The commercial market for technologies to monitor and improve personal health and sports performance is ever expanding. A wide range of smart watches, bands, garments, and patches with embedded sensors, small portable devices and mobile applications now exist to record and provide users with feedback on many different physical performance variables. These variables include cardiorespiratory function, movement patterns, sweat analysis, tissue oxygenation, sleep, emotional state, and changes in cognitive function following concussion. In this review, we have summarized the features and evaluated the characteristics of a cross-section of technologies for health and sports performance according to what the technology is claimed to do, whether it has been validated and is reliable, and if it is suitable for general consumer use. Consumers who are choosing new technology should consider whether it (1) produces desirable (or non-desirable) outcomes, (2) has been developed based on real-world need, and (3) has been tested and proven effective in applied studies in different settings. Among the technologies included in this review, more than half have not been validated through independent research. Only 5% of the technologies have been formally validated. Around 10% of technologies have been developed for and used in research. The value of such technologies for consumer use is debatable, however, because they may require extra time to set up and interpret the data they produce. Looking to the future, the rapidly expanding market of health and sports performance technology has much to offer consumers. To create a competitive advantage, companies producing health and performance technologies should consult with consumers to identify real-world need, and invest in research to prove the effectiveness of their products. To get the best value, consumers should carefully select such products, not only based on their personal needs, but also according to the strength of supporting evidence and effectiveness of the products.
Sure, Rachel Dawson knew that GPS could tell athletes how far they had gone.
But she had no idea that it could also show how fast an athlete accelerates or decelerates, or how far or high they jump.
“I knew nothing,” the senior kinesiology major said.
That’s just a sampling of what she’s learned in “Sports Science & Athlete Monitoring,” a new three-week course taught by David Bell, an assistant professor of kinesiology at the UW–Madison and the director of the Wisconsin Injury in Sport Laboratory.
Designers can extract vital signs and raw data for their wearable designs with the MAX-HEALTH-BAND, as well as monitor clinical-grade electrocardiograph (ECG) and heart rate with the MAX-ECG-MONITOR from Maxim Integrated Products, Inc. (NASDAQ: MXIM). The MAX-HEALTH-BAND evaluation and development platform, a wrist-worn heart-rate and activity monitor, features the MAX86140 optimized optical pulse-oximeter/heart-rate sensor, the MAX20303 wearable power-management solution, and Maxim’s motion-compensated algorithms. The MAX-ECG-MONITOR evaluation and development platform features the MAX30003 ultra-low-power, clinical-grade analog front end (AFE), which monitors ECG and heart-rate signals. It comes in two form factors—a wet electrode patch for clinical applications and a chest strap for fitness applications.
Health and wellness has been one of the biggest categories for development in the tech industry, with huge range of wearable devices and connected equipment being built to track how we are moving and helping us stay fit. Now, a startup whose app offers users a selection of audio-based, personal-trainer-led workouts that also monitors users as they progress through them, has closed a major round of funding that underscores how software — and specifically apps — are also capitalising on that trend. Aaptiv, which makes what its founder and CEO Ethan Agarwal described to me as “the Netflix of fitness” — providing streams of music-based fitness training on demand — has raised $22 million in funding.
(It’s not the only big wellness software app that’s announcing funding today: Calm, the meditation app, announced a $27 million round of funding today, too.)
The Series C round brings the total raised by New York-based Aaptiv to $52 million, and while the company is not disclosing its valuation, sources close to it tell me that it is now over $200 million. The company has picked up 200,000 paying members in the last two years, and says that its audio classes have been streaming more than 14 million times.
For all you users who enjoy using your Garmin watches not just as sport/activity trackers, which features do you find the most useful. Which ConnectIQ apps (3rd party or official) have you found to be either fun or useful? I, for example, have found the SMS app, though limited, to be great for sending short replies without getting my phone out!
… researchers in Japan have taken an entirely new approach. They use something called a local oscillator to steer the beams. These local oscillators, in combination with a mixer, can change the frequency of the signal. The scientists, from the Tokyo Institute of Technology, believe that this new approach will lead to large-scale phased-array transceivers capable of increasing communication distance, data rate, and network capacity.
These days, many retailers and manufacturers are tracking their products using RFID, or radio-frequency identification tags. Often, these tags come in the form of paper-based labels outfitted with a simple antenna and memory chip. When slapped on a milk carton or jacket collar, RFID tags act as smart signatures, transmitting information to a radio-frequency reader about the identity, state, or location of a given product.
In addition to keeping tabs on products throughout a supply chain, RFID tags are used to trace everything from casino chips and cattle to amusement park visitors and marathon runners.
The Auto-ID Lab at MIT has long been at the forefront of developing RFID technology. Now engineers in this group are flipping the technology toward a new function: sensing. They have developed a new ultra-high-frequency, or UHF, RFID tag-sensor configuration that senses spikes in glucose and wirelessly transmits this information. In the future, the team plans to tailor the tag to sense chemicals and gases in the environment, such as carbon monoxide.
Horizon: the EU Research & Innovation magazine from
… Dr Ana Neves, a researcher from the University of Exeter in the UK who specialises in wearable electronics, thinks bulky design is partly to blame.
‘The user needs to feel comfortable,’ she said. ‘Most smart textiles still rely on integrating conventional electronics onto fabrics, attaching them to the surface and removing them when the textile needs to be washed.’
As part of the E-TEX project, Dr Neves and her colleagues are using a different strategy, by building devices directly into the fibres of textiles using flexible and lightweight components. A t-shirt, for example, could be designed to monitor the wearer’s heartbeat without the need for embedded electronics.
