Design of a Control System for an Autonomous Vehicle Based on Adaptive-PID

Design of a Control System for an Autonomous Vehicle Based on Adaptive-PID

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International Journal of Advanced Robotic Systems
Design of a Control System for an Autonomous Vehicle Based on Adaptive-PID
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Pan Zhao, Jiajia Chen, Yan Song, …
First Published January 1, 2012 Research Article
PDF download for Design of a Control System for an Autonomous Vehicle Based on Adaptive-PID Article Information
Article has an altmetric score of 11 Open Access Creative Commons Attribution 3.0 License
The autonomous vehicle is a mobile robot integrating multi-sensor navigation and positioning, intelligent decision making and control technology. This paper presents the control system architecture of the autonomous vehicle, called “Intelligent Pioneer”, and the path tracking and stability of motion to effectively navigate in unknown environments is discussed. In this approach, a two degree-of-freedom dynamic model is developed to formulate the path-tracking problem in state space format. For controlling the instantaneous path error, traditional controllers have difficulty in guaranteeing performance and stability over a wide range of parameter changes and disturbances. Therefore, a newly developed adaptive-PID controller will be used. By using this approach the flexibility of the vehicle control system will be increased and achieving great advantages. Throughout, we provide examples and results from Intelligent Pioneer and the autonomous vehicle using this approach competed in the 2010 and 2011 Future Challenge of China. Intelligent Pioneer finished all of the competition programmes and won first position in 2010 and third position in 2011.

Keywords Autonomous vehicle, path-tracking, vehicle control, adaptive-PID control
1. Introduction
As a significant platform to show the level of artificial intelligence technology and the future of the vehicle industry, research into autonomous vehicles has become a focus of the field of robotics worldwide. An autonomous vehicle is an intelligent mobile robot which covers a set of frontier research fields including environment perception, pattern recognition, navigation and positioning, intelligent decision and control and computer science. The purpose of the research is to realize autonomous driving by the vehicle instead of human drivers and to improve traffic safety and transport efficiency.

In China, a series of annual competitions called “Future Challenge of Intelligent Vehicles” has been organized by “Cognitive Computing of Visual and Auditory Information”, a Major Research Plan (MRP) of the National Nature Science Foundation of China since 2009. The third competition the 2011 Future Challenge of Intelligent Vehicles was held in Ordos city on Oct. 18, 2011 [1]. The competition took place on the urban roads in the Kangbashi district, a newly developed district with few residents. Eight teams from 10 universities attended the competition. However, only three autonomous vehicles finished the 10 km journey within the required time (50 minutes). Our intelligent vehicle named Intelligent Pioneer took 42 minutes to arrive at the finish line in second place and won the third position overall.

The path-tracking control of an autonomous vehicle is one of the most difficult automation challenges because of constraints on mobility, speed of motion, high-speed operation, complex interaction with the environment and typically a lack of prior information. The vehicle control can be separated into lateral and longitudinal controls. Here we focus on the lateral control to follow a given trajectory with a minimum of track error. In the previous studies, various theories and methods have been investigated. These include the PID control method [2][3], the predictive control method [4], the fuzzy control method [5][6], the model reference adaptive method [7][8], the neural-network control method [9][10], the SVR (support vector regression) method [11], the fractional-order control method [12], etc. Recently much attention has been attracted by the use of the PID control method. PID control has such advantages as a simple structure, good control effect and robust and easy implementation [13]. Unfortunately, this method does not deal with parameter optimization and automatically adapt to the environment caused by the complexity of vehicle dynamics, uncertainty of the external environments and the non-holonomic constraint of the vehicle[14]. To overcome these difficulties, an adaptive control algorithm has received attention and been studied.

Currently adaptive control has been a great success in the industrial field [15]. The adaptive control system has the ability of adaptation, it can undertake corrective control action because of change in environments, achieving optimal or suboptimal control effects. At present the control method of adaptive control includes model reference adaptive PID control [16], fuzzy adaptive PID control [17], adaptive PID control based on neural network [18][19], adaptive PID control based on genetic algorithm [20][21] and so on. But in solving the problem of trajectory tracking of unmanned vehicles, the reference model of adaptive PID control based on the model reference is hard to ascertain because the motion model of the vehicle is influenced greatly by environments. The design of fuzzy adaptive PID control requires much priori knowledge. The vehicle finds it hard to obtain comprehensive priori knowledge when the vehicle travels in unknown environments. Adaptive PID control based on a neural network generally use supervised learning to optimize the parameters, so it is also limited by some application conditions, for instance, the teacher signal of supervised learning is hard to obtain exactly. Although the design of adaptive PID control based on evolutionary algorithm requires less priori knowledge, it has the disadvantage of long computing times, i.e., not real time on line optimization. For the reason that we required the fluctuation of output in the control procedure caused by the disturbance to be as small as possible, namely the output of steady state equation is as small as possible, we designed the adaptive controller based on the generalized minimum variance method. The principle of this adaptive controller is simple and it is easy to realize. It can correct parameters constantly online and set the parameters of the controller constantly, so we can obtain real dynamic behaviour of the process gradually. This adaptive control method is increasingly used for industrial process control [22][23] and it is better than PID control.

In this paper we describe the intelligent control system designed for an autonomous vehicle in this challenge. We first introduce briefly the system architecture used by Intelligent Pioneer, then describe the control algorithm used to generate every move of the vehicle based on the vehicle’s lateral dynamics and adaptive PID control. Then we discuss the performance of the control strategy in the simulative environment and provide results from Intelligent Pioneer in the actual test. Finally, the discussion and future work are presented.

2. System Architecture
Figure 1. shows the hardware platform of Intelligent Pioneer. It is based on a 1.6L Tiggo3 SUV made by Chery Automobile Co., outfitted with two 4-core computers and a suite of sensors. It includes four subsystems: perception system, navigation system, decision system and control system. A distributed automatic control system of the vehicle is constructed by the CAN2.0 B bus that controls objects. The state detection units can connect to each other and the control system computer can receive the information of the vehicle’s state directly using the vehicle’s body – CAN. Through the CAN bus the control commands are sent to the control mechanisms, which are used to control the vehicle’s steering gear, brakes, accelerator, lights and horn respectively. The steering gear is a transformed original steering column shown in Figure 2. It realizes the accurate control of steering through a large-power servo motor.

Figure 1. Intelligent Pioneer, our entry in the Future Challenge 2011.

Figure 2. The steering gear

Figure 3. describes the model of the driver-vehicle-road [24] closed loop system, where the vehicle is the controlled object, the road and the environment are constraints of its motions, and the driver is in charge of environment cognition, planning decisions and vehicle operation and control [25]. The purpose of this paper is to design a controller, which will realize the vehicle’s autonomous driving instead of a human driver.

Figure 3. Model of the driver-vehicle-road closed loop system.

