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Understanding day-to-day variations in symptoms and medication management can be important in describing patient centered outcomes for people with constipation. Patient Generated Health Data (PGHD) from digital devices is a potential solution, but its utility as a tool for describing experiences of people with frequent constipation is unknown. We conducted a virtual, 16-week prospective study of individuals with frequent constipation from an online wellness platform that connects mobile consumer digital devices including wearable monitors capable of passively collecting steps, sleep, and heart rate data. Participants wore a Fitbit monitoring device for the study duration and were administered daily and monthly surveys assessing constipation symptom severity and medication usage. A set of 38 predetermined day-level behavioral activity metrics were computed from minute-level data streams for steps, sleep and heart rate. Mixed effects regression models were used to compare activity metrics between constipation status (irregular or constipated vs. regular day), medication use (medication day vs. non-medication day) and the interaction of medication day with irregular or constipation days, as well as to model likelihood to treat with constipation medications based on daily self-reported symptom severity. Correction for multiple comparisons was performed with the Benjamini–Hochberg procedure for false discovery rate. This study analyzed 1540 enrolled participants with completed daily surveys (mean age 36.6 sd 10.0, 72.8% female, 88.8% Caucasian). Of those, 1293 completed all monthly surveys and 756 had sufficient Fitbit data density for analysis of activity metrics. At a daily-level, 22 of the 38 activity metrics were significantly associated with bowel movement or medication treatment patterns for constipation. Participants were measured to have fewer steps on irregular days compared to regular days (−200 steps, 95% CI [−280, −120]), longer periods of inactivity on constipated days (9.1 min, 95% CI [5.2, 12.9]), reduced total sleep time on irregular and constipated days (−2.4 min, 95% CI [−4.3, −0.4] and −4.0 min, 95% CI [−6.5, −1.4], respectively). Participants reported greater severity of symptoms for bloating, hard stool, difficulty passing, and painful bowel movements on irregular, constipation and medication days compared to regular days with no medication. Interaction analysis of medication days with irregular or constipation days observed small increases in severity compared to non-medication days. Participants were 4.3% (95% CI 3.2, 5.3) more likely to treat with medication on constipated days versus regular. No significant increase in likelihood was observed for irregular days. Daily likelihood to treat increased for each 1-point change in symptom severity of bloating (2.4%, 95% CI [2.0, 2.7]), inability to pass (2.2%, 95% CI [1.4, 3.0]) and incomplete bowel movements (1.3%, 95% CI [0.9, 1.7]). This is the first large scale virtual prospective study describing the association between passively collected PGHD and constipation symptoms and severity at a day-to-day granularity level. Constipation status, irregular or constipated, was associated with a number of activity metrics in steps and sleep, and likelihood to treat with medication increased with increasing severity for a number of constipation symptoms. Given the small magnitude of effect, further research is needed to understand the clinical relevance of these results. PGHD may be useful as a tool for describing real world patient centered experiences for people with constipation.

Commercial wearable devices are surfacing as an appealing mechanism to detect COVID-19 and potentially other public health threats, due to their widespread use. To assess the validity of wearable devices as population health screening tools, it is essential to evaluate predictive methodologies based on wearable devices by mimicking their real-world deployment. Several points must be addressed to transition from statistically significant differences between infected and uninfected cohorts to COVID-19 inferences on individuals. We demonstrate the strengths and shortcomings of existing approaches on a cohort of 32,198 individuals who experience influenza like illness (ILI), 204 of which report testing positive for COVID-19. We show that, despite commonly made design mistakes resulting in overestimation of performance, when properly designed wearables can be effectively used as a part of the detection pipeline. For example, knowing the week of year, combined with naive randomised test set generation leads to substantial overestimation of COVID-19 classification performance at 0.73 AUROC. However, an average AUROC of only 0.55 +/- 0.02 would be attainable in a simulation of real-world deployment, due to the shifting prevalence of COVID-19 and non-COVID-19 ILI to trigger further testing. In this work we show how to train a machine learning model to differentiate ILI days from healthy days, followed by a survey to differentiate COVID-19 from influenza and unspecified ILI based on symptoms. In a forthcoming week, models can expect a sensitivity of 0.50 (0-0.74, 95% CI), while utilising the wearable device to reduce the burden of surveys by 35%. The corresponding false positive rate is 0.22 (0.02-0.47, 95% CI). In the future, serious consideration must be given to the design, evaluation, and reporting of wearable device interventions if they are to be relied upon as part of frequent COVID-19 or other public health threat testing infrastructures.

