Geographical variation in perceptions, attitudes and barriers to mental health care-seeking across the UK: a cross-sectional study.

OBJECTIVES: To examine the relative importance of the drivers of mental health care-seeking intention and how these, along with intention itself, are geographically distributed across integrated care systems (ICS) and health boards (HBs) in the UK. Also, to examine the degree of acceptance of virtual modes of care. DESIGN: Community-based cross-sectional survey. PARTICIPANTS AND SETTING: A national online survey of 17 309 adults between August and September 2021 recruited via a research technology company, Lucid. Sample size quotas were set to ensure coverage across the UK and match population distributions for gender, age and ethnicity. After exclusions, 16 835 participants remained (54% female, 89% white). MAIN OUTCOME MEASURES: Care-seeking intention, using a continuous measure of likelihood and a categorical measure of estimated time to seek professional help for a future mental health difficulty. RESULTS: 20.5% (95% CI 19.8% to 21.2%) reported that they would significantly delay or never seek mental healthcare, ranging from 8.3% to 25.7% across ICS/HBs. Multilevel regression analysis showed mental health knowledge was the most predictive of care-seeking intention, followed by attitudes towards others with mental illness and a combination of stigma, negative attitudes to treatment and instrumental barriers to accessing care. The model explained 17% of the variance. There was substantial geographical variation in prevalence of preclinical symptoms of depression and anxiety, attitudes to mental health, and barriers to care, leading to complex ICS/HB profiles. Remote and self-guided therapies did not pose as a major barrier to care with more than half of respondents likely or very likely to use them. CONCLUSIONS: Our locally relevant and actionable findings suggest possible interventions that may improve care-seeking intention and indicate which of these interventions need to be geographically tailored to have maximal effect.

Black-White disparities in maternal vulnerability and adverse pregnancy outcomes: an ecological population study in the United States, 2014-2018.

Background: Systematic information on the association between community-level determinants and maternal health outcomes and disparities is needed. We aimed to investigate multi-dimensional place-based contributions to Black-White maternal health disparities in the United States. Methods: We constructed the Maternal Vulnerability Index, a geospatial measure of vulnerability to poor maternal health. The index was linked to 13m live births and maternal deaths to mothers aged 10-44 for 2014-2018 in the United States. We quantified racial disparities in exposure to higher risk environments, and used logistic regression to estimate associations between race, vulnerability, and maternal death (n = 3633), low birthweight (n = 1.1m), and preterm birth (n = 1.3m). Findings: Black mothers lived in disproportionately higher maternal vulnerability counties, when compared to White mothers (median of 55 vs 36/100 points). Giving birth in the highest-quartile MVI counties was associated with an increase in the odds of poor outcomes when compared to the lowest-quartile (aOR 1.43 [95% CI 1.20-1.71] for mortality, 1.39 [1.37-1.41] for low birthweight and 1.41 [1.39-1.43] for preterm birth, adjusted for age, educational attainment level and race/ethnicity). Racial disparities exist in low- and high-vulnerability counties: Black mothers in the least vulnerable counties remain at higher risk of maternal mortality, preterm birth, and low birthweight as White mothers in the most vulnerable. Interpretation: Exposure to community maternal vulnerability is associated with increased odds of adverse outcomes, but the Black-White gap in outcomes remained under all vulnerability levels. Our findings suggest that locally-informed precision health interventions and further research into racism are needed to achieve maternal health equity. Funding: Bill & Melinda Gates Foundation (grant number INV-024583).

Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data.

BACKGROUND: The COVID-19 pandemic has impacted people's lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States. OBJECTIVE: We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with-or precede-real-life events? METHODS: We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states. RESULTS: The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor's appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others. CONCLUSIONS: COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.

Bringing Greater Precision to Interactions Between Community Health Workers and Households to Improve Maternal and Newborn Health Outcomes in India.

