The beta version of our self testing platform is open access at: www.guidedselftesting.com.

For a quick tutorial, see the PDF below called "gst_quick_tutorial".  

For a deeper dive full-length article, see the PDF below called "GST_manuscript".

Please note: This app is not meant to provide medical advice, and has not been validated for clinical use.  Anyone with concerns about their sleep should speak with their doctor or trained professional.  

This article summarizes our pilot work using the ResMed S+ (radar-like) sleep monitor as a form of feedback for adults with insomnia symptoms.  We were inspired by the work of Dr Harvey using actigraphy-based feedback over a decade ago.  To read the study, click on the PDF link below. 

Abstract
​Chronic insomnia may be exacerbated and/or perpetuated by a variety of factors, including anxiety about sleep and misperception of sleep-wake times.  Limited prior evidence suggests that providing objective feedback, based on actigraphy measures, can improve insomnia symptoms.  It is unknown whether this finding can generalize to increasingly available consumer sleep monitors.  We conducted a randomized cross-over wait-list control pilot study of device-based feedback for insomnia.  After a seven-day run-in period of diary entries, subjects were randomized to either waitlist (sleep hygiene) or to active feedback of sleep duration with a ResMed S+ monitor.  The waitlist group crossed over to active feedback after one week.  Daily electronic diaries were kept throughout. Feedback was associated with significant improvements in the Insomnia Severity Index, Pittsburgh Sleep Quality Index, and Functional Outcomes of Sleep Questionnaire. These symptomatic improvements occurred despite no change in subjective (diary) or objective (device) measures of sleep latency, wake after sleep onset, number of awakenings, or total sleep time. At the exit interview, 89% reported the device feedback was useful, and 63% would consider device-based feedback as a long-term treatment for insomnia.  Device-based feedback is a simple, feasible intervention that may benefit some patients with insomnia.  Future studies in larger cohorts will inform predictors and durability of response. 

“What if it were your own mother, what would you tell her?”
     Patients may use this or similar language, invoking both the merits of familial loyalty and candid advice when facing different – and difficult – options for their care.  This question can anchor their own decision making in a more humane and empathetic context, and sidestep overly scientific or statistical discussions.  Whether the physician’s response literally or figuratively considers their own mother, patients may understandably find solace to navigate their situation more confidently in times of fear and vulnerability through their doctor’s personal perspective.
     Yet this “mother-statistic”, like p-values and other statistics, can be misunderstood and inadvertently steer the decision ship into uncertain waters.  Ultimately, navigating uncertainty in medical decision making involves two types of information: evidence from past experience and research about the medical condition in question, and the risk tolerance of those choosing between options that each have some uncertainty. 
      Even if we prefer to use qualitative thinking to assess the information of these two categories, it is useful to at least consider how statistics can provide a framework for decision making under uncertain circumstances.  Let’s think about two examples where the translation of a statistical concept into “normal” language could result in muddy waters. 
      First, if we think of p-values as a measure of how likely an effect is to be real (versus chance), even putting aside the critiques of p-values in general [1], it is unknown whether a patient would apply the standard 5% threshold if judging their own treatment and chances of success.  In fact, more liberal and more strict thresholds would both alter the way in which medical evidence would be considered, but such discussions rarely if ever occur it seems.
      Second, let’s consider how the patient perspective on treatment choice might be swayed by the way data is presented.  Take the example of carotid endarterectomy, a surgery to open up clogged neck artery to reduce stroke risk.  Extensive evidence suggests that, for patients at high risk of having a stroke, there is a substantial reduction in risk of future strokes by this endarterectomy procedure.  We may counsel patients that this approach makes sense: since the cholesterol plaque likely causes strokes, we remove the offending clog, and thus prevent future stroke events. A simple plumbing problem, solved by highly specialized plumbing procedure.  The medical evidence is among the most clear and convincing in neurology, and the narrative of why this surgery works is indeed clear and compelling.  But now let’s phrase the same problem a different way.  Line up 8 men, each with high stroke risk and a clogged carotid.  Then say to them: “We need to operate on all 8 of you in order to prevent a stroke in one of you”.  This might be interpreted in a different light than the more qualitative narrative of why the surgery works. 
      Stating the benefit in this way is known as the number needed to treat, an index of how effective a treatment is that takes into account the baseline risk of some event before versus the risk after some intervention. Here, the stroke risk is reduced from approximately 20% down to 8%, a 12% absolute benefit.  The inverse of this fraction (1 / 0.12) is about 8.  This risk reduction is quite favorable compared to many other medical treatments.  On a “population” level, performing endarterectomies makes perfect sense and prevents thousands of strokes every year.  Phrasing it in the second way, however, carries a less favorable view compared to the earlier plumbing narrative. The exact same data are used to create each narrative. In neither case is the “real” semantic – that of probability – expressly stated.  In the second narrative, though, one is confronted with the implication of probability: it is possible (perhaps even likely) that the operation will not actually prevent the stroke as intended.  But with an understanding of probability, a patient might still rationally opt for surgery to improve the probability of stroke. 
      In the end, whether we know or care about statistics and decision theory, we all have some heuristic for risk tolerance. When patients request that I calculate the proverbial mother-statistic for their case, I phrase my answer by sharing the wisdom of both of my parents: my father is relatively risk tolerant while my mother is relatively risk averse.  This reminds me to describe how people at each end of the risk tolerance continuum might reason through a medical decision. Whatever method a person actually uses to reason through decisions (including the enteric route of pure gut reaction), recognizing that complicated decisions may not have a clear mother-statistic answer may be as important to the decision process as the final decision itself.
 
