Resting energy expenditure differs among individuals with different levels of perceived thermal sensitivity: A cross-sectional study.

Metabolic rate has been used in thermophysiological models for predicting the thermal response of humans. However, only a few studies have investigated the association between an individual's trait-like thermal sensitivity and resting energy expenditure (REE), which resulted in inconsistent results. This study aimed to explore the association between REE and perceived thermal sensitivity. The REE of healthy adults was measured using an indirect calorimeter, and perceived thermal intolerance and sensation in the body were evaluated using a self-administered questionnaire. In total, 1567 individuals were included in the analysis (women = 68.9%, age = 41.1 ±â€…13.2 years, body mass index = 23.3 ±â€…3.3 kg/m2, REE = 1532.1 ±â€…362.4 kcal/d). More women had high cold intolerance (31.8%) than men (12.7%), and more men had high heat intolerance (23.6%) than women (16.1%). In contrast, more women experienced both cold (53.8%) and heat (40.6%) sensations in the body than men (cold, 29.1%; heat, 27.9%). After adjusting for age, fat-free mass, and fat mass, lower cold intolerance, higher heat intolerance, and heat sensation were associated with increased REE only in men (cold intolerance, P for trend = .001; heat intolerance, P for trend = .037; heat sensation, P = .046), whereas cold sensation was associated with decreased REE only in women (P = .023). These findings suggest a link between the perceived thermal sensitivity and REE levels in healthy individuals.

Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data.

BACKGROUND: Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient's subjective response is still important information that cannot be replaced by wearable devices. OBJECTIVE: To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals' time perception in comparison with wearable data. METHODS: Sleep data were acquired for 2 weeks using a Fitbit. Participants' sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared. RESULTS: In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants' actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant's AST was within ±30 and ±60 minutes of the Fitbit TST-WASO was 50.7% and 74.3%, respectively. CONCLUSIONS: The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants' sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices.

Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea.

BACKGROUND: Metabolic syndrome (MetS) is a complex condition that appears as a cluster of metabolic abnormalities, and is closely associated with the prevalence of various diseases. Early prediction of the risk of MetS in the middle-aged population provides greater benefits for cardiovascular disease-related health outcomes. This study aimed to apply the latest machine learning techniques to find the optimal MetS prediction model for the middle-aged Korean population. METHODS: We retrieved 20 data types from the Korean Medicine Daejeon Citizen Cohort, a cohort study on a community-based population of adults aged 30-55 years. The data included sex, age, anthropometric data, lifestyle-related data, and blood indicators of 1991 individuals. Participants satisfying two (pre-MetS) or ≥ 3 (MetS) of the five NECP-ATP III criteria were included in the MetS group. MetS prediction used nine machine learning models based on the following algorithms: Decision tree, Gaussian Naïve Bayes, K-nearest neighbor, eXtreme gradient boosting (XGBoost), random forest, logistic regression, support vector machine, multi-layer perceptron, and 1D convolutional neural network. All analyses were performed by sequentially inputting the features in three steps according to their characteristics. The models' performances were compared after applying the synthetic minority oversampling technique (SMOTE) to resolve data imbalance. RESULTS: MetS was detected in 33.85% of the subjects. Among the MetS prediction models, the tree-based random forest and XGBoost models showed the best performance, which improved with the number of features used. As a measure of the models' performance, the area under the receiver operating characteristic curve (AUC) increased by up to 0.091 when the SMOTE was applied, with XGBoost showing the highest AUC of 0.851. Body mass index and waist-to-hip ratio were identified as the most important features in the MetS prediction models for this population. CONCLUSIONS: Tree-based machine learning models were useful in identifying MetS with high accuracy in middle-aged Koreans. Early diagnosis of MetS is important and requires a multidimensional approach that includes self-administered questionnaire, anthropometric, and biochemical measurements.

Evaluation of thermal sensitivity is of potential clinical utility for the predictive, preventive, and personalized approach advancing metabolic syndrome management.

