Background: Wii Fitwas originally designed as a health and fitness interactive training experience for the general public. There are, however, many examples ofWii Fitbeing utilized in clinical settings. This article aims to identify the contribution ofWii Fitin the field of health promotion and rehabilitation by: (1) identifying the health-related domains for which theWii Fitseries has been tested, (2) clarifying the effect ofWii Fitin those identified health-related domains and (3) quantifying this effect.Method: A systematic literature review was undertaken. The MEDLINE database andGames for Health Journalpublished content were explored using the search term “Wii-Fit.” Occurrences resulting from manual searches on Google and material suggested by experts in the field were also considered. Included articles were required to have measurements fromWii Fitactivities for at least one relevant health indicator. The effect ofWii Fitinterventions was assessed using meta-analyses for the following outcomes: activity-specific balance confidence score, Berg balance score (BBC) and time-up-and-go test (TUG).Findings: A total of 115 articles highlighted that theWii Fithas been tested in numerous healthy and pathological populations. Out of these, only a few intervention studies have focused on the prevention of chronic diseases. A large proportion of the studies focus on balance training (N= 55). This systematic review highlights several potential benefits ofWii Fitinterventions and these positive observations are supported by meta-analyses data (N= 25). For example, the BBC and the TUG respond to a similar extend toWii Fitinterventions compared with traditional training.Conclusion: Wii Fithas the potential to be used as a rehabilitation tool in different clinical situations. However, the current literature includes relatively few randomized controlled trials in each population. Further research is therefore required.

Many activity trackers have been developed, but steps can still be inconsistent from one monitor to another. What are the differences and associations between the steps of 13 selected consumer-based and research-grade wearable devices during 1 standardized day in a metabolic chamber and 15-day free-living trials? In total, 19 healthy adults between 21 and 50 years-old participated in this study. Participants were equipped with 12 accelerometer-based active trackers and one pedometer (Yamasa) in order to monitor the number of steps per day. The devices were worn on the waist (ActiGraph, Omron, Actimarker, Lifedorder, Withings, and Yamasa) or non-dominant wrist (Fitbit, Garmin, Misfit, EPSON, and Jawbone), or placed in a pocket (Omron CaloriScan, and TANITA). Participants performed structured activities over a 24 h period in a chamber (Standardized day), and steps were monitored in the same participants in free-living trials for 15 successive days using the same monitors (free-living days). When the 13 monitors were ranked by their steps, waist-worn ActiGraph was located at the center (7th) of the monitors both in the Standardized (12,252 ± 598 steps/day, mean ± SD) and free-living days (9295 ± 4027 steps/day). The correlation between the accelerometer-based devices was very high (r = 0.87-0.99). However, the steps of Yamasa was significantly lower in both trials than ActiGraph. The wrist-worn accelerometers had significantly higher steps than other devices both trials (P < 0.05). The differences between ActiGraph and Actimarker or Lifecorder was less than 100 steps/day in the Standardized day, and the differences between ActiGraph and Active Style Pro was less than 100 steps/day in the free-living days. Regression equation was also performed for inter-device compatibility. Step obtained from the wrist-worn, waist-worn, and pocket-type activity trackers were significantly different from each other but still highly correlated in free-living conditions.

Extended sitting time at work is viewed as a crucial public health issue. Encouraging workers to stand during their office hours via the installation of standing desks maybe one effective option to combat this. Here, we investigate whether the installation of high desks in the workplace can induce positive changes in the amount of physical activity (PA) and thereby lead to subsequent improvements in anthropometric parameters. Thirty-two white-collar workers (22 men and 10 women, mean age 44.2) were randomly divided into two groups. A randomised crossover trial was performed for 13 weeks. During the experimental period, subjects completed their office work in a standing position using stationary high desks (standing work, SW) for 10 hours per week or more (SW period). The subjects were asked to maintain their normal sitting working habits during the control period (CONT period). The primary outcome was PA, which was assessed objectively using a triaxial accelerometer during weekdays and weekends. The secondary outcomes were anthropometric measurements. For each group and each parameter, the mean values during each period were recorded and were compared by paired t test. The daily total PA (10.2 ± 2.4 vs. 9.7 ± 2.3 METs · h/day, P = 0.043), MVPA (4.2 ± 2.2 vs. 3.7 ± 1.8 METs · h/day, P = 0.025), time spent in moderate PA (58.2 ± 20.7 vs. 53.4 ± 17.0 min/day, P = 0.019) and time spent in MVPA (62.8 ± 25.1 vs. 57.0 ± 20.3 min/day, P = 0.019) were significantly higher during the SW period compared to the CONT period. A weekdays verses weekends subanalysis revealed that these parameters were significantly higher during the SW period compared to the CONT period during weekdays only. No significant differences were noted before and after SW periods for most of the anthropometric measures, except waist circumference (83.7 ± 7.9 vs. 83.0 ± 7.9 cm, respectively, P = 0.007). Standing work, via the installation of high desks, significantly increases moderate to vigorous physical activity, especially on weekdays. UMIN-CRT, UMIN000016731 , 7th March 2015.