3. Mathematic Model Analysis of the Vehicle Control System
The external forces and torques acting on the vehicle are two main types: tyre contact forces and aerodynamic forces [26]. But the vehicle motion dealt with in this paper is mainly generated by the tyre forces produced by the vehicle motion itself. Three forces act upon the tyre, namely longitudinal force, lateral force and vertical force. The effect of longitudinal force will cause vehicle traction and braking. Driver controls the magnitude of the vehicle’s driving force by the acceleration pedal and shift gear, and controls the magnitude of braking force by the braking system. The effect of lateral force is to make the vehicle turn. The driver makes the tyres generate a steering angle using the steering system to control the lateral force of the tyres. The effect of vertical force is good adhesion of the vehicle to the road. For a general vehicle travelling on a city road, the effect of aerodynamics is little, therefore, we can ignore aerodynamic force in this problem of designing the vehicle’s controller as mentioned below. Several different models have been used to simulate the dynamics of the vehicle. One common approach is to treat a four-wheeled vehicle as a two-wheeled system, also called the “bicycle model”, which makes the analysis of vehicle motion simpler [27]. Under the following assumption, we can build a two degree-of-freedom dynamic model for describing the motion of the vehicle.

Supposing that the vehicle travels on a flat and level road, and that there is no input of vertical angle caused by road unevenness, we can ignore the vertical force and its coupling effects related with vehicle dynamics.

The structure of the vehicle is rigid including the suspension system.

Putting the input on the tyre directly ignoring the steering system; or supposing the steering system is rigid, which puts the input imposed on the turning tyres through the steering wheel with a fixed transmission ratio.

Ignoring aerodynamic force.

The vehicle is disturbed merely by the small perturbation in the equilibrium point, this means the input angle of the front wheel is small enough to ensure the linearity of equations of the vehicle motion.

As the left and right tyre side-slip angles are equal, the steer angle is small and there is negligible roll motion. This is suitable for the left and right tyres of the front and rear wheels to be concentrated at the intersecting point of the vehicle x-axis with the front and rear axles as shown in Figure 4. In this model, we set up a vehicle-centred coordinate system, O’-xyz. The rigid body vehicle has a velocity component of u in the longitudinal, x direction, and v in the lateral, y direction. The vehicle also has an angular velocity of r around the centre of gravity. The net force components in x and y direction are ΣFx and ΣFx, and the external torque around z axis is ΣMz. The lateral motion of the vehicle is described below:

Figure 4. Equivalent bicycle model.

−vr)=∑Fx (1)
+ur)=∑Fy (2)
=∑Mz (3)
here, m is the vehicle inertia mass. I is vehicle yaw moment inertia.

Usually the velocity component of u in the longitudinal is larger than the velocity component of u in the lateral. Therefore, we can represent u as:

u=uc+Δu (4)
Here, uc is the velocity in heading direction and Δu is a disturbance of the velocity. We consider the vehicle is driving at uniform velocity, therefore, ΣFx=0, and with this small disturbance, Δur and vr can be ignored as negligible. Then the lateral motion of the vehicle can be described using the two degree-of-freedom model by the decoupling equations as below:

+ucr)=∑Fy (5)
=∑Mz (6)
Usually, if there is no difference in the characteristics in the left and right tyres, the lateral forces of the left and right tyres will be equal. Taking the front and rear lateral forces as Fyf and Fyr, and the distances of the front and rear wheel axles from the centre of gravity are a and b, then the equations are expressed as:

+ucr)=Fyf+Fyr (7)
=aFyf-bFyr (8)
If the tyre side-slip stiffness C is known, Fy is proportional to the side-slip angle α. When a side-slip angle is positive, Fy acts in the negative y-direction and can be written as below:

Fy=-Cα (9)
Because the rear wheel is not the steering wheel, the side-slip angle of the rear wheel can be approximated as:


In response to an arbitrary front wheel steer angle, δf. the side-slip angle of the rear wheel can be approximated as:


−δf (11)
Substituting Eqns (3.8) and (3.9) into the previous Eqns (3.4) and (3.5), the equations now become the fundamental equations of motion describing the vehicle plane motion as below:



r (12)


r (13)
Rearranging equations (2.1) and (2.2) yields the state space system described as below:

=AX+BU (14)

A=[ a11 a12 a21 a22 ],B=[ b1 b2 ],X=[ v r ],U=δf(t)







Assume that the vehicle is driving at a constant velocity with 20m/s (uc=20m/s) and referring to the table of pertinent vehicle parameters given in Table 1, substituting these into Equation 14, we have:

Table 1. Pertinent vehicle parameters of Intelligent Pioneer.

Table 1. Pertinent vehicle parameters of Intelligent Pioneer.

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]=[ −3.785 −19.167 0.469 0.976 ][ v r ]+[ 34.409 27.686 ]δf(t) (15)
In the dynamic equation above, the two state variables are yaw rate and lateral velocity. In this paper a lateral controller is designed to reduce lateral error E of trajectory tracking. The lateral path error E is a function of the lateral velocity V, the heading θ, and the longitudinal velocity V. This relation is shown in Equations 16 and 17.

=v+ucθ (16)
=r (17)
The augmented state space model is shown in Equation 18.

]=[ −3.785 −19.167 0 0 0.469 0.976 0 0 0 1 0 0 1 0 20 0 ][ v r θ E ]+[ 34.409 27.686 0 0 ]δf(t) (18)
It is assumed that the lateral path error E is the measurable output of the system. Consequently, the equation can be described as:

Y=CX+DU (19)
Here the C matrix is described as:

C=[ 0 0 0 1 ],D=0
The lateral path error E is also the quantity which must be controlled. This system’s open-loop control transfer function of interest is thus the transfer function from steering angle input to path error output. This may be determined using Equation 20.

G(s)=C(sI−A)−1B+D   =
34.41 s2 - 10.52 s + 2419
s4 + 2.809 s3 + 5.295 s2

Note that the relative degree is 2, the numerator is Hurwitz, the denominator has a double root at the origin and the sign of the high frequency gain is known (positive). Now that the structure of the plant is known, the next section describes the design of a model reference adaptive controller for controlling the lateral path error.

4. Controller Design Based on Adaptive PID
In the design of the controller, the study is based on the performance index of these:

Settling time less than 2s, within 1% of final value;

Overshot of step responsive less than 10%;

Steady-state error of step responsive is 0.

Equation 20 can be written as below:


34.41 s2 - 10.52 s + 2419
s2 + 2.809 s+ 5.295 


C(s) (21)
Because 0 is the double pole of the system, the system will be unstable. We must use velocity feedback, see as Figure 5.

Figure 5. Closed-loop control strategy.

Figure 6 shows the structure of the control strategy used. It is simulated using MATLAB.

Figure 6. Performance without PID control.

The coefficient is chosen at K=10 to make the additional zero point nearby the origin, therefore, H(s) = (1 + 10s). The system’s open-loop transfer function becomes:


(1+10s)(34.41 s2 - 10.52 s + 2419)
s2(s2 + 2.809 s+ 5.295) 

In the optimal response of step response the overshot is 30.9% and the settling time is 1.63s. The performance of sinusoid response is shown in Figure 6. This shows that we have to further design a PID controller to improve the performance. The PID control law is given by:


+KDs (23)
We chose Kp=1, Kl=0.5, KD=10. The block diagram of closed-loop PID control is shown in Figure 8. The result of the system simulation using MATLAB with sinusoid response shows that the system has better performance, as shown in Figure 7.

Figure 7. Performance of PID control.

Figure 8. Block diagram of PID control.

