Takahiro Miki

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: Whether mobile health (mHealth) interventions positively influence glycaemic control remains unclear. This study aimed to identify heterogeneity in the effects of mHealth-based disease management programmes on glycaemic control over a 1-year follow-up period within a target trial emulation framework. Methods: The study included participants diagnosed with hypertension, diabetes or dyslipidaemia between June 2021 and December 2023 who were either receiving medication or had a history of coronary artery disease or stroke and had a haemoglobin A1c (HbA1c) level of ≥ 7.0%. Hypothetical treatment regimens involve mobile health-based disease management combined with conventional treatment or conventional treatment alone. The primary outcome of this study was poor glycaemic control, operationally defined as a HbA1c level of ≥ 7.0% sustained over a 1-year follow-up period. Average treatment effects and heterogeneity were assessed using G-computation modelling. Effect modifiers were identified using cluster modelling. Results: The mHealth disease management programmes had an estimated risk difference in poor glycaemic control of −0.15 compared with conventional treatment. Through our clustering modelling, age ≤ 49 years, no dyslipidaemia diagnosis or prescription of related medication in the past 12 months, Charlson Comorbidity Index ≤ 3 and HbA1c ≥ 8.22% were identified to be associated with high benefit from the mHealth disease management programme. Conclusions: mHealth-based disease management programmes may reduce the risk of poor glycaemic control. Young age, high baseline HbA1c levels and lower comorbidity burden were identified as the factors affecting efficacy. These results highlight the need for targeted interventions in mHealth disease management programmes.

Background: Understanding health behaviour patterns is critical to addressing public health challenges. In Japan, lifestyle factors such as smoking, physical inactivity, poor diet and insufficient sleep significantly contribute to non-communicable diseases (NCDs). Identifying how these behaviours cluster within populations offers valuable insights for effective prevention and intervention strategies. Latent class analysis (LCA) enables the identification of behavioural patterns to inform tailored public health initiatives. Purpose: To identify and characterise demographic-behavioural profiles among Japanese adults using LCA. Method: Data from 137 357 individuals participating in a health promotion programme in Japan (1 January 2022 to 31 December 2022) were analysed. Participants aged 20 years and older with complete records for all relevant health behaviour variables were included. Variables included age, gender, smoking status, alcohol consumption, sleep duration, physical activity, sedentary behaviour and salt intake. LCA was conducted to identify behaviour clusters, with the optimal number of classes determined using Bayesian and Akaike information criteria. Results: From a cohort of 137 357 individuals, nine clusters were identified: (1) middle-aged women with short sleep duration (20.8%), (2) middle-aged men with elevated health behaviour risks (8.4%), (3) young adults with short sleep duration (18.4%), (4) healthy middle-aged men (2.6%), (5) sedentary middle-aged men with short sleep duration (25.3%), (6) typical middle-aged women (5.2%), (7) physically active young men with poor dietary habits (2.5%), (8) unhealthy middle-aged men with inconsistent behaviours (8.5%) and (9) older adults with moderate health concerns (8.4%). Conclusion: LCA revealed diverse health behaviour patterns, emphasising the need for tailored health interventions targeting specific subgroups to mitigate NCD risks.

Long-term effectiveness of digital health interventions for hypertension remains unclear, particularly regarding individual variability in treatment response. This study examined the association of a mobile health-based disease management program for uncontrolled hypertension and assessed treatment effect heterogeneity using a target trial emulation framework. We analyzed health checkup data of employees June 2021–December 2023. Individuals with hypertension, diabetes, or dyslipidemia were invited to participate in a six-month mobile health-based disease management program incorporating lifestyle tracking via a mobile application and remote behavioral coaching. We compared the following two treatments using a target trial emulation framework: mobile health-based disease management program combined with conventional treatment, versus conventional treatment alone. The primary outcome was uncontrolled hypertension at the one-year follow-up (systolic ≥140 mmHg or diastolic ≥90 mmHg). We estimated average and individual treatment effects using outcome regression based on the G-formula with ensemble machine learning methods for model specification. Clustering analysis was used to identify heterogeneous subgroups and potential effect modifiers. Mobile health-based disease management program was associated with a 5.2% (95% confidence interval: 4.4% to 6.0%) lower prevalence of uncontrolled hypertension compared with conventional treatment. Treatment response varied, with greater benefits observed in individuals with a strong intention to improve lifestyle habits, higher diastolic blood pressure, and more favorable behavioral and metabolic characteristics. Age was associated with benefit, though it had relatively lower importance. Participation in a mobile health-based disease management program was associated with better blood pressure control over one year. The substantial variation in treatment effectiveness highlights the need for personalized digital health strategies.

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: Personality traits influence behavioral changes and may impact engagement and outcomes in obesity management programs. However, their role in mobile app-based interventions for obesity remains underexplored. Understanding the relationship between personality traits and health behaviors can inform the design of personalized programs, potentially enhancing adherence and long-term success. Mobile health technologies offer scalable solutions for lifestyle modification, making this investigation particularly relevant. Methods: This study analyzed 2716 participants with a body mass index of 25.0 or higher enrolled in a six-month mobile app-based lifestyle modification program. Personality traits were assessed using the five-factor model, and participants were categorized into resilient, overcontrolled, and undercontrolled prototypes through cluster analysis. App engagement metrics, such as session frequency, usage duration, and weight input rates, were recorded. Weight loss outcomes were measured as the percentage reduction in body weight, calculated by comparing the first and last 14 days of the program. Additional variables, including daily steps, blood pressure, and cholesterol levels, were evaluated to assess overall health changes. Results: The undercontrolled group exhibited the lowest engagement metrics, including fewer app sessions (2.63 times/day) and a lower weight input rate (63.06%). In contrast, the overcontrolled group achieved the highest weight loss, with a mean reduction of −3.08% compared to −2.55% in the undercontrolled group ( p = 0.015). The resilient group demonstrated the highest daily app usage duration (9.09 min/day). Across all groups, no significant differences were observed in blood pressure, daily steps, or cholesterol levels. Cluster analysis confirmed that three prototypes effectively captured behavioral and programmatic differences among participants. Conclusion: Personality prototypes significantly influence engagement and weight loss in a mobile app-based lifestyle modification support program. These findings highlight the importance of considering personality traits to enhance program design and effectiveness. Further research is required to validate these outcomes across diverse populations and examine the long-term sustainability of behavioral changes.

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.