Thomas Svensson

Objective: The long‐term impact of coping behaviors and strategies on the risk of geriatric depression remains unclear. We examined coping strategies and behaviors in mid‐to‐late life as predictors of major depressive disorder (MDD) during approximately 15 years of follow‐up. Method: Overall, 1039 participants were assessed for coping behaviors and strategies in 2000, and their MDD status was diagnosed by a standard psychiatrist‐based assessment in 2014–2015. The Stress and Coping Inventory was used to assess the frequency of six coping behaviors: consulting, planning, positive reappraisal, avoidance, fantasizing, and self‐blame. Subsequently, we assessed two coping strategies: approach‐oriented and avoidance‐oriented. Logistic regression examined associations between coping behaviors and strategies and MDD, reported as odds ratios (ORs) with 95% confidence intervals (CIs). Results: Among 1039 participants (mean age: 72.6 ± 5.4 years in 2014–2015), 86 (8.3%) were diagnosed with MDD. Among the six coping behaviors, planning (OR: 0.47, 95% CI: 0.24–0.90) and positive reappraisal (OR: 0.36, 95% CI: 0.17–0.74) were associated with a lower risk of MDD, whereas fantasizing (OR: 3.20, 95% CI: 1.64–6.24) was associated with a higher risk. An approach‐oriented coping strategy was associated with a lower risk of MDD (OR: 0.42, 95% CI: 0.18–0.94), while an avoidance‐oriented coping strategy was associated with a higher risk (OR: 3.88, 95% CI: 1.28–11.75). Conclusion: Coping behaviors and strategies in mid‐to‐late life were associated with subsequent MDD risk among older adults, suggesting that coping style is a potentially modifiable target for geriatric depression prevention.

Background/Objectives: Meal sequence (MS) has been proven to suppress the postprandial blood glucose variability, potentially preventing the onset of diabetes and its complications. However, existing evidence in healthy individuals has not been conclusive because it solely focuses on single-meal postprandial glucose, overlooking the potential influence of preceding meals. Methods: A 24-day randomized, single-blinded crossover study investigated the MS effect in real-life settings. Among 12 participants, 8 (66.7%) were female, with 158/342 (46.2%) observations available for analysis. Results: In per-protocol (PP) analysis, the effect of MS on incremental glucose peak (IGP) was -11.47 (95% confidence interval [CI] -38.04 to 15.09, p-value = 0.395) mg/dL. After adjusting for confounders in model 2 and model 3, the effect of MS on IGP was -12.24 (95% CI -37.09 to 12.61) and -11.25 (95% CI -40.63 to 20.13), respectively. In intention-to-treat analysis (ITT), the effect of MS on IGP was -12.09 (95% CI -38.82 to 14.65, p-value = 0.373) mg/dL. After adjusting for confounders in model 2 and model 3, the effect of MS on IGP was -12.75 (95% CI -37.62 to 12.12) and -14.31 (95% CI -47.14 to 18.53), respectively. Conclusions: This result contradicts previous studies conducted in diabetes patients under free-living conditions and in both healthy individuals and diabetes participants in experimental settings. Discrepancies may be due to different instruction designs, low MS adherence, and irregular meal timing in healthy participants under the free-living conditions of our study. This study reported no evidence of MS effect on the IGP in young, healthy individuals in a real-life time setting. The improved meal quality was seen while following MS by increasing fiber intake and prolonging meal duration. Further studies should aim to stabilize meal timing and strictly instruct the MS implementation.

Background: Several studies have investigated the association between metabolic syndrome (MetS) and physical activity (PA). However, further research is needed using objective measures of PA in free-living conditions, while also accounting for the time-variant nature of MetS. This study aimed to: (1) investigate the association between wearable device-measured step count and 5-year MetS incidence in generally healthy Japanese participants, using annual health check-up (AHC) data and interval-censored survival analysis; and (2) assess the current, and recently revised, reference value (8,000 steps/day) of Japan’s Ministry of Health, Labour and Welfare (MHLW), and (3) investigate the possibility of non-linear associations between daily step count and MetS. Methods: This longitudinal prospective observation study identified average daily step count per year/person as the main exposure, and MetS incidence, defined according to Japanese guidelines, as the main outcome. The main analysis included 730 participants without MetS or pre-MetS at baseline. An interval-censored Cox model was applied to assess MetS incidence using time-to-event data. Results: Every 1,000 steps added to the average step count was significantly and inversely associated with incident MetS in adjusted models [Model 1: HR = 0.92; 95% CI: 0.85, 1.00; Model 2: HR = 0.91; 95% CI: 0.83, 0.99] (p < 0.05). The current reference value (8,000 steps/day) also indicated a significant inverse association [Model 1: HR = 0.32; 95% CI: 0.14, 0.71; Model 2: HR = 0.26; 95% CI: 0.11, 0.61] (p < 0.01). Higher daily step counts lowered the risk of MetS incidence according to the amount of steps up to a step count of 12,000, however, the dose-response effect was attenuated beyond 12,000 steps/day. Conclusions: Each additional 1,000 daily steps was associated with a 9% reduction in the risk of developing MetS among healthy participants. The reference value (8,000 steps/day) was associated with a 74% reduction in MetS risk.