The severity of viral infections can vary widely, from asymptomatic cases to complications leading to hospitalizations and death. Milder cases, despite being more prevalent, often go undocumented and their public health impact unaccounted for. We estimated the burden of influenza-like-illness (ILI) by leveraging the widespread use of commercial activity trackers. Analysing data from 15,382 US participants who reported ILI symptoms during the 2018–2019 flu season (before the COVID-19 pandemic) and who had high-density wearable sensor data at symptom onset, we estimated an overall nationwide reduction in mobility equivalent to 257 billion steps lost due to ILI symptoms. This finding reflects significant changes in routines, mobility, and employment and is equivalent to 15% of the active US population becoming completely immobilized for 1 day. Moreover, ∼60% of this impact occurred among individuals who sought no medical care, who would otherwise be invisible to healthcare and public health reporting systems. We validated our measure against self-reported measures of disease severity. We believe this method has applications for public health, healthcare, and clinical research, from estimating costs of lost productivity at population scale, to measuring effectiveness of anti-ILI treatments, to monitoring recovery after acute viral syndromes, such as during ‘long COVID’.

Understanding day-to-day variations in symptoms and medication management can be important in describing patient centered outcomes for people with constipation. Patient Generated Health Data (PGHD) from digital devices is a potential solution, but its utility as a tool for describing experiences of people with frequent constipation is unknown. We conducted a virtual, 16-week prospective study of individuals with frequent constipation from an online wellness platform that connects mobile consumer digital devices including wearable monitors capable of passively collecting steps, sleep, and heart rate data. Participants wore a Fitbit monitoring device for the study duration and were administered daily and monthly surveys assessing constipation symptom severity and medication usage. A set of 38 predetermined day-level behavioral activity metrics were computed from minute-level data streams for steps, sleep and heart rate. Mixed effects regression models were used to compare activity metrics between constipation status (irregular or constipated vs. regular day), medication use (medication day vs. non-medication day) and the interaction of medication day with irregular or constipation days, as well as to model likelihood to treat with constipation medications based on daily self-reported symptom severity. Correction for multiple comparisons was performed with the Benjamini-Hochberg procedure for false discovery rate. This study analyzed 1540 enrolled participants with completed daily surveys (mean age 36.6 sd 10.0, 72.8% female, 88.8% Caucasian). Of those, 1293 completed all monthly surveys and 756 had sufficient Fitbit data density for analysis of activity metrics. At a daily-level, 22 of the 38 activity metrics were significantly associated with bowel movement or medication treatment patterns for constipation. Participants were measured to have fewer steps on irregular days compared to regular days (-200 steps, 95% CI [-280, -120]), longer periods of inactivity on constipated days (9.1 min, 95% CI [5.2, 12.9]), reduced total sleep time on irregular and constipated days (-2.4 min, 95% CI [-4.3, -0.4] and -4.0 min, 95% CI [-6.5, -1.4], respectively). Participants reported greater severity of symptoms for bloating, hard stool, difficulty passing, and painful bowel movements on irregular, constipation and medication days compared to regular days with no medication. Interaction analysis of medication days with irregular or constipation days observed small increases in severity compared to non-medication days. Participants were 4.3% (95% CI 3.2, 5.3) more likely to treat with medication on constipated days versus regular. No significant increase in likelihood was observed for irregular days. Daily likelihood to treat increased for each 1-point change in symptom severity of bloating (2.4%, 95% CI [2.0, 2.7]), inability to pass (2.2%, 95% CI [1.4, 3.0]) and incomplete bowel movements (1.3%, 95% CI [0.9, 1.7]). This is the first large scale virtual prospective study describing the association between passively collected PGHD and constipation symptoms and severity at a day-to-day granularity level. Constipation status, irregular or constipated, was associated with a number of activity metrics in steps and sleep, and likelihood to treat with medication increased with increasing severity for a number of constipation symptoms. Given the small magnitude of effect, further research is needed to understand the clinical relevance of these results. PGHD may be useful as a tool for describing real world patient centered experiences for people with constipation.