Contributed by: Matt Bianchi, MD PhD
A version of this blog was originally posted on HorseAndZebra (no longer active), in 2011
 
References
[1]  www.ncbi.nlm.nih.gov/pmc/articles/PMC3058025/

     Dress codes stand at the precarious interface between three spheres of thinking: (1) our long-held children’s parable-worthy ethics of not judging a book by its cover, (2) the occasionally obvious utility that appearance can provide some “warning” information (eg, stumbling inebriated person), and perhaps most commonly, (3) that our own garb choices can be both an expression of style and can actually influence how we feel about ourselves.
     Sphere 1 seems at odds with the pragmatic spirit of Sphere 2, and with the self-lifting spirit of Sphere 3. Of course, each has its merits, and aside from extreme cases, none seems particularly unreasonable.  Where does the issue of physician dress code land?  This topic pops up every now and again in local department “rules,” as well as media stories, and even in the medical literature.  The discussions span practical, traditional, and even sometimes patient safety arenas like infection being potentially spread by coats and ties.  Perhaps the only moment of solidarity comes in the form of White Coat Ceremonies at the heart of most medical school curricula, and all of its associated symbolism.
     However, even in my own training years across medical school and residency, the proportion of white-coat-wearing people had changed over time.  MDs might opt against the coat, while other practitioners might don a very similar or identical looking coat.  The media doesn’t seem to pay much attention to physician garb, except it seems in sporadic re-discovery of the discussions involving infection risk with long sleeves, neck ties, jewelry, and so forth.
     Sometimes the question of physician attire, with respect to infection risk, even takes on an “evidence-based medicine” air, though our typical randomized double-blind clinical trial design would be tricky to implement when it comes to clothing.  Even if we tried to brainstorm at the whiteboard, we might quickly enter the rabbit hole of stratifying even a naturalistic trial to span all possible combinations of clothing, jewelry, and even medical accoutrements used in examinations.  If you are already seeing the stage set for a tempting satirical crossover trial, well, there is nothing new under the sun (I’m not making this up: see Nair et al 2002).
     But seriously, there is a literature out there attempting to study physician dress and patient response to it.  Delving into this area has all the anticipatory excitement of finding the Cracker Jack toy: you know it’s not going to be that great, but you can’t help but look.  Curiosity got a hold of me, I’ll admit, when I was a resident, and eventually I felt compelled to write a critique on the prevailing dress code literature in medicine (Bianchi 2008).
     So it turns out that physicians wearing white coats is (a) fairly new and (b) not restricted to physicians – two simple points that make the “tradition” and “identification” arguments a bit shaky for a pro-dress-code stance.  Even if the original Hippocratic oath itself contained attire recommendations, multiple recent commentaries outline numerous “revisions” to the language to fit what current society thinks is reasonable.  A medical historian, Dr Markel (2004) wrote an elegant piece in the New England Journal of Medicine, and conveniently it was published the same year as my medical school graduation. Among other pearls, we learn that surgery itself, now obviously central to the practice of medicine, was viewed quite oppositely in the Apollo-swearing oath of antiquity which explicitly prohibited it!  
     Perhaps the bottom line is that patient trust is critical, and what might seem superficially about outward appearances is really about the big picture of how trust is earned in the physician-patient relationship, which for better or for worse, may involve attire.  Even when taking this perspective, it is curious that the literature adds an interesting twist: it turns out perception can actually supersede reality in this interaction.  In a now-20-year old study (Pronchik et al 1998), patients were asked about their physician experience after the fact, and it turned out their memory revealed improved formality of clothing worn by doctors who with whom they had positive interactions. 
     Perhaps we physicians can thus take some solace in simply being the best doctor that we can be, and each patient’s memory will dress us in retrospect as it sees fit.


