Physical activity is a crucial aspect of health benefits in the public society. Although studies on the temporal physical activity patterns might lead to the protocol for efficient intervention/program, a standardized procedure to determine and analyze the temporal physical activity patterns remains to be developed. Here, we attempted to develop a procedure to cluster 24-hour patterns of physical activity as step counts measured with an accelerometer-based wearable sensor. The step-counting data from forty-two healthy adults were analyzed using unsupervised machine learning. We could identify six 24-hour step-counting patterns and five daily step-behavioral clusters. When the amount of physical activity was categorized into tertile groups reflecting highly active, moderately active, and low active, each tertile group consisted of different proportions of six 24-hour step-counting patterns. Our procedure would be reliable for finding and clustering physical activity patterns/behaviors and reveal heterogeneity in the categorization by a traditional tertile procedure using total step amount.

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Depression is a global burden with profound personal and economic consequences. Previous studies have reported that the amount of physical activity is associated with depression. However, the relationship between the temporal patterns of physical activity and depressive symptoms are not well understood. We hypothesize that the temporal patterns of daily physical activity could better explain the association of physical activity with depressive symptoms. To address the hypothesis, we investigated the association between depressive symptoms and daily dominant activity behaviors based on 24-hour temporal patterns of physical activity. We found that evening dominant behavior was positively associated with depressive symptoms compared to morning dominant behavior as the control group. Our results might contribute to monitoring and identifying individuals with latent depressive symptoms, emphasizing the importance of nuanced activity patterns and their probability in assessing depressive symptoms effectively.

Background: Previous studies have focused on the relationship between specific dietary factors (such as sodium intake or consumption of fruits and vegetables) and the development of hypertension. However, less is known about the role of overall dietary patterns (food intake, dietary behaviors, and cooking methods) in the development of hypertension. This study aims to address this gap in the literature by using unsupervised machine-learning techniques to identify dietary patterns associated with the incidence of hypertension. Methods: Data were obtained from Japanese participants enrolled in a prospective cohort study between August 2008 and August 2010. A total of 447 male participants were included in the analysis. Dimension reduction using Uniform Manifold Approximation and Projection (UMAP) and subsequent K-means clustering was used to derive dietary patterns. In addition, multivariable logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CI) to evaluate the association between dietary patterns and the incidence of hypertension. Results: We identified four dietary patterns: ‘Sweet Tooth,’ ‘Herbivorous,’ ‘Meat-based healthy,’ and ‘Seafood and Alcohol.’ Approximately 13.4% of the cohort study participants developed hypertension in the following two years. Compared with ‘Seafood and Alcohol’ as a reference, the protective dietary patterns for hypertension were ‘Herbivorous’ (OR = 0.39, 95% CI = 0.19–0.80, p = 0.013) and the ‘Meat-based healthy’ (OR = 0.37, 95% CI = 0.16–0.86, p = 0.022) after adjusting for potential confounding factors, including age, body mass index, smoking, education, physical activity, dyslipidemia, and diabetes. An age-matched sensitivity analysis confirmed this finding. Conclusions:From a methodological perspective, we successfully identified clear dietary patterns by clustering using the UMAP and K-means algorithms in an epidemiological dataset with a small sample size. The ‘Herbivorous’ and ‘Meat-based healthy’ dietary patterns were associated with a lower risk of hypertension in Japanese males than the ‘Seafood and Alcohol’ pattern. These findings provide helpful insights into hypertension-preventive interventions in Japanese males through dietary pattern regulation.

Background: Low back pain (LBP) is a common health problem — sitting on a chair for a prolonged time is considered a significant risk factor. Furthermore, the level of LBP may vary at different times of the day. However, the role of the time-sequence property of sitting behavior in relation to LBP has not been considered. During the dynamic sitting, small changes, such as slight or big sways, have been identified. Therefore, it is possible to identify the motif consisting of such changes, which may be associated with the incidence, exacerbation, or improvement of LBP.Method: Office chairs installed with pressure sensors were provided to a total of 22 office workers (age = 43.4 ± 8.3 years) in Japan. Pressure sensors data were collected during working days and hours (from morning to evening). The participants were asked to answer subjective levels of pain including LBP. Center of pressure (COP) was calculated from the load level, the changes in COP were analyzed by applying the Toeplitz inverse covariance-based clustering (TICC) analysis, COP changes were categorized into several states. Based on the states, common motifs were identified as a recurring sitting behavior pattern combination of different states by motif-aware state assignment (MASA). Finally, the identified motif was tested as a feature to infer the changing levels of LBP within a day. Changes in the levels of LBP from morning to evening were categorized as exacerbated, did not change, or improved based on the survey questions. Here, we present a novel approach based on social spider algorithm (SSA) and probabilistic neural network (PNN) for the prediction of LBP. The specificity and sensitivity of the LBP inference were compared among ten different models, including SSA-PNN.Result: There exists a common motif, consisting of stable sitting and slight sway. When LBP level improved toward the evening, the frequency of motif appearance was higher than when LBP was exacerbated (p < 0.05) or the level did not change. The performance of the SSA-PNN optimization was better than that of the other algorithms. Accuracy, precision, recall, and F1-score were 59.20, 72.46, 40.94, and 63.24%, respectively.Conclusion: A lower frequency of a common motif of the COP dynamic changes characterized by stable sitting and slight sway was found to be associated with the exacerbation of LBP in the evening. LBP exacerbation is predictable by AI-based analysis of COP changes during the sitting behavior of the office workers.