Predictability of VO\u3csub\u3e2max\u3c/sub\u3e using a commercially available GPS sports watch

Using accurate submaximal methodologies to estimate VO2max is a convenient alternative to maximal exercise testing. Submaximal testing is practical because it provides a cheaper, more time-efficient method to determine VO2max and allows a wider range of individuals to be tested. Purpose: The purpose of this study was to examine the predictability of VO2max using a GPS sports watch. Methods: Thirty participants, 16 males and 14 females between the ages of 18 and 55, volunteered for this study. A total of three separate VO2max values were recorded during the study: (a) directly measured VO2max, (b) a predicted VO2max value based on a 15-minute outdoor run, and (c) an adjusted predicted VO2max value based on three subsequent outdoor runs of at least 30 minutes in duration. Participants came to the Running Science Laboratory at Eastern Michigan University (EMU) on two separate occasions. On day one, participants completed a treadmill-based graded exercise test (GXT) to determine VO2max. Participants completed the test using a self-selected pace (mph) that was determined during a 3-minute warm-up period. The self-selected pace remained constant throughout the test while the grade increased at a rate of 2% every 2 minutes. On day two, participants arrived at EMU and completed a 15-minute submaximal outdoor run. Participants were fitted with a GPS sports watch, which was used to predict VO2max based on subject characteristics (gender, age, height [in], weight [lbs.]), as well as total distance of the run, pace, time (15 minutes), and heart rate (HR) during exercise. Participants were then required to take the watch home and record three additional runs of at least 30 minutes to produce an adjusted predicted VO2max value. A two-way (2 fitness groups x 3 VO2max time points) repeated measures ANOVA was conducted to determine if there was a significant difference between directly measured VO2max, predicted VO2max, and adjusted predicted VO2max. Participants were placed into two fitness groups determined by directly measured VO2max (VO2max of greater than [high] or less than [low] 50 ml/kg/min). A one-way repeated measures ANOVA was conducted to determine if a significant difference in recorded VO2max values was observed within groups. Statistical significance was determined using a p-value of .05. Results: Two participants (two males) were excluded from the analysis due to failing to return for visit two. The remaining 28 participants were 24.71 ± 5.69 years old, had a height of 168.94 ± 6.94 cm, and weighed 67.22 ± 14.85 kg. A statistically significant difference was observed between directly measured (55.09 ± 9.73 ml/kg/min) and predicted VO2max (51.75 ± 5.16 ml/kg/min);(p-value \u3c .05), directly measured and adjusted predicted VO2max (50.68 ± 5.98 ml/kg/min);(p-value \u3c .001), and predicted and adjusted predicted VO2max (p-value \u3c .05). A significant difference was observed in the high VO2max group between directly measured and predicted VO2max and directly measured and adjusted predicted VO2max (p-value \u3c .001). No significant difference was observed between predicted and adjusted predicted VO2max in the high VO2max group (p-value \u3e .05). No significant difference was observed between values in the low VO2max group (p-value \u3e .05). Conclusion: Major limitations of this study included participants performing all activities at a self-selected pace and measuring HR using the radial pulse with an optical sensor. A self-selected pace could have led to inaccuracies in VO2max prediction as participants may not have performed to their full potential. Future research could enforce stricter pace and distance requirements for additional activity recording to test both anaerobic and aerobic energy systems. Additionally, measuring HR using an optical sensor within the watch at the radial pulse has been shown to underestimate average HR values when compared to HR measurement using a chest strap. While the purpose of this study was to test the predictability of only the GPS sports watch, a lower overall average HR for a given activity could have produced overestimates of VO2max

Wearable technology: role in respiratory health and disease

In the future, diagnostic devices will be able to monitor a patient's physiological or biochemical parameters continuously, under natural physiological conditions and in any environment through wearable biomedical sensors. Together with apps that capture and interpret data, and integrated enterprise and cloud data repositories, the networks of wearable devices and body area networks will constitute the healthcare's Internet of Things. In this review, four main areas of interest for respiratory healthcare are described: pulse oximetry, pulmonary ventilation, activity tracking and air quality assessment. Although several issues still need to be solved, smart wearable technologies will provide unique opportunities for the future or personalised respiratory medicine

Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study

BACKGROUND: Atrial fibrillation (AF) is often paroxysmal and minimally symptomatic, hindering its diagnosis. Smartwatches may enhance AF care by facilitating long-term, noninvasive monitoring. OBJECTIVE: This study aimed to examine the accuracy and usability of arrhythmia discrimination using a smartwatch. METHODS: A total of 40 adults presenting to a cardiology clinic wore a smartwatch and Holter monitor and performed scripted movements to simulate activities of daily living (ADLs). Participants\u27 clinical and sociodemographic characteristics were abstracted from medical records. Participants completed a questionnaire assessing different domains of the device\u27s usability. Pulse recordings were analyzed blindly using a real-time realizable algorithm and compared with gold-standard Holter monitoring. RESULTS: The average age of participants was 71 (SD 8) years; most participants had AF risk factors and 23% (9/39) were in AF. About half of the participants owned smartphones, but none owned smartwatches. Participants wore the smartwatch for 42 (SD 14) min while generating motion noise to simulate ADLs. The algorithm determined 53 of the 314 30-second noise-free pulse segments as consistent with AF. Compared with the gold standard, the algorithm demonstrated excellent sensitivity (98.2%), specificity (98.1%), and accuracy (98.1%) for identifying irregular pulse. Two-thirds of participants considered the smartwatch highly usable. Younger age and prior cardioversion were associated with greater overall comfort and comfort with data privacy with using a smartwatch for rhythm monitoring, respectively. CONCLUSIONS: A real-time realizable algorithm analyzing smartwatch pulse recordings demonstrated high accuracy for identifying pulse irregularities among older participants. Despite advanced age, lack of smartwatch familiarity, and high burden of comorbidities, participants found the smartwatch to be highly acceptable

Stress monitoring using wearable sensors:a pilot study and stress-predict dataset

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the “Stress-Predict Dataset”, created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201