Contributed by: Matt Bianchi MD PhD


A version of this blog was originally posted on HorseAndZebra (no longer active), in 2011
 
 
References

Markel H. “I swear by Apollo”– on taking the Hippocratic oath. N Engl J Med. 2004 May 13;350(20):2026-9.
Nair BR, Attia JR, Mears SR, Hitchcock KI. Evidence-based physicians’ dressing: a crossover trial. Med J Aust. 2002;177(11–12):681–2.
Pronchik DJ, Sexton JD, Melanson SW, Patterson JW, Heller MB. Does wearing a necktie influence patient perceptions of emergency department care? J Emerg Med. 1998;16(4):541–3.
Bianchi MT. Desiderata or dogma: what the evidence reveals about physician attire.J Gen Intern Med. 2008 May;23(5):641-3.
 
 
 

Welcome to another addition of the Bayesian Statistics Avenger and his 3 minions.

     Everyone agrees that patient safety and physician performance are important.  Yet translating these seemingly straightforward fundamentals into real-world policy is not trivial, and the question of addressing sleep deprivation during residency continues to evoke polarizing rhetoric, especially in the wake of the FIRST[1] trial and the ACGME decision in 2017 to relax residency work hours restrictions.      
     
     Those arguing for and against work hours restrictions theoretically have access to the same body of literature, yet arrive at opposite conclusions.  The basic logic of restricting hours is: experimental sleep deprivation impairs performance; long shifts increase the risk of sleep deprivation; sleep deprivation in residents leads to medical errors; shortening shifts will mitigate said deprivation; thus fewer medical errors will occur.  As with many topics in medicine, what seems straightforward in the world of principle may not enjoy such luxury in the world of practice.  Even in controlled experimental settings, the literature is strikingly mixed in evaluating the impact of sleep deprivation in the healthcare setting[2,3].  This not only reminds us of the complexity of studying this topic, but also sets the precarious stage for selective citing.  Looking back to the landmark intensive care unit (ICU) experiments in 2004[4,5], widely cited as evidence supporting restricted work hours, provides clues to anchor ongoing discussions as we grapple with increasing concerns that the restrictions, seen by many as reasonable if not imperative, have in many ways failed to bear convincing fruits.  Drilling deeper into this early work, not in criticism, but rather in inferential reflection, may provide context to reconciling the mixed literature and soften the polarizing rhetoric so that ongoing efforts to improve safety and performance will see measured discussion (e.g. [6]) that avoids over-simplifying a complex issue.
​
    First, the study carefully quantified what is at stake in a high-risk ICU environment: 1.3 serious medical errors per 10 patient-days in usual care, which was reduced to 1.0 in the intervention group. Placing this difference into context is a challenging task, because where policy is concerned, the absolute risk is more important than the relative risk.  For medical error rates, the statistically pertinent value is arguably not error per-patient but rather error per-decision.  However, the required denominator for per-decision risk is not known: how many decisions are at-risk for serious error per patient-day in an ICU?  If it is 100 (it could easily be so, across categories of exam, labs, medications, and diagnostics), then in 10 days, the base error rate would be ~0.1%.  Similar values (and implications) exist for multi-center hand-off interventions to reduce errors[7].  Whether one thinks a 0.1% error rate is unacceptably high is inferentially orthogonal to the question of rationalizing interventions to reduce already-small base error rates.  We would be challenged to demonstrate a reduction from a base risk even as high as 1% in almost any medical field.  Framed this way, it is perhaps not surprising that mixed results have emerged from an indirect intervention (work hours restrictions) to address a heterogeneous problem (sleep deprivation) in a complex system (residency) applied uniformly across diverse practice settings, coupled with individual heterogeneity of vulnerability to deprivation, and placed within a broader context that includes unintended consequences (handoffs, shift-culture, educational, etc). 