INTRODUCTION: Community health workers (CHWs) play a key role in the health of mothers and newborns in low- and middle-income countries. However, it remains unclear by what actions and messages CHWs enable good outcomes and respectful care. METHODS: We collected a uniquely linked set of questions on behaviors, beliefs, and care pathways from recently delivered women (n=5,469), their husbands (n=3,064), mothers-in-law (n=3,626), and CHWs (accredited social health activists; n=1,052) in Uttar Pradesh, India. We used logistic regression to study associations between CHW actions and household behaviors during antenatal, delivery, and postnatal periods. RESULTS: Pregnant women who were visited earlier in pregnancy and who received multiple visits were more likely to perform recommended health behaviors including attending multiple checkups, consuming iron and folic acid tablets, and delivering in a health facility (ID), compared to women visited later or receiving fewer visits, respectively. Counseling the woman was associated with higher likelihood of attending 3+ checkups and consuming 100+ iron and folic acid tablets, whereas counseling the husband and mother-in-law was associated with higher rates of ID. Certain behavior change messages, such as the danger of complications, were associated with more checkups and ID, but were only used by 50%-80% of CHWs. During delivery, 57% of women had the CHW present, and their presence was associated with respectful care, early initiation of breastfeeding, and exclusive breastfeeding, but not with delayed bathing or clean cord care. The newborn was less likely to receive delayed bathing if the CHW incorrectly believed that newborns could be bathed soon after birth (which is believed by 30% of CHWs). CHW presence was associated with health behaviors more strongly for home than facility deliveries. Home visits after delivery were associated with higher rates of clean cord care and exclusive breastfeeding. Counseling the mother-in-law (but not the husband or woman) was associated with exclusive breastfeeding. CONCLUSION: We identified potential ways in which CHW impact could be improved, specifically by emphasizing the importance of home visits, which household members are targeted during these visits, and what messages are shared. Achieving this change will require training CHWs in counseling and behavior change and providing supervision and modern tools such as apps that can help the CHW keep track of her beneficiaries, suggest behavior change strategies, and direct attention to households that stand to gain the most from support.

Preliminary Outcomes of a Digital Therapeutic Intervention for Smoking Cessation in Adult Smokers: Randomized Controlled Trial.

BACKGROUND: Tobacco smoking remains the leading cause of preventable death and disease worldwide. Digital interventions delivered through smartphones offer a promising alternative to traditional methods, but little is known about their effectiveness. OBJECTIVE: Our objective was to test the preliminary effectiveness of Quit Genius, a novel digital therapeutic intervention for smoking cessation. METHODS: A 2-arm, single-blinded, parallel-group randomized controlled trial design was used. Participants were recruited via referrals from primary care practices and social media advertisements in the United Kingdom. A total of 556 adult smokers (aged 18 years or older) smoking at least 5 cigarettes a day for the past year were recruited. Of these, 530 were included for the final analysis. Participants were randomized to one of 2 interventions. Treatment consisted of a digital therapeutic intervention for smoking cessation consisting of a smartphone app delivering cognitive behavioral therapy content, one-to-one coaching, craving tools, and tracking capabilities. The control intervention was very brief advice along the Ask, Advise, Act model. All participants were offered nicotine replacement therapy for 3 months. Participants in a random half of each arm were pseudorandomly assigned a carbon monoxide device for biochemical verification. Outcomes were self-reported via phone or online. The primary outcome was self-reported 7-day point prevalence abstinence at 4 weeks post quit date. RESULTS: A total of 556 participants were randomized (treatment: n=277; control: n=279). The intention-to-treat analysis included 530 participants (n=265 in each arm; 11 excluded for randomization before trial registration and 15 for protocol violations at baseline visit). By the quit date (an average of 16 days after randomization), 89.1% (236/265) of those in the treatment arm were still actively engaged. At the time of the primary outcome, 74.0% (196/265) of participants were still engaging with the app. At 4 weeks post quit date, 44.5% (118/265) of participants in the treatment arm had not smoked in the preceding 7 days compared with 28.7% (76/265) in the control group (risk ratio 1.55, 95% CI 1.23-1.96; P<.001; intention-to-treat, n=530). Self-reported 7-day abstinence agreed with carbon monoxide measurement (carbon monoxide <10 ppm) in 96% of cases (80/83) where carbon monoxide readings were available. No harmful effects of the intervention were observed. CONCLUSIONS: The Quit Genius digital therapeutic intervention is a superior treatment in achieving smoking cessation 4 weeks post quit date compared with very brief advice. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN) 65853476; https://www.isrctn.com/ISRCTN65853476.