To address the challenges of an ageing population, it is important to promote health by identifying factors for healthy ageing. The aim of this study was to investigate the bidirectional association between physical activity (PA) and sleep in healthy Japanese super-seniors over the age of 80. For approximately 1 year, 124 participants wore wearable devices and answered daily lifestyle questionnaires. PA was defined as daily step count and minutes in light activity. Sleep was measured using 24-h total sleep time (TST) and time in bed (TIB). Associations were analysed bidirectionally using multilevel mixed-effects linear regression models. Fully adjusted models revealed significant and inverse associations between sleep and PA from the same 24-h period. Similarly, the results were significant and inverse with PA from the day before as exposure and the next 24-h sleep measures as outcome. The between-individual associations between sleep measures and PA from the subsequent day were significant and inverse. However, there was a positive within-individual association between TST and step count from the subsequent day. The study suggests that associations between PA and sleep in super-seniors differ from patterns previously described in younger adults. Very old individuals with increasing 24-h total sleep time may compensate for a lower step count on one day by increasing their step count the following day.

Sleep disturbances are prevalent among elderly populations and are linked to various health complications. Understanding the underlying biological mechanisms contributing to sleep disorders is crucial for developing targeted interventions. In this study, we measured 355 plasma proteins in an elderly Japanese cohort (n=77) using a high-throughput proteomic platform. Additionally, we collected over 25,000 person-days of physical activity and sleep behavior data from wrist-worn wearable devices, focusing on total sleep time (TST) across 24 h and daytime sleep. Fragmented sleep was observed as one of the most prevalent sleep disturbances in this population. In protein expression analysis, we identified 9 protein biomarkers associated with increased secondary sleep TST, defined as additional sleep episodes outside of the main sleep episode within 24 h. These findings may suggest disruptions in circadian rhythms or underlying health conditions. Functional analysis revealed that biological processes related to inflammation play a significant role in regulating sleep behavior. Further analysis showed an association of 12 proteins with daytime sleep and 5 proteins with afternoon sleep. Overall, this study identified inflammatory biomarkers and biological processes associated with sleep behavior in the elderly, presenting promising opportunities for developing diagnostic tools and targeted clinical interventions.

Objectives: Many Japanese adults experience poor sleep quality. Prolonged sedentary behavior negatively affects health, including sleep-associated problems. We investigated the association between sedentary behavior and sleep quality among urban white-collar workers in Japan with metabolic syndrome or at risk of metabolic syndrome, a population thought to have particularly prolonged sedentary time. Methods: This study was conducted using a prospective cohort study design as a secondary analysis of participants’ data in a lifestyle intervention program using a smartphone application in 5 different companies (>1000 employees) in Tokyo from December 2018 to March 2019. The primary outcome was sleep quality, obtained as the daily score of relevant items in the St Mary’s Hospital Sleep Questionnaire (SMHSQ) during the study period. The primary exposure was daily sedentary time (hours), which was objectively measured using a wristwatch-type wearable device (Fitbit Versa). Multilevel linear mixed effects analysis adjusted for covariates was used to estimate between-person and within-person associations. Sensitivity analyses were used to investigate between-/within-person associations between sedentary time and the scores of each item related to sleep quality on the SMHSQ. Results: A longer sedentary time was inversely associated with the SMHSQ scores related to sleep quality in multivariable-adjusted models at the individual level (β coefficient −.046; SE 0.016; 95% CI, −.077 to −.015). Longer sedentary time was also inversely associated with each sleep satisfaction-related SMHSQ item. However, no significant associations were observed at the population level. Conclusions: Longer sedentary time was inversely associated with sleep quality at the individual level.