     Second, the randomization of that pioneering ICU study did not evenly distribute workload: the longer shift group happened to have ~30% more admissions and patient-days.  This is a reminder that the inpatient world is hard to predict, and even prospective trials are vulnerable to ineffective randomization (as seen more recently, for example, in the controversial SERVE-HF trial[8]).  Workload heterogeneity, even within one center, reminds us of the challenges inherent in mandating hours-limits equally across specialties and hospitals with distinct operational ecosystems. 

     Third, while the study reported increased EEG-defined attention lapses in the usual work-hours group, over one-third of the intern participants did not show this effect, and no within-individual prediction was reported to allow association between EEG lapses and medical errors.  This is a reminder of the long-recognized reality of inter-individual variability in sleep deprivation vulnerability[9], and raises the question of whether EEG lapses are in fact biomarkers for medical errors.   

     Fourth, crediting sleep for the reported lower rate of serious medical error rates under the shorter shift intervention is confounded by several other factors that differed between the groups (workloads, staffing, handoffs). Interestingly, to this point, the intervention group did not use much of their extra time for sleep (~45 minutes more per day), though actually this is consistent with other work[10].  This reminds us of how challenging traditional evidence-based causal attributions remain in this arena of trainee performance.

     One interpretation of the evolving work hours saga is that we need even stricter schedules, more rigorous tracking, and improved compliance[11,12].  An alternative approach is to query whether work hours restrictions are the best avenue to improve patient safety, even if we could solve the entrenched issues of increased cost, handoff risk, culture shift, and education paradigms.  The mixed literature and results of the FIRST trial should not be viewed as a criticism of sleep research, or as proof that sleep is not important.  A reasonable interpretation is that sleep is one of many factors impacting resident performance, which is arguably near its statistical ceiling, such that stratifying factors for pragmatic interventions that deliver measurable results is far from straightforward.  That many factors are involved in patient safety during residency training was recognized in the Libby Zion tragedy (1984) that is widely cited as triggering residency training reform: work hours represented one of five contributing factors noted in trial.  Bertrand Bell himself, of the Bell commission, lamented that trainee regulations following the Zion case emphasized work hours over the stated key factor of supervision[13]. 

      For any complex issue, it is inevitable that published data and stakeholder opinions will be mixed.  For this issue in particular, recognizing uncertainties is a key step grounding a discussion that struggles to reconcile experimental and real-world validation in the wake of the FIRST trial and the ACGME decision to relax duty hour requirements[14,15].  Ultimately, one can believe that sleep deprivation impacts performance, and that patient and physician safety are important, and yet still conclude that work hours restrictions may not be the best use of resources to reliably mitigate the attributable risk.

