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The ability of statins to reduce the morbidity and mortality of cardiovascular disease has ensured that they are among the most prescribed drugs in modern medicine. Unfortunately, most patients who start taking statins will end up stopping them, most commonly due to side effects. Confusingly, however, in blinded placebo-controlled trials, side effects appear no more common in those taking statins than those taking placebo. One possible explanation is that ever-present background symptoms are being falsely attributed to statins. However, another explanation is the nocebo effect, where the act of just taking a tablet (even a placebo) causes genuine side effects in patients. Two recent randomized placebo-controlled personalized (N-of-1) trials have been reported: StatinWise and SAMSON. In these trials, each participant was randomized to multiple periods of statin and placebo, with regular symptom burden assessments. Together, these trials support the existence of a significant nocebo effect from taking statins. Possibly even more importantly, they demonstrate the ability of personalized trials to inform and empower patients: up to half of the patients in these trials were able to successfully restart statins after taking part, despite previously having been statin intolerant. StatinWise and SAMSON have raised public awareness of the nocebo effect in statin intolerance. However, they also demonstrate a potential role for the personalized design outside of clinical trials. If taking part in these personalized experiments allows half of our patients to successfully restart life-saving medications, maybe we should be able to prescribe personalized experiments to our patients in the clinical setting?

N-of-1 trials are multiple crossover trials done over time within a single person; they can also be done with a series of individuals. Their focus on the individual as the unit of analysis maintains statistical power while accommodating greater differences between patients than most standard clinical trials. This makes them particularly useful in rare diseases, while also being applicable across many health conditions and populations. Best practices recommend the use of reporting guidelines to publish research in a standardized and transparent fashion. N-of-1 trials have the SPIRIT extension for N-of-1 protocols (SPENT) and the CONSORT extension for N-of-1 trials (CENT). Open science is a recent movement focused on making scientific knowledge fully available to anyone, increasing collaboration, and sharing of scientific efforts. Open science goals increase research transparency, rigor, and reproducibility, and reduce research waste. Many organizations and articles focus on specific aspects of open science, for example, open access publishing. Throughout the trajectory of research (idea, development, running a trial, analysis, publication, dissemination, knowledge translation/reflection), many open science ideals are addressed by the individual-focused nature of N-of-1 trials, including issues such as patient perspectives in research development, personalization, and publications, enhanced equity from the broader inclusion criteria possible, and easier remote trials options. However, N-of-1 trials also help us understand areas of caution, such as monitoring of post hoc analyses and the nuances of confidentiality for rare diseases in open data sharing. The N-of-1 reporting guidelines encourage rigor and transparency of N-of-1 considerations for key aspects of the research trajectory.

This article, an introduction to HDSR 's "Personalized (N-of-1) Trials: Methods, Applications, and Impact" special issue, describes the rationale for a primer of the methods, data types and management, designs, and use cases for personalized (N-of-1) trials. It explains that the design and implementation of personalized (N-of-1) trials is only useful if patients volunteer for research involving them and actively participate in clinical services that use them. However, 'N-of-1 trials' may be an inadequate name to enact such patient engagement. The authors briefly review what patients have reported about the 'N-of-1' label and propose a more consumer-friendly moniker for this type of research and clinical approach to improve evidence-based science.

In this article we briefly examine the unique features of Single-Case Designs (SCDs) (studies in a single participant), their history and current trends, and real-world clinical applications. The International Collaborative Network for N-of-1 Trials and Single-Case Designs (ICN) is a formal collaborative network for individuals with an interest in SCDs. The ICN was established in 2017 to support the SCD scientific community and provide opportunities for collaboration, a global communication channel, resource sharing and knowledge exchange. In May 2021, there were more than 420 members in 31 countries. A member survey was undertaken in 2019 to identify priorities for the ICN for the following few years. This article outlines the key priorities identified and the ICN's progress to date in these key areas including network activities (developing a communications strategy to increase awareness, collecting/sharing a comprehensive set of resources, guidelines and tips, and incorporating the consumer perspective) and scientific activities (writing position papers and guest editing special journal issues, exploring key stakeholder perspectives about SCDs, and working to streamline ethical approval processes for SCDs). The ICN provides a practical means to engage with this methodology through membership. We encourage clinicians, researchers, industry, and healthcare consumers to learn more about and conduct SCDs, and to join us in our mission of using SCDs to improve health outcomes for individuals and populations., Competing Interests: Authors declare that the article was prepared in the absence of any financial relationships that could be construed as a conflict of interest. Authors JN and SM have a commercial interest in N-of-1 Hub Pty Ltd consultancy company. N-of-1 Hub Pty Ltd has no commercial or financial relationship with the International Collaborative Network for N-of-1 Trials and Single-Case Designs (ICN)., (© 2021 Published by Elsevier Inc.)

