Step-count monitors (pedometers, body-worn trackers and smartphone applications) can increase walking, helping to tackle physical inactivity. We aimed to assess the effect of step-count monitors on ...physical activity (PA) in randomised controlled trials (RCTs) amongst community-dwelling adults; including longer-term effects, differences between step-count monitors, and between intervention components.
Systematic literature searches in seven databases identified RCTs in healthy adults, or those at risk of disease, published between January 2000-April 2020. Two reviewers independently selected studies, extracted data and assessed risk of bias. Outcome was mean differences (MD) with 95% confidence intervals (CI) in steps at follow-up between treatment and control groups. Our preferred outcome measure was from studies with follow-up steps adjusted for baseline steps (change studies); but we also included studies reporting follow-up differences only (end-point studies). Multivariate-meta-analysis used random-effect estimates at different time-points for change studies only. Meta-regression compared effects of different step-count monitors and intervention components amongst all studies at ≤4 months.
Of 12,491 records identified, 70 RCTs (at generally low risk of bias) were included, with 57 trials (16,355 participants) included in meta-analyses: 32 provided change from baseline data; 25 provided end-point only. Multivariate meta-analysis of the 32 change studies demonstrated step-counts favoured intervention groups: MD of 1126 steps/day 95%CI 787, 1466 at ≤4 months, 1050 steps/day 602, 1498 at 6 months, 464 steps/day 301, 626 at 1 year, 121 steps/day - 64, 306 at 2 years and 434 steps/day 191, 676 at 3-4 years. Meta-regression of the 57 trials at ≤4 months demonstrated in mutually-adjusted analyses that: end-point were similar to change studies (+ 257 steps/day - 417, 931); body-worn trackers/smartphone applications were less effective than pedometers (- 834 steps/day - 1542, - 126); and interventions providing additional counselling/incentives were not better than those without (- 812 steps/day - 1503, - 122).
Step-count monitoring leads to short and long-term step-count increases, with no evidence that either body-worn trackers/smartphone applications, or additional counselling/incentives offer further benefit over simpler pedometer-based interventions. Simple step-count monitoring interventions should be prioritised to address the public health physical inactivity challenge.
PROSPERO number CRD42017075810 .
The smartphone penetrates many facets of everyday life, including travel. As such, this article argues that since travel can be considered a special stage of technology use, understanding how the ...smartphone shapes the tourist experience cannot be separated from the way it is used in one’s everyday life. On the basis of a study of American travelers, this study uses adaptive structuration theory as a lens to identify a number of spillover effects from smartphone use in everyday life into travel. The results of this study offer several important implications for both research and practice as well as future directions for the study of mobile technology in tourism.
Patulin (PAT) is a mycotoxin that adversely affects the health of humans and animals. PAT can be particularly found in products such as apples and apple juice and can cause many health problems if ...consumed. Therefore, accurate and sensitive determination of PAT is very important for food quality and human and animal health. A voltammetric aptasensor was introduced in this study for PAT determination while measuring the changes at redox probe signal. The limit of detection (LOD) was found to be 0.18 pg/mL in the range of 1-10
pg/mL of PAT in buffer medium under optimum experimental conditions. The selectivity of the PAT aptasensor against ochratoxin A, fumonisin B1 and deoxynivalenol mycotoxins was examined and it was found that the aptasensor was very selective to PAT. PAT determination was performed in an apple juice medium for the first time by using a smartphone-integrated portable device, and accordingly, an LOD of 0.47 pg/mL was achieved in diluted apple juice medium. A recovery range of 91.24-93.47% was obtained for PAT detection.
Abstract
Introduction
The ever-evolving market for sleep technologies far outpaces the ability of providers to understand and counsel patients about developments in this area. Although significant ...literature has validated the performance of sleep tracking technologies, there is little evidence regarding sleep enhancement technologies. Our study systematically surveys currently available commercial sleep enhancement smartphone applications to empower both providers and patients alike.
Methods
We systematically searched the App Store (Apple) and Google Play Store (Android) in the US on 26 MAY 2020 using the keyword “sleep.” This survey is inclusive of all smartphone applications found.
Results
We identified 342 apps: 70.2% were found on Android (N=240) and 29.8% on Apple (N=102). Ninety-five percent of apps offer a free version. The majority of sleep apps are intended for use during wake (65.8% exclusively during wake; 28.7% during both wake and sleep), with only 5.6% intended to be used during sleep alone. Most apps purport to enhance rather than measure sleep (78.7% versus 1.8%). The vast majority of apps claim to enhance sleep via reductions in sleep latency (65.8%). Reduced sleep latency is primarily achieved using a combination of non-verbal auditory stimuli such as nature sounds (84.4%), artificial stimuli (64.5%), and instrumental music (77.1%).
