User Experience of a Large-Scale Smartphone-Based Observational Study in Multiple Sclerosis: Global, Open-Access, Digital-Only Study.

BACKGROUND: The Floodlight Open app is a digital health technology tool (DHTT) that comprises remote, smartphone sensor-based tests (daily activities) for assessing symptoms of multiple sclerosis (MS). User acquisition, engagement, and retention remain a barrier to successfully deploying such tools. OBJECTIVE: This study aims to quantitatively and qualitatively investigate key user experience (UX) factors associated with the Floodlight Open app. METHODS: Floodlight Open is a global, open-access, digital-only study designed to understand the drivers and barriers in deploying a DHTT in a naturalistic setting without supervision and onboarding by a clinician. Daily activities included tests assessing cognition (Information Processing Speed and Information Processing Speed Digit-Digit), hand-motor function (Pinching Test and Draw a Shape Test), and postural stability and gait (Static Balance Test, U-Turn Test, and Two-Minute Walk Test [2MWT]). All daily activities except the 2MWT were taken in a fixed sequence. Qualitative UX was studied through semistructured interviews in a substudy of US participants with MS. The quantitative UX analysis investigated the impact of new UX design features on user engagement and retention in US participants for 3 separate test series: all daily activities included in the fixed sequence (DA), all daily activities included in the fixed sequence except the Static Balance Test and U-Turn Test (DAx), and the 2MWT. RESULTS: The qualitative UX substudy (N=22) revealed the need for 2 new UX design features: a more seamless user journey during the activation process that eliminates the requirement of switching back and forth between the app and the email that the participants received upon registration, and configurable reminders and push notifications to help plan and remind the participants to complete their daily activities. Both UX design features were assessed in the quantitative UX analysis. Introducing the more seamless user journey (original user journey: n=608; more seamless user journey: n=481) improved the conversion rate of participants who enrolled in the study and proceeded to successfully activate the app from 53.9% (328/608) to 74.6% (359/481). Introducing reminders and push notifications (with reminders and notifications: n=350; without reminders and notifications: n=172) improved continuous usage time (proportion of participants with ≥3 consecutive days of usage: DA and DAx: ~30% vs ~12%; 2MWT: ~30% vs ~20%); test completion rates (maximum number of test series completed: DA: 279 vs 64; DAx: 283 vs 126; 2MWT: 302 vs 76); and user retention rates (at day 30: DA: 53/172, 30.8% vs 34/350, 9.7%; DAx: 53/172, 30.8% vs 60/350, 17.1%; 2MWT: 39/172, 22.6% vs 22/350, 6.2%). Inactivity times remained comparable. CONCLUSIONS: The remote assessment of MS with DHTTs is a relatively nascent but growing field of research. The continued assessment and improvement of UX design features can play a crucial role in the successful long-term adoption of new DHTTs.

Dynamic causal modelling highlights the importance of decreased self-inhibition of the sensorimotor cortex in motor fatigability.

Motor fatigability emerges when challenging motor tasks must be maintained over an extended period of time. It is frequently observed in everyday life and affects patients as well as healthy individuals. Motor fatigability can be measured using simple tasks like finger tapping at maximum speed for 30 s. This typically results in a rapid decrease of tapping frequency, a phenomenon called motor slowing. In a previous study (Bächinger et al, eLife, 8 (September), https://doi.org/10.7554/eLife.46750 , 2019), we showed that motor slowing goes hand in hand with a gradual increase in blood oxygen level dependent signal in the primary sensorimotor cortex (SM1), supplementary motor area (SMA), and dorsal premotor cortex (PMd). It is unclear what drives the activity increase in SM1 caused by motor slowing and whether motor fatigability affects the dynamic interactions between SM1, SMA, and PMd. Here, we performed dynamic causal modelling (DCM) on data of 24 healthy young participants collected during functional magnetic resonance imaging to answer this question. The regions of interest (ROI) were defined based on the peak activation within SM1, SMA, and PMd. The model space consisted of bilateral connections between all ROI, with intrinsic self-modulation as inhibitory, and driving inputs set to premotor areas. Our findings revealed that motor slowing was associated with a significant reduction in SM1 self-inhibition, as uncovered by testing the maximum à posteriori against 0 (t(23)=-4.51, p < 0.001). Additionally, the model revealed a significant decrease in the driving input to premotor areas (t(23) > 2.71, p < 0.05) suggesting that structures other than cortical motor areas may contribute to motor fatigability.

Upper limb sensorimotor recovery in Asian stroke survivors: a study protocol for the development and implementation of a Technology-Assisted dIgitaL biOmaRker (TAILOR) platform.