Identifying population segments for effective intervention design and targeting using unsupervised machine learning: an end-to-end guide.

One-size-fits-all interventions that aim to change behavior are a missed opportunity to improve human health and well-being, as they do not target the different reasons that drive people's choices and behaviors. Psycho-behavioral segmentation is an approach to uncover such differences and enable the design of targeted interventions, but is rarely implemented at scale in global development. In part, this may be due to the many choices program designers and data scientists face, and the lack of available guidance through the process. Effective segmentation encompasses conceptualization and selection of the dimensions to segment on, which often requires the design of suitable qualitative and quantitative primary research. The choice of algorithm and its parameters also profoundly shape the resulting output and how useful the results are in the field. Analytical outputs are not self-explanatory and need to be subjectively evaluated and described. Finally, segments can be prioritized and targeted with matching interventions via appropriate channels. Here, we provide an end-to-end overview of all the stages from planning, designing field-based research, analyzing, and implementing a psycho-behavioral segmentation solution. We illustrate the choices and critical steps along the way, and discuss a case study of segmentation for voluntary medical male circumcision that implemented the method described here. Though our examples mostly draw on health interventions in the developing world, the principles in this approach can be used in any context where understanding human heterogeneity in driving behavior change is valuable.

Randomized controlled trial of a 12-week digital care program in improving low back pain.

Low back pain (LBP) is the leading cause of disability throughout the world and is economically burdensome. The recommended first line treatment for non-specific LBP is non-invasive care. A digital care program (DCP) delivering evidence-based non-invasive treatment for LBP can aid self-management by engaging patients and scales personalized therapy for patient-specific needs. We assessed the efficacy of a 12-week DCP for LBP in a two-armed, pre-registered, randomized, controlled trial (RCT). Participants were included based on self-reported duration of LBP, but those with surgery or injury to the lower back in the previous three months were excluded. The treatment group (DCP) received the 12-week DCP, consisting of sensor-guided exercise therapy, education, cognitive behavioral therapy, team and individual behavioral coaching, activity tracking, and symptom tracking - all administered remotely via an app. The control group received three digital education articles only. All participants maintained access to treatment-as-usual. At 12 weeks, an intention-to-treat analysis showed each primary outcome-Oswestry Disability Index (p < 0.001), Korff Pain (p < 0.001) and Korff Disability (p < 0.001)-as well as each secondary outcome improved more for participants in the DCP group compared to control group. For participants who completed the DCP (per protocol), average improvement in pain outcomes ranged 52-64% (Korff: 48.8-23.4, VAS: 43.6-16.5, VAS impact on daily life: 37.3-13.4; p < 0.01 for all) and average improvement in disability outcomes ranged 31-55% (Korff: 33.1-15, ODI: 19.7-13.5; p < 0.01 for both). Surgical interest significantly reduced in the DCP group. Participants that completed the DCP had an average engagement, each week, of 90%. Future studies will further explore the effectiveness of the DCP for long-term outcomes beyond 12 weeks and for a LBP patient population with possibly greater baseline pain and disability. In conclusion, the DCP resulted in improved LBP outcomes compared to treatment-as-usual and has potential to scale personalized evidence-based non-invasive treatment for LBP patients.

Harnessing citizen science through mobile phone technology to screen for immunohistochemical biomarkers in bladder cancer.

