95% ci: -4.65, 3.00;p = 0.670;DBP: -1.71 mmHg;95% ci: -3.71, 0.29;p = 0.090)并无获益。 结论: 在患有2型糖尿病的老年人中, 与常规护理相比, 移动健康干预与改善心脏代谢结果相关。由于当前阶段的移动医疗发展尚处于起步阶段, 其疗效在未来可以得到改进。解决技术等障碍可能有助于策略的制定, 进一步提高患有2型糖尿病的老年人对移动健康干预措施的接受度和有效性。.

BACKGROUND: The stigma faced by people living with HIV causes difficulties in the treatment of HIV/AIDS. Decreasing this stigma is thus no less urgent than implementing behavioral interventions. Serious games are being increasingly adopted as an intervention mechanism to control HIV/AIDS around the world. However, the development and evaluation of these games in China are far from adequate.
OBJECTIVE: This research aimed to help decrease HIV-related stigma in China via the development and evaluation of a serious game, as well as promote a participatory gamification culture for health interventions.
METHODS: Initially, a serious game was developed using free resources from a user-generated content website. Then, quantitative and qualitative methods were employed for game evaluation. A randomized controlled trial was conducted to explore the game\'s effect on HIV-related stigma. The trial included 167 university students, who were randomly allocated to game and control groups. After the experimental evaluation, focus group discussions were held with 64 participants, who were invited to form 16 groups.
RESULTS: The game was called The Second Kind of Life with HIV (SKLWH), which is a free online game that can be played on computers and smartphones. This game hopes to publicize that people living with HIV can live a normal life, that is, a second life different from that imagined by the public. Based on the gamification practice of SKLWH, the participatory serious game development model (PSGDM) was proposed, which guided the development of 3 other HIV-themed games. The trial showed that intimacy stigma was much more severe than morality stigma and personal interaction stigma. Females were more tolerant of morality stigma than males (mean score: 1.29 vs 1.50; P=.01). The game intervention showed an advantage in decreasing intimacy stigma (mean score [game vs control]: 2.43 vs 2.73; P=.04). The group discussions validated the quantitative results and provided further in-depth information. The game intervention was largely preferred by participants, and the belief in intimacy impossibility was commonly expressed by participants when considering their relationship with people living with HIV.
CONCLUSIONS: HIV/AIDS education should adopt appropriate media interventions to mitigate different dimensions of HIV-related stigma. Serious games should be used to decrease intimacy stigma, which is the hardest form to diminish. It is expected that the PSGDM can promote the development of more health games. Furthermore, HIV/AIDS intervention requires interdisciplinary efforts and cooperation that will allow more people to participate and share the responsibility of promoting health.

BACKGROUND: Under the paradigm of precision medicine (PM), patients with the same disease can receive different personalized therapies according to their clinical and genetic features. These therapies are determined by the totality of all available clinical evidence, including results from case reports, clinical trials, and systematic reviews. However, it is increasingly difficult for physicians to find such evidence from scientific publications, whose size is growing at an unprecedented pace.
OBJECTIVE: In this work, we propose the PM-Search system to facilitate the retrieval of clinical literature that contains critical evidence for or against giving specific therapies to certain cancer patients.
METHODS: The PM-Search system combines a baseline retriever that selects document candidates at a large scale and an evidence reranker that finely reorders the candidates based on their evidence quality. The baseline retriever uses query expansion and keyword matching with the ElasticSearch retrieval engine, and the evidence reranker fits pretrained language models to expert annotations that are derived from an active learning strategy.
RESULTS: The PM-Search system achieved the best performance in the retrieval of high-quality clinical evidence at the Text Retrieval Conference PM Track 2020, outperforming the second-ranking systems by large margins (0.4780 vs 0.4238 for standard normalized discounted cumulative gain at rank 30 and 0.4519 vs 0.4193 for exponential normalized discounted cumulative gain at rank 30).
CONCLUSIONS: We present PM-Search, a state-of-the-art search engine to assist the practicing of evidence-based PM. PM-Search uses a novel Bidirectional Encoder Representations from Transformers for Biomedical Text Mining-based active learning strategy that models evidence quality and improves the model performance. Our analyses show that evidence quality is a distinct aspect from general relevance, and specific modeling of evidence quality beyond general relevance is required for a PM search engine.