Objective: This study aimed to investigate the safety and effectiveness of probiotics in osteoarthritic pain for one individual.Methods: The study was an N-of-1 trial design, divided into 3 blocks of 10 weeks. Each block included one pair of randomized interventions (AB), separated by a washout period. The trial took place in a private naturopathic practice in Sydney, Australia. The participant was a 67 year old female with osteoarthritis in her lower back and right ankle. The active intervention was two daily capsules that contained Lactobacillus rhamnosus (LGG®), Saccharomyces cerevisiae (boulardii) and Bifidobacterium animalis ssp lactis. The placebo was an identical capsule that did not contain probiotics. The primary outcome was daily pain scores, measured by the participant on a Visual Analogue Scale (VAS). Secondary outcome measures included patient preference (of intervention), General Health Questionnaire (GHQ-12), Patient Specific Functional Scale (PSFS), Comprehensive Digestive Stool Analysis (CDSA) and rescue medication usage. A dependent t-test analysed mean pain scores for the last week of each intervention across the three blocks of the study.Results: The probiotic intervention was associated with lower pain scores and was the preferred intervention chosen by the participant. The mean pain score on the VAS was 4.9 ± 2.2 in the placebo condition compared to 4.0 ± 1.7 in the probiotic condition (t(20) = 2.2, p =  0.04, difference = 0.9, 95 % CI [0.04, 1.77]).Conclusions: The reduction in pain scores associated with the probiotic intervention was small but clinically significant for this patient. A holistic view of the patient focusing on digestive integrity and function may be crucial for clinical applications of interventions such as probiotics. N-of-1 trial designs allow for the measurement of a holistic approach to an individual, which is aligned with naturopathic practice. Further trials are required to generate data to enable reliable estimation of population effects. [ABSTRACT FROM AUTHOR]

Complementary medicines and therapies are popular forms of healthcare with a long history of traditional use. Yet, despite increasing consumer demand, there is an ongoing exclusion of complementary medicines from mainstream healthcare systems. A lack of evidence is often cited as justification. Until recently, high-quality evidence of treatment efficacy was defined as findings from well-conducted systematic reviews and meta-analyses of randomized controlled trials. In a recent and welcome move by the Oxford Centre for Evidence-Based Practice, however, the N-of-1 trial design has also been elevated to the highest level of evidence for treatment efficacy of an individual, placing this research design on par with the meta-analysis. N-of-1 trial designs are experimental research methods that can be implemented in clinical practice. They incorporate much of the rigor of group clinical trials, but are designed for individual patients. Individualizing treatment interventions and outcomes in research designs is consistent with the movement towards patient-centered care and aligns well with the principles of holism as practiced by naturopaths and many other complementary medicine practitioners. This paper explores whether rigorously designed and conducted N-of-1 trials could become a new 'gold standard' for demonstrating treatment efficacy for complementary medicine interventions in individual patients in clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: Little is known about the effectiveness of bright white light therapy (BWL) for depressive symptoms in cancer survivors, many of whom prefer non-pharmacological treatments. The purpose of this study was to compare the effectiveness of BWL versus dim red light therapy (DRL) on depressive symptoms within individual cancer survivors using personalized (N-of-1) trials. Methods: Cancer survivors with at least mild depressive symptoms were randomized to one of two treatment sequences consisting of counterbalanced crossover comparisons of three-weeks of lightbox-delivered BWL (intervention) or DRL (sham) for 30 min each morning across 12 weeks. A smartphone application guided cancer survivors through the treatment sequence and facilitated data collection. Cancer survivors tracked end-of-day depressive symptoms (primary outcome) and fatigue using visual analog scales. Within-patient effects of BWL were assessed using an autoregressive model with adjustment for linear time trends. Results: Eight of nine cancer survivors completed the 12-week protocol. Two survivors reported significantly (i.e., p < 0.05) lower depressive symptoms (−1.3 ± 0.5 and −1.30 ± 0.9 points on a 10-point scale), five reported no difference in depressive symptoms, and one reported higher depressive symptoms (+1.7 ± 0.6 points) with BWL versus DRL. Eight of nine cancer survivors recommended personalized trials of BWL to others. Conclusions: There were heterogeneous effects of three-week BWL on self-reported depressive symptoms among cancer survivors, with some finding a benefit but others finding no benefit or even harm. Implications for Cancer Survivors: Personalized trials can help cancer survivors learn if BWL is helpful for improving their depressive symptoms. [ABSTRACT FROM AUTHOR]