BACKGROUND: Immunohistochemistry (IHC) is often used in personalisation of cancer treatments. Analysis of large data sets to uncover predictive biomarkers by specialists can be enormously time-consuming. Here we investigated crowdsourcing as a means of reliably analysing immunostained cancer samples to discover biomarkers predictive of cancer survival. METHODS: We crowdsourced the analysis of bladder cancer TMA core samples through the smartphone app 'Reverse the Odds'. Scores from members of the public were pooled and compared to a gold standard set scored by appropriate specialists. We also used crowdsourced scores to assess associations with disease-specific survival. RESULTS: Data were collected over 721 days, with 4,744,339 classifications performed. The average time per classification was approximately 15 s, with approximately 20,000 h total non-gaming time contributed. The correlation between crowdsourced and expert H-scores (staining intensity × proportion) varied from 0.65 to 0.92 across the markers tested, with six of 10 correlation coefficients at least 0.80. At least two markers (MRE11 and CK20) were significantly associated with survival in patients with bladder cancer, and a further three markers showed results warranting expert follow-up. CONCLUSIONS: Crowdsourcing through a smartphone app has the potential to accurately screen IHC data and greatly increase the speed of biomarker discovery.

Effects of a 12-Week Digital Care Program for Chronic Knee Pain on Pain, Mobility, and Surgery Risk: Randomized Controlled Trial.

BACKGROUND: Chronic knee pain, most commonly caused by knee osteoarthritis, is a prevalent condition which in most cases can be effectively treated through conservative, non-surgical care involving exercise therapy, education, psychosocial support, and weight loss. However, most people living with chronic knee pain do not receive adequate care, leading to unnecessary use of opiates and surgical procedures. OBJECTIVE: Assess the efficacy of a remotely delivered digital care program for chronic knee pain. METHODS: We enrolled 162 participants into a randomized controlled trial between January and March 2017. Participants were recruited from participating employers using questionnaires for self-assessment of their knee pain, and randomized into treatment (n=101) and control (n=61) groups. Participants in the treatment group were enrolled in the Hinge Health digital care program for chronic knee pain. This is a remotely delivered, home-based 12-week intervention that includes sensor-guided exercise therapy, education, cognitive behavioral therapy, weight loss, and psychosocial support through a personal coach and team-based interactions. The control group received three education pieces regarding self-care for chronic knee pain. Both groups had access to treatment-as-usual. The primary outcome was the Knee Injury and Osteoarthritis Outcome Score (KOOS) Pain subscale and KOOS Physical Function Shortform (KOOS-PS). Secondary outcomes were visual analog scales (VAS) for pain and stiffness respectively, surgery intent, and self-reported understanding of the condition and treatment options. Outcome measures were analyzed by intention to treat (excluding 7 control participants who received the digital care program due to administrative error) and per protocol. RESULTS: In an intent-to-treat analysis the digital care program group had a significantly greater reduction in KOOS Pain compared to the control group at the end of the program (greater reduction of 7.7, 95% CI 3.0 to 12.3, P=.002), as well as a significantly greater improvement in physical function (7.2, 95% CI 3.0 to 11.5, P=.001). This was also reflected in the secondary outcomes VAS pain (12.3, 95% CI 5.4 to 19.1, P<.001) and VAS stiffness (13.4, 95% CI 5.6 to 21.1, P=.001). Participants' self-reported likelihood (from 0% to 100%) of having surgery also reduced more strongly in the digital care program group compared to the control group over the next 1 year (-9.4 percentage points, pp, 95% CI -16.6 to -2.2, P=.01), 2 years (-11.3 pp, 95% CI -20.1 to -2.5, P=.01), and 5 years (-14.6 pp, 95% CI -23.6 to -5.5, P=.002). Interest in surgery (from 0 to 10) also reduced more so in the digital care program compared to control group (-1.0, 95% CI -1.7 to -0.2, P=.01). Participants' understanding of the condition and treatment options (on a scale from 0 to 4) increased more substantially for participants in the digital care program than those in the control group (0.9, 95% CI 0.6 to 1.3, P<.001). In an analysis on participants that completed the intervention (per protocol analysis) all primary and secondary outcomes remained significant at greater effect magnitudes compared to intention to treat, with those completing the program showing a 61% (95% CI 48 to 74) reduction in VAS pain compared to 21% (95% CI 5 to 38) in the control group (P<.001). Accounting for the cost of administering the program, we estimate net cost savings on surgery alone of US $4340 over 1 year and $7900 over 5 years for those participants completing the digital care program compared to those in the control group receiving treatment-as-usual. In an exploratory subgroup analysis including only participants exhibiting clinical symptoms of osteoarthritis the program proved equally effective. CONCLUSIONS: This trial provides strong evidence that a comprehensive 12-week digital care program for chronic knee pain, including osteoarthritis, yields significantly improved outcomes for pain, physical function, stiffness, surgery risk, and understanding of the condition, compared to a control group. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN) 13307390; http://www.isrctn.com/ISRCTN13307390 (Archived by WebCite at http://www.webcitation.org/6ycwjGL73).

