Yuta Hagiwara

Introduction: Despite the established effectiveness of Motivational Interviewing (MI) in promoting health behavior change, intensive training formats may be impractical for busy healthcare professionals. This study evaluated changes in self-reported confidence and MI knowledge following brief, multisession online MI training for healthcare professionals engaged in lifestyle disease management. Methods: A quasiexperimental pre-post study included 25 healthcare professionals participating in six monthly 30-minute online MI sessions. Participants were categorized into complete (n = 15) and incomplete (n = 10) attendance groups. Confidence was measured using an 8-item questionnaire and knowledge using the Motivational Interviewing Knowledge and Attitudes Test (MIKAT). Results: The complete attendance group showed a trend toward increased self-reported confidence across all items, with changes ranging from +0.07 to +0.60 points on a 5-point scale. The largest increases involved empathy/reflection and alcohol dependency support; however, confidence changes were not statistically significant, and knowledge outcomes showed no consistent improvement. Discussion: This study presents an innovative model integrating brief, spaced online MI training into a mobile health (mHealth)–based lifestyle management service. Embedding learning within a remote care platform allows healthcare professionals to learn about MI alongside real-world digital interventions. Complete attendees showed a trend toward increased confidence, whereas MI knowledge did not improve consistently. The program's scalability and effects on clinical practice require further evaluation. These findings support reinforcement strategies and objective assessment of MI skills.

Objectives: Despite the emphasis on weight control, heterogeneity in response to weight loss among individuals with or at high risk for knee osteoarthritis (OA) remains unclear. This study examined heterogeneity in the associations of weight reduction with pain among overweight and obese individuals using a machine learning approach. Methods: We analyzed longitudinal data from the Osteoarthritis Initiative, comprising community-dwelling adults aged 45–79 years with a body mass index ≥25 who completed baseline assessments between 2008 and 2010, with a 24-month follow-up (n=3,644). The exposure was a ≥5% reduction in body weight from baseline to 24 months. The primary outcome of this study was the pain subscale of the Western Ontario and McMaster Universities (WOMAC) Osteoarthritis Index. Results: The average treatment effect (ATE) was estimated using a doubly robust estimator, and treatment effect heterogeneity was assessed by estimating conditional average treatment effects (CATEs) using a causal forest approach. A reduction of ≥5% in body weight was not significantly associated with a lower WOMAC pain score (ATE=–0.25; 95% confidence interval [CI], –0.51 to 0.01). The causal forest analysis demonstrated substantial heterogeneity, with CATE estimates of –0.77 (95% CI, −1.24 to −0.30) for the high-benefit subgroup (top 20% of estimated CATEs) and 0.41 (95% CI, −0.35 to 1.17) for the low-benefit subgroup (bottom 20%). Discussion: Our findings highlight the potential value of individualized weight-loss strategies and suggest that patient characteristics may help identify those most likely to benefit from weight reduction.

Background: While biopsychosocial interventions are effective for low back pain (LBP), implementing them in workplace settings remains challenging due to resource constraints. We adapted a comprehensive biopsychosocial approach into a single, brief, individualized advice session to enhance real-world feasibility. This study aimed to evaluate the reach, adoption, implementation, and preliminary effectiveness of this program using the RE-AIM (Reach, Adoption, Implementation, Effectiveness, and Maintenance) framework. Methods: A prospective pre–post quasi-experimental study was conducted in a Japanese insurance company. Employees with LBP were invited to a single 30-min occupational physiotherapy advice session. Outcomes were assessed at baseline and 3 months. Effectiveness was evaluated using mean changes in pain (numerical rating scale) and disability (Roland–Morris Disability Questionnaire), supplemented by a responder analysis based on minimal important change (MIC). Results: Of 1201 eligible employees, 154 (12.8%) attended the session (Reach), and 68 (44.2%) provided completed the 3-month follow-up (Implementation). Regarding effectiveness, mean pain intensity showed no significant change (+0.12). However, the responder analysis revealed that 17.6% of participants achieved MIC in pain (≥2 points), and 35.3% were classified as meeting MIC in disability (≥2 points). Additionally, the proportion of participants perceiving LBP as the primary cause of productivity loss decreased from 58.8% to 16.2%. Between-group analyses showed no statistically significant differences. Conclusions: This quasi-experimental evaluation suggests that a single 30-min workplace physiotherapy advice session is feasible to implement. Effectiveness findings were exploratory: mean pain intensity changed little and between-group differences were not statistically significant, while some attendees who completed follow-up met the MIC threshold for disability.