N-of-1 trials may provide insights into the impact of deprescribing medications in populations where evidence is currently lacking, such as the effect of statins on cognition in people with dementia. For this pilot, N-of-1, double-blinded, deprescribing trial, adults over 80 years of age with dementia taking statins for at least 6-months were recruited from a hospital's geriatric medicine outpatient clinic in Sydney, Australia. Participants discontinued and restarted statins over the study period. At enrolment, the hospital pharmacy—using a random number generator, randomised recruited participants to their usual statin or placebo regimen, with assessment and switching of treatment every 5 weeks from baseline (0-weeks) until Visit 4 (15-weeks). Primary outcome was measured using the rate of change in Alzheimer's Disease Assessment Score-Cognitive Subscale (ADAS-CoG). Over 6-months, 81 participants were screened, 14 were eligible, and four were randomised. One participant (female, 88 years) completed all four assessments with no major harms reported. Cognitive impairment, as measured by ADAS-CoG score, was similar on placebo (15.5/70) compared to statin (15/70). This study suggests there are significant challenges in performing N-of-1 trials and recruiting people with dementia into deprescribing trials from outpatient settings. [ABSTRACT FROM AUTHOR]

N-of-1 trial designs have rarely been used in bodywork research. Using a recent trial as a methodological pilot, critical issues related to the applicability of N-of-1 trials to bodywork are discussed. These include the issues of carry-over effects, bias-controlling approaches and statistical analysis. The discussion highlights the importance of mixed methods and draws some suggestions for a future research program. N-of-1 trials could be used to provide insights about some essential elements of bodywork modalities and their effectiveness., (Copyright © 2021 Shanghai Changhai Hospital. Published by Elsevier B.V. All rights reserved.)

Background and Significance: Parallel-group randomized controlled trials (PG-RCTs) are the gold standard for detecting differences in mean improvement across treatment conditions. However, PG-RCTs provide limited information about individuals, making them poorly optimized for quantifying the relationship of a biomarker measured at baseline with treatment response. In N-of-1 trials, an individual subject moves between treatment conditions to determine their specific response to each treatment. Aggregated N-of-1 trials analyze a cohort of such participants, and can be designed to optimize both statistical power and clinical or logistical constraints, such as allowing all participants to begin with an open-label stabilization phase to facilitate the enrollment of more acutely symptomatic participants. Here, we describe a set of statistical simulation studies comparing the power of four different trial designs to detect a relationship between a predictive biomarker measured at baseline and subjects' specific response to the PTSD pharmacotherapeutic agent prazosin. Methods: Data was simulated from 4 trial designs: (1) open-label; (2) open-label + blinded discontinuation; (3) traditional crossover; and (4) open label + blinded discontinuation + brief crossover (the N-of-1 design). Designs were matched in length and assessments. The primary outcome, analyzed with a linear mixed effects model, was whether a statistically significant association between biomarker value and response to prazosin was detected with 5% Type I error. Simulations were repeated 1,000 times to determine power and bias, with varied parameters. Results: Trial designs 2 & 4 had substantially higher power with fewer subjects than open label design. Trial design 4 also had higher power than trial design 2. Trial design 4 had slightly lower power than the traditional crossover design, although power declined much more rapidly as carryover was introduced. Conclusions: These results suggest that an aggregated N-of-1 trial design beginning with an open label titration phase may provide superior power over open label or open label and blinded discontinuation designs, and similar power to a traditional crossover design, in detecting an association between a predictive biomarker and the clinical response to the PTSD pharmacotherapeutic prazosin. This is achieved while allowing all participants to spend the first 8 weeks of the trial on open-label active treatment., (Copyright © 2020 Hendrickson, Thomas, Schork and Raskind.)