Background: Stroke is a leading cause of lifelong disability worldwide, partially driven by a reduced ability to use the upper limb in daily life causing increased dependence on caregivers. However, post-stroke functional impairments have only been investigated using limited clinical scores, during short-term longitudinal studies in relatively small patient cohorts. With the addition of technology-based assessments, we propose to complement clinical assessments with more sensitive and objective measures that could more holistically inform on upper limb impairment recovery after stroke, its impact on upper limb use in daily life, and on overall quality of life. This paper describes a pragmatic, longitudinal, observational study protocol aiming to gather a uniquely rich multimodal database to comprehensively describe the time course of upper limb recovery in a representative cohort of 400 Asian adults after stroke. Particularly, we will characterize the longitudinal relationship between upper limb recovery, common post-stroke impairments, functional independence and quality of life. Methods: Participants with stroke will be tested at up to eight time points, from within a month to 3 years post-stroke, to capture the influence of transitioning from hospital to community settings. We will perform a battery of established clinical assessments to describe the factors most likely to influence upper limb recovery. Further, we will gather digital health biomarkers from robotic or wearable sensing technology-assisted assessments to sensitively characterize motor and somatosensory impairments and upper limb use in daily life. We will also use both quantitative and qualitative measures to understand health-related quality of life. Lastly, we will describe neurophysiological motor status using transcranial magnetic stimulation. Statistics: Descriptive analyses will be first performed to understand post-stroke upper limb impairments and recovery at various time points. The relationships between digital biomarkers and various domains will be explored to inform key aspects of upper limb recovery and its dynamics using correlation matrices. Multiple statistical models will be constructed to characterize the time course of upper limb recovery post-stroke. Subgroups of stroke survivors exhibiting distinct recovery profiles will be identified. Conclusion: This is the first study complementing clinical assessments with technology-assisted digital biomarkers to investigate upper limb sensorimotor recovery in Asian stroke survivors. Overall, this study will yield a multimodal data set that longitudinally characterizes post-stroke upper limb recovery in functional impairments, daily-life upper limb use, and health-related quality of life in a large cohort of Asian stroke survivors. This data set generates valuable information on post-stroke upper limb recovery and potentially allows researchers to identify different recovery profiles of subgroups of Asian stroke survivors. This enables the comparisons between the characteristics and recovery profiles of stroke survivors in different regions. Thus, this study lays out the basis to identify early predictors for upper limb recovery, inform clinical decision-making in Asian stroke survivors and establish tailored therapy programs. Clinical trial registration: ClinicalTrials.gov, identifier: NCT05322837.

The Patient Matters in the End(point).

Digital health technologies such as wearable sensors are increasingly being used in clinical trials. However, the endpoints created from these useful tools are wide and varied. Often, digital health technologies such as wearable sensors are used either to collect a raw metric like "step count" or with artificial intelligence algorithms to define a biomarker for improvement. In the case of the former, improvements in such a raw metric is difficult to attribute to the patient health in a meaningful way. In the case of the latter, despite the potential predictive accuracies of machine learning and artificial intelligence approaches, the resulting biomarkers are a black box, which has limited direct interpretability to the patient's specific health concerns. The paper represents a call to arms to really place the patient at the heart of the endpoint. By designing trial endpoints which are measured by digital health technologies using a patient centered approach from the outset, the patient benefits from understanding the implications of approved medication for their life.

Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot.

Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms (p = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance.

Neurorehabilitation From a Distance: Can Intelligent Technology Support Decentralized Access to Quality Therapy?

Current neurorehabilitation models primarily rely on extended hospital stays and regular therapy sessions requiring close physical interactions between rehabilitation professionals and patients. The current COVID-19 pandemic has challenged this model, as strict physical distancing rules and a shift in the allocation of hospital resources resulted in many neurological patients not receiving essential therapy. Accordingly, a recent survey revealed that the majority of European healthcare professionals involved in stroke care are concerned that this lack of care will have a noticeable negative impact on functional outcomes. COVID-19 highlights an urgent need to rethink conventional neurorehabilitation and develop alternative approaches to provide high-quality therapy while minimizing hospital stays and visits. Technology-based solutions, such as, robotics bear high potential to enable such a paradigm shift. While robot-assisted therapy is already established in clinics, the future challenge is to enable physically assisted therapy and assessments in a minimally supervized and decentralized manner, ideally at the patient's home. Key enablers are new rehabilitation devices that are portable, scalable and equipped with clinical intelligence, remote monitoring and coaching capabilities. In this perspective article, we discuss clinical and technological requirements for the development and deployment of minimally supervized, robot-assisted neurorehabilitation technologies in patient's homes. We elaborate on key principles to ensure feasibility and acceptance, and on how artificial intelligence can be leveraged for embedding clinical knowledge for safe use and personalized therapy adaptation. Such new models are likely to impact neurorehabilitation beyond COVID-19, by providing broad access to sustained, high-quality and high-dose therapy maximizing long-term functional outcomes.