Objectives: This study investigated the associations between cognitive and personality traits and work productivity loss among desk workers with low back pain (LBP). Methods: A cross-sectional study was conducted among 2,223 corporate employees in Japan. Participants completed an online questionnaire assessing work productivity (quantity-quality method), pain intensity (numerical rating scale), disability (Roland-Morris Disability Questionnaire), beliefs about LBP (Back Beliefs Questionnaire), and personality traits (Ten Item Personality Inventory). Multiple linear regression analysis examined factors associated with productivity loss. To confirm robustness, a sensitivity analysis was performed limited to participants who identified LBP as the primary cause of their productivity loss. Results: Among the participants, 1,201 (54.0%) reported LBP. Regression analysis indicated that beliefs about LBP (β=−0.40, p=0.011), conscientiousness (β=−0.99, p=0.006), neuroticism (β=1.02, p=0.007), and age (β=−0.18, p=0.002) were significantly associated with work productivity loss. Pain intensity (p=0.139) and functional disability (p=0.574) were not significantly associated with productivity loss. These findings were consistent in the sensitivity analysis, where pain intensity remained unrelated to productivity loss. Conclusions: Psychological and cognitive factors, particularly beliefs about LBP and personality traits, may have a stronger impact on work productivity than physical symptoms among desk workers. Interventions focusing on cognitive restructuring and personality-tailored strategies are necessary to mitigate productivity losses.

Background: Mobile health (mHealth) apps are increasingly used for health promotion, particularly for managing noncommunicable diseases (NCDs) through behavior modification. Understanding the factors associated with successful weight loss in such interventions can improve program effectiveness. Objective: This study examined factors influencing weight change and the relationship between weight recording frequency and chat volume with health care professionals on weight loss in individuals with obesity and NCDs. Methods: The participants had obesity (BMI ≥25 kg/m²) and were diagnosed with NCDs (eg, hypertension, diabetes, dyslipidemia). The program included 12 telephone consultations with health care professionals. Only participants who completed the full 6-month program, including all 12 telephone consultations, and provided an end-of-study weight were included in the analysis. The primary outcome was the rate of weight change, defined as the percentage change in weight from the initial period (first 14 days) to the final period (2 weeks before the last consultation), relative to the initial weight. The key independent variables were proportion of days with weight recording and chat communication volume (total messages exchanged). An interaction term between these variables was included to assess moderation effects in the regression analysis. The volume of communication was measured as the total number of messages exchanged, with each message, regardless of who sent it, being counted as 1 interaction. Health care staffs were instructed to send a single scheduled chat message per week following each biweekly phone consultation. These scheduled messages primarily included personalized feedback, reminders, and motivational support. In addition, providers responded to participant-initiated messages at any time during the program. Furthermore, 1 professional responded to each participant. Hierarchical multiple regression and simple slope analyses were conducted to identify relationships and interactions among these variables. Results: The final analysis of this study included 2423 participants. Significant negative associations were found between the rate of weight change and baseline BMI (β=−.10; P<.001), proportion of days with weight recording (β=−.017; P<.001), and communication volume (β=−.193; P<.001). The interaction between proportion of days with weight recording and chat frequency also showed a significantly negative effect on weight change (β=−.01; P<.001). Simple slope analysis showed that when the proportion of days with weight recording was +1 SD above the mean, frequent chats were associated with greater weight reduction (slope=−0.60; P<.001), whereas no significant effect was observed at −1 SD (slope=−0.01; P=.94) Conclusions: The findings suggest that both the proportion of days with weight recording and communication volume independently and interactively influence weight change in individuals with obesity and NCDs.