Conclusion
Interestingly, most sleep apps are designed to be used while awake, prior to sleep, and focus on the enhancement of sleep, rather than measurement, by targeting sleep latency. Given the multitude of available sleep enhancement apps, many of which are free to try, these should be considered a reasonable strategy for providers and consumers to consider for empowering patients to improve sleep!
Support (if any)
Department of Defense Military Operational Medicine Research Program (MOMRP)
Abstract
Introduction
The proliferation of wearable and smartphone technologies has enabled continuous monitoring of sleep using data from different channels (physiological wearables, behavioural ...phone usage and ecological momentary assessment EMA self-report). As these modalities use different methods to assess sleep, information gaps suggested by discrepancies between estimates may be filled in through cross-referencing among the modalities to produce a more accurate sleep measurement. Moreover, the pattern of discrepancies could inform about specific sleep and peri-sleep behaviors (e.g. phone use before bedtime).
Methods
198 staff and students from the National University of Singapore (61 male, mean age 26.15±5.83 years) were recruited for an 8-week study. Sleep timings were assessed daily from three modalities: a wearable sleep and activity tracker (Oura ring), estimations from smartphone touchscreen interactions (tappigraphy) and smartphone derived EMA self-reports. Sleep estimates from the different modalities were compared for agreement (bivariate correlation) and discrepancies (t-test). Additionally, clustering analysis of high-discrepancy nights (>1h discrepancy between modalities) was performed to identify pattens of sleep behaviors that could lead to specific discrepancies.
Results
Adherence throughout the 8-week monitoring period (total 11,088 nights) was = high for the Oura ring; 9826 nights 80%), Tappigraphy; 9740 nights 88%)), and EMA; 9166 nights 83%). Sleep estimates across the three modalities showed high agreement (r=0.79-.91), with some discrepancies: Relative to self-report data, Oura wake time tended to be a later (Mean diff=9mins, t=18.58, p<.001), while tappigraphy estimates of bedtime tended to be early (Mean diff=15mins, t=26.48, p<.001). On 23% of nights (1755 nights), however, large discrepancies were detected (>1h). K-means clustering identified three distinct patterns of discrepancy, which were dominantly expressed in different individuals. Group comparison revealed that these individuals differed in demographic variables (age, student/work status), sleep variables (sleep timing, duration, subjective sleepiness), and phone usage characteristics (overall and pre-bedtime phone usage).
Conclusion
These data show that the combined use of three streams of data concerning sleep is complementary. Moreover, discrepancy patterns provide specific insights into sleep and peri-sleep behaviors facilitating digital phenotyping.
Support (if any)
This research was supported by the National Medical Research Council Singapore (NMRC/STaR/015/2013 and NMRC/STaR19may-0001).
Measuring joint range of motion is an important skill for many allied health professionals. While the Universal Goniometer is the most commonly utilised clinical tool for measuring joint range of ...motion, the evolution of smartphone technology and applications (apps) provides the clinician with more measurement options. However, the reliability and validity of these smartphones and apps is still somewhat uncertain. The aim of this study was to systematically review the literature regarding the intra- and inter-rater reliability and validity of smartphones and apps to measure joint range of motion. Eligible studies were published in English peer-reviewed journals with full text available, involving the assessment of reliability and/or validity of a non-videographic smartphone app to measure joint range of motion in participants >18 years old. An electronic search using PubMed, Medline via Ovid, EMBASE, CINAHL, and SPORTSDiscus was performed. The risk of bias was assessed using a standardised appraisal tool. Twenty-three of the eligible 25 studies exceeded the minimum 60% score to be classified as a low risk of bias, although 3 of the 13 criteria were not achieved in >50% of the studies. Most of the studies demonstrated adequate intra-rater or inter-rater reliability and/or validity for >50% of the range of motion tests across all joints assessed. However, this level of evidence appeared weaker for absolute (e.g. mean difference ± limit of agreement, minimal detectable change) than relative (e.g. intraclass correlation, correlation) measures; and for spinal rotation than spinal extension, flexion and lateral flexion. Our results provide clinicians with sufficient evidence to support the use of smartphones and apps in place of goniometers to measure joint motion. Future research should address some methodological limitations of the literature, especially including the inclusion of absolute and not just relative reliability and validity statistics.
