Integrated Solution for Physical Activity Monitoring Based on Mobile Phone and PC

A virtual reality physical activity pattern assessment: Mixed crossover experiments and cluster analysis

Objective

The subjects' physical activity levels and enjoyment of exercise after 15 min of virtual reality (VR) physical activity of different intensities were compared.

Methods

Thirty-two subjects were selected for a mixed crossover experiment. They were randomly assigned to exercise in three VR games with different exercise intensities. Acceleration data of the subjects were collected and subjects' exercise enjoyment and exercise levels were compared. The subjects' emotional efficacy and arousal during exercise were measured and evaluated using the Feeling Scale (FS) and the Felt Arousal Scale (FAS), and the acceleration data were evaluated by clustering using the fuzzy c-mean (FCM) clustering algorithm.

Results

A one-way ANOVA was performed on FS and FAS before and after VR physical activity, P overall p = .003 in FS, before and after low-intensity (LI), medium-intensity (MI), and high-intensity (HI) VR physical activity, the p-values were.087, p = .027, and p = .021, respectively. p < .001 in FAS, before and after LI, MI, and HI VR physical activity, the p-values were .029, < .001, < .001. According to the FCM clustering of acceleration activity counts by LI, MI, and HI, the clustering centers of the right arm acceleration counts were 2016.77, 6118.31, and 9923.45; the clustering centers of the right thigh acceleration counts were 248.30, 1895.22, and 3485.60; and the clustering centers of the combined upper and lower limb acceleration counts were 1443.83, 4415.47, and 7149.13.

Conclusion

VR physical activity enhances subjects' sense of enjoyment of exercise and emotional arousal, with moderate intensity VR physical activity having the best effect. VR physical activity is skewed toward high upper-extremity activity and low lower-extremity activity. The combined intensity of VR physical activity matches that of traditional exercise, and it can achieve the workout effect of the traditional workout modality.

A systematic review of smartphone-based human activity recognition methods for health research

Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarized the existing approaches to smartphone-based HAR. For this purpose, we systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Consequently, we identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives. We conclude that smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving the quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.

Effect of Mobile Health on Obese Adults: A Systematic Review and Meta-Analysis

OBJECTIVES

This study was conducted to examine the effects of mobile health (mHealth), using mobile phones as an intervention for weight loss in obese adults.

METHODS

An electronic search was carried out using multiple databases. A meta-analysis of selected studies was performed. The effects of mHealth were analyzed using changes in body weight and body mass index (BMI).

RESULTS

We identified 20 randomized controlled trials (RCTs) involving 2,318 participants who fit our inclusion criteria. The meta-analysis showed that body weight was reduced with a weighted mean difference (WMD) of -2.35 kg (95% confidence interval [CI], -2.84 to -1.87). An examination of the impact of duration of intervention showed that weight loss was greater after 6 months of mHealth (WMD = -2.66 kg) than between three and four months (WMD = -2.25 kg); it was maintained for up to 9 months (WMD = -2.62 kg). At 12 months, weight loss was reduced to a WMD of -1.23 kg. BMI decreased with a WMD of -0.77 kg/m2 (95% CI, -1.01 to -0.52). BMI changes were not statistically significant at 3 months (WMD = -1.10 kg/m2), but they were statistically significant at 6 months (WMD = -0.67 kg/m2).

CONCLUSIONS

The use of mHealth for obese adults showed a modest short-term effect on body weight and BMI. Although the weight loss associated with mHealth did not meet the recommendation of the Scottish Intercollegiate Guideline Network, which considers a reduction of approximately 5 to 10 kg of the initial body weight as a successful intervention. Well-designed RCTs are needed to reveal the effects of mHealth interventions.

A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study

The links between increased participation in Physical Activity (PA) and improvements in health are well established. As this body of evidence has grown, so too has the search for measures of PA with high levels of methodological effectiveness (i.e. validity, reliability and responsiveness to change). The aim of this “review of reviews” was to provide a comprehensive overview of the methodological effectiveness of currently employed measures of PA, to aid researchers in their selection of an appropriate tool. A total of 63 review articles were included in this review, and the original articles cited by these reviews were included in order to extract detailed information on methodological effectiveness.

Self-report measures of PA have been most frequently examined for methodological effectiveness, with highly variable findings identified across a broad range of behaviours. The evidence-base for the methodological effectiveness of objective monitors, particularly accelerometers/activity monitors, is increasing, with lower levels of variability observed for validity and reliability when compared to subjective measures. Unfortunately, responsiveness to change across all measures and behaviours remains under-researched, with limited information available.

Other criteria beyond methodological effectiveness often influence tool selection, including cost and feasibility. However, researchers must be aware of the methodological effectiveness of any measure selected for use when examining PA. Although no “perfect” tool for the examination of PA in adults exists, it is suggested that researchers aim to incorporate appropriate objective measures, specific to the behaviours of interests, when examining PA in free-living environments.

Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity

Physical activity is closely linked with health and well-being; however, many Americans do not engage in regular exercise. Older adults and those with low socioeconomic status are especially at risk for poor health, largely due to their sedentary lifestyles. Fitness technology, including trackers and smartphone applications (apps), has become increasingly popular for measuring and encouraging physical activity in recent years. However, many questions remain regarding the effectiveness of this technology for promoting behavior change. Behavior change techniques such as goal setting, feedback, rewards, and social factors are often included in fitness technology. However, it is not clear which components are most effective and which are actually being used by consumers. We discuss additional strategies not typically included in fitness technology devices or apps that are promising for engaging inactive, vulnerable populations. These include action planning, restructuring negative attitudes, enhancing environmental conditions, and identifying other barriers to regular physical activity. We consider which strategies are most conducive to motivating behavior change among sedentary adults. Overall, fitness technology has the potential to significantly impact public health, research, and policies. We suggest ways in which app developers and behavior change experts can collaborate to develop successful apps. Advances are still needed to help inactive individuals determine how, when, where, and with whom they can increase their physical activity.

An Automatic User-Adapted Physical Activity Classification Method Using Smartphones

In recent years, an increasing number of people have become concerned about their health. Most chronic diseases are related to lifestyle, and daily activity records can be used as an important indicator of health. Specifically, using advanced technology to automatically monitor actual activities can effectively prevent and manage chronic diseases. The data used in this paper were obtained from acceleration sensors and gyroscopes integrated in smartphones. We designed an efficient Adaboost-Stump running on a smartphone to classify five common activities: cycling, running, sitting, standing, and walking and achieved a satisfactory classification accuracy of 98%. We designed an online learning method, and the classification model requires continuous training with actual data. The parameters in the model then become increasingly fitted to the specific user, which allows the classification accuracy to reach 95% under different use environments. In addition, this paper also utilized the OpenCL framework to design the program in parallel. This process can enhance the computing efficiency approximately ninefold.

Sensitivity of fNIRS measurement to head motion: an applied use of smartphones in the lab

BACKGROUND

Powerful computing capabilities in small, easy to use hand-held devices have made smart technologies such as smartphones and tablets ubiquitous in today's society. The capabilities of these devices provide scientists with many tools that can be used to improve the scientific method.

METHOD

Here, we demonstrate how smartphones may be used to quantify the sensitivity of functional near-infrared spectroscopy (fNIRS) signal to head motion. By attaching a smartphone to participants' heads during the fNIRS scan, we were able to capture data describing the degree of head motion.

RESULTS

Our results demonstrate that data recorded from an off-the-shelf smartphone accelerometer may be used to identify correlations between head-movement and fNIRS signal change. Furthermore, our results identify correlations between the magnitudes of head-movement and signal artifact, as well as a relationship between the direction of head movement and the location of the resulting signal noise.

CONCLUSIONS

These data provide a valuable proof-of-concept for the use of off-the-shelf smart technologies in neuroimaging applications.

Measuring and influencing physical activity with smartphone technology: a systematic review

BACKGROUND

Rapid developments in technology have encouraged the use of smartphones in physical activity research, although little is known regarding their effectiveness as measurement and intervention tools.

OBJECTIVE

This study systematically reviewed evidence on smartphones and their viability for measuring and influencing physical activity.

DATA SOURCES

Research articles were identified in September 2013 by literature searches in Web of Knowledge, PubMed, PsycINFO, EBSCO, and ScienceDirect.

STUDY SELECTION

The search was restricted using the terms (physical activity OR exercise OR fitness) AND (smartphone* OR mobile phone* OR cell phone*) AND (measurement OR intervention). Reviewed articles were required to be published in international academic peer-reviewed journals, or in full text from international scientific conferences, and focused on measuring physical activity through smartphone processing data and influencing people to be more active through smartphone applications.

STUDY APPRAISAL AND SYNTHESIS METHODS

Two reviewers independently performed the selection of articles and examined titles and abstracts to exclude those out of scope. Data on study characteristics, technologies used to objectively measure physical activity, strategies applied to influence activity; and the main study findings were extracted and reported.

RESULTS

A total of 26 articles (with the first published in 2007) met inclusion criteria. All studies were conducted in highly economically advantaged countries; 12 articles focused on special populations (e.g. obese patients). Studies measured physical activity using native mobile features, and/or an external device linked to an application. Measurement accuracy ranged from 52 to 100% (n = 10 studies). A total of 17 articles implemented and evaluated an intervention. Smartphone strategies to influence physical activity tended to be ad hoc, rather than theory-based approaches; physical activity profiles, goal setting, real-time feedback, social support networking, and online expert consultation were identified as the most useful strategies to encourage physical activity change. Only five studies assessed physical activity intervention effects; all used step counts as the outcome measure. Four studies (three pre-post and one comparative) reported physical activity increases (12-42 participants, 800-1,104 steps/day, 2 weeks-6 months), and one case-control study reported physical activity maintenance (n = 200 participants; >10,000 steps/day) over 3 months.

LIMITATIONS

Smartphone use is a relatively new field of study in physical activity research, and consequently the evidence base is emerging.