The above analysis is just a preliminary approach under ideal dynamics models, but we have ignored many factors which make automatic lateral control of vehicles difficult. These include changing vehicle parameters with time, changing road conditions, as well as disturbances caused by GPS signal attenuation and other factors. Traditional controllers have difficulty in guaranteeing performance and stability over a wide range of parameter changes. In order to solve these problems and make the system automatically adapt to changes of the environment and parameters, we designed an adaptive PID controller. The requirement of the adaptive PID control is control of the system’s internal parameters independent of a precise mathematic model and that the parameters can adjust automatically online by real-time performance requirements. The adaptive PID control combines with the advantages of adaptive control and conventional PID control. Using an adaptive PID controller, the PID parameters can be changed with the state of control object to obtain better control performance.

In this paper we have established a single input single output control system. We describe the control object using the controlled auto regressive model as:

A(z)y(t)=z−dB(z)u(t)+ω(t) (24)
Where y(t), u(t) and ω(t) are system output, input and zero mean white noise sequence. d is pure delay of system. A(z) and B(z) are

A(z−1)=1+a1z−1+a2z−2+a3z−3+⋯+anaz−na B(z−1)=1+b1z−1+b2z−2+b3z−3+⋯+bnbz−nb
We propose a control strategy based on minimum variance [28]. This means that the output variance of the control system must be a minimum, so it can improve the smoothness and give a comfortable ride during the vehicle’s movement. The performance index function is defined as:

J=E{y2(t)}=min (25)
According to Diophantine equation [29], there are polynomials D(z) and E(z) that make J minimize. Thus, the control law is:


We ignore the disturbances of higher order term in the system and set E(z) as a two order polynomial, as below:

E(z)=e(k)+e(k−1)z−1+e(k−2)z−2 (27)
To ensure that the system has a zero steady-state error after any disturbance, we chose:

ψ=E(z)|z=1=e(k)+e(k−1)+e(k−2) (28)
According to generalized minimum variance law, an adaptive PID controller can be described as:

Δu0(k)=(1−z−1)u0(k)      =k(e(k)+e(k−1)+e(k−2))r(k)      −ke(k−1)y(k−1)−ke(k−2)y(k−2) (29)
And the control equation of ordinary PID controller is:



] (30)
Here Kp, Ti and Td are proportional gain, integration time constant and derivative time constant, and e(t) is the lateral path error which we need to control.

If we take Ts as the sampling period, the controller equation can be discretized as below:



j=1 e(j)+

(e(k)−e(k−1))] (31)
Then transform Equation 31 as the incremental form:




)y(k)    +Kp(1+2


y(k−2) (32)
Comparing the two Equations 29 and 32, the control parameters can be deduced as:

Kp=−k(e(k−1)+2e(k−2)) (33)


Where, Ts is the control period which in our control system is 0.1s. The parameters k and Kp play similar control roles. That means the system has a low damping response when k is bigger. Conversely, the system has a high damping response when k is smaller.

5. Experimental Results and Discussion
The performance of this control system is tested on Intelligent Pioneer with different kinds of trajectories. Figure 9 is a screenshot from Google Map, which shows an example of a trajectory that consists of a long. straight part, a long, curved part and a sharp curve. The test is driven at a top speed of approximately 20 m/s. The desired trajectory is reconstructed from the data points with 1m spacing recorded from GPS/INS data (latitude and longitude) and many related data are all recorded in real time at a 30-Hz update rate. The steering angles are determined by calculating the position errors of the output from the controller which has been experimentally validated. Through the actuation system, the desired steering angle is converted to an absolute encoder position and sent to the steering gear. The performance result of the path followed using the classical PID control algorithm is shown in Figure 10. This illustrates the actual path followed by the vehicle centre mass relative to its desired trajectory reconstructed from real time data. The performance of the control strategy in terms of track error can be seen in Figure 11. It can be seen that the vehicle largely follows the trajectory quite satisfactorily, but the path error becomes larger when turning. The result of path following using the adaptive PID control is shown in Figure 12 and Figure 13. It can be seen that the vehicle follows the trajectory quite satisfactorily both at straight and turning, and in both cases, the errors are kept well within 0.5m less than the PID control (1m) which achieves the desired purpose of this research. In addition, Figure 14 shows the variation of PID parameters. It can be seen that the percentage gain Kp increases with the curvature of the trajectory. Kp is smaller when going straight, ensuring high-speed stability. Kp is larger when turning, making the dynamic response faster to decrease the lateral error.

Figure 9. Screenshot from Google Map of the test site.

Figure 10. Trajectory with classical PID control.

Figure 11. Track error of the trajectory above.

Figure 12. Trajectory with adaptive PID control.

Figure 13. Track error of the trajectory above.

Figure 14. The variation of PID parameters.

Our control system has been developed over the course of more than a year and tested in a multitude of scenarios of autonomous operation, as well as at the Future Challenge of Intelligent Vehicles in China competitions in 2010 and 2011. Through these all components were corrected and the controller was incrementally improved upon. During the tests and competitions, the trajectory tracking system demonstrated high control accuracy and the speed control system kept excellent stability. Intelligent Pioneer could drive autonomously at a maximum speed of 60km/h. In the competition in 2011, the maximum speed was set to 35km/h and the speed control error was less than 2km/h, while the trajectory tracking error was less than 0.5m. Intelligent Pioneer is one of the three autonomous vehicles which finished the 10 km journey within the required time (50 minutes) and in this journey autonomous vehicles have to finish some necessary missions including: overtaking, merging into the traffic flow, passing through the barricades, recognition of traffic signs and traffic lights, U-turn and stopping at a stop line. The autonomous vehicle arrived at the finish line in 42 minutes and won the third position overall. The track records of the journey during the test are shown in Figure 15.

Figure 15. Track record of the journey.

6. Conclusions
In this paper a simplified bicycle model of an automobile was used to model the vehicle’s lateral dynamics. Based on the vehicle parameters for Intelligent Pioneer, a suitable reference model was developed. In addition, the adaptive PID control system described in this paper used for Intelligent Pioneer proved good adaptability and stability. The approach presented here features some innovations which were well grounded in past research on autonomous driving and mobile robotics. These innovations include: a controller that is capable of both following the rules of the road and intersections, and a controller based on adaptive PID which can change the parameters automatically when faced with changing environments. Although simplicity was central to our control system development, approximate control algorithms could work well in most cases, and it made the interfacing between processes simpler, thus it simplified the system’s software and hardware. Combining the vehicle model with an efficient on-road controller we could safely handle the high-speed driving involved which was also central to the vehicle’s control performance.

Above all, it delivers higher expansibility which became the important foundation for continually refining the design of the autonomous vehicle’s upper subsystems. Thus, we designed a practical approach to engineering an autonomous vehicle to effectively navigate in unknown environment. This has been implemented on an autonomous vehicle that completed all of the competition programmes and won first position in 2010 and third position in 2011 at the Future Challenge of Intelligent Vehicles competitions. The issues we encountered during the competitions are invaluable to advancing Intelligence Pioneer’s capabilities. The research and design of a stability control algorithm to improve the dynamic characteristic of the vehicle is the most important challenge. The overall requirements of the control systems are not near the vehicle or actuator limits, nor were their specific scoring parameters based on how precisely trajectory following. Because of this, no optimal control system’s design is performed and only rudimentary modelling of actual subsystems is performed. Our existing algorithm on motion planning is relatively rough. We have only considered the accuracy of s trajectory tracking so in the future, we will improve the ability of the vehicle body’s control system and we will mainly hope to enhance the stability and security of Intelligence Pioneer to make it more like a human driver.