We developed an algorithm for the early screening of autism spectrum disorder using baby cry sound. We collected the sound using a smartphone application to record it in their daily life. We employed the Japanese version of the Modified Checklist for Autism in Toddlers (M-CHAT) for the evaluation of the autism spectrum. We built a prediction model for binary classification using least absolute shrinkage and selection operator (LASSO) logistic regression. We evaluated the validity of the features as explanatory variables used in the model by cross-validation, and the accuracy of the file-wise prediction in the cross-validation was 0.63. This result proved the feasibility of the very early-stage screening of autism spectrum using baby cry.

Background: Associations between subjective sleep quality and stage-specific heart rate (HR) may have important clinical relevance when aiming to optimize sleep and overall health. The majority of previously studies have been performed during short periods under laboratory-based conditions. The aim of this study was to investigate the associations of subjective sleep quality with heart rate during REM sleep (HR REMS) and non-REM sleep (HR NREMS) using a wearable device (Fitbit Versa). Methods: This is a secondary analysis of data from the intervention group of a randomized controlled trial (RCT) performed between December 3, 2018, and March 2, 2019, in Tokyo, Japan. The intervention group consisted of 179 Japanese office workers with metabolic syndrome (MetS), Pre-MetS or a high risk of developing MetS. HR was collected with a wearable device and sleep quality was assessed with a mobile application where participants answered The St. Mary’s Hospital Sleep Questionnaire. Both HR and sleep quality was collected daily for a period of 90 days. Associations of between-individual and within-individual sleep quality with HR REMS and HR NREMS were analyzed with multi-level model regression in 3 multivariate models. Results: The cohort consisted of 92.6% men (n=151) with a mean age (± standard deviation) of 44.1 (± 7.5) years. A non-significant inverse between-individual association was observed for sleep quality with HR REMS (HR REMS − 0.18; 95% CI − 0.61, 0.24) and HR NREMS (HR NREMS − 0.23; 95% CI − 0.66, 0.21), in the final multivariable adjusted models; a statistically significant inverse within-individual association was observed for sleep quality with HR REMS (HR REMS − 0.21 95% CI − 0.27, − 0.15) and HR NREMS (HR NREMS − 0.21 95% CI − 0.27, − 0.14) after final adjustments for covariates. Conclusion: The present study shows a statistically significant within-individual association of subjective sleep quality with HR REMS and HR NREMS. These findings emphasize the importance of considering sleep quality on the individual level. The results may contribute to early detection and prevention of diseases associated with sleep quality which may have important implications on public health given the high prevalence of sleep disturbances in the population.

Background: The molecular pathways linking short and long sleep duration with incident diabetes mellitus (iDM) and incident coronary heart disease (iCHD) are not known. We aimed to identify circulating protein patterns associated with sleep duration and test their impact on incident cardiometabolic disease. Methods: We assessed sleep duration and measured 78 plasma proteins among 3336 participants aged 46–68 years, free from DM and CHD at baseline, and identified cases of iDM and iCHD using national registers. Incident events occurring in the first 3 years of follow-up were excluded from analyses. Tenfold cross-fit partialing-out lasso logistic regression adjusted for age and sex was used to identify proteins that significantly predicted sleep duration quintiles when compared with the referent quintile 3 (Q3). Predictive proteins were weighted and combined into proteomic scores (PS) for sleep duration Q1, Q2, Q4, and Q5. Combinations of PS were included in a linear regression model to identify the best predictors of habitual sleep duration. Cox proportional hazards regression models with sleep duration quintiles and sleep-predictive PS as the main exposures were related to iDM and iCHD after adjustment for known covariates. Results: Sixteen unique proteomic markers, predominantly reflecting inflammation and apoptosis, predicted sleep duration quintiles. The combination of PSQ1 and PSQ5 best predicted sleep duration. Mean follow-up times for iDM (n = 522) and iCHD (n = 411) were 21.8 and 22.4 years, respectively. Compared with sleep duration Q3, all sleep duration quintiles were positively and significantly associated with iDM. Only sleep duration Q1 was positively and significantly associated with iCHD. Inclusion of PSQ1 and PSQ5 abrogated the association between sleep duration Q1 and iDM. Moreover, PSQ1 was significantly associated with iDM (HR = 1.27, 95% CI: 1.06–1.53). PSQ1 and PSQ5 were not associated with iCHD and did not markedly attenuate the association between sleep duration Q1 with iCHD. Conclusions: We here identify plasma proteomic fingerprints of sleep duration and suggest that PSQ1 could explain the association between very short sleep duration and incident DM.