Background: Chronic diseases such as diabetes and cardiovascular disease are global health challenges, affecting millions of people worldwide. Traditional health care often falls short in chronic disease management. This has led to the exploration of innovative solutions, such as mobile health (mHealth) technologies. mHealth, which leverages mobile and wireless technologies, has the potential to transform health care delivery by providing continuous, accessible, and personalized care. However, the effectiveness of mHealth, particularly when integrated with traditional health care interventions delivered by professionals, warrants comprehensive investigation. Understanding the combined impact of mHealth and professional-led interventions is critical to maximizing the potential of mHealth to improve patient outcomes and adherence. Objective: This study aims to investigate the effectiveness of combining mHealth and health professional–led intervention for improving health-related outcomes in chronic diseases Methods: This systematic review and meta-analysis focused on randomized controlled trials. We searched Web of Science, CENTRAL, MEDLINE, and CINAHL through July 17, 2023. The study targeted patients aged 18 years and older, experiencing at least 1 chronic condition. The interventions were a combination of mHealth and the use of a health care professional. The comparison groups consisted of participants receiving either general care and follow-up or those using mHealth devices without any health care professional involvement. The outcomes measured in this review included hemoglobin A1c (HbA1c), quality of life (QoL), and physical activity. Results: The study included 26 research papers, encompassing 7360 individuals. Meta-analysis was conducted for HbA1c, QoL, and physical activity. For HbA1c, short-term improvement was significant (standardized mean difference [SMD] –0.43; 95% CI –0.64 to –0.21; I2=69%) and medium term (SMD –0.49; 95% CI –0.49 to –0.09; I2=21%). However, in the long term, the improvement was not significant (SMD –0.23; 95% CI –0.49 to 0.03; I2=88%). For QoL, significant improvements were observed in the short term (SMD –0.23; 95% CI –0.42 to –0.05; I2=62%), and in the medium term (SMD –0.16; 95% CI –0.24 to –0.07; I2=0%). In the long term, however, the improvement was not significant (SMD –0.12; 95% CI –0.41 to 0.16; I2=71%). For physical activity, both subjective (questionnaire) and objective (number of steps) outcomes were analyzed. In the short term, subjective outcomes showed significant improvement (SMD 0.31; 95% CI 0.12-0.50; I2=0%), while objective outcomes did not (SMD 0.11; 95% CI –0.05 to 0.27; I2=0%). Medium- and long-term subjective outcomes showed no significant improvement. Meta-analysis for objective outcomes in the medium and long term was not possible due to insufficient studies. Conclusions: This study confirmed short- and medium-term benefits of mHealth combined with professional interventions for HbA1c, QoL, and short-term physical activity, supporting effective chronic disease management.

Background: Ischemic heart disease (IHD) is a leading cause of morbidity and mortality worldwide, requiring innovative management strategies. Traditional disease management programs often struggle to maintain patient engagement and ensure long-term adherence to lifestyle modifications and treatment plans. Mobile health (mHealth) technologies have emerged as a promising approach to address these challenges by providing continuous, personalized support and monitoring. However, the reported use and effectiveness of mHealth in the management of chronic diseases, such as IHD, have not been fully explored. Objective: The primary aim of this study was to evaluate the feasibility and initial impact of an mHealth-based disease management program on coronary risk factors, specifically focusing on low-density lipoprotein cholesterol (LDL-C) levels, in individuals with chronic IHD. This formative study assessed changes in LDL-C and other metabolic health indicators over a 6-month period to determine the initial impact of the program on promoting cardiovascular health and lifestyle modification. Methods: This study was conducted using data from 266 individuals enrolled in an mHealth-based disease management program between December 2018 and October 2022. Eligibility was based on a documented history of IHD, with participants undergoing a comprehensive cardiac risk assessment before enrollment. The program included biweekly telephone sessions, health tracking via a smartphone app, and regular progress reports to physicians. The study measured change in LDL-C levels as the primary outcome, with secondary outcomes including body weight, triglyceride levels, and other metabolic health indicators. Statistical analysis used paired 2-tailed t tests and stratified analyses to assess the impact of the program. Results: Participants experienced a significant reduction in LDL-C, with LDL-C levels decreasing from a mean of 98.82 (SD 40.92) mg/dL to 86.62 (SD 39.86) mg/dL (P<.001). The intervention was particularly effective in individuals with high baseline LDL-C levels. Additional improvements were seen in body weight and triglyceride levels, suggesting a broader impact on metabolic health. Program adherence and engagement metrics suggested high participant satisfaction and compliance. Conclusions: The results of this study suggest that the mHealth-based disease management program is feasible and has an initial positive impact on reducing LDL-C levels and improving metabolic health in individuals with chronic IHD. However, the study design does not allow for a definitive conclusion regarding whether mHealth-based disease management programs are more effective than traditional face-to-face care. Future studies are needed to further validate these findings and to examine the comparative effectiveness of these interventions in more detail.