A possible association between metabolic disorders and ambient temperature has been suggested, and cold exposure as a way of increasing energy expenditure has gained considerable interest for preventative/therapeutic measures toward metabolic disorders. Although thermal sensitivity, which has recently been studied in regard to its utility as a risk assessment/patient stratification for various diseases, might influence physiological responses to ambient temperature on an individual basis, more studies are needed. We aimed to investigate the association between self-identified thermal intolerance/sensation and metabolic syndrome (MetS) to verify the working hypothesis that individuals with altered thermal sensitivity may have a predisposition to MetS. We fitted generalized additive models for thermal intolerance/sensation using body mass index (BMI) and waist-hip ratio in women, and identified those with higher/lower thermal intolerance/sensation than those predicted by the models. Higher heat intolerance, higher heat sensation, and lower cold intolerance were associated with a higher prevalence of MetS. The risk of having MetS was increased in those who had two or three associated conditions compared with those with none of these conditions. In an analysis for MetS components, significant associations of thermal sensitivity were present with high glucose, triglyceride, and blood pressure levels. Overall, higher heat intolerance/sensation and lower cold intolerance were associated with increased prevalence of MetS even at a similar level of obesity. Our study indicates that evaluation of thermal sensitivity may help identify individuals at high risk for MetS, and lead to more advanced patient stratification and personalized treatment strategies for MetS, including cold-induced thermogenesis. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00273-6.

Metabolic Syndrome Prediction Models Using Machine Learning and Sasang Constitution Type.

BACKGROUND: Machine learning may be a useful tool for predicting metabolic syndrome (MetS), and previous studies also suggest that the risk of MetS differs according to Sasang constitution type. The present study investigated the development of MetS prediction models utilizing machine learning methods and whether the incorporation of Sasang constitution type could improve the performance of those prediction models. METHODS: Participants visiting a medical center for a health check-up were recruited in 2005 and 2006. Six kinds of machine learning were utilized (K-nearest neighbor, naive Bayes, random forest, decision tree, multilayer perceptron, and support vector machine), as was conventional logistic regression. Machine learning-derived MetS prediction models with and without the incorporation of Sasang constitution type were compared to investigate whether the former would predict MetS with higher sensitivity. Age, sex, education level, marital status, body mass index, stress, physical activity, alcohol consumption, and smoking were included as potentially predictive factors. RESULTS: A total of 750/2,871 participants had MetS. Among the six types of machine learning methods investigated, multiplayer perceptron and support vector machine exhibited the same performance as the conventional regression method, based on the areas under the receiver operating characteristic curves. The naive-Bayes method exhibited the highest sensitivity (0.49), which was higher than that of the conventional regression method (0.39). The incorporation of Sasang constitution type improved the sensitivity of all of the machine learning methods investigated except for the K-nearest neighbor method. CONCLUSION: Machine learning-derived models may be useful for MetS prediction, and the incorporation of Sasang constitution type may increase the sensitivity of such models.

Association between Resting Energy Expenditure and Heat Pattern in Traditional Medicine.

Many symptoms of heat pattern (HP) in traditional medicine are related to thermoregulation; however, research on the association between energy expenditure characteristics and HP is limited. We aimed to study the association between HP and resting energy expenditure (REE). A total of 109 participants were divided into the HP and non-HP groups based on a self-administered questionnaire and their REE was measured using an indirect calorimeter. Multiple logistic regression models were used to calculate the odds ratio (OR) of HP according to the level of REE. No significant differences in age, anthropometric, and body composition characteristics were observed between the HP and the non-HP groups. The likelihood of having an HP significantly increased with higher REE after adjustment for fat-free mass (OR 1.22 [95% CI 1.03-1.46]) and also after additional adjustment for sex and age (OR 1.21 [95% CI 1.01-1.46]). These results suggest that increased REE could be a biological characteristic of HP. Future studies are necessary to investigate the underlying mechanisms associated with the differing energy expenditure in HP.