7. Acknowledgments
We would like to acknowledge Huawei Liang, Bin Kong, Jing Yang, Hui Zhu, Bichun Li, Weizhong Zhang, Hu Wei and Jun Wang for their contributions to developing the autonomous vehicle. We would also like to thank Chery Automobile Co. for the technical support in on board CAN. This work was supported by “Key technologies and platform for unmanned vehicle in urban integrated environment”, a National Nature Science Foundation of China (91120307).

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View Abstract

1. Introduction
2. System Architecture
3. Mathematic Model Analysis of the Vehicle Control System
4. Controller Design Based on Adaptive PID
5. Experimental Results and Discussion
6. Conclusions
7. Acknowledgments
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By MICHAEL MCFAUL 05/19/2018 06:36 AM EDT
The U.S. Ambassador to Russia Michael McFaul arrives at Foreign Ministry headquarters in Moscow, Russia, Wednesday, May 15, 2013. | AP Photo/Misha Japaridze
The U.S. Ambassador to Russia Michael McFaul arrives at Foreign Ministry headquarters in Moscow, Russia, Wednesday, May 15, 2013. | AP Photo/Misha Japaridze

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In the fall of 2012, for reasons that remain mysterious to me, it became clear that my family and I were being followed. I had been in Moscow as ambassador then for less than a year. As I wrote to the head of our security team on October 7, “My guards informed me that I was followed today while attending my son’s soccer game. And they then kept with us as we went to McDonald’s.” My head of security replied that if we saw them, it was because they wanted us to see them.

A few weeks later, agents from Russia’s security service FSB, or so we assumed, sat in the pew behind us in church, which truly unnerved my wife. They followed us on the streets, and closely tailed our Cadillac. On one occasion, one of my drivers overreacted to being followed. With my family in the car, he began driving faster and more erratically, weaving through Russia’s crazy traffic until I finally intervened and urged him to relax. After all, our situation was not like in the movies. We could never lose them for good. They knew where we lived.

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The car chase episode scared me, as it illustrated that the Russian intelligence officers were succeeding in getting under our collective skin. It also was getting dangerous.

During my first year as ambassador to Russia in the Obama administration, the Russian authorities conducted a ground campaign of harassment against my colleagues at the embassy, myself and, from time to time, even my family. I was the architect of the Reset plan to improve relations between the United States and Russia, and here I was, witnessing firsthand just how deeply and how quickly the relationship was deteriorating—and how little anyone could do to stop it.

In my first week in Moscow, Nashi, the Kremlin-created “youth group” that sometimes acted as stunt journalists or street protesters in the government’s service, threatened to organize a demonstration outside our residence. As the regional security officer circulated emails in red ink urging embassy employees to stay away from my new home, Spaso House, I wondered what I was supposed to do, as my family was at the residence at the time and I was at the embassy. The red-letter email turned out to be a false alarm. All we saw were some grandmothers walking their dogs in the park in front of our house. The experience, however, kept us on alert that day and for the rest of our time in Moscow, just as our hosts desired.

Outside the American Embassy, the Russian government authorities also made their presence known. Formally, the Russian police officers posted at the gates of our compound were deployed to protect us, even though we had our own Marine guard just inside the gates. In reality, the Russian officers’ main assignment in this new era of confrontation was to harass and subject to surveillance everyone entering the embassy. Even our American employees often had to stand helplessly in the cold, waiting for the Russian officers to take and then inspect their passports and record their data before being allowed to proceed. They even detained my wife from time to time. As one embassy report on harassment in the spring of 2012 documented, “January 27: Donna Norton, the Ambassador’s spouse. Harassed and held by police at South Gate in sub-zero weather.” Surely Russian intelligence was good enough to know who my wife was and that she was from Southern California!

This level of police harassment at the American Embassy and Spaso House was new. No one could remember a time even during the Soviet era when our hosts were so aggressive. More than once, we delivered formal letters of complaint to the Ministry of Foreign Affairs about the behavior, noting that we did not station police outside the Russian Embassy in Washington. Nothing changed.

When we could have used these Russian guards, however, they suddenly seemed unable to act. One day early in my tenure, on February 10, 2012, a group of several hundred demonstrators strangely attired in white plastic lay down on the street in front of the main embassy gate, blocking all traffic from leaving or entering the compound. (I’m assuming the plastic jumpsuits were to protect the demonstrators’ clothing.) My car pulled up to the embassy just after the group arrived; it was a strange feeling being denied access to the American Embassy, which is sovereign territory of the United States. I also worried about the embassy children, including two of my own, who were on their way back home from school at the time. Were they going to have to sit on the bus outside the gates of the compound, where their homes were located? We asked the police at our gate to clear the street, but they pleaded that they had no authority to break up a public demonstration. Of course, the irony was that Russian law made it illegal to convene a public demonstration without a permit, but the Russian authorities didn’t seem too anxious to enforce their laws against these particular demonstrators.

Our security cameras later revealed that Nashi leader Tikhon Chumakov had organized the demonstration. Chumakov and his Kremlin-backed comrades were above the law. One of my very frustrated embassy colleagues suggested that the demonstration violated the Vienna Convention on Diplomatic Relations. If so, the convention wasn’t doing us much good that afternoon. Eventually the protesters dispersed, but the exposed feeling we all endured that day lingered long thereafter. What if they had tried to enter our compound? The low brick wall surrounding our embassy office buildings and townhouses, after all, was easily scalable. What would we do then? Our security team started making contingency plans. None of our options provided much reassurance.

Not long afterward, I encountered Chumakov face-to-face during a visit to the CEO of Rusnano, Anatoly Chubais, at his company’s headquarters. It was a routine courtesy call. Rusnano is a giant state-owned Russian company that invests in high-tech companies all over the world, including in the United States. Why Nashi operatives would want to harass me outside this office building was unclear. As I opened my car door, they sprang—Chumakov and two or three others with video cameras. My meeting with Chubais was not a public event, so it was clear they had obtained access to my calendar—perhaps electronically, perhaps from a Russian informant working at the embassy. They bombarded me with questions about supporting the opposition—a widespread rumor that Nashi and other state-aligned activist groups, often posing as journalists, had been spreading about me since the beginning of my time as ambassador. Against the judgment of my bodyguards, I decided to answer, in Russian.

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I reconfirmed that the United States provided no financial support to the Russian opposition. After a short exchange, I finally recognized Chumakov. I recalled that he had previously been assigned to follow and harass the former British ambassador, Anthony Brenton. Nashi encounters with Ambassador Brenton were aggressive and sometimes violent; he had even been attacked in the driveway of his residence. During our chat outside of Rusnano, Chumakov threatened me with similar treatment, promising that his group would chase me out of the country just as they had with Brenton. How pleasant, I thought. Welcome to Moscow!

My run-in with these Nashi agents at Rusnano ended uneventfully. None of the tape from the “interview” ever aired, because I hadn’t said anything useful to them. And my bodyguards thankfully avoided physical contact with these “youth leaders” (yes, that’s polite diplospeak), even though they were quite aggressive with me. But the event reminded me that I was under constant surveillance. How did Chumakov know that I was coming to Rusnano that day? Who was helping him obtain such information? Should I expect such a greeting party everywhere I visited?