Introduction: Statins reduce the incidence of cardiovascular disease (CVD) and cause few adverse effects. Half of patients prescribed statins discontinue treatment due to perceived intolerance. Placebo-controlled (blinded) n-of-1 trials have shown people with perceived intolerance that the statin does not cause adverse events and most resume treatment. However, blinded n-of-1 trials are impractical to deliver in routine practice. Tackling Statin Intolerance using n-of-1 trials (TaSINI) will test the feasibility of a general practitioner (GP)-delivered behavioural intervention endorsing an unblinded n-of-1 trial to increase adherence to statins relative to usual care., Methods and Analysis: TaSINI is a feasibility randomised controlled trial with a nested qualitative substudy. Ninety primary care patients who have discontinued statins due to intolerance or refused treatment will be randomised to an unblinded n-of-1 trial, a blinded n-of-1 trial (positive control) or usual care (negative control). Participants randomised to usual care will be advised to take statin therapy to prevent CVD. In both n-of-1 trial arms, GPs will deliver a behaviourally informed intervention that accessibly explains the benefits of statins, the prevalence of adverse effects and endorse the benefit of experimenting with medication. Participants will alternate between 4 weeks of medication and no medication (unblinded arm) or randomly sorted active and placebo (blinded arm) and will record adherence, symptoms and symptom attributions throughout. After 6 months, GPs will feedback symptom data during active/inactive treatment periods. All participants will be asked if they would like to initiate statin treatment. Measures of feasibility will be met if 4% of invited patients enrol, 50% of participants randomised to n-of-1 trials engage with the experiment and 25% more participants initiate statin in the unblinded n-of-1 arm than in usual care., Ethics and Dissemination: This study has been granted ethical approval by North of Scotland Research Ethics Service. The results will be written up for publication and show whether to progress to an effectiveness trial where the primary outcome would be differences in low-density lipoprotein concentration., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: N-of-1 trials promise to help individuals make more informed decisions about treatment selection through structured experiments that compare treatment effectiveness by alternating treatments and measuring their impacts in a single individual. We created a digital platform that automates the design, administration, and analysis of N-of-1 trials. Our first N-of-1 trial, the app-based Brain Boost Study, invited individuals to compare the impacts of two commonly consumed substances (caffeine and L-theanine) on their cognitive performance., Objective: The purpose of this study is to evaluate critical factors that may impact the completion of N-of-1 trials to inform the design of future app-based N-of-1 trials. We will measure study completion rates for participants that begin the Brain Boost Study and assess their associations with study duration (5, 15, or 27 days) and notification level (light or moderate)., Methods: Participants will be randomized into three study durations and two notification levels. To sufficiently power the study, a minimum of 640 individuals must begin the study, and 97 individuals must complete the study. We will use a multiple logistic regression model to discern whether the study length and notification level are associated with the rate of study completion. For each group, we will also compare participant adherence and the proportion of trials that yield statistically meaningful results., Results: We completed the beta testing of the N1 app on a convenience sample of users. The Brain Boost Study on the N1 app opened enrollment to the public in October 2019. More than 30 participants enrolled in the first month., Conclusions: To our knowledge, this will be the first study to rigorously evaluate critical factors associated with study completion in the context of app-based N-of-1 trials., Trial Registration: ClinicalTrials.gov NCT04056650; https://clinicaltrials.gov/ct2/show/NCT04056650., International Registered Report Identifier (irrid): PRR1-10.2196/16362., (©Jason R R Bobe, Jacqueline Buros, Eddye Golden, Matthew Johnson, Michael Jones, Bethany Percha, Ryan Viglizzo, Noah Zimmerman. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 08.01.2020.)

