Causal relations of health indices inferred statistically using the DirectLiNGAM

Causal relations among many statistical variables have been assessed using a Linear non-Gaussian Acyclic Model (LiNGAM). Using access to large amounts of health checkup data from Osaka prefecture obtained during the six fiscal years of years 2012–2017, we applied the DirectLiNGAM algorithm as a trial to extract causal relations among health indices for age groups and genders. Results show that LiNGAM yields interesting and reasonable results, suggesting causal relations and correlation among the statistical indices used for these analyses

Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data

We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction models were developed using a light gradient boosting machine (LightGBM), which is an effective GBDT implementation algorithm, and LR. Their reliabilities were measured based on expected calibration error (ECE), negative log-likelihood (Logloss), and reliability diagrams. Similarly, their classification accuracies were measured in the area under the curve (AUC). We further analyzed their reliabilities while changing the sample size for training. Among the 277,651 participants, 15,900 (7978 males and 7922 females) were newly diagnosed with diabetes within 3 years. LightGBM (LR) achieved an ECE of 0.0018 ± 0.00033 (0.0048 ± 0.00058), a Logloss of 0.167 ± 0.00062 (0.172 ± 0.00090), and an AUC of 0.844 ± 0.0025 (0.826 ± 0.0035). From sample size analysis, the reliability of LightGBM became higher than LR when the sample size increased more than 104. Thus, we confirmed that GBDT provides a more reliable model than that of LR in the development of diabetes prediction models using big data. ML could potentially produce a highly reliable diabetes prediction model, a helpful tool for improving lifestyle and preventing diabetes

Social robot for older adults with cognitive decline: a preliminary trial

The number of older adults living alone is rapidly increasing. Loneliness in older adults not only degrade their quality of life but also causes troubles such as heavy burden on the medical staff, especially when cognitive decline is present. Social robots could be used in several ways to reduce such problems. As a first step towards this goal, we introduced conversation robots into the homes of older adults with cognitive decline to evaluate the robot’s availability and acceptance during several months. The study involved two steps, one for evaluating the robustness of the proposed robotic system, and the second one to examine the long-term acceptance of social robots by older adults with cognitive decline living alone. Our data shows that after several weeks of human-robot interaction, the participants continued to use the robot and successfully integrated them into their lives. These results open the possibility of further research involving how sustained interaction can be achieved, as well as which factors contributed to the acceptance of the robot

Mentoring of nursing students—A comparative study of Japan and five European countries

Aims: This study aimed to explore mentoring competence in nursing student mentors during clinical practice by identifying different mentor profiles and connections between different competence areas among five European countries and Japan. Methods: The study implemented a cross-sectional design in Finland, Italy, Lithuania, Slovenia, Spain, and Japan during 2016 and 2019. In total, 6208 mentors were invited, and 1862 participated from 58 healthcare organizations. The data were collected with a survey questionnaire by including background question items with the Mentor Competence Instrument. K-clustering and structural equation modeling were used for data analysis. Results: Four mentor profiles, A (43%), B (30%), C (18%), and D (9%), were identified according to the seven mentoring competence areas with high statistical significance (mean >3.50) was observed among Finnish, Lithuanian, and Slovenian mentors with university education in nursing, older ages, more work experience, and previous education in mentoring. Lower competence (mean <2.49) was observed among Japanese and Italian mentors with diplomas in nursing, younger ages, less work experience, and no previous education in mentoring. Conclusion: Mentoring requires motivated, highly competent mentors since mentoring is a critical aspect of nursing education. Mentoring roles should be given to nurses with higher education and mentoring training. Younger, less experienced nurses without formal mentoring training may need support from senior nurses when performing mentoring roles and could also facilitate a more balanced workload between patient care and mentoring for senior nurses

ニンチショウ カンジャ ノ カゾク カイゴシャ ニ タイスル モニター キキ ヲ モチイタ カイニュウ ニ ツイテ ノ アンブレラ レビュー

目的：｢見守り｣に関するモニター機器の使用によって、認知症患者の家族介護者への介護負担が、軽減されるのかを調査し、次世代型介護技術の有用性を検討することを目的とした。研究方法：MEDLINE、PsycINFO、CINAHL Plus の3つのデータベースを用いて、全年を対象に検索を行い、最終的に3件のシステマティックレビューを対象とした。結果：モニター機器が介護者の不安を軽減し、患者・家族どちらにも利益があると示した文献が2件であり、介護者の負担軽減に対して利益がなかったと示した文献が1件であった。使用されたモニター機器は位置情報を用いた機器、センサー、アラームが多数を占めており、認知症患者の安全を守ること、徘徊の検出を使用目的としていた。考察：介護負担を検討した文献が少ないことや、負担感の尺度として統一した指標を用いていないことから、結果の信頼性は、高いとは言えず、新たに評価指標を統一した研究を行う必要がある。総説Review Article

シュウショウジ フウフ ドウシツ ノ ザイタク コウキ コウレイシャ ニ タイスル センシング ギジュツ ヲ モチイタ スイミン ジッタイ チョウサ ヤカン ミマモリ ノ ヒツヨウセイ ノ ケントウ