… “You actually find a lot of the Masters are getting better in their 40s, because they sometimes have more time to themselves than in their 20s and 30s,” Henwood says.
“A guy I know, he’s 42 years old and he took three minutes off his half marathon time and ran 70 minutes. I met a girl in the gym who was 47 and I helped her run under three hours for the Boston Marathon—which was a ten-minute PR. A lot of these New Yorkers in their 40s have never been on a decent training plan before and usually have no problem getting a PR. I have athletes on my team who are improving in their 60s.”
… Garmin: You had to self-support for this race. How do you begin planning for a race of this nature and how did Garmin assist you with on-course logistics?
RR: This kind of a race is right up my alley when it comes to navigating, planning logistics and being self-supported. I have years of experience doing this kind of stuff in the backcountry, but this was the longest non-stop gravel ride I’ve ever done. The planning included reviewing the route file that we were sent before the race. At home, I was looking at key elements like mileage, elevation gain, distances between towns and resupply points and the type of terrain. By reviewing the course file and details at home, I was able to strategize and make decisions on what equipment to bring, how much water and food capacity I needed, how many hours my electronics like my 1030 and lights needed to run and how I would carry all of these things. After the planning at home and the packing and repacking of the bike configuration, then it’s time to do the ride. I used my beloved Edge 1030 for this event because it has great navigation features along with all of the cycling features such as power and heart rate. While I don’t use those stats to dictate my speed while riding a distance this long, it is really fun to capture that data for such a big ride.
Knee functional disorders are one of the most common lower extremity non-traumatic injuries reported by cyclists. Incorrect bicycle configuration may predispose cyclist to injury but the evidence of an effect of saddle setback on knee pain remains inconclusive. The aim of this study was to determine the effect of saddle setback on knee joint forces during pedalling using a musculoskeletal modelling approach. Ten cyclists were assessed under three saddle setback conditions (range of changes in saddle position ~6 cm) while pedalling at a steady power output of 200 W and cadence of 90 rpm. A cycling musculoskeletal model was developed and knee joint forces were estimated using an inverse dynamics method associated with a static optimisation procedure. Our results indicate that moving the saddle forwards was not associated with an increase of patellofemoral joint forces. On the contrary, the tibiofemoral mean and peak compression force were 14 and 15% higher in the Backward than in the Forward condition, respectively. The peak compression force was related to neither pedal force nor quadriceps muscle force but coincided with the eccentric contraction of knee flexor muscles. These findings should benefit bike fitting practitioners and coaches in the design of specific training/rehabilitation protocols.
… “Our goal is to mimic what really happens in the real world so we take each helmet and subject it to a variety of impacts including six different locations and two velocities that are really representing how a cyclist would really crash,” said Megan Bland, a graduate research assistant at Virginia Tech.
The ratings are based on a five-star system with five stars being the highest rating.
The first 30 helmets tested showed a range of performance with four earning five stars, two earning two stars, and the rest earning three or four stars.
Cost wasn’t a good predictor of helmet performance as both a $200 Bontrager Ballista MIPS helmet and a $75 Specialized Charmonix MIPS helmet earned a five-star rating.
The purpose of this study was to classify runners in sex-specific groups as either competitive or recreational based on center of mass (CoM) accelerations. Forty-one runners participated in the study (25 male and 16 female), and were labeled as competitive or recreational based on age, sex, and race performance. Three-dimensional acceleration data were collected during a 5-minute treadmill run, and 24 features were extracted. Support vector machine classification models were used to examine the utility of the features in discriminating between competitive and recreational runners within each sex-specific subgroup. Competitive and recreational runners could be classified with 82.63 % and 80.4 % in the male and female models, respectively. Dominant features in both models were related to regularity and variability, with competitive runners exhibiting more consistent running gait patterns, but the specific features were slightly different in each sex-specific model. Therefore, it is important to separate runners into sex-specific competitive and recreational subgroups for future running biomechanical studies. In conclusion, we have demonstrated the ability to analyze running biomechanics in competitive and recreational runners using only CoM acceleration patterns. A runner, clinician, or coach may use this information to monitor how running patterns change as a result of training.
Running power meters rely on a combination of accelerometer motion sensors—vector detectors—and complex software algorithms to convert the measured data to assign a power value. It’s a lot different than how most bike power meters work, which gauge real measured energy output from the force applied to the pedal, calculating an objective wattage figure based on your power, multiplied by the stroke rate.
Comparatively, running power meters aren’t as accurate of a measure. Consider them more an indicator of relative effort. As Martyn Shorten, founder of the Portland-based sports product biomechanics lab, BioMechanica, puts it: “These devices are not measuring ‘power’ but trying to measure something that correlates with ‘effort.’”
And that measurement itself can be a useful training tool. Just like in cycling, a runner training with “power” can use their relative numbers to understand pacing on hills and pacing during races, regardless of conditions or heart rate (which can be affected by outside influences like sleep, temperature, and even caffeine intake). The goal: run at the lowest wattage for the fastest pace—a sign of efficiency.
Just as we’re getting used to the idea of heart rate zone training in our running, there’s a new metric in town: Heart Rate Variability (HRV). But before you start stamping your running shoes in protest at yet another complex new number to grapple with, hear us out, because HRV doesn’t have to be complicated.
With the right tools HRV can be simple to monitor and with a little knowledge it’s even easier to put into practice to help you train better, whether your goal is a sub-three-hour marathon or your first 5km.
Here’s our guide to using HRV for marginal running gains without the need for a sports science degree.