Anxiety is characterized by low confidence in daily decisions, coupled with high levels of phenomenological stress. Ventromedial prefrontal cortex (vmPFC) plays an integral role in maladaptive anxious behaviors via decreased sensitivity to threatening vs. non-threatening stimuli (fear generalization). vmPFC is also a key node in approach-avoidance decision making requiring two-dimensional integration of rewards and costs. More recently, vmPFC has been implicated as a key cortical input to the sympathetic branch of the autonomic nervous system. However, little is known about the role of this brain region in mediating rapid stress responses elicited by changes in confidence during decision making. We used an approach-avoidance task to examine the relationship between sympathetically mediated cardiac stress responses, vmPFC activity and choice behavior over long and short time-scales. To do this, we collected concurrent fMRI, EKG and impedance cardiography recordings of sympathetic drive while participants made approach-avoidance decisions about monetary rewards paired with painful electric shock stimuli. We observe first that increased sympathetic drive (shorter pre-ejection period) in states lasting minutes are associated with choices involving reduced decision ambivalence. Thus, on this slow time scale, sympathetic drive serves as a proxy for “mobilization” whereby participants are more likely to show consistent value-action mapping. In parallel, imaging analyses reveal that on shorter time scales (estimated with a trial-to-trial GLM), increased vmPFC activity, particularly during low-ambivalence decisions, is associated with decreased sympathetic state. Our findings support a role of sympathetic drive in resolving decision ambivalence across long time horizons and suggest a potential role of vmPFC in modulating this response on a moment-to-moment basis.

Summary The fight against COVID-19 is hindered by similarly presenting viral infections that may confound detection and monitoring. We examined person-generated health data (PGHD), consisting of survey and commercial wearable data from individuals' everyday lives, for 230 people who reported a COVID-19 diagnosis between March 30, 2020, and April 27, 2020 (n = 41 with wearable data). Compared with self-reported diagnosed flu cases from the same time frame (n = 426, 85 with wearable data) or pre-pandemic (n = 6,270, 1,265 with wearable data), COVID-19 patients reported a distinct symptom constellation that lasted longer (median of 12 versus 9 and 7 days, respectively) and peaked later after illness onset. Wearable data showed significant changes in daily steps and prevalence of anomalous resting heart rate measurements, of similar magnitudes for both the flu and COVID-19 cohorts. Our findings highlight the need to include flu comparator arms when evaluating PGHD applications aimed to be highly specific for COVID-19., Highlights • We use data from smartphones and wearables from ~7,000 people to compare flu and COVID-19 • While symptoms have some overlap, patients report longer COVID-19 illnesses than flu • Elevated resting heart rate measures are more frequent around ILI symptoms onset • It is important to consider flu as a confounder in COVID-19 real-world studies, The Bigger Picture In this study, we integrate longitudinal symptoms reports and continuous data from commercial wearables to compare and contrast flu and COVID-19 presentations. We found that, while symptoms constellation between COVID-19 and flu have large overlap, symptoms are significantly more prolonged and severe for COVID-19 than for flu. Similarly, physiological data from commercial wearables showed increased resting heart rate around symptoms onset date that were more severe for COVID-19, but present in milder form for flu as well. Person-generated health data (PGHD), including data from smartphones and other connected sensors, has the potential to enable applications ranging from individual-level early warnings or population-level hotspot detection for COVID-19. However, for these applications to become a reality, our findings suggest that it is crucial to develop and validate them in the context of other potentially confounding respiratory illnesses, such as the flu., Person-generated health data (PGHD), including data from smartphones and other connected sensors, has the potential to enable applications ranging from individual-level early warnings or population-level hotspot detection for COVID-19. We show that, when studied outside the clinic, COVID-19 and flu may have overlapping, yet distinct, symptom presentations. For COVID-19 PGHD applications to become a reality, our findings suggest that it is crucial to develop and validate them in the context of other potentially confounding respiratory illnesses, such as the flu.