Patients undergoing heart surgery may experience a range of physiological changes, and the postoperative recovery time is long. Patients and their families often have concerns about quality of life (QoL) after discharge. eHealth interventions may improve patient participation, ensure positive and effective health management, improve the quality of at-home care and the patient\'s quality of life, and reduce rates of depression.
The purpose of this study was to evaluate the effects of eHealth interventions on the physiology, psychology, and compliance of adult patients after cardiac surgery to provide a theoretical basis for clinical practice.
We conducted systematic searches of the following 4 electronic databases: PubMed, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials. Mean (SD) values were used to calculate the pooled effect sizes for all consecutive data, including QoL, anxiety, and depression. Where the same results were obtained using different instruments, we chose the standardized mean difference with a 95% CI to represent the combined effect size; otherwise, the mean difference (MD) with a 95% CI was used. Odds ratios were used to calculate the combined effect size for all dichotomous data. The Cohen Q test for chi-square distribution and an inconsistency index (I2) were used to test for heterogeneity among the studies. We chose a fixed-effects model to estimate the effect size if there was no significant heterogeneity in the data (I2≤50%); otherwise, a random-effects model was used. The quality of the included studies was assessed using the Cochrane risk-of-bias tool for randomized trials (RoB 2).
The search identified 3632 papers, of which 19 met the inclusion criteria. In terms of physical outcomes, the score of the control group was lower than that of the intervention group (MD 0.15, 95% CI 0.03-0.27, I2=0%, P=.02). There was no significant difference in the mental outcomes between the intervention and control groups (MD 0.10, 95% CI -0.03 to 0.24, I2=46.4%, P=.14). The control group\'s score was lower than that of the intervention group for the depression outcomes (MD -0.53, 95% CI -0.89 to -0.17, I2=57.1%, P=.004). Compliance outcomes improved in most intervention groups. The results of the sensitivity analysis were robust. Nearly half of the included studies (9/19, 47%) had a moderate to high risk of bias. The quality of the evidence was medium to low.
eHealth improved the physical component of quality of life and depression after cardiac surgery; however, there was no statistical difference in the mental component of quality of life. The effectiveness of eHealth on patient compliance has been debated. Further high-quality studies on digital health are required.
PROSPERO CRD42022327305; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=327305.

Progress in medical and surgical care has tremendously improved the survival rates of children with congenital heart disease (CHD). However, reduced aerobic capacity and health-related issues remain a threaten to quality survival and prevention of related complications among children and adolescents with CHD. This research program aims to develop and evaluate a WeChat-based health platform (HeartFIT) to facilitate cardiac rehabilitation and promote physical fitness for this rapidly expanding young population. The study protocol describes the use of an iterative process of using a mixed-methods strategy to develop, refine, and pilot test the proposed HeartFIT platform. A sequential problem-solving process comprising four iterative phases with ongoing end-user input will be implemented. In phase 1, relevant literature was systematically reviewed (completed) and then child-parent dyads will be interviewed to understand the broad context and the requirements and considerations of the target population toward the WeChat-based rehabilitation platform. In phase 2, key features and priority functionalities for the platform will be ideated and refined, and a digital interactive prototype will be created. In phase 3, heuristic evaluation and three rounds of end-user testing will be conducted to ensure further refinement and usability of the prototype. In phase 4, a prospective pilot study will be performed to investigate the feasibility, acceptability, and preliminary efficacy of the developed platform over a 12-week intervention period. If HeartFIT intervention is feasible, acceptable, and demonstrates promising efficacy, an adequately powered randomized controlled trial (future work) will be deployed to test the real-world effectiveness of the intervention.