BACKGROUND Ischemic heart disease (IHD) is a leading cause of morbidity and mortality worldwide, requiring innovative management strategies. Traditional disease management programs often struggle to maintain patient engagement and ensure long-term adherence to lifestyle modifications and treatment plans. Mobile health (mHealth) technologies have emerged as a promising approach to address these challenges by providing continuous, personalized support and monitoring. However, the reported use and effectiveness of mHealth in the management of chronic diseases, such as IHD, have not been fully explored. OBJECTIVE The primary aim of this study was to evaluate the feasibility and initial impact of an mHealth-based disease management program on coronary risk factors, specifically focusing on low-density lipoprotein cholesterol (LDL-C) levels, in individuals with chronic IHD. This formative study assessed changes in LDL-C and other metabolic health indicators over a 6-month period to determine the initial impact of the program on promoting cardiovascular health and lifestyle modification. METHODS This study was conducted using data from 266 individuals enrolled in an mHealth-based disease management program between December 2018 and October 2022. Eligibility was based on a documented history of IHD, with participants undergoing a comprehensive cardiac risk assessment before enrollment. The program included biweekly telephone sessions, health tracking via a smartphone app, and regular progress reports to physicians. The study measured change in LDL-C levels as the primary outcome, with secondary outcomes including body weight, triglyceride levels, and other metabolic health indicators. Statistical analysis used paired 2-tailed t tests and stratified analyses to assess the impact of the program. RESULTS Participants experienced a significant reduction in LDL-C, with LDL-C levels decreasing from a mean of 98.82 (SD 40.92) mg/dL to 86.62 (SD 39.86) mg/dL (P<.001). The intervention was particularly effective in individuals with high baseline LDL-C levels. Additional improvements were seen in body weight and triglyceride levels, suggesting a broader impact on metabolic health. Program adherence and engagement metrics suggested high participant satisfaction and compliance. CONCLUSIONS The results of this study suggest that the mHealth-based disease management program is feasible and has an initial positive impact on reducing LDL-C levels and improving metabolic health in individuals with chronic IHD. However, the study design does not allow for a definitive conclusion regarding whether mHealth-based disease management programs are more effective than traditional face-to-face care. Future studies are needed to further validate these findings and to examine the comparative effectiveness of these interventions in more detail.

BACKGROUND Chronic diseases such as diabetes and cardiovascular disease are global health challenges, affecting millions of people worldwide. Traditional health care often falls short in chronic disease management. This has led to the exploration of innovative solutions, such as mobile health (mHealth) technologies. mHealth, which leverages mobile and wireless technologies, has the potential to transform health care delivery by providing continuous, accessible, and personalized care. However, the effectiveness of mHealth, particularly when integrated with traditional health care interventions delivered by professionals, warrants comprehensive investigation. Understanding the combined impact of mHealth and professional-led interventions is critical to maximizing the potential of mHealth to improve patient outcomes and adherence. OBJECTIVE This study aims to investigate the effectiveness of combining mHealth and health professional–led intervention for improving health-related outcomes in chronic diseases METHODS This systematic review and meta-analysis focused on randomized controlled trials. We searched Web of Science, CENTRAL, MEDLINE, and CINAHL through July 17, 2023. The study targeted patients aged 18 years and older, experiencing at least 1 chronic condition. The interventions were a combination of mHealth and the use of a health care professional. The comparison groups consisted of participants receiving either general care and follow-up or those using mHealth devices without any health care professional involvement. The outcomes measured in this review included hemoglobin A1c (HbA1c), quality of life (QoL), and physical activity. RESULTS The study included 26 research papers, encompassing 7360 individuals. Meta-analysis was conducted for HbA1c, QoL, and physical activity. For HbA1c, short-term improvement was significant (standardized mean difference [SMD] –0.43; 95% CI –0.64 to –0.21; I2=69%) and medium term (SMD –0.49; 95% CI –0.49 to –0.09; I2=21%). However, in the long term, the improvement was not significant (SMD –0.23; 95% CI –0.49 to 0.03; I2=88%). For QoL, significant improvements were observed in the short term (SMD –0.23; 95% CI –0.42 to –0.05; I2=62%), and in the medium term (SMD –0.16; 95% CI –0.24 to –0.07; I2=0%). In the long term, however, the improvement was not significant (SMD –0.12; 95% CI –0.41 to 0.16; I2=71%). For physical activity, both subjective (questionnaire) and objective (number of steps) outcomes were analyzed. In the short term, subjective outcomes showed significant improvement (SMD 0.31; 95% CI 0.12-0.50; I2=0%), while objective outcomes did not (SMD 0.11; 95% CI –0.05 to 0.27; I2=0%). Medium- and long-term subjective outcomes showed no significant improvement. Meta-analysis for objective outcomes in the medium and long term was not possible due to insufficient studies. CONCLUSIONS This study confirmed short- and medium-term benefits of mHealth combined with professional interventions for HbA1c, QoL, and short-term physical activity, supporting effective chronic disease management.