We eventually learned to expect them. The Nashi posse did not meet me at the entrance of every meeting I attended. They never showed up, for instance, outside the gates of the Kremlin or in the parking lot at the Ministry of Foreign Affairs. But they showed up frequently enough that we planned for encounters.

As per guidance from our security team, I also had to assume that every phone call I made, every email I sent (on the unclassified system), every website I visited, every conversation I had, and even every movement I made inside Spaso House was being monitored by the Russian government. All the large apartment buildings next to Spaso House in downtown Moscow had for rent signs hanging in them, yet no one ever moved in. In our first security briefing upon arrival, Donna and I were told that we should use one of our secure rooms at the embassy if we ever needed to have a serious argument. (Thankfully, we never needed to use that service!) The technological advances in cyber surveillance over the last decade, as well as voice and video monitoring, are mind-boggling. We had to operate in Russia as if we were being monitored all the time. I had adjusted to a life with minimal privacy as a White House official. Living in Russia, I had no privacy at all.

Harassment was not limited to my immediate security team and me. Anyone who worked at the embassy could become a target. They slashed the tires of one of my junior staffers. They broke into the homes of embassy employees, oftentimes just rearranging the furniture or turning on all the lights to let people know that they were vulnerable. During my second year on the job, the State Department’s Office of Inspector General did a comprehensive review of all activities at the embassy. On security, their final unclassified report noted, “Across Mission Russia, employees face intensified pressure by the Russian security services at a level not seen since the days of the Cold War.”

Russian officials also regularly interrogated our Russian employees, pressuring them to report on us. My Russian bodyguards were brought in for questioning. We assumed that some of our Russian employees were informants for Russian intelligence.

The FSB, Russia’s state security agency, also aggressively recruited informants among our American staff, offering large sums of money for sensitive information, just like one sees in the movies. And “honey traps” —the deployment of beautiful young women and men to lure American employees into doing things that could make them vulnerable to blackmail—occasionally work. One of the hardest parts of the job as ambassador was signing papers to curtail someone’s assignment in Moscow because they had become a counterintelligence risk.

These harassment techniques were not new, but the number of incidents spiked noticeably in the winter of 2012. A memo prepared by my regional security officer and his team counted “nearly 500 additional instances of harassment against U.S. Mission personnel” between January 17 and March 30, 2012. Even during the Soviet era, no one on our staff could remember a period of harassment so intense.

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The worst form of harassment, however, was when my children were followed. One day, in the spring of 2013, my security team reported that a car was following my kids’ vehicle to school, an activity we verified through a proper investigation. It wasn’t hard to confirm that my kids’ car had been followed. Whoever was responsible wanted us to know.

Then it happened again. On May 6, 2013, one of my senior staff members reported to me that another car had been observed following my sons to school. I concurred with the plan to issue another note of complaint to the Ministry of Foreign Affairs, but also added, “I do not want us to assume that we know this car is the FSB. We should also keep open the possibility that this is a security threat to my children.” There were many people in Russia who didn’t like me or the United States. Anyone could have been driving that car.

We did push back with the Russian government regarding these family-related incidents. Through various government-to-government channels, we presented evidence of harassment to our Russian hosts. Even President Obama got involved a couple of times, asking both President Medvedev and later President Putin to stop harassing “his guy” in Russia. We could never really tell if these pushbacks worked. The harassment, especially the overt surveillance, would come and go. We could never detect a clear pattern related to my activities as ambassador or developments in U.S.-Russia relations. And maybe that was the plan.

I mostly stayed calm, but once, under such constant pressure, I cracked and made a mistake. On March 29, 2012, as a crowd of Kremlin-aligned activists gathered outside a meeting—which, again, was not publicly announced—I decided to stop and talk with the crowd. When they asked if they could interview me on other topics, I agreed, but requested they go through my press office instead of stopping me on the street in the cold when I had no coat on. As they kept pressing me, however, I became agitated, and stated that Russia was a dikaya strana (“wild country”) for tracking and harassing diplomats the way it did.

Some opposition leaders who later watched the exchange on video on social media loved it. Alexei Navalny, for instance, tweeted that I should have punched the agitators, being that I had diplomatic immunity. Russian officialdom had a very different reaction, and I agreed with them. I had wanted to say that the behavior of those Nashi activists was inconsistent with international norms. American political groups do not obtain the Russian ambassador’s calendar and then follow him wherever he goes. And Nashi and NTV were instruments of the Russian regime, so their treatment of me was a Russian government operation. But in the heat of the moment, those words had not come to me. Of course, after the “dikaya strana” clip aired I apologized immediately on Twitter, tweeting, “Just watched NTV. I misspoke in bad Russian. Did not mean to say ‘wild country.’ Meant to say NTV actions ‘wild.’ I greatly respect Russia.” But the tape of that sound bite would loop a long time.

I reached out to some friends of mine at the White House to apologize for letting “the boss” down. Obama, I was reminded, had made his share of inappropriate remarks. Still, I was disappointed in myself. Of course, the Russian government was behaving wildly at the time. It was doing things that normal governments simply don’t do, both to their own citizens and to me. And wilder stuff was yet to come. But diplomats should not say such things in public, and I was now a diplomat. The Nashi strategy of constant harassment had generated dividends for the government that day.

The Kremlin had pivoted on us, portraying the United States as Russia’s enemy. The Kremlin was the one rolling back the Reset, not the U.S. administration. Its attacks on me were part of this larger campaign. It felt personal at times, but it wasn’t only personal. I eventually came to understand that these negative trends in our relationship were much bigger than myself. I was not a cause of the problems, but my troubles as ambassador were a symptom of larger forces over which I had little, if any, control.

Excerpted from FROM COLD WAR TO HOT PEACE: An American Ambassador in Putin’s Russia by Michael McFaul. Copyright © 2018 by Michael McFaul. Reprinted by permission of Houghton Mifflin Harcourt Publishing Company. All rights reserved.

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Six Years of Exercise — or Lack of It — May Be Enough to Change Heart Failure Risk

Six Years of Exercise — or Lack of It — May Be Enough to Change Heart Failure Risk

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Six Years of Exercise — or Lack of It — May Be Enough to Change Heart Failure Risk
Release Date: May 15, 2018
Increasing physical activity over six years may be enough to lower heart failure risk. – Click to TweetSix years of physical inactivity enough to increase heart failure risk. – Click to TweetMore evidence that it’s never too late to get moving to reduce heart failure risk. – Click to Tweet
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By analyzing reported physical activity levels over time in more than 11,000 American adults, Johns Hopkins Medicine researchers conclude that increasing physical activity to recommended levels over as few as six years in middle age is associated with a significantly decreased risk of heart failure, a condition that affects an estimated 5 million to 6 million Americans.

The same analysis found that as little as six years without physical activity in middle age was linked to an increased risk of the disorder.

Unlike heart attack, in which heart muscle dies, heart failure is marked by a long-term, chronic inability of the heart to pump enough blood, or pump it hard enough, to bring needed oxygen to the body. The leading cause of hospitalizations in those over 65, the disorder’s risk factors include high blood pressure, high cholesterol, diabetes, smoking and a family history.