Hybrid Ubiquitous Coaching With a Novel Combination of Mobile and Holographic Conversational Agents Targeting Adherence to Home Exercises: Four Design and Evaluation Studies.

BACKGROUND: Effective treatments for various conditions such as obesity, cardiac heart diseases, or low back pain require not only personal on-site coaching sessions by health care experts but also a significant amount of home exercises. However, nonadherence to home exercises is still a serious problem as it leads to increased costs due to prolonged treatments. OBJECTIVE: To improve adherence to home exercises, we propose, implement, and assess the novel coaching concept of hybrid ubiquitous coaching (HUC). In HUC, health care experts are complemented by a conversational agent (CA) that delivers psychoeducation and personalized motivational messages via a smartphone, as well as real-time exercise support, monitoring, and feedback in a hands-free augmented reality environment. METHODS: We applied HUC to the field of physiotherapy and conducted 4 design-and-evaluate loops with an interdisciplinary team to assess how HUC is perceived by patients and physiotherapists and whether HUC leads to treatment adherence. A first version of HUC was evaluated by 35 physiotherapy patients in a lab setting to identify patients' perceptions of HUC. In addition, 11 physiotherapists were interviewed about HUC and assessed whether the CA could help them build up a working alliance with their patients. A second version was then tested by 15 patients in a within-subject experiment to identify the ability of HUC to address adherence and to build a working alliance between the patient and the CA. Finally, a 4-week n-of-1 trial was conducted with 1 patient to show one experience with HUC in depth and thereby potentially reveal real-world benefits and challenges. RESULTS: Patients perceived HUC to be useful, easy to use, and enjoyable, preferred it to state-of-the-art approaches, and expressed their intentions to use it. Moreover, patients built a working alliance with the CA. Physiotherapists saw a relative advantage of HUC compared to current approaches but initially did not see the potential in terms of a working alliance, which changed after seeing the results of HUC in the field. Qualitative feedback from patients indicated that they enjoyed doing the exercise with an augmented reality-based CA and understood better how to do the exercise correctly with HUC. Moreover, physiotherapists highlighted that HUC would be helpful to use in the therapy process. The longitudinal field study resulted in an adherence rate of 92% (11/12 sessions; 330/360 repetitions; 33/36 sets) and a substantial increase in exercise accuracy during the 4 weeks. CONCLUSIONS: The overall positive assessments from both patients and health care experts suggest that HUC is a promising tool to be applied in various disorders with a relevant set of home exercises. Future research, however, must implement a variety of exercises and test HUC with patients suffering from different disorders.

Human motor fatigability as evoked by repetitive movements results from a gradual breakdown of surround inhibition.

Motor fatigability emerges when demanding tasks are executed over an extended period of time. Here, we used repetitive low-force movements that cause a gradual reduction in movement speed (or 'motor slowing') to study the central component of fatigability in healthy adults. We show that motor slowing is associated with a gradual increase of net excitability in the motor network and, specifically, in primary motor cortex (M1), which results from overall disinhibition. Importantly, we link performance decrements to a breakdown of surround inhibition in M1, which is associated with high coactivation of antagonistic muscle groups. This is consistent with the model that a loss of inhibitory control might broaden the tuning of population vectors such that movement patterns become more variable, ill-timed and effortful. We propose that the release of inhibition in M1 is an important mechanism underpinning motor fatigability and, potentially, also pathological fatigue as frequently observed in patients with brain disorders.

Food-Predicting Stimuli Differentially Influence Eye Movements and Goal-Directed Behavior in Normal-Weight, Overweight, and Obese Individuals.

Obese individuals have been shown to exhibit abnormal sensitivity to rewards and reward-predicting cues as for example food-associated cues frequently used in advertisements. It has also been shown that food-associated cues can increase goal-directed behavior but it is currently unknown, whether this effect differs between normal-weight, overweight, and obese individuals. Here, we investigate this question by using a Pavlovian-to-instrumental transfer (PIT) task in normal-weight (N = 20), overweight (N = 17), and obese (N = 17) individuals. Furthermore, we applied eye tracking during Pavlovian conditioning to measure the participants' conditioned response as a proxy of the incentive salience of the predicted reward. Our results show that the goal-directed behavior of overweight individuals was more strongly influenced by food-predicting cues (i.e., stronger PIT effect) than that of normal-weight and obese individuals (p < 0.001). The weight groups were matched for age, gender, education, and parental education. Eye movements during Pavlovian conditioning also differed between weight categories (p < 0.05) and were used to categorize individuals based on their fixation style into "high eye index" versus "low eye index" as well. Our main finding was that the fixation style exhibited a complex interaction with the weight category. Furthermore, we found that normal-weight individuals of the group "high eye index" had higher body mass index within the healthy range than individuals of the group "low eye index" (p < 0.001), but this relationship was not found within in the overweight or obese groups (p > 0.646). Our findings are largely consistent with the incentive sensitization theory predicting that overweight individuals are more susceptible to food-related cues than normal-weight controls. However, this hypersensitivity might be reduced in obese individuals, possibly due to habitual/compulsive overeating or differences in reward valuation.