Value encoding in the globus pallidus: fMRI reveals an interaction effect between reward and dopamine drive.

The external part of the globus pallidus (GPe) is a core nucleus of the basal ganglia (BG) whose activity is disrupted under conditions of low dopamine release, as in Parkinson's disease. Current models assume decreased dopamine release in the dorsal striatum results in deactivation of dorsal GPe, which in turn affects motor expression via a regulatory effect on other nuclei of the BG. However, recent studies in healthy and pathological animal models have reported neural dynamics that do not match with this view of the GPe as a relay in the BG circuit. Thus, the computational role of the GPe in the BG is still to be determined. We previously proposed a neural model that revisits the functions of the nuclei of the BG, and this model predicts that GPe encodes values which are amplified under a condition of low striatal dopaminergic drive. To test this prediction, we used an fMRI paradigm involving a within-subject placebo-controlled design, using the dopamine antagonist risperidone, wherein healthy volunteers performed a motor selection and maintenance task under low and high reward conditions. ROI-based fMRI analysis revealed an interaction between reward and dopamine drive manipulations, with increased BOLD activity in GPe in a high compared to low reward condition, and under risperidone compared to placebo. These results confirm the core prediction of our computational model, and provide a new perspective on neural dynamics in the BG and their effects on motor selection and cognitive disorders.

Association of Neural and Emotional Impacts of Reward Prediction Errors With Major Depression.

Importance: Major depressive disorder (MDD) is associated with deficits in representing reward prediction errors (RPEs), which are the difference between experienced and predicted reward. Reward prediction errors underlie learning of values in reinforcement learning models, are represented by phasic dopamine release, and are known to affect momentary mood. Objective: To combine functional neuroimaging, computational modeling, and smartphone-based large-scale data collection to test, in the absence of learning-related concerns, the hypothesis that depression attenuates the impact of RPEs. Design, Setting, and Participants: Functional magnetic resonance imaging (fMRI) data were collected on 32 individuals with moderate MDD and 20 control participants who performed a probabilistic reward task. A risky decision task with repeated happiness ratings as a measure of momentary mood was also tested in the laboratory in 74 participants and with a smartphone-based platform in 1833 participants. The study was conducted from November 20, 2012, to February 17, 2015. Main Outcomes and Measures: Blood oxygen level-dependent activity was measured in ventral striatum, a dopamine target area known to represent RPEs. Momentary mood was measured during risky decision making. Results: Of the 52 fMRI participants (mean [SD] age, 34.0 [9.1] years), 30 (58%) were women and 32 had MDD. Of the 74 participants in the laboratory risky decision task (mean age, 34.2 [10.3] years), 44 (59%) were women and 54 had MDD. Of the smartphone group, 543 (30%) had a depression history and 1290 (70%) had no depression history; 918 (50%) were women, and 593 (32%) were younger than 30 years. Contrary to previous results in reinforcement learning tasks, individuals with moderate depression showed intact RPE signals in ventral striatum (z = 3.16; P = .002) that did not differ significantly from controls (z = 0.91; P = .36). Symptom severity correlated with baseline mood parameters in laboratory (ρ = -0.54; P < 1 × 10-6) and smartphone (ρ = -0.30; P < 1 × 10-39) data. However, participants with depression showed an intact association between RPEs and happiness in a computational model of momentary mood dynamics (z = 4.55; P < .001) that was not attenuated compared with controls (z = -0.42; P = .67). Conclusions and Relevance: The neural and emotional impact of RPEs is intact in major depression. These results suggest that depression does not affect the expression of dopaminergic RPEs and that attenuated RPEs in previous reports may reflect downstream effects more closely related to aberrant behavior. The correlation between symptom severity and baseline mood parameters supports an association between depression and momentary mood fluctuations during cognitive tasks. These results demonstrate a potential for smartphones in large-scale computational phenotyping, which is a goal for computational psychiatry.