【目的】就床時夫婦同室の在宅後期高齢者の夜間見守りの検討に、睡眠状況についてセンシング技術を用いて調査する。【方法】高齢夫妻2 組に対し、センシング機器を用いて測定、自記式記録、家族介護者の観察等を実施した。【結果】1 組目の夫はCPAP を装着していても睡眠時無呼吸が観察され、妻は喘息だが、睡眠効率は高かった。妻は難聴もあり、夫の睡眠状況の影響を受けなかった。2 組目の夫は夜間頻尿による排泄のための離床があったが、妻は片側難聴と背を向けて側臥位で寝ており覚醒しなかった。両夫婦とも就床と起床時刻については、同室者の干渉を認めたが、夫婦の長年の工夫などで中途覚醒については、干渉を認めず、同室とする目的である、お互いの見守りは完全ではなかった。【結論】同室者の干渉は、中途覚醒については認められず、お互いの睡眠を妨害することは少ない一方で、夫婦同士による見守りでは、不十分であった。センシングデータ、観察等の統合により、在宅環境における現状把握のためのさらなる調査が必要である。研究報告Report

Randomised controlled trials addressing how the clinical application of information and communication technology impacts the quality of patient care—A systematic review and meta‐analysis

Background The number of people with chronic and long-term conditions has increased during recent decades; this has been addressed by leveraging information and communication technology (ICT) to develop new self-care solutions. However, many of the developed technological solutions have not been tested in terms of impact(s) on patients' quality of care. Objectives This systematic review aimed to identify the current best evidence on the types of interventions that have been developed to improve the quality of patient care through the clinical application of ICT in primary, tertiary or home care. Design A systematic review, including a meta-analysis, was conducted according to the JBI Manual for Evidence Synthesis guidelines. Data sources Relevant data were identified from four electronic databases: CINAHL, PUBMED, SCOPUS and MEDIC. Review methods The eligibility criteria were formatted according to PICOS inclusion and exclusion criteria. At least two researchers performed the screening process separately, after which they agreed upon the results. The Cochrane Risk of Bias Assessment and JBI Critical Appraisal tool for randomised controlled studies (RCTs) were used to assess research quality. Data were extracted, and a meta-analysis was performed if the research met quantitative requirements. Results Of the 528 initially identified studies, 11 studies were chosen for final data synthesis. All of the interventions integrated ICT solutions into patient care to improve the quality of care. Patients across all of the RCTs were educated through direct training, the provision of information relevant to their disease or one-to-one educational coaching. The interventions included various interactions, e.g. nurse expert visits and support, and support provided by peers, groups or family members. These interactions occurred through face-to-face coaching, virtual human coaching or virtual coaching that relied on an algorithm. The performed meta-analysis included 6 of the 11 identified studies. The overall effect was nonsignificant, with three studies demonstrating a significant postintervention effect on patients' quality of care and quality of life and three studies a nonsignificant effect. Conclusions The presented results suggest that ICT-based care should be developed in collaboration with nurses and other health care professionals, involve patients in decision-making and combine ICT solutions with human interaction and coaching. ICT education was found to be essential to the success of an intervention

Exploring the impact of socially assistive robots on health and wellbeing across the lifespan: An umbrella review and meta-analysis

Background: Socially assistive robots offer an alternate source of connection for interventions within health and social care amidst a landscape of technological advancement and reduced staff capacity. There is a need to summarise the available systematic reviews on the health and wellbeing impacts to evaluate effectiveness, explore potential moderators and mediators, and identify recommendations for future research and practice. Objective: To explore the effect of socially assistive robots within health and social care on psychosocial, behavioural, and physiological health and wellbeing outcomes across the lifespan (PROSPERO registration number: CRD42023423862). Design: An umbrella review utilising meta-analysis, narrative synthesis, and vote counting by direction of effect. Methods: 14 databases were searched (ProQuest Health Research Premium collection, Scopus, PubMed, Web of Science, ASM Digital Library, IEEE Xplore, Cochrane Reviews, and EPISTEMONIKOS) from 2005 to May 4, 2023. Systematic reviews including the effects of socially assistive robots on health outcomes were included and a pooled meta-analysis, vote counting by direction of effect, and narrative synthesis were applied. The second version of A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2) was applied to assess quality of included reviews. Results: 35 reviews were identified, most focusing on older adults with or without dementia (n = 24). Pooled meta-analysis indicated no effect of socially assistive robots on quality of life (standard mean difference (SMD) = 0.43), anxiety (SMD = -0.02), or depression (SMD = 0.21), although vote counting identified significant improvements in social interaction, mood, positive affect, loneliness, stress, and pain across the lifespan, and narrative synthesis identified an improvement in anxiety in children. However, some reviews reported no significant difference between the effects of socially assistive robots and a plush toy, and there was no effect of socially assistive robots on psychiatric outcomes including agitation, neuropsychiatric symptoms, and medication use. Discussion: Socially assistive robots show promise for improving non-psychiatric outcomes such as loneliness, positive affect, stress, and pain, but exert no effect on psychiatric outcomes such as depression and agitation. The main mechanism of effect within group settings appeared to be the stimulation of social interaction with other humans. Limitations include the low quality and high amount of overlap between included reviews. Conclusion: Socially assistive robots may help to improve loneliness, social interaction, and positive affect in older adults, decrease anxiety and distress in children, and improve mood, stress, and reduce pain across the lifespan. However, before recommendations for socially assistive robots can be made, a cost-effectiveness analysis of socially assistive robots to improve mood across the lifespan, and a quantitative analysis of the effects on pain, anxiety, and distress in children are required