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•An electrochemical sensor for SARS-CoV-2 was constructed based on a smartphone.•The signal amplification of the sensor was based on supersandwich-type recognition strategy.•The ...detectable ratios of the sensor were higher than those obtained by RT-qPCR.•The sensor has the lowest limit of detection among the published approaches to date.•The sensor showed high specificity and selectivity.
The recent pandemic outbreak of COVID-19 caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a threat to public health globally. Thus, developing a rapid, accurate, and easy-to-implement diagnostic system for SARS-CoV-2 is crucial for controlling infection sources and monitoring illness progression. Here, we reported an ultrasensitive electrochemical detection technology using calixarene functionalized graphene oxide for targeting RNA of SARS-CoV-2. Based on a supersandwich-type recognition strategy, the technology was confirmed to practicably detect the RNA of SARS-CoV-2 without nucleic acid amplification and reverse-transcription by using a portable electrochemical smartphone. The biosensor showed high specificity and selectivity during in silico analysis and actual testing. A total of 88 RNA extracts from 25 SARS-CoV-2-confirmed patients and eight recovery patients were detected using the biosensor. The detectable ratios (85.5 % and 46.2 %) were higher than those obtained using RT-qPCR (56.5 % and 7.7 %). The limit of detection (LOD) of the clinical specimen was 200 copies/mL, which is the lowest LOD among the published RNA measurement of SARS-CoV-2 to date. Additionally, only two copies (10 μL) of SARS-CoV-2 were required for per assay. Therefore, we developed an ultrasensitive, accurate, and convenient assay for SARS-CoV-2 detection, providing a potential method for point-of-care testing.
The prevalence of smartphones today, paired with the increasing precision and therapeutic potential of digital capabilities, offers unprecedented opportunity in the field of digital medicine. ...Smartphones offer novel accessibility, unique insights into physical and cognitive behavior, and diverse resources designed to aid health. Many of these digital resources, however, are developed and shared at a faster rate than they can be assessed for efficacy, safety, and security-presenting patients and clinicians with the challenge of distinguishing helpful tools from harmful ones.
Leading regulators, such as the FDA in the USA and the NHS in the UK, are working to evaluate the influx of mobile health applications entering the market. Efforts to regulate, however, are challenged by the need for more transparency. They require real-world data on the actual use, effects, benefits, and harms of these digital health tools. Given rapid product cycles and frequent updates, even the most thorough evaluation is only as accurate as the data it is based on.
In this debate piece, we propose a complementary approach to ongoing efforts via a dynamic self-certification checklist. We outline how simple self-certification, validated or challenged by app users, would enhance transparency, engage diverse stakeholders in meaningful education and learning, and incentivize the design of safe and secure medical apps.
As smartphone becomes increasingly prevalent and affordable, more youths today can own a smartphone device and download applications in various application stores. Smartphone applications have been ...proven to be useful for youths in various aspects. However, there has been a paucity of data looking into the preferences of Vietnamese youths and adolescents with regards to health-related applications and their receptiveness towards smartphone apps. Therefore, this study aimed to determine the receptiveness and preferences of health-related smartphone applications (mHealth apps) among online Vietnamese youths and adolescents.
An online cross-sectional study was conducted between the periods of August till October 2015 in Vietnam. Respondent-driven sampling technique (RDS) was utilized to recruit participants. Participants were asked questions about their history of downloading and using health-related smartphone applications and their receptiveness when using these applications. Moreover, socio-demographic characteristics and health status were also self-reported. Multivariate logistic regression was employed to determine associated factors.
Among 1028 participants, 57.4% owned a smartphone and only 14.1% of smartphone users have used a health-related smartphone application, and most of these individuals downloaded the applications for disease prevention (66.3%). 66.4% of the participants who owned these applications reported that health applications were useful and 92.8% reported being satisfied with the functionalities of the applications which they owned. Among smartphone users, people who were employed (OR = 15.46; 95%CI = 4.93-48.47) were more likely to download mHealth apps. Meanwhile, youths with higher EQ-5D index had a lower likelihood of downloading healthcare-related smartphone applications (OR = 0.17; 95%CI = 0.04-0.81).
This study highlighted a low rate of mHealth apps utilization among online Vietnamese youths and adolescents but a high acceptance of individuals who already used these apps. Developing mHealth apps or interventions towards the disease prevention and quality of life improvement could be feasible to proliferate the benefits of such applications in youths and adolescents in Vietnam. Further research should be conducted to optimize the contents and interfaces of mHealth apps that meet the needs of these populations.
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