Local striatal reward signals can be predicted from corticostriatal connectivity.

A defining feature of the basal ganglia is their anatomical organization into multiple cortico-striatal loops. A central tenet of this architecture is the idea that local striatal function is determined by its precise connectivity with cortex, creating a functional topography that is mirrored within cortex and striatum. Here we formally test this idea using both human anatomical and functional imaging, specifically asking whether within striatal subregions one can predict between-voxel differences in functional signals based on between-voxel differences in corticostriatal connectivity. We show that corticostriatal connectivity profiles predict local variation in reward signals in bilateral caudate nucleus and putamen, expected value signals in bilateral caudate nucleus, and response effector activity in bilateral putamen. These data reveal that, even within individual striatal regions, local variability in corticostriatal anatomical connectivity predicts functional differentiation.

Translating Comprehensive Conservative Care for Chronic Knee Pain Into a Digital Care Pathway: 12-Week and 6-Month Outcomes for the Hinge Health Program.

BACKGROUND: Chronic knee pain (CKP) affects a large number of adults, many of whom do not receive best-practice care and are at high risk for unnecessary surgery. OBJECTIVE: The aim of this study was to investigate the effect of the Hinge Health 12-week digital care program (DCP) for CKP on knee pain and function, with secondary outcomes of surgery interest and satisfaction, at 12 weeks and 6 months after starting the program. METHODS: Individuals with CKP were recruited onto the 12-week program, comprising sensor-guided physical exercises, weekly education, activity tracking, and psychosocial support such as personal coaching and cognitive behavioral therapy (CBT). We used a single-arm design with assessment of outcomes at baseline, 12 weeks, and 6 months after starting the program. We used a linear mixed effects model with Tukey contrasts to compare timepoints and report intention-to-treat statistics with last observation carried forward. RESULTS: The cohort consisted of 41 individuals (32 female, mean age 52 years, SD 9 years). Between baseline and week 12, participants reported clinically significant improvements in the Knee Injury and Osteoarthritis Outcome Score (KOOS) pain and Knee Injury and Osteoarthritis Outcome Score-Physical Function Short Form (KOOS-PS) function scales of 16 points (95% CI 12-21, P<.001) and 10 points (95% CI 6-14, P<.001), respectively. Significant reductions of 57% (mean difference 30, 95% CI 21-38, P<.001) and 51% (mean difference 25, 95% CI 16-33, P<.001) in visual analog scale (VAS) knee pain and stiffness, respectively, were observed at 12 weeks, as well as a 67% reduction in surgery interest (mean reduction 2.3 out of 10, 95% CI 1.5-3.1, P<.001). Average satisfaction at week 12 was 9.2 out of 10. Critically, all improvements were maintained at 6 months at similar or greater magnitude. CONCLUSIONS: Participants on the Hinge Health DCP for CKP showed substantial clinical improvements that were maintained 6 months after enrolling in the program. This shows that DCPs carry strong potential to deliver evidence-based, cost-effective care to those suffering from CKP.