Purpose : Little is known about the effectiveness of bright white light therapy (BWL) for depressive symptoms in cancer survivors, many of whom prefer non-pharmacological treatments. The purpose of this study was to compare the effectiveness of BWL versus dim red light therapy (DRL) on depressive symptoms within individual cancer survivors using personalized (N-of-1) trials. Methods : Cancer survivors with at least mild depressive symptoms were randomized to one of two treatment sequences consisting of counterbalanced crossover comparisons of three-weeks of lightbox-delivered BWL (intervention) or DRL (sham) for 30 min each morning across 12 weeks. A smartphone application guided cancer survivors through the treatment sequence and facilitated data collection. Cancer survivors tracked end-of-day depressive symptoms (primary outcome) and fatigue using visual analog scales. Within-patient effects of BWL were assessed using an autoregressive model with adjustment for linear time trends. Results : Eight of nine cancer survivors completed the 12-week protocol. Two survivors reported significantly (i.e., p < 0.05) lower depressive symptoms (-1.3 ± 0.5 and -1.30 ± 0.9 points on a 10-point scale), five reported no difference in depressive symptoms, and one reported higher depressive symptoms (+1.7 ± 0.6 points) with BWL versus DRL. Eight of nine cancer survivors recommended personalized trials of BWL to others. Conclusions : There were heterogeneous effects of three-week BWL on self-reported depressive symptoms among cancer survivors, with some finding a benefit but others finding no benefit or even harm. Implications for Cancer Survivors: Personalized trials can help cancer survivors learn if BWL is helpful for improving their depressive symptoms.

Background: Gastric cancer has been the second cause of cancer death worldwide. Chemical comprehensive treatment programs primarily were the main therapy method with modest efficacy to gastric cancer. Traditional Chinese medicine (TCM) has been reported to alleviate adverse events induced by chemotherapy, but has not yet developed clinical trials to test and needs scientific evidence for making policy. Single-patient (N-of-1) trials might be an eligible study design for TCM since it well represented the individualized treatment philosophy of TCM. The aim of this study is to obtain information necessary to design a more series trial., Methods: Individuals who underwent gastrectomy were included. Each patient suffered 3-week standard chemotherapy and 3-day treatment periods (decoction with Astragalus mongholicus and Semen Cuscutae or placebo: decoction without Astragalus mongholicus and Semen Cuscutae). Each trial lasted up to a maximum of 30 weeks or a minimum of 20 weeks. Staffs and participants were blinded to the randomization. This study was approved by Ethics Committee of First Hospital, Lanzhou University in November, 2014., Results: From August, 2014 to March, 2015, 6 participants were included. There were 16 cycles compared between intervention and control decoction (2.28, 95% CI: 1.24-5.47), P<0.0001. The quality of life (QoL) score after the trial was reported is a little higher than before, t=3.87, P=0.01. Two participants reported symptoms had improved after taken trial decoction., Conclusions: This is the first N-of-1 trials of testing the effectiveness of TCM decoction on alleviative treatment to gastric cancer. The feasibility study will help to develop a practical design for the more series trial., Competing Interests: Conflicts of Interest: The authors have no conflicts of interest to declare.

Background: N-of-1 (individual comparison) trials are a promising approach for comparing the effectiveness of 2 or more treatments for individual patients; yet, few studies have qualitatively examined how patients use and make sense of their own patient-generated health data (PGHD) in the context of N-of-1 trials. Objective: The objective of our study was to explore chronic pain patients' perceptions about the PGHD they compiled while comparing 2 chronic pain treatments and tracking their symptoms using a smartphone N-of-1 app in collaboration with their clinicians. Methods: Semistructured interviews were recorded with 33 patients, a consecutive subset of the intervention group in a primary study testing the feasibility and effectiveness of the Trialist N-of-1 app. Interviews were transcribed verbatim, and a descriptive thematic analysis was completed. Results: Patients were enthusiastic about recording and accessing their own data. They valued sharing data with clinicians but also used their data independently. Conclusions: N-of-1 trials remain a promising approach to evidence-based decision making. Patients appear to value their roles as trial participants but place as much or more importance on the independent use of trial data as on comparative effectiveness results. Future efforts to design patient-centered N-of-1 trials might consider adaptable designs that maximize patient flexibility and autonomy while preserving a collaborative role with clinicians and researchers. [ABSTRACT FROM AUTHOR]