BackgroundSince the beginning of the COVID-19 pandemic, data from smartphones and connected sensors have been used to better understand presentation and management outside the clinic walls. However, reports on the validity of such data are still sparse, especially when it comes to symptom progression and relevance of wearable sensors.ObjectiveTo understand the relevance of Person-Generated Health Data (PGHD) as a means for early detection, monitoring, and management of COVID-19 in everyday life. This type of data include quantifying prevalence and progression of symptoms from self-reports as well as changes in activity and physiological parameters continuously measured from wearable sensors, and contextualizing findings for COVID-19 patients with those from cohorts of flu patients.Design, Setting, and ParticipantsRetrospective digital cohort study of individuals with a self-reported positive SARS-CoV-2 or influenza test followed over the period 2019-12-02 to 2020-04-27. Three cohorts were derived: Patients who self-reported being diagnosed with flu prior to the SARS-CoV-2 pandemic (N=6270, of which 1226 also contributed sensor PGHD); Patients who reported being diagnosed with flu during the SARS-CoV-2 pandemic (N=426, of which 85 also shared sensor PGHD); and patients who reported being diagnosed with COVID-19 (N=230, of which sensor PGHD was available for 41). The cohorts were derived from a large-scale digital participatory surveillance study designed to track Influenza-like Illness (ILI) incidence and burden over time.ExposuresSelf-reported demographic data, comorbidities, and symptoms experienced during a diagnosed ILI episode, including SARS-CoV-2. Physiological and behavioral parameters measured daily from commercial wearable sensors, including Resting Heart Rate (RHR), total step count, and nightly sleep hours.Main Outcomes and MeasuresWe investigated the percentage of individuals experiencing symptoms of a given type (e.g. shortness of breath) across demographic groups and over time. We examined illness duration, and care seeking behavior, and how RHR, step count, and nightly sleep hours deviated from expected behavior on healthy days over the course of the infection episode.ResultsSelf-reported symptoms of COVID-19 present differently from flu. COVID-19 cases tended to last longer than flu (median of 12 vs. 9 days), are uniquely characterized by chest pain/pressure, shortness of breath, and anosmia. The fraction of elevated RHR measurements collected daily from commercial wearable devices rise significantly in the 2 days surrounding ILI symptoms onset, but does not appear to do so in a way specific to COVID-19. Steps lost due to COVID-19 persists for longer than for flu.Conclusion and RelevancePGHD can be a valid source of longitudinal real world data to detect and monitor COVID-19-related symptoms and behaviors at population scale. PGHD may provide continuous, near real-time feedback to intervention effectiveness that otherwise requires waiting for symptoms to develop into contacts with the healthcare system. It has also the potential to increase pre-test probability of other downstream diagnostics. To effectively leverage PGHD for participatory surveillance it is crucial to invest in the creation of trusted, long-term communication channels with individuals through which data can be efficiently collected, consented, and contextualized, while protecting the privacy of individuals and ultimately facilitating the transition in and out of care.

Two fundamental goals of decision making are to select actions that maximize rewards while minimizing costs and to have strong confidence in the accuracy of a judgment. Neural signatures of these two forms of value: the subjective value (SV) of choice alternatives and the value of the judgment (confidence), have both been observed in ventromedial prefrontal cortex (vmPFC). However, the relationship between these dual value signals and their relative time courses are unknown. We recorded fMRI while 28 men and women performed a two-phase Ap-Av task with mixed-outcomes of monetary rewards paired with painful shock stimuli. Neural responses were measured during offer valuation (offer phase) and choice valuation (commit phase) and analyzed with respect to observed decision outcomes, model-estimated SV and confidence. During the offer phase, vmPFC tracked SV and decision outcomes, but it not confidence. During the commit phase, vmPFC tracked confidence, computed as the quadratic extension of SV, but it bore no significant relationship with the offer valuation itself, nor the decision. In fact, vmPFC responses from the commit phase were selective for confidence even for rejected offers, wherein confidence and SV were inversely related. Conversely, activation of the cognitive control network, including within lateral prefrontal cortex (lPFC) and dorsal anterior cingulate cortex (dACC) was associated with ambivalence, during both the offer and commit phases. Taken together, our results reveal complementary representations in vmPFC during value-based decision making that temporally dissociate such that offer valuation (SV) emerges before decision valuation (confidence).

Numerous functional neuroimaging studies suggest that widespread bilateral parietal, temporal, and frontal regions are involved in tool-related and pantomimed gesture performance, but the role of these regions in specific aspects of gestural tasks remains unclear. In the largest prospective study of apraxia-related lesions to date, we performed voxel-based lesion–symptom mapping with data from 71 left hemisphere stroke participants to assess the critical neural substrates of three types of actions: gestures produced in response to viewed tools, imitation of tool-specific gestures demonstrated by the examiner, and imitation of meaningless gestures. Thus, two of the three gesture types were tool-related, and two of the three were imitative, enabling pairwise comparisons designed to highlight commonalities and differences. Gestures were scored separately for postural (hand/arm positioning) and kinematic (amplitude/timing) accuracy. Lesioned voxels in the left posterior temporal gyrus were significantly associated with lower scores on the posture component for both of the tool-related gesture tasks. Poor performance on the kinematic component of all three gesture tasks was significantly associated with lesions in left inferior parietal and frontal regions. These data enable us to propose a componential neuroanatomic model of action that delineates the specific components required for different gestural action tasks. Thus, visual posture information and kinematic capacities are differentially critical to the three types of actions studied here: the kinematic aspect is particularly critical for imitation of meaningless movement, capacity for tool-action posture representations are particularly necessary for pantomimed gestures to the sight of tools, and both capacities inform imitation of tool-related movements. These distinctions enable us to advance traditional accounts of apraxia.