CONCLUSIONS

Few studies identified in this review considered the validity of phone-based assessment of physical activity. Those that did report on measurement properties found average-to-excellent levels of accuracy for different behaviors. The range of novel and engaging intervention strategies used by smartphones, and user perceptions on their usefulness and viability, highlights the potential such technology has for physical activity promotion. However, intervention effects reported in the extant literature are modest at best, and future studies need to utilize randomized controlled trial research designs, larger sample sizes, and longer study periods to better explore the physical activity measurement and intervention capabilities of smartphones.

Comparison of physical activity measures using mobile phone-based CalFit and Actigraph

Background

Epidemiological studies on physical activity often lack inexpensive, objective, valid, and reproducible tools for measuring physical activity levels of participants. Novel sensing technologies built into smartphones offer the potential to fill this gap.

Objective

We sought to validate estimates of physical activity and determine the usability for large population-based studies of the smartphone-based CalFit software.

Methods

A sample of 36 participants from Barcelona, Spain, wore a smartphone with CalFit software and an Actigraph GT3X accelerometer for 5 days. The ease of use (usability) and physical activity measures from both devices were compared, including vertical axis counts (VT) and duration and energy expenditure predictions for light, moderate, and vigorous intensity from Freedson’s algorithm. Statistical analyses included (1) Kruskal-Wallis rank sum test for usability measures, (2) Spearman correlation and linear regression for VT counts, (3) concordance correlation coefficient (CCC), and (4) Bland-Altman plots for duration and energy expenditure measures.

Results

Approximately 64% (23/36) of participants were women. Mean age was 31 years (SD 8) and mean body mass index was 22 kg/m² (SD 2). In total, 25/36 (69%) participants recorded at least 3 days with at least 10 recorded hours of physical activity using CalFit. The linear association and correlations for VT counts were high (adjusted R²=0.85; correlation coefficient .932, 95% CI 0.931-0.933). CCCs showed high agreement for duration and energy expenditure measures (from 0.83 to 0.91).

Conclusions

The CalFit system had lower usability than the Actigraph GT3X because the application lacked a means to turn itself on each time the smartphone was powered on. The CalFit system may provide valid estimates to quantify and classify physical activity. CalFit may prove to be more cost-effective and easily deployed for large-scale population health studies than other specialized instruments because cell phones are already carried by many people.

An objective pronator drift test application (iPronator) using handheld device

BACKGROUND

The pronator drift test is widely used to detect mild arm weakness. We developed an application that runs on a handheld device to objectify the pronator drift test and investigated its feasibility in stroke patients.

METHODS

The iPronator application, which uses the built-in accelerometer in handheld devices, was developed. We enrolled acute ischemic stroke patients (n = 10) with mild arm weakness and healthy controls (n = 10) to validate the iPronator. In addition to conventional neurological examinations, the degree of average, maximum, and oscillation in drift and pronation were measured and compared using the iPronator. Follow-up tests using the iPronator were also conducted in the patient group one week later.

RESULTS

There was a strong correlation between the average degree of pronation and drift measured by the iPronator (r = 0.741, p<0.001). The degrees of average and maximum in pronation were greater in the patient group than in the control group [in average, 28.9°, interquartile range (IQR) 18.7-40.3 vs. 3.8° (IQR 0.3-7.5), p<0.001], in maximum, 33.0° (IQR 24.0-52.1) vs. 6.2° (IQR 1.4-9.4), p<0.001]. The degree of oscillation in pronation was not different between the groups (p = 0.166). In drift, the degrees of average, maximum, and oscillation were greater in the patient group. In stroke patients, a follow-up study at one week revealed improvements in the degrees of pronation and drift compared with baseline parameters.

CONCLUSIONS

The iPronator can reliably detect mild arm weakness of stroke patients and was also useful in detecting functional recovery for one week in patients with acute stroke.

The uses of the smartphone for doctors: an empirical study from samsung medical center

Objectives

In healthcare, mobile computing made possible by smartphones is becoming an important tool among healthcare professionals. However, currently there is very little research into the effectiveness of such applications of technology. This study aims to present a framework for a smartphone application to give doctors mobile access to patient information, then review the consequences of its use and discuss its future direction.

Methods

Since 2003 when Samsung Medical Center introduced its first mobile application, a need to develop a new application targeting the latest smartphone technology was identified. To that end, an application named Dr. SMART S was officially launched on December 22nd, 2010.

Results

We analyzed the usage data of the application for a month until April 25th, 2011. On average, 170 doctors (13% of the entire body of doctors) logged on 2.4 times per day and that number keeps growing. The number was uniformly distributed across all working hours, with exceptions of heavy accesses around 6-8 AM and 4-6 PM when doctors do their regular rounds to see the patients. The most commonly accessed content was inpatient information, this constituted 78.6% of all accesses, within this 50% was to accesses lab results.

Conclusions

Looking at the usage data, we can see the use of Dr. SMART S by doctors is growing in sync with the popularity of smartphones. Since u-Health seem an inevitable future trend, a more rigorous study needs to be conducted on how such mobile applications as Dr. SMART S affect the quality of care and patient safety to derive directions for further improvements.