Objective: Despite their promise for increasing treatment precision, Personalized Trials (i.e., N-of-1 trials) have not been widely adopted. We aimed to ascertain patient preferences for Personalized Trials., Study Design and Setting: We recruited 501 adults with ≥2 common chronic conditions from Harris Poll Online. We used Sawtooth Software to generate 45 plausible Personalized Trial designs comprising combinations of eight key attributes (treatment selection, treatment type, clinician involvement, blinding, time commitment, self-monitoring frequency, duration, and cost) at different levels. Conditional logistic regression was used to assess relative importance of different attributes using a random utility maximization model., Results: Overall, participants preferred Personalized Trials with no costs vs. $100 cost (utility difference 1.52 [standard error 0.07], P < 0.001) and with less vs. more time commitment/day (0.16 [0.07], P < 0.015) but did not hold preferences for the other six attributes. In subgroup analyses, participants ≥65 years, white, and with income ≤$50,000 were more averse to costs than their counterparts (P all <0.05)., Conclusion: To optimize dissemination, Personalized Trial designers should seek to minimize out-of-pocket costs and time burden of self-monitoring. They should also consider adaptive designs that can accommodate subgroup differences in design preferences., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Systems biology uses mathematical models to analyze large datasets and simulate system behavior. It enables integrative analysis of different types of data and can thereby provide new insight into complex biological systems. Here will be discussed the challenges of using systems medicine for advancing the development of personalized and precision medicine to treat metabolic diseases like insulin resistance, obesity, NAFLD, NASH, and cancer. It will be illustrated how the concept of genome-scale metabolic models can be used for integrative analysis of big data with the objective of identifying novel biomarkers that are foundational for personalized and precision medicine., (Copyright © 2017 Elsevier Inc. All rights reserved.)

There is a growing interest in personalized and preventive medicine initiatives that leverage serious patient engagement, such as those initiated and pursued among participants in the quantified-self movement. However, many of the self-assessments that result are not rooted in good scientific practices, such as exploiting controls, dose escalation strategies, multiple endpoint monitoring, etc. Areas where individual monitoring and health assessments have great potential involve sleep and behavior, as there are a number of very problematic sleep and behavior-related conditions that are hard to treat without personalization. For example, winter depression or seasonal affective disorder (SAD) is a serious, recurrent, atypical depressive disorder impacting millions each year. In order to prevent yearly recurrence antidepressant drugs are used to prophylactically treat SAD. In turn, these antidepressant drugs can affect sleep patterns, further exacerbating the condition. Because of this, possibly unique combinatorial or 'polypharmaceutical' interventions involving sleep aids may be prescribed. However, little research into the effects of such polypharmacy on the long-term sleep quality of treated individuals has been pursued. Employing wireless monitoring in a patient-centered study we sought to gain insight into the influence of polypharmacy on sleep patterns and the optimal course of therapy for an individual being treated for SAD with duloxetine (Cymbalta) and temazepam. We analyzed continuous-time sleep data while dosages and combinations of these agents were varied. We found that the administration of Cymbalta led to an exacerbation of the subject's symptoms in a statistically significant way. We argue that such analyses may be necessary to effectively treat individuals with similar overall clinical manifestations and diagnosis, despite their having a unique set of symptoms, genetic profiles and exposure histories. We also consider the limitations of our study and areas for further research., Competing Interests: Competing interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

There is a growing interest in personalized and preventive medicine initiatives that leverage serious patient engagement, such as those initiated and pursued among participants in the quantified-self movement. However, many of the self-assessments that result are not rooted in good scientific practices, such as exploiting controls, dose escalation strategies, multiple endpoint monitoring, etc. Areas where individual monitoring and health assessments have great potential involve sleep and behavior, as there are a number of very problematic sleep and behavior-related conditions that are hard to treat without personalization. For example, winter depression or seasonal affective disorder (SAD) is a serious, recurrent, atypical depressive disorder impacting millions each year. In order to prevent yearly recurrence antidepressant drugs are used to prophylactically treat SAD. In turn, these antidepressant drugs can affect sleep patterns, further exacerbating the condition. Because of this, possibly unique combinatorial or 'polypharmaceutical' interventions involving sleep aids may be prescribed. However, little research into the effects of such polypharmacy on the long-term sleep quality of treated individuals has been pursued. Employing wireless monitoring in a patient-centered study we sought to gain insight into the influence of polypharmacy on sleep patterns and the optimal course of therapy for an individual being treated for SAD with duloxetine (Cymbalta) and temazepam. We analyzed continuous-time sleep data while dosages and combinations of these agents were varied. We found that the administration of Cymbalta led to an exacerbation of the subject's symptoms in a statistically significant way. Further, we unmasked and monitored treatment effects on a latent obstructive sleep apnea condition. We argue that such analyses may be necessary to effectively treat individuals with similar overall clinical manifestations and diagnosis, despite their having a unique set of symptoms, genetic profiles and exposure histories. We also consider the limitations of our study and areas for further research., Competing Interests: Competing interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.