Complementary and Alternative Medicine Use in Amyotrophic Lateral Sclerosis Cases in South Korea.

Patients with amyotrophic lateral sclerosis (ALS) sometimes consider complementary and alternative medicine (CAM) because of ineffective treatment. This study investigated the prevalence and utilization pattern of CAM among patients with ALS in South Korea. Participants were recruited through homecare services for mechanical ventilation in South Korea. This study comprised a face-to-face cross-sectional survey with staff members available to address any queries. Fifty-five participants were included; all had used >1 CAM treatment option for ALS symptoms. Dietary treatments were most common, followed by functional food and massages. Most participants had obtained relevant information from family members or friends. The main reason for CAM use was an expectation that symptoms will improve with CAM; most patients were unsure of the effects. CAM use was previously discontinued by the majority of patients because of unsatisfactory effects. The mean expenditure on CAM was 288,385.28 ± 685,265.14 won per month, and the mean duration of CAM use was 11.54 ± 20.09 months. The results indicate that there is a high prevalence of CAM use among ALS patients. Healthcare providers should inquire about CAM use and openly provide accurate CAM information. Further evidence of CAM efficacy is required, as is specific guidance for consulting ALS patients regarding CAM.

Effect of Traditional East Asian Medicinal herbal tea (HT002) on insomnia: a randomized controlled pilot study.

BACKGROUND: Complementary and alternative medicine treatment for insomnia has been sought due to the possible adverse effects of conventional pharmacotherapies. We performed a preliminary evaluation of the feasibility of using, and of the effect of a herbal tea (HT002), based on Traditional East Asian Medicine, in mild-to-moderate insomnia. METHODS: Patients (n = 40) with mild-to-moderate insomnia were randomized to the HT002 (n = 20) or waitlist (n = 20) groups. The HT002 group consumed HT002 twice daily for 4 weeks. Outcomes were assessed using the Insomnia Severity Scale (ISI), Pittsburgh Sleep Quality Index (PSQI), and 12-item Short Form Health Survey (SF-12) at baseline and after 4 and 8 weeks. RESULTS: The ISI score differences from baseline at weeks 4 and 8 were significantly greater in the HT002 than that in the waitlist group (week 4: -4.0 ± 0.8 vs. -0.4 ± 0.8, p < 0.05; week 8: -4.8 ± 0.7 vs. -0.9 ± 0.7, p < 0.05). Changes in PSQI and SF-12 physical component scores in the HT002 group were significantly greater at weeks 4 and 8 (p < 0.05), while SF-12 mental component scores were only significantly larger at 4 weeks (p < 0.05). HT002 was well-tolerated, with only one (5.0%) dropout, and no significant mean liver and renal function test changes post-treatment. CONCLUSION: Our preliminary results suggest that a 4-week treatment with HT002 may reduce the severity of insomnia symptoms and improve the quality of life. Further studies devoid of the limitations of our protocol may provide stronger conclusions. TRIAL REGISTRATION: Clinical Research Information Service (CRIS), KCT0001900.

Three-Component Herbal Tea Alleviates Prolonged Fatigue and Improves Sleep Quality: A Randomized Controlled Pilot Study.

INTRODUCTION: Prolonged fatigue is common among adults, yet only a small percentage of affected individuals seek medical attention. Following findings that complementary and alternative medicine can be effective strategies for improving fatigue, we evaluated the effect of an herbal tea regimen on prolonged fatigue. METHODS: In this pilot randomized controlled trial, participants with prolonged fatigue were randomly allocated to either a tea (n = 20) or control (n = 20) group. The tea group consumed herbal tea consisting of Astragali Radix, Angelicae gigantis Radix, and Zizyphi Fructus twice daily for 4 weeks, whereas the control group received no intervention. Outcomes were assessed at baseline and 4 and 8 weeks. The primary outcome was fatigue severity, measured using the Chalder Fatigue Scale. Secondary outcomes included sleep quality, depression symptoms, and quality of life. RESULTS: Among the 40 individuals screened, 35 completed the study. Fatigue severity was significantly lower in the tea group than the control group at 4 weeks, and within the tea group, fatigue severity and sleep quality improved at the 4- and 8-week follow-ups vs. baseline in the tea group. Depression also significantly improved in the tea group at 8 weeks. There were no changes within the control group on any measure. CONCLUSION: The findings suggest that our herbal tea formulation reduces fatigue severity and improves sleep quality in adults with prolonged fatigue.