Observing back pain provoking lifting actions modulates corticomotor excitability of the observer's primary motor cortex.

Observing another person experiencing exogenously inflicted pain (e.g. by a sharp object penetrating a finger) modulates the excitability of the observer' primary motor cortex (M1). By contrast, far less is known about the response to endogenously evoked pain such as sudden back pain provoked by lifting a heavy object. Here, participants (n=26) observed the lifting of a heavy object. During this action the actor (1) flexed and extended the legs (LEG), (2) flexed and extended the back (BACK) or (3) flexed and extended the back which caused visible pain (BACKPAIN). Corticomotor excitability was measured by applying a single transcranial magnetic stimulation pulse to the M1 representation of the muscle erector spinae and participants scored their perception of the actor's pain on the numeric pain rating scale (NPRS). The participants scored vicarious pain as highest during the BACKPAIN condition and lowest during the LEG condition. MEP size was significantly lower for the LEG than the BACK and BACKPAIN condition. Although we found no statistical difference in the motor-evoked potential (MEP) size between the conditions BACK and BACKPAIN, there was a significant correlation between the difference in NPRS scores between the conditions BACKPAIN and BACK and the difference in MEP size between these conditions. Participants who believed the vicarious pain to be much stronger in the BACKPAIN than in the BACK condition also exhibited higher MEPs for the BACKPAIN than the BACK condition. Our results indicate that observing how others lift heavy objects facilitates motor representations of back muscles in the observer. Modulation occurs in a movement-specific manner and is additionally modulated by the extent to which the participants perceived the actor's pain. Our findings suggest that movement observation might be a promising paradigm to study the brain's response to back pain.

Disrupted prediction errors index social deficits in autism spectrum disorder.

Social deficits are a core symptom of autism spectrum disorder; however, the perturbed neural mechanisms underpinning these deficits remain unclear. It has been suggested that social prediction errors-coding discrepancies between the predicted and actual outcome of another's decisions-might play a crucial role in processing social information. While the gyral surface of the anterior cingulate cortex signalled social prediction errors in typically developing individuals, this crucial social signal was altered in individuals with autism spectrum disorder. Importantly, the degree to which social prediction error signalling was aberrant correlated with diagnostic measures of social deficits. Effective connectivity analyses further revealed that, in typically developing individuals but not in autism spectrum disorder, the magnitude of social prediction errors was driven by input from the ventromedial prefrontal cortex. These data provide a novel insight into the neural substrates underlying autism spectrum disorder social symptom severity, and further research into the gyral surface of the anterior cingulate cortex and ventromedial prefrontal cortex could provide more targeted therapies to help ameliorate social deficits in autism spectrum disorder.

Monetary, Food, and Social Rewards Induce Similar Pavlovian-to-Instrumental Transfer Effects.

Multiple types of reward, such as money, food or social approval, are capable of driving behavior. However, most previous investigations have only focused on one of these reward classes in isolation, as such it is not clear whether different reward classes have a unique influence on instrumental responding or whether the subjective value of the reward, rather than the reward type per se, is most important in driving behavior. Here, we investigate behavior using a well-established reward paradigm, Pavlovian-to-instrumental transfer (PIT), and three different reward types: monetary, food and social rewards. The subjective value of each reward type was matched using a modified Becker-DeGroot-Marschak (BDM) auction where subjective reward value was expressed through physical effort using a bimanual grip force task. We measured the influence of reward-associated stimuli on how participants distributed forces between hands when reaching a target effort range on the screen bimanually and on how much time participants spent in this target range. Participants spent significantly more time in the target range (15% ± 2% maximal voluntary contraction) when a stimulus was presented that was associated with a reward used during instrumental conditioning or Pavlovian conditioning compared to a stimulus associated with a neutral outcome (i.e., general PIT). The strength of the PIT effect was modulated by subjective value (i.e., individuals who showed a stronger PIT effect rated the value of rewards more highly), but not by reward type, demonstrating that stimuli of all reward types were able to act as appetitive reinforcers and influenced instrumental responding, when matched to the same subjective reward value. This is the first demonstration that individually matched monetary, food and social rewards are equally effective as appetitive reinforcers in PIT. These findings strengthen the hypotheses that the subjective value is crucial for how much reward-associated stimuli influence behavior.