Crowdsourcing for translational research: analysis of biomarker expression using cancer microarrays.

BACKGROUND: Academic pathology suffers from an acute and growing lack of workforce resource. This especially impacts on translational elements of clinical trials, which can require detailed analysis of thousands of tissue samples. We tested whether crowdsourcing - enlisting help from the public - is a sufficiently accurate method to score such samples. METHODS: We developed a novel online interface to train and test lay participants on cancer detection and immunohistochemistry scoring in tissue microarrays. Lay participants initially performed cancer detection on lung cancer images stained for CD8, and we measured how extending a basic tutorial by annotated example images and feedback-based training affected cancer detection accuracy. We then applied this tutorial to additional cancer types and immunohistochemistry markers - bladder/ki67, lung/EGFR, and oesophageal/CD8 - to establish accuracy compared with experts. Using this optimised tutorial, we then tested lay participants' accuracy on immunohistochemistry scoring of lung/EGFR and bladder/p53 samples. RESULTS: We observed that for cancer detection, annotated example images and feedback-based training both improved accuracy compared with a basic tutorial only. Using this optimised tutorial, we demonstrate highly accurate (>0.90 area under curve) detection of cancer in samples stained with nuclear, cytoplasmic and membrane cell markers. We also observed high Spearman correlations between lay participants and experts for immunohistochemistry scoring (0.91 (0.78, 0.96) and 0.97 (0.91, 0.99) for lung/EGFR and bladder/p53 samples, respectively). CONCLUSIONS: These results establish crowdsourcing as a promising method to screen large data sets for biomarkers in cancer pathology research across a range of cancers and immunohistochemical stains.

Adaptive integration of habits into depth-limited planning defines a habitual-goal-directed spectrum.

Behavioral and neural evidence reveal a prospective goal-directed decision process that relies on mental simulation of the environment, and a retrospective habitual process that caches returns previously garnered from available choices. Artificial systems combine the two by simulating the environment up to some depth and then exploiting habitual values as proxies for consequences that may arise in the further future. Using a three-step task, we provide evidence that human subjects use such a normative plan-until-habit strategy, implying a spectrum of approaches that interpolates between habitual and goal-directed responding. We found that increasing time pressure led to shallower goal-directed planning, suggesting that a speed-accuracy tradeoff controls the depth of planning with deeper search leading to more accurate evaluation, at the cost of slower decision-making. We conclude that subjects integrate habit-based cached values directly into goal-directed evaluations in a normative manner.

Risk Taking for Potential Reward Decreases across the Lifespan.

The extent to which aging affects decision-making is controversial. Given the critical financial decisions that older adults face (e.g., managing retirement funds), changes in risk preferences are of particular importance [1]. Although some studies have found that older individuals are more risk averse than younger ones [2-4], there are also conflicting results, and a recent meta-analysis found no evidence for a consistent change in risk taking across the lifespan [5]. There has as yet been little examination of one potential substrate for age-related changes in decision-making, namely age-related decline in dopamine, a neuromodulator associated with risk-taking behavior. Here, we characterized choice preferences in a smartphone-based experiment (n = 25,189) in which participants chose between safe and risky options. The number of risky options chosen in trials with potential gains but not potential losses decreased gradually over the lifespan, a finding with potentially important economic consequences for an aging population. Using a novel approach-avoidance computational model, we found that a Pavlovian attraction to potential reward declined with age. This Pavlovian bias has been linked to dopamine, suggesting that age-related decline in this neuromodulator could lead to the observed decrease in risk taking.

Proactive and Reactive Response Inhibition across the Lifespan.