Contributed by: Dr Matt Bianchi

References
1.         Bilimoria KY, Chung JW, Hedges LV, et al. National Cluster-Randomized Trial of Duty-Hour Flexibility in Surgical Training. The New England Journal of Medicine. Feb 25 2016;374(8):713-727.
2.         Friedman WA. Resident duty hours in American neurosurgery. Neurosurgery. Apr 2004;54(4):925-931; discussion 931-923.
3.         Philibert I, Nasca T, Brigham T, Shapiro J. Duty-hour limits and patient care and resident outcomes: can high-quality studies offer insight into complex relationships? Annual Review of Medicine. 2013;64:467-483.
4.         Landrigan CP, Rothschild JM, Cronin JW, et al. Effect of reducing interns' work hours on serious medical errors in intensive care units. The New England Journal of Medicine. Oct 28 2004;351(18):1838-1848.
5.         Lockley SW, Cronin JW, Evans EE, et al. Effect of reducing interns' weekly work hours on sleep and attentional failures. The New England Journal of Medicine. Oct 28 2004;351(18):1829-1837.
6.         Shea JA, Willett LL, Borman KR, et al. Anticipated consequences of the 2011 duty hours standards: views of internal medicine and surgery program directors. Academic Medicine: Jul 2012;87(7):895-903.
7.         Starmer AJ, Spector ND, Srivastava R, et al. Changes in medical errors after implementation of a handoff program. The New England Journal of Medicine. Nov 06 2014;371(19):1803-1812.
8.         Cowie MR, Woehrle H, Wegscheider K, et al. Adaptive Servo-Ventilation for Central Sleep Apnea in Systolic Heart Failure. The New England journal of medicine. Sep 17 2015;373(12):1095-1105.
9.         Van Dongen HP, Vitellaro KM, Dinges DF. Individual differences in adult human sleep and wakefulness: Leitmotif for a research agenda. Sleep. Apr 2005;28(4):479-496.
10.       Baldwin DC, Jr., Daugherty SR. Sleep deprivation and fatigue in residency training: results of a national survey of first- and second-year residents. Sleep. Mar 15 2004;27(2):217-223.
11.       AASM urges ACGME to limit resident work periods to 16 hours. 2016; http://www.aasmnet.org/articles.aspx?id=6647, 2017.
12.       Volpp KG, Landrigan CP. Building physician work hour regulations from first principles and best evidence. JAMA : the journal of the American Medical Association. Sep 10 2008;300(10):1197-1199.
13.       Bell BM. Resident duty hour reform and mortality in hospitalized patients. JAMA : the journal of the American Medical Association. Dec 26 2007;298(24):2865-2866; author reply 2866-2867.
14.       AMSA and Public Citizen Send Complaint Letters Concerning FIRST and iCompare Trials. 2016; http://www.amsa.org/about/amsa-press-room/first-icompare-complaint-letters/, 2017.
15.       Landrigan CP, Czeisler CA. A health-care change that could prove catastrophic. 2017; https://www.washingtonpost.com/opinions/a-health-care-change-that-could-prove-catastrophic/2017/02/22/2a4970d2-f30e-11e6-a9b0-ecee7ce475fc_story.html?utm_term=.aab3f9a29c45, 2017.
 
Disclosure: A version of this article was rejected from two major US medical journals in 2017.

​Link: www.ncbi.nlm.nih.gov/pubmed/28835407
 
Welcome to another addition of the Bayesian Statistics Avenger and his 3 minions.

Link: https://www.ncbi.nlm.nih.gov/pubmed/2059654

Welcome to another addition of the Bayesian Statistics Avenger and his 3 minions.

Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4751423/​

     If I had hair, it would have stood up on the back of my neck when I first saw this video.  Heroes and rock stars can inspire, motivate, and even console.  The video is gripping because we might not think of heroes and rock stars in certain setting.  This is perhaps never more true than in statistics, or more generally, how we make sense of data in medical research.  I admit to even being embarrassed in the past about this topic, which seems at best to evoke yawns, and at worst takes the blame for obfuscating arguments and twisting facts.  But what if we had a hero of statistics – no superpowers, just a mortal who would be like Holden Caulfield, catching those who wander too close to the risky edge of data mis-interpretation, stepping in to keep us safe but also aware of the edge.
     My hero is the Bayesian Statistics Avenger.  And every hero needs a sidekick.  Or three.  The Avenger is more Socrates than Hulk, and less saving from an enemy, and more protection from wandering without context.  The three minions each see data from a different perspective.  Dr Yes is the optimist, singing the praises of the research.  You’d think Dr Yes was a publicist for the researchers.  Dr No is the pessimist, with a keen eye for spotting weakness and vulnerability.  You’d think Dr No was a contrarian gladiator hired by competitors.  Dr Go is the moderate soothsayer who wants everyone to just get along, offering concrete advice on how to bridge the gaps between Dr Yes and Dr No. 
     You may find that you gravitate toward one of these minions, or that you have some of each of them within you.  Hopefully glancing at all three meets the goal: context.  We’ll start with some of my own work, to kick off this series of entries… enjoy!
 
Contributed by Dr Matt Bianchi.

The circadian rhythm dominates discussions of jet lag.  This is no accident: what could be more obvious than the body’s internal clock as the focus of how we should adjust to external clock changes as we travel across time zones?  Well, like everything in life, the story is deeper than it first appears.  Luckily, in the case of jet lag, to get the most out of travel planning, we only need to peak just beyond the circadian rhythm, to another internal “clock”, also managed by the brain (surprise!) called the sleep homeostat. 
Before we talk about the sleep homeostat, let’s briefly visit the circadian part.  When we travel west across time zones, we “gain” time before bed, in a sense, as the destination clock time is earlier compared to the home (departure) zone.  Many people find such travel easier to manage, since it seems easier to stay up later than usual for many of us.  But when we travel east across time zones, that is harder for most people, since it seems harder to go to bed earlier than usual.  As far as wake up times, traveling west is awesome because we can sleep in (at least theoretically) compared to home – but traveling east is a bummer because we need to get up earlier than we are used to.  This is all roughly true for typical travel of 2-8 time zones.  The more the time zone change, the harder it is to adjust in general, and as we approach 12 hours of change, the direction of travel plays less of a direct role.   Techniques to reduce jetlag that target the circadian system involve schedule shifts (moving your sleep schedule gradually over several days prior to travel to approach the destination zone), light exposure (to match ideal times or at least avoid counter-productive times of bright light), and sometimes also taking melatonin.  Sites like www.jetlagrooster.com are useful for this kind of advice. 
Now, back to the homeostat.  This clock starts ticking when you wake up, and keeps track of how long you’ve been awake.  For a person who typically sleeps from, say, 10pm to 6am, when night rolls in, there are two clocks say it’s time for sleep: the circadian clock that pays attention to the actual clock on the wall, and the homeostatic clock that says “you’ve been awake since 6am, so yes, you can sleep now”.  Fun fact: when you drink coffee, it is thought that the caffeine tricks the homeostatic clock into thinking you have not been awake as long as you have.  The homeostatic clock can also be “tricked” by naps. If you are a 10pm to 6am kind of person, and you happen to take a nap late in the day, say, from 7-9pm, the homeostatic clock senses the recent sleep, so it might be difficult if you try to sleep again at 10pm.   
How does this relate to jet lag?  Well, let’s say you are flying east, across 5 time zones, and you land at 9pm in the destination time zone.  Say you are hoping to be in bed shortly thereafter, by 10pm.  Your circadian system does not like this, because it is only 5pm in your home zone, which it is accustomed to.  Well, let’s also say you happened to also doze for 3 hours, in the second half of the flight. Now, your homeostat is also not happy, since you just slept recently, before trying to go to bed at 10.  You have two clocks working against you!  The point is, depending on the time of day you are landing in the destination time zone, you would be wise to plan your in-flight dozing to please your sleep homeostat.  For evening destination landings, you’d be wise to limit your in flight sleep, and if you do sleep, try to front-load it at the beginning of the flight, so you have been awake for a while upon landing, and have built up some homeostatic drive to sleep. This thinking applies to morning arrivals as well: in that case, sleep as much as you can and as close to landing as you can, so you wake up with your homeostat on your side to maintain alertness during the day at destination. 
 
Contributed by:  Dr Matt Bianchi.