Directional selectivity during visually guided hand movements is a fundamental characteristic of neural populations in multiple motor areas of the primate brain. In the current study, we assessed how directional selectivity changes when reaching movements are dissociated from their visual feedback by rotating the visual field. We recorded simultaneous movement kinematics and fMRI activity while human subjects performed out-and-back movements to four peripheral targets before and after adaptation to a 45° visuomotor rotation. A classification algorithm was trained to identify movement direction according to voxel-by-voxel fMRI patterns in each of several brain areas. The direction of movements was successfully decoded with above-chance accuracy in multiple motor and visual areas when training and testing the classifier on trials within each condition, thereby demonstrating the existence of directionally selective fMRI patterns within each stage of the experiment. Most importantly, when training the classifier on baseline trials and decoding rotated trials, motor brain areas exhibited above-chance decoding according to the original movement direction and visual brain areas exhibited above-chance decoding according to the rotated visual target location, while posterior parietal cortex (PPC) exhibited chance-level decoding according to both. These results reveal that directionally selective fMRI patterns in motor system areas faithfully represent movement direction regardless of visual feedback, while fMRI patterns in visual system areas faithfully represent target location regardless of movement direction. Directionally selective fMRI patterns in PPC, however, were altered following adaptation learning, thereby suggesting that the novel visuomotor mapping, which was learned during visuomotor adaptation, is stored in PPC.

Substantial evidence suggests that conceptual processing of manipulable objects is associated with potentiation of action. Such data have been viewed as evidence that objects are recognized via access to action features. Many objects, however, are associated with multiple actions. For example, a kitchen timer may be clenched with a power grip to move it but pinched with a precision grip to use it. The present study tested the hypothesis that action evocation during conceptual object processing is responsive to the visual scene in which objects are presented. Twenty-five healthy adults were asked to categorize object pictures presented in different naturalistic visual contexts that evoke either move- or use-related actions. Categorization judgments (natural vs. artifact) were performed by executing a move- or use-related action (clench vs. pinch) on a response device, and response times were assessed as a function of contextual congruence. Although the actions performed were irrelevant to the categorization judgment, responses were significantly faster when actions were compatible with the visual context. This compatibility effect was largely driven by faster pinch responses when objects were presented in use-compatible, as compared with move-compatible, contexts. The present study is the first to highlight the influence of visual scene on stimulus-response compatibility effects during semantic object processing. These data support the hypothesis that action evocation during conceptual object processing is biased toward context-relevant actions. © 2013 Psychonomic Society, Inc.

Sir, It is intriguing that our article ‘Critical brain regions for tool-related and imitative actions: a componential analysis’ was the sole subject of a recent positive Scientific Commentary in Brain by Professor Georg Goldenberg ‘Challenging traditions in apraxia’ (Goldenberg, 2014) noting that the paper ‘excels by virtue of its methodical astuteness, large sample size and the clarity of its results’, as well as a more critical Letter to the Editor by Francois Osiurak and Didier Le Gall (2014). We are very pleased that our paper has stimulated discussion. Drs Osiurak and Le Gall raise three points of concern, all focused on our interpretation of results. The first concern is with our suggestion that the inferior parietal lobe provides the basis for the kinematic component of praxis actions, contributing to the planning of movement trajectories in terms of extent, direction, and timing. The second is with our claim that the posterior temporal lobe supports the stored, representational components of praxis. Finally, they note a concern that our account has been modified since it was originally proposed in 2001. We will address each of these criticisms in turn. Osiurak and Le Gall suggest that the kinematic hypothesis is unable to account for the difficulty of many patients with left brain damage to select the appropriate tool to solve problems. Rather, they prefer to conceptualize the difficulty as a failure of technical reasoning. But what are the core underlying mechanisms that enable one to ‘reason’ about physical object properties? We suggest that selection of the appropriate tool (and pantomime of tool use) require a simulation (which some have likened to a predictive ‘forward model’) (Tian and Poeppel, 2012; Pickering and Clark, 2014 …