The Association of Quantitative Facial Color Features with Cold Pattern in Traditional East Asian Medicine.

INTRODUCTION: Facial diagnosis is a major component of the diagnostic method in traditional East Asian medicine. We investigated the association of quantitative facial color features with cold pattern using a fully automated facial color parameterization system. METHODS: The facial color parameters of 64 participants were obtained from digital photographs using an automatic color correction and color parameter calculation system. Cold pattern severity was evaluated using a questionnaire. RESULTS: The a⁎ values of the whole face, lower cheek, and chin were negatively associated with cold pattern score (CPS) (whole face: B = -1.048, P = 0.021; lower cheek: B = -0.494, P = 0.007; chin: B = -0.640, P = 0.031), while b⁎ value of the lower cheek was positively associated with CPS (B = 0.234, P = 0.019). The a⁎ values of the whole face were significantly correlated with specific cold pattern symptoms including cold abdomen (partial ρ = -0.354, P < 0.01) and cold sensation in the body (partial ρ = -0.255, P < 0.05). CONCLUSIONS: a⁎ values of the whole face were negatively associated with CPS, indicating that individuals with increased levels of cold pattern had paler faces. These findings suggest that objective facial diagnosis has utility for pattern identification.

Perception of cold and heat pattern identification in diseases: a survey of Korean medicine doctors.

BACKGROUND: This study aimed to investigate the extent to which Korean Medicine doctors consider cold and heat pattern identification when prescribing herbal treatment for a disease. METHODS: A survey was sent by e-mail to 15,841 members of the Association of Korean Medicine for whom member information was registered. Of these, 699 (4.4%) members participated in the survey. The survey included questions regarding the frequency of use of cold and heat pattern identification in deciding a herbal treatment prescription, the diseases for which cold and heat pattern identification-related herbal treatment was most efficacious, the type of herbal treatment prescribed, and the duration of the treatment. RESULTS: Of the 699 respondents, 591 (84.5%) reported that they considered cold and heat when prescribing herbal treatment. The diseases for which consideration of cold and heat patterns was effective were, in order, menopausal disorder (124, 18.3%), chronic rhinitis (98, 14.5%), dyspepsia (94, 13.9%), hwa-byung (92, 13.6%), diarrhea (83, 12.3%), dysmenorrhea (61, 9.0%), headache (59, 8.7%), inflammation in the digestive tract (58, 8.6%), coldness in hands and feet (58, 8.6%), and atopic dermatitis (55, 8.1%). The typical treatment duration differed widely for different diseases: atopic dermatitis was most frequently treated for >2 months (38, 34.5%), whereas diarrhea was most frequently treated for ≤ 10 days (73, 43.6%). CONCLUSION: These findings indicate that cold and heat pattern identification is a useful tool employed by Korean Medicine doctors. This study may provide a basis for clinical research investigating the effect of pattern identification-based treatment of diseases.

Cold and Spleen-Qi Deficiency Patterns in Korean Medicine Are Associated with Low Resting Metabolic Rate.