“In everyday terms our findings suggest that consistently participating in the recommended 150 minutes of moderate to vigorous activity each week, such as brisk walking or biking, in middle age may be enough to reduce your heart failure risk by 31 percent,” says Chiadi Ndumele, M.D., M.H.S., the Robert E. Meyerhoff Assistant Professor of Medicine at the Johns Hopkins University School of Medicine, and the senior author of a report on the study. “Additionally, going from no exercise to recommended activity levels over six years in middle age may reduce heart failure risk by 23 percent.”

The researchers caution that their study, described in the May 15 edition of the journal Circulation, was observational, meaning the results can’t and don’t show a direct cause-and-effect link between exercise and heart failure. But they say the trends observed in data gathered on middle-aged adults suggest that it may never be too late to reduce the risk of heart failure with moderate exercise.

“The population of people with heart failure is growing because people are living longer and surviving heart attacks and other forms of heart disease,” says Roberta Florido, M.D., cardiology fellow at the Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease. “Unlike other heart disease risk factors like high blood pressure or high cholesterol, we don’t have specifically effective drugs to prevent heart failure, so we need to identify and verify effective strategies for prevention and emphasize these to the public.” There are drugs used to treat heart failure, such as beta blockers and ACE inhibitors, but they are essentially “secondary” prevention drugs, working to reduce the heart’s workload after dysfunction is already there.

Several studies, Florido says, suggest that in general people who are more physically active have lower risks of heart failure than those who are less active, but little was known about the impact of changes in exercise levels over time on heart failure risk.

For example, if you are sedentary most of your life but then start exercising in middle age, does that decrease your risk of heart failure? Or, if you are active much of your life but then stop being active at middle age, will that increase your risk?

To address those questions, the researchers used data already gathered from 11,351 participants in the federally funded, long term Atherosclerosis Risk in Communities (ARIC) study, recruited from 1987 to 1989 in Forsyth County, North Carolina; Jackson, Mississippi; greater Minneapolis, Minnesota; and Washington County, Maryland.

The participants’ average age was 60, 57 percent were women and most were either white or African-American.

Participants were monitored annually for an average of 19 years for cardiovascular disease events such as heart attack, stroke and heart failure using telephone interviews, hospital records and death certificates. Over the course of the study there were 1,693 hospitalizations and 57 deaths due to heart failure.

In addition to those measures, at the first and third ARIC study visits (six years apart), each participant filled out a questionnaire, which asked them to evaluate their physical activity levels, which were then categorized as poor, intermediate or “recommended,” in alignment with guidelines issued by the American Heart Association.

The “recommended” amount is at least 75 minutes per week of vigorous intensity or at least 150 minutes per week of moderate intensity exercise. One to 74 minutes per week of vigorous intensity or one to 149 minutes per week of moderate exercise per week counted as intermediate level activity. And physical activity qualified as “poor” if there was no exercise at all.

After the third visit, 42 percent of participants (4,733 people) said they performed recommended levels of exercise; 23 percent (2,594 people) said they performed intermediate levels; and 35 percent (4,024 people) said they had poor levels of activity. From the first to the third visit over about six years, 24 percent of participants increased their physical activity, 22 percent decreased it and 54 percent stayed in the same category.

Those with recommended activity levels at both the first and third visits showed the highest associated heart failure risk decrease, at 31 percent compared with those with consistently poor activity levels.

Heart failure risk decreased by about 12 percent in the 2,702 participants who increased their physical activity category from poor to intermediate or recommended, or from intermediate to recommended, compared with those with consistently poor or intermediate activity ratings.

Conversely, heart failure risk increased by 18 percent in the 2,530 participants who reported decreased physical activity from visit one to visit three, compared with those with consistently recommended or intermediate activity levels.

Next, the researchers determined how much of an increase in exercise, among those initially doing no exercise, was needed to reduce the risk of future heart failure. Exercise was calculated as METs (metabolic equivalents), where one MET is 1 kilocalorie per kilogram per hour. Essentially, sitting watching television is 1 MET, fast walking is 3 METs, jogging is 7 METs and jumping rope is 10 METs. The researchers calculated outcomes in METs times the number of minutes of exercise.

The researchers found that each 750 MET minutes per week increase in exercise over six years reduced heart failure risk by 16 percent. And each 1,000 MET minutes per week increase in exercise was linked to a reduction in heart failure risk by 21 percent.

According to the American Heart Association, fewer than 50 percent of Americans get recommended activity levels.

Other authors on the study include Lucia Kwak, Mariana Lazo, Gary Gerstenblith, Roger Blumenthal, Elizabeth Selvin and Josef Coresh of Johns Hopkins; Vijay Nambi and Christie Ballantyne of Baylor College of Medicine; Haitham Ahmed of Cleveland Clinic; Sheila Hegde of Brigham and Women’s Hospital and Aaron Folsom of University of Minnesota.

The research was funded by a Robert E. Meyerhoff Professorship, a Robert Wood Johnson Amos Medical Faculty Development Award, a JHU Catalyst Award and grants from the National Heart, Lung, and Blood Institute (K23HL12247) and the National Institute of Diabetes and Digestive and Kidney Diseases (K24DK106414).

Related Stories:
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Weight: A Silent Heart Risk
Severe Obesity Revealed As a Stand-Alone High-Risk Factor for Heart Failure
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© The Johns Hopkins University, The Johns Hopkins Hospital, and Johns Hopkins Health System. All rights reserved. Configuring Beyond Compare with Git

Configuring Beyond Compare with Git

Beyond Compare has been my favorite comparison tool for a while now. It is cross-platform and makes diffs and 3-way merges very easy to understand and visualize.

Configuring Beyond Compare 4
Git for Linux
To get it working on any linux flavor is pretty straight forward. Run these commands from terminal:

git config –global diff.tool bc
git config –global difftool.prompt false
git config –global difftool.bc trustExitCode true

git config –global merge.tool bc
git config –global mergetool.bc trustExitCode true
In order to get directory diffs working (e.g. git difftool –dir-diff), I had to tweak the settings a little bit. By default, git uses symlinks to do the directory diff and BC4 will not follow those by default yielding something that looks like this:

broken directory diff

There are a couple of ways to fix this (courtesy of StackOverflow). The approach I like is updating Beyond Compare to follow the symlinks.

In the Folder Compare, click the Rules toolbar button (referee icon). Go to the Handling tab. Check Follow symbolic links. To make this affect all new sessions, change the dropdown at the bottom of the dialog from Use for this view only to Also update session defaults before you click OK.

Git for Windows
On Windows, in addition to the above commands, you need to tell git the path to bcomp.exe:

git config –global difftool.bc.path “c:/program files/beyond compare 4/bcomp.exe”
git config –global mergetool.bc.path “c:/program files/beyond compare 4/bcomp.exe”
You don’t need to do anything special to get directory diffs working on Windows.

Git for Visual Studio
Visual Studio includes a builtin diff viewer that is pretty nice. However, I don’t find it as useful as I do Beyond Compare. Not to mention, it doesn’t support 3-way merge. Instead of using your global git config, it forces you to change the git config file in the repo. Specifically, you need to open git/config in the repo and add the following:

tool = bc4
[difftool “bc4″]
cmd = \”C:\\Program Files\\Beyond Compare 4\\BComp.exe\” \”$LOCAL\” \”$REMOTE\”
tool = bc4
[mergetool “bc4″]
cmd = \”C:\\Program Files\\Beyond Compare 4\\BComp.exe\” \”$REMOTE\” \”$LOCAL\” \”$BASE\” \”$MERGED\”
Now, among other things, you can right-click files in VS and choose Compare with Unmodified to open Beyond Compare.