Background: Wearable activity trackers are regarded as a new opportunity to deliver health promotion interventions. Indeed, while the prediction of active behaviors is currently primarily relying on the processing of accelerometer sensor data, the emergence of smart clothes with multi-sensing capacities is offering new possibilities. Algorithms able to process data from a variety of smart devices and classify daily life activities could therefore be of particular importance to achieve a more accurate evaluation of physical behaviors. This study aims to (1) develop an activity recognition algorithm based on the processing of plantar pressure information provided by a smart-shoe prototype and (2) to determine the optimal hardware and software configurations. Method: Seventeen subjects wore a pair of smart-shoe prototypes composed of plantar pressure measurement insoles, and they performed the following nine activities: sitting, standing, walking on a flat surface, walking upstairs, walking downstairs, walking up a slope, running, cycling, and completing office work. The insole featured seven pressure sensors. For each activity, at least four minutes of plantar pressure data were collected. The plantar pressure data were cut in overlapping windows of different lengths and 167 features were extracted for each window. Data were split into training and test samples using a subject-wise assignment method. A random forest model was trained to recognize activity. The resulting activity recognition algorithms were evaluated on the test sample. A multi hold-out procedure allowed repeating the operation with 5 different assignments. The analytic conditions were modulated to test (1) different window lengths (1–60 seconds), (2) some selected sensor configurations and (3) different numbers of data features. Results: A window length of 20 s was found to be optimum and therefore used for the rest of the analysis. Using all the sensors and all 167 features, the smart shoes predicted the activities with an average success of 89%. “Running” demonstrated the highest sensitivity (100%). “Walking up a slope” was linked with the lowest performance (63%), with the majority of the false negatives being “walking on a flat surface” and “walking upstairs.” Some 2- and 3-sensor configurations were linked with an average success rate of 87%. Reducing the number of features down to 20 does not alter significantly the performance of the algorithm. Conclusion: High-performance human behavior recognition using plantar pressure data only is possible. In the future, smart-shoe devices could contribute to the evaluation of daily physical activities. Minimalist configurations integrating only a small number of sensors and computing a reduced number of selected features could maintain a satisfying performance. Future experiments must include a more heterogeneous population.

Background: Frailty and falls are two adverse characteristics of aging that impair the quality of life of senior people and increase the burden on the healthcare system. Various methods exist to evaluate frailty, but none of them are considered the gold standard. Technological methods have also been proposed to assess the risk of falling in seniors. This study aims to propose an objective method for complementing existing methods used to identify the frail state and risk of falling in older adults. Method: A total of 712 subjects (age: 71.3 ± 8.2 years, including 505 women and 207 men) were recruited from two Japanese cities. Two hundred and three people were classified as frail according to the Kihon Checklist. One hundred and forty-two people presented with a history of falling during the previous 12 months. The subjects performed a 45 s standing balance test and a 20 m round walking trial. The plantar pressure data were collected using a 7-sensor insole. One hundred and eighty-four data features were extracted. Automatic learning random forest algorithms were used to build the frailty and faller classifiers. The discrimination capabilities of the features in the classification models were explored. Results: The overall balanced accuracy for the recognition of frail subjects was 0.75 ± 0.04 (F1-score: 0.77 ± 0.03). One sub-analysis using data collected for men aged > 65 years only revealed accuracies as high as 0.78 ± 0.07 (F1-score: 0.79 ± 0.05). The overall balanced accuracy for classifying subjects with a recent history of falling was 0.57 ± 0.05 (F1-score: 0.62 ± 0.04). The classification of subjects relative to their frailty state primarily relied on features extracted from the plantar pressure series collected during the walking test. Conclusion: In the future, plantar pressures measured with smart insoles inserted in the shoes of senior people may be used to evaluate aspects of frailty related to the physical dimension (e.g., gait and balance alterations), thus allowing assisting clinicians in the early identification of frail individuals.