Background. Korean medicine (KM) patterns such as cold, heat, deficiency, and excess patterns have been associated with alterations of resting metabolic rate (RMR). However, the association of KM patterns with accurately measured body metabolic rate has not been investigated. Methods. Data on cold (CP), heat (HP), spleen-qi deficiency (SQDP), and kidney deficiency (KDP) patterns were extracted by a factor analysis of symptoms experienced by 954 participants. A multiple regression analysis was conducted to determine the association between KM patterns and RMR measured by an indirect calorimeter. Results. The CP and SQDP scores were higher and the HP score was lower in women. The HP and SQDP scores decreased with age, while KDP scores increased with age. A multiple regression analysis revealed that CP and SQDP scores were negatively associated with RMR independently of gender and age, and the CP remained significantly and negatively associated with RMR even after adjustment for fat-free mass. Conclusions. The underlying pathology of CP and SQDP might be associated with the body's metabolic rate. Further studies are needed to investigate the usefulness of RMR measurement in pattern identification and the association of CP and SQDP with metabolic disorders.

Interrelationships among common symptoms in the elderly and their effects on health-related quality of life: a cross-sectional study in rural Korea.

BACKGROUND: Because the world population is aging, it has become increasingly important to focus on and meet the healthcare needs of elderly individuals. This study aims to evaluate the relationships among common symptoms experienced by the elderly, including fatigue, pain, sleep disturbance, indigestion, and depression/anger/anxiety, and to assess how these symptoms affect health-related quality of life (HRQoL) in the elderly population after adjusting for sociodemographic characteristics and diagnosed diseases. METHODS: In a cross-sectional study conducted in 2014 in a rural area of Korea, we extracted data on 1328 elderly individuals aged 60 years or older. Their HRQoL was assessed using the EuroQol Five-Dimension (EQ-5D) questionnaire. The pairwise associations between each symptom and the influence of the symptoms on HRQoL were measured using logistic regression and multiple regression analysis. RESULTS: Each symptom was positively correlated with the other symptoms. The strongest association was observed between fatigue and pain (adjusted odds ratio = 8.127), and the weakest correlation was observed between sleep and indigestion (adjusted odds ratio = 2.521). Of the individuals experiencing symptoms other than sleep disturbance, those who reported comorbid symptoms tended to report higher symptom severity and a higher prevalence of symptoms persisting for ≥ 3 days compared with individuals who reported only one symptom. The number of symptoms was significantly correlated with the EQ-5D index (Spearman correlation coefficient = -0.370, p < 0.01) and the EQ Visual Analog Scale (EQ VAS) scores (Spearman correlation coefficient = -0.226, p < 0.01). Fatigue, pain, and sleep disturbance showed negative effects on all dimensions of EQ-5D. In multiple regression analysis, fatigue (ß = -0.073, p < 0.01), pain (ß = -0.140, p < 0.01), sleep disturbance (ß = -0.061, p < 0.05), and depression/anger/anxiety (ß = -0.065, p < 0.05) showed significant independent effects on the EQ-5D index when we adjusted for socioeconomic characteristics and diagnosed diseases. CONCLUSIONS: Fatigue, pain, sleep disturbance, and depression/anger/anxiety were correlated with one another, and they presented significant independent effects on the HRQoL of elderly individuals. Thus, multidisciplinary healthcare programs are required to address these common symptoms.

Economic evaluation of manual therapy for musculoskeletal diseases: a protocol for a systematic review and narrative synthesis of evidence.

