Can self-monitoring mobile health apps reduce sedentary behavior? A randomized controlled trial.

OBJECTIVE: To examine whether the self-monitoring interventions of a mobile health app reduce sedentary behavior in the short and long terms. METHOD: We designed a double-blind randomized control trial. Participants were selected from among the staff of a medical institution and registrants of an online research firm. Forty-nine participants were randomly assigned to either a control group (n = 25) or an intervention group (n = 24). The control group was given only the latest information about sedentary behavior, and the intervention was provided real-time feedback for self-monitoring in addition to the information. These interventions provided for 5 weeks (to measure the short-term effect) and 13 weeks (to measure the long-term effect) via the smartphone app. Measurements were as follows: subjective total sedentary time (SST), objective total sedentary time (OST), mean sedentary bout duration (MSB), and the number of sedentary breaks (SB). Only SST was measured by self-report based on the standardized International Physical Activity Questionnaire and others were measured with the smartphone. RESULTS: No significant results were observed in the short term. In the long term, while no significant results were also observed in objective sedentary behavior (OST, MSB, SB), the significant differences were observed in subjective sedentary behavior (SST, ßint  - ßctrl between baseline and 9/13 weeks; 1.73 and 1.50 h/d, respectively). CONCLUSIONS: Real-time feedback for self-monitoring with smartphone did not significantly affect objective sedentary behavior. However, providing only information about sedentary behavior to users with smartphones may make misperception on the amount of their subjective sedentary behavior.

Identifying characteristics of indicators of sedentary behavior using objective measurements.

OBJECTIVE: Recent attention has been focused on sedentary behavior (SB) affecting health outcomes, but the characteristics of indicators reflecting SB remain to be identified. This cross-sectional study aims to identify the characteristics of indicators of SB, focusing on the examination of correlations, reliability, and validity of sedentary variables assessed by the smartphone app. METHOD: Objectively measured data of SB of eligible 46 Japanese workers obtained from smartphones were used. We assessed the characteristics of current indicators being used with a 10-minute or 30-minute thresholds, in addition to the conventional indicators of total sedentary time, mean sedentary bout duration, and total number of sedentary bouts. They were evaluated from three perspectives: (a) association among the indicators, (b) reliability of the indicators, and (c) criterion validity. RESULTS: Total sedentary time under 10 minutes (U10) and U30 had negative associations with Total sedentary time (r = -.47 and -.21 respectively). The correlation between Mean sedentary bout duration and Total number of sedentary bouts was -.84, whereas between Mean sedentary bout duration 10, 30 and Total number of sedentary bouts were -.54 and -.21, respectively. The intraclass correlation coefficients of almost all indicators were around .80. Mean sedentary bout duration, Mean sedentary bout duration 10, Total number of sedentary bouts, Total sedentary time 30, U30 and U10 have significant differences between three BMI groups. CONCLUSION: This study comprehensively revealed the rationale of advantage in the current indicator being used with a 10-minute or 30-minute threshold, rather than the conventional total amount of SB.

Reliability of smartphone-based gait measurements for quantification of physical activity/inactivity levels.

OBJECTIVES: Objective measurements using built-in smartphone sensors that can measure physical activity/inactivity in daily working life have the potential to provide a new approach to assessing workers' health effects. The aim of this study was to elucidate the characteristics and reliability of built-in step counting sensors on smartphones for development of an easy-to-use objective measurement tool that can be applied in ergonomics or epidemiological research. METHODS: To evaluate the reliability of step counting sensors embedded in seven major smartphone models, the 6-minute walk test was conducted and the following analyses of sensor precision and accuracy were performed: 1) relationship between actual step count and step count detected by sensors, 2) reliability between smartphones of the same model, and 3) false detection rates when sitting during office work, while riding the subway, and driving. RESULTS: On five of the seven models, the inter-class correlations coefficient (ICC (3,1)) showed high reliability with a range of 0.956-0.993. The other two models, however, had ranges of 0.443-0.504 and the relative error ratios of the sensor-detected step count to the actual step count were ±48.7%-49.4%. The level of agreement between the same models was ICC (3,1): 0.992-0.998. The false detection rates differed between the sitting conditions. CONCLUSIONS: These results suggest the need for appropriate regulation of step counts measured by sensors, through means such as correction or calibration with a predictive model formula, in order to obtain the highly reliable measurement results that are sought in scientific investigation.

Visualizing physiological condition of two persons during cooperative motion.

The purpose of this study is to develop a visualizing system which represents human motion and physiological conditions such as muscle activities, autonomic nervous balances of multi persons simultaneously. Our system generates avatar animation representing muscle activity and heart rate variability of multi persons by color of muscles and heart. Three kinds of assistance motion were visualized to evaluate our system. In these experiments, muscle activity and heart rate variability of assisted and service persons were represented. Physiological conditions of multi persons are presented intuitively using this system. This visualizing system is useful for analysis of workload in cooperative motion.

Development of health monitoring system based on three-dimensional imaging using bio-signals and motion data.

The purpose of this study is to develop a health monitoring system. This system represents human health condition and human motion simultaneously. This system obtains motion data by optical motion capture system. Electromyogram and electrocardiogram are used for health condition estimation. These are measured synchronously with motion data. Two experiments were performed to evaluate our system. In these experiments, muscle activity, motion of the heart and the autonomic nervous balance were represented as health conditions. This system contributes intuitive recognitions of health condition and high accurate estimation of health conditions.

Estimation of bio-signal based on human motion for integrated visualization of daily-life.

This paper describes a method for the estimation of bio-signals based on human motion in daily life for an integrated visualization system. The recent advancement of computers and measurement technology has facilitated the integrated visualization of bio-signals and human motion data. It is desirable to obtain a method to understand the activities of muscles based on human motion data and evaluate the change in physiological parameters according to human motion for visualization applications. We suppose that human motion is generated by the activities of muscles reflected from the brain to bio-signals such as electromyograms. This paper introduces a method for the estimation of bio-signals based on neural networks. This method can estimate the other physiological parameters based on the same procedure. The experimental results show the feasibility of the proposed method.