Configuring Beyond Compare 3
Git for Linux
Pretty much the only difference from above is using bc3 instead of bc:

git config –global diff.tool bc3
git config –global difftool.prompt false
git config –global difftool.bc3 trustExitCode true

git config –global merge.tool bc3
git config –global mergetool.bc3 trustExitCode true
Git for Windows
On Windows, it is a little more work. In addition to the above, you need to tell git the path to bcomp.exe:

git config –global difftool.bc3.path “c:/program files (x86)/beyond compare 3/BCompare.exe”
git config –global mergetool.bc3.path “c:/program files (x86)/beyond compare 3/bcomp.exe”
The reason I am using BCompare.exe for the difftool instead of bcomp.exe is described here. When using the new –dir-diff option of the git difftool command:

git difftool 4e560^^ –dir-diff
#4e560 is your commit hash that you want to show a whole directory-diff on in BC
Beyond Compare 3 has a bug that is not fixed as of v3.3.12 where you need to employ this workaround. If you don’t do it, bcomp.exe will exit too early, and no files will be available to diff while you are viewing the directory diff.

This is fixed in BC4, so you should probably just upgrade to that.

Using It
Once you have it setup, you can easily run

git difftool path/to/my/file.js
in your working copy to show unstaged changes. If you want to see a diff of the whole directory, you can run:

git difftool –dir-diff
which opens BC’s directory comparison which can’t be beat IMO.

To resolve merge conflicts:

git mergetool
That will cycle through each merge conflict in the working copy and as long as you save the file from BC, the conflict will be marked resolved by git (hence the mergetool.bc trustExitCode setting).

Chad Lee
Technical Lead at CivicSource, OSS developer. Expert in distributed systems, REST, messaging, domain-driven design, test-driven development, & CQRS. Beginner dad, novice human.

The myopia boom Short-sightedness is reaching epidemic proportions. Some scientists think they have found a reason why.

The myopia boom Short-sightedness is reaching epidemic proportions. Some scientists think they have found a reason why.
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The myopia boom
Short-sightedness is reaching epidemic proportions. Some scientists think they have found a reason why.

Elie Dolgin
18 March 2015
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The southern city of Guangzhou has long held the largest eye hospital in China. But about five years ago, it became clear that the Zhongshan Ophthalmic Center needed to expand.

More and more children were arriving with the blurry distance vision caused by myopia, and with so many needing eye tests and glasses, the hospital was bursting at the seams. So the centre began adding new testing rooms — and to make space, it relocated some of its doctors and researchers to a local shopping mall. Now during the summer and winter school holidays, when most diagnoses are made, “thousands and thousands of children” pour in every day, says ophthalmologist Nathan Congdon, who was one of those uprooted. “You literally can’t walk through the halls because of all the children.”

Ian Morgan talks about about ways to prevent myopia

East Asia has been gripped by an unprecedented rise in myopia, also known as short-sightedness. Sixty years ago, 10–20% of the Chinese population was short-sighted. Today, up to 90% of teenagers and young adults are. In Seoul, a whopping 96.5% of 19-year-old men are short-sighted.

Other parts of the world have also seen a dramatic increase in the condition, which now affects around half of young adults in the United States and Europe — double the prevalence of half a century ago. By some estimates, one-third of the world’s population — 2.5 billion people — could be affected by short-sightedness by the end of this decade. “We are going down the path of having a myopia epidemic,” says Padmaja Sankaridurg, head of the myopia programme at the Brien Holden Vision Institute in Sydney, Australia.

The condition is more than an inconvenience. Glasses, contact lenses and surgery can help to correct it, but they do not address the underlying defect: a slightly elongated eyeball, which means that the lens focuses light from far objects slightly in front of the retina, rather than directly on it. In severe cases, the deformation stretches and thins the inner parts of the eye, which increases the risk of retinal detachment, cataracts, glaucoma and even blindness. Because the eye grows throughout childhood, myopia generally develops in school-age children and adolescents. About one-fifth of university-aged people in East Asia now have this extreme form of myopia, and half of them are expected to develop irreversible vision loss.

This threat has prompted a rise in research to try to understand the causes of the disorder — and scientists are beginning to find answers. They are challenging old ideas that myopia is the domain of the bookish child and are instead coalescing around a new notion: that spending too long indoors is placing children at risk. “We’re really trying to give this message now that children need to spend more time outside,” says Kathryn Rose, head of orthoptics at the University of Technology, Sydney.

Vision quest
For many years, the scientific consensus held that myopia was largely down to genes. Studies in the 1960s showed that the condition was more common among genetically identical twins than non-identical ones, suggesting that susceptibility is strongly influenced by DNA1. Gene-finding efforts have now linked more than 100 regions of the genome to short-sightedness.

Singapore National Myopia Programme/Health Promotion Board

A Singapore poster encourages children to spend time outside to prevent myopia.

But it was obvious that genes could not be the whole story. One of the clearest signs came from a 1969 study of Inuit people on the northern tip of Alaska whose lifestyle was changing2. Of adults who had grown up in isolated communities, only 2 of 131 had myopic eyes. But more than half of their children and grandchildren had the condition. Genetic changes happen too slowly to explain this rapid change — or the soaring rates in myopia that have since been documented all over the world (see ‘The march of myopia’). “There must be an environmental effect that has caused the generational difference,” says Seang Mei Saw, who studies the epidemiology and genetics of myopia at the National University of Singapore.

There was one obvious culprit: book work. That idea had arisen more than 400 years ago, when the German astronomer and optics expert Johannes Kepler blamed his own short-sightedness on all his study. The idea took root; by the nineteenth century, some leading ophthalmologists were recommending that pupils use headrests to prevent them from poring too closely over their books.

Related stories
Curing blindness: Vision quest
Vision science: Seeing without seeing
Neuroscience: Learning to see
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The modern rise in myopia mirrored a trend for children in many countries to spend more time engaged in reading, studying or — more recently — glued to computer and smartphone screens. This is particularly the case in East Asian countries, where the high value placed on educational performance is driving children to spend longer in school and on their studies. A report last year3 from the Organisation for Economic Co-operation and Development showed that the average 15-year-old in Shanghai now spends 14 hours per week on homework, compared with 5 hours in the United Kingdom and 6 hours in the United States.

Researchers have consistently documented a strong association between measures of education and the prevalence of myopia. In the 1990s, for example, they found that teenage boys in Israel who attended schools known as Yeshivas (where they spent their days studying religious texts) had much higher rates of myopia than did students who spent less time at their books4. On a biological level, it seemed plausible that sustained close work could alter growth of the eyeball as it tries to accommodate the incoming light and focus close-up images squarely on the retina.

Attractive though the idea was, it did not hold up. In the early 2000s, when researchers started to look at specific behaviours, such as books read per week or hours spent reading or using a computer, none seemed to be a major contributor to myopia risk5. But another factor did. In 2007, Donald Mutti and his colleagues at the Ohio State University College of Optometry in Columbus reported the results of a study that tracked more than 500 eight- and nine-year-olds in California who started out with healthy vision6. The team examined how the children spent their days, and “sort of as an afterthought at the time, we asked about sports and outdoorsy stuff”, says Mutti.