One expression of executive control involves proactive preparation for future events, and this contrasts with stimulus driven reactive control exerted in response to events. Here we describe findings from a response inhibition task, delivered using a smartphone-based platform, that allowed us to index proactive and reactive inhibitory self-control in a large community sample (n = 12,496). Change in stop-signal reaction time (SSRT) when participants are provided with advance information about an upcoming trial, compared to when they are not, provides a measure of proactive control while SSRT in the absence of advance information provides a measure of reactive control. Both forms of control rely on overlapping frontostriatal pathways known to deteriorate in healthy aging, an age-related decline that occurs at an accelerated rate in men compared to women. Here we ask whether these patterns of age-related decline are reflected in similar changes in proactive and reactive inhibitory control across the lifespan. As predicted, we observed a decline in reactive control with natural aging, with a greater rate of decline in men compared to women (~10 ms versus ~8 ms per decade of adult life). Surprisingly, the benefit of preparation, i.e. proactive control, did not change over the lifespan and women showed superior proactive control at all ages compared to men. Our results suggest that reactive and proactive inhibitory control partially rely on distinct neural substrates that are differentially sensitive to age-related change.

Age-related changes in working memory and the ability to ignore distraction.

A weakened ability to effectively resist distraction is a potential basis for reduced working memory capacity (WMC) associated with healthy aging. Exploiting data from 29,631 users of a smartphone game, we show that, as age increases, working memory (WM) performance is compromised more by distractors presented during WM maintenance than distractors presented during encoding. However, with increasing age, the ability to exclude distraction at encoding is a better predictor of WMC in the absence of distraction. A significantly greater contribution of distractor filtering at encoding represents a potential compensation for reduced WMC in older age.

Crowdsourcing for cognitive science--the utility of smartphones.

By 2015, there will be an estimated two billion smartphone users worldwide. This technology presents exciting opportunities for cognitive science as a medium for rapid, large-scale experimentation and data collection. At present, cost and logistics limit most study populations to small samples, restricting the experimental questions that can be addressed. In this study we investigated whether the mass collection of experimental data using smartphone technology is valid, given the variability of data collection outside of a laboratory setting. We presented four classic experimental paradigms as short games, available as a free app and over the first month 20,800 users submitted data. We found that the large sample size vastly outweighed the noise inherent in collecting data outside a controlled laboratory setting, and show that for all four games canonical results were reproduced. For the first time, we provide experimental validation for the use of smartphones for data collection in cognitive science, which can lead to the collection of richer data sets and a significant cost reduction as well as provide an opportunity for efficient phenotypic screening of large populations.

Greater striatal responses to medication in Parkinson׳s disease are associated with better task-switching but worse reward performance.

Dopaminergic medication in Parkinson's disease has been proposed to improve cognitive processing by modulating the severely depleted dorsal striatum, while impairing reward processing by modulating the relatively intact ventral striatum. However, there is no direct (neural) evidence for this hypothesis. Here we fill this gap by scanning Parkinson's disease patients (n=15) ON and relatively OFF their dopaminergic medication using functional magnetic resonance imaging. During scanning, patients performed a task that enabled the simultaneous measurement of task-switching and reward-related processing. Brain-behavior correlations revealed that medication-related increases (ON-OFF) in switch-related BOLD signal (switch-repeat) in the dorsomedial striatum were associated, on an individual basis, with improvements in task-switching (i.e. a decreased switch cost). Conversely, medication-related increases (ON-OFF) in reward-related BOLD signal (high-low) in the ventromedial striatum were associated, on an individual basis, with impairments in performance in anticipation of reward (i.e. an increased reward cost). Linear regression analyses demonstrated that the positive relationship between medication-related changes in BOLD and the reward cost was unique to the ventromedial striatum, whereas the negative relationship between medication-related changes in BOLD and the switch cost was not unique to the dorsomedial striatum. These findings extend the dopamine overdose hypothesis, according to which dopamine-induced changes in dorsal and ventral striatal processing lead to cognitive improvement and impairment respectively.