INTRODUCTION: Manual therapy is the non-surgical conservative management of musculoskeletal disorders using the practitioner's hands on the patient's body for diagnosing and treating disease. The aim of this study is to systematically review trial-based economic evaluations of manual therapy relative to other interventions used for the management of musculoskeletal diseases. METHODS AND ANALYSIS: Randomised clinical trials (RCTs) on the economic evaluation of manual therapy for musculoskeletal diseases will be included in the review. The following databases will be searched from their inception: Medline, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Econlit, Mantis, Index to Chiropractic Literature, Science Citation Index, Social Science Citation Index, Allied and Complementary Medicine Database (AMED), Cochrane Database of Systematic Reviews (CDSR), National Health Service Database of Abstracts of Reviews of Effects (NHS DARE), National Health Service Health Technology Assessment Database (NHS HTA), National Health Service Economic Evaluation Database (NHS EED), CENTRAL, five Korean medical databases (Oriental Medicine Advanced Searching Integrated System (OASIS), Research Information Service System (RISS), DBPIA, Korean Traditional Knowledge Portal (KTKP) and KoreaMed) and three Chinese databases (China National Knowledge Infrastructure (CNKI), VIP and Wanfang). The evidence for the cost-effectiveness, cost-utility and cost-benefit of manual therapy for musculoskeletal diseases will be assessed as the primary outcome. Health-related quality of life and adverse effects will be assessed as secondary outcomes. We will critically appraise the included studies using the Cochrane risk of bias tool and the Drummond checklist. Results will be summarised using Slavin's qualitative best-evidence synthesis approach. ETHICS AND DISSEMINATION: The results of the study will be disseminated via a peer-reviewed journal and/or conference presentations. TRIAL REGISTRATION NUMBER: PROSPERO CRD42015026757.

Development of a template for the classification of traditional medical knowledge in Korea.

ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Medical Knowledge (TMK) is a form of Traditional Knowledge associated with medicine that is handed down orally or by written material. There are efforts to document TMK, and make database to conserve Traditional Medicine and facilitate future research to validate traditional use. Despite of these efforts, there is no widely accepted template in data file format that is specific for TMK and, at the same time, helpful for understanding and organizing TMK. AIM OF THE STUDY: We aimed to develop a template to classify TMK. MATERIALS AND METHODS: First, we reviewed books, articles, and health-related classification systems, and used focus group discussion to establish the definition, scope, and constituents of TMK. Second, we developed an initial version of the template to classify TMK, and applied it to TMK data. Third, we revised the template, based on the results of the initial template and input from experts, and applied it to the data. RESULTS: We developed the template for classification of TMK. The constituents of the template were summary, properties, tools/ingredients, indication/preparation/application, and international standard classification. We applied International Patent Classification, International Classification of Diseases (Korea version), and Classification of Korean Traditional Knowledge Resources to provide legal protection of TMK and facilitate academic research. The template provides standard terms for ingredients, preparation, administration route, and procedure method to assess safety and efficacy. CONCLUSIONS: This is the first template that is specialized for TMK for arranging and classifying TMK. The template would have important roles in preserving TMK, and protecting intellectual property. TMK data classified with the template could be used as the preliminary data to screen potential candidates for new pharmaceuticals.

Self-care use patterns in the UK, US, Australia, and Japan: a multinational web-based survey.

BACKGROUND: The trend toward patient- or consumer-centered healthcare has been accelerated by advances in technology, consumer empowerment, and a shift from infectious to chronic diseases. The purpose of this study was to examine the growing self-care market by analyzing self-care patterns. METHODS: We conducted a cross-sectional, web-based survey involving adults from nine major cities in the UK, the USA, Australia, and Japan. This study examined the extent and frequency of self-care, self-care expenditure, sources of self-care information, and reasons for self-care in each country. RESULTS: The results showed that the prevalence of self-care was highest in Japan (54.9%), followed by the UK (43.1%), the USA (42.5%), and Australia (40.4%). The primary reason for practicing self-care was "to manage my healthcare myself" (cited by 45.7%, 59.5%, 49.2%, and 4.1% of participants in Australia, Japan, the UK, and the USA, respectively). Significant linear associations were observed between age and the prevalence of self-care in all countries (p < 0.05), indicating that self-care prevalence decreased with age in the UK, the USA, and Australia, and increased with age in Japan. The frequency with which self-care was practiced was positively correlated with age in the USA (p < 0.05), Australia (p < 0.01), and Japan (p < 0.05). In addition to acquaintances, internet search engines and information obtained from pharmacies were considered reliable and widely used sources of self-care information. CONCLUSION: When developing self-care products or services, healthcare providers and policymakers should consider self-care patterns.