It was a good thing they did. After five years, one in five of the children had developed myopia, and the only environmental factor that was strongly associated with risk was time spent outdoors6. “We thought it was an odd finding,” recalls Mutti, “but it just kept coming up as we did the analyses.” A year later, Rose and her colleagues arrived at much the same conclusion in Australia7. After studying more than 4,000 children at Sydney primary and secondary schools for three years, they found that children who spent less time outside were at greater risk of developing myopia.

Source: Ian Morgan, Australian Natl Univ.

Rose’s team tried to eliminate any other explanations for this link — for example, that children outdoors were engaged in more physical activity and that this was having the beneficial effect. But time engaged in indoor sports had no such protective association; and time outdoors did, whether children had played sports, attended picnics or simply read on the beach. And children who spent more time outside were not necessarily spending less time with books, screens and close work. “We had these children who were doing both activities at very high levels and they didn’t become myopic,” says Rose. Close work might still have some effect, but what seemed to matter most was the eye’s exposure to bright light.

See the light
Some researchers think that the data to support the link need to be more robust. Most epidemiological studies have estimated children’s time outdoors from questionnaires — but Christine Wildsoet, an optometrist at the University of California, Berkeley, says that such data should be treated with caution. In a small, pilot study of wearable light sensors8, she found that people’s estimates often do not match up with their actual exposure. And Ian Flitcroft, a myopia specialist at Children’s University Hospital in Dublin, questions whether light is the key protective factor of being outdoors. He says that the greater viewing distances outside could affect myopia progression, too. “Light is not the only factor, and making it the explanation is a gross over-simplification of a complex process,” he says.

Yet animal experiments support the idea that light is protective. Researchers first demonstrated this in chicks, a common lab model for studying vision. By fitting chicks with goggles that alter the resolution and contrast of incoming images, it is possible to induce the development of myopia while raising the birds under controlled conditions in which only light intensity is changed. In 2009, Regan Ashby, Arne Ohlendorf and Frank Schaeffel from the University of Tübingen’s Institute for Ophthalmic Research in Germany showed that high illumination levels — comparable to those encountered outside — slowed the development of experimentally induced myopia in chicks by about 60% compared with normal indoor lighting conditions9. Researchers elsewhere have found similar protective effects in tree shrews and rhesus monkeys10.

But what scientists really needed was a mechanism: something to explain how bright light could prevent myopia. The leading hypothesis is that light stimulates the release of dopamine in the retina, and this neurotransmitter in turn blocks the elongation of the eye during development. The best evidence for the ‘light–dopamine’ hypothesis comes — again — from chicks. In 2010, Ashby and Schaeffel showed that injecting a dopamine-inhibiting drug called spiperone into chicks’ eyes could abolish the protective effect of bright light11.

Retinal dopamine is normally produced on a diurnal cycle — ramping up during the day — and it tells the eye to switch from rod-based, nighttime vision to cone-based, daytime vision. Researchers now suspect that under dim (typically indoor) lighting, the cycle is disrupted, with consequences for eye growth. “If our system does not get a strong enough diurnal rhythm, things go out of control,” says Ashby, who is now at the University of Canberra. “The system starts to get a bit noisy and noisy means that it just grows in its own irregular fashion.”

Time out
Based on epidemiological studies, Ian Morgan, a myopia researcher at the Australian National University in Canberra, estimates that children need to spend around three hours per day under light levels of at least 10,000 lux to be protected against myopia. This is about the level experienced by someone under a shady tree, wearing sunglasses, on a bright summer day. (An overcast day can provide less than 10,000 lux and a well-lit office or classroom is usually no more than 500 lux.) Three or more hours of daily outdoor time is already the norm for children in Morgan’s native Australia, where only around 30% of 17-year-olds are myopic. But in many parts of the world — including the United States, Europe and East Asia — children are often outside for only one or two hours.

“Sort of as an afterthought, we asked about sports and outdoorsy stuff.”

In 2009, Morgan set out to test whether boosting outdoor time would help to protect the eyesight of Chinese children. He and a team from the Zhongshan Ophthalmic Center (where Morgan also works) launched a three-year trial in which they added a 40-minute outdoor class to the end of the school day for a group of six- and seven-year-olds at six randomly selected schools in Guangzhou; children at six other schools had no change in schedule and served as controls. Of the 900-plus children who attended the outside class, 30% developed myopia by age nine or ten compared with 40% of those at the control schools. The study is being prepared for publication.

A stronger effect was found at a school in southern Taiwan, where teachers were asked to send children outside for all 80 minutes of their daily break time instead of giving them the choice to stay inside. After one year, doctors had diagnosed myopia in 8% of the children, compared with 18% at a nearby school12.

Morgan is buoyed by the preliminary findings, but thinks that he can do even better. “We’ve got proof of principle that increasing the amount of time children spend outside actually works,” he says. “The question then is how do we make this work in practice at a level that would have a significant impact?” He recognizes that many schools do not have the flexibility to add time outdoors. So last year, in collaboration with Congdon, he began piloting the idea of teaching kids in a classroom made of glass to let in more natural light. “This glass classroom idea is quite applicable for whole swathes of China,” Congdon says.

Rose points out that additional outdoor time “has to be mandated through the schools, because getting parents to voluntarily do this is extremely difficult”. Saw and her colleagues learned this when they trialled a 9-month programme to teach parents in Singapore about the importance of outdoor time in order to prevent myopia. They provided step-counters, organized outdoor weekend activities for families and even offered cash prizes for cooperation. But by the end of the trial, the time spent outdoors was not statistically different from that for a control group with no such campaign13.

In some places, children cannot get any more outdoor light: there are too few hours of daylight, the sun is too fierce, or the cold too intense. Animal research10 has suggested that powerful indoor lights could do the trick instead: light boxes currently sold to treat seasonal affective disorder, for example, can deliver up to 10,000 lux illumination, but their effects on myopia have not been tested extensively in humans.

Meanwhile, researchers have been working on ways to prevent myopia from worsening. Sankaridurg and her colleagues have developed special glasses and contact lenses that can alter eye growth by focusing light from distant images across the entire field of view, rather than just at the centre, as standard lenses do. Other research groups have shown that nightly eye drops with a neurotransmitter-blocking drug called atropine can also help to control myopia progression14, although the mechanism remains unclear. “We want to take a holistic approach” to tackling myopia, Sankaridurg says.

But eye drops and light boxes do not have quite the appeal of sending children outside to play, which has plenty of other benefits besides those for the eyes. “It probably also increases physical activity, which decreases likelihood of obesity and enhances mood,” Rose says. “I can only see it as a win — and it’s free.”

More than a century ago, Henry Edward Juler, a renowned British eye surgeon, offered similar advice. In 1904, he wrote in A Handbook of Ophthalmic Science and Practice that when “the myopia had become stationary, change of air — a sea voyage if possible — should be prescribed”. As Wildsoet points out: “We’ve taken a hundred years to go back to what people were intuitively thinking was the case.”

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Related stories and links
Curing blindness: Vision quest
10 September 2014

Vision science: Seeing without seeing
19 January 2011

Neuroscience: Learning to see
27 October 2010

From elsewhere
Facts about myopia
Ian Morgan discusses the results of myopia prevention study
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Elie Dolgin is a science writer in Somerville, Massachusetts.
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