Exploiting telerobotics for sensorimotor rehabilitation: a locomotor embodiment.

BACKGROUND: Manual treadmill training is used for rehabilitating locomotor impairments but can be physically demanding for trainers. This has been addressed by enlisting robots, but in doing so, the ability of trainers to use their experience and judgment to modulate locomotor assistance on the fly has been lost. This paper explores the feasibility of a telerobotics approach for locomotor training that allows patients to receive remote physical assistance from trainers. METHODS: In the approach, a trainer holds a small robotic manipulandum that shadows the motion of a large robotic arm magnetically attached to a locomoting patient's leg. When the trainer deflects the manipulandum, the robotic arm applies a proportional force to the patient. An initial evaluation of the telerobotic system's transparency (ability to follow the leg during unassisted locomotion) was performed with two unimpaired participants. Transparency was quantified by the magnitude of unwanted robot interaction forces. In a small six-session feasibility study, six individuals who had prior strokes telerobotically interacted with two trainers (separately), who assisted in altering a targeted gait feature: an increase in the affected leg's swing length. RESULTS: During unassisted walking, unwanted robot interaction forces averaged 3-4 N (swing-stance) for unimpaired individuals and 2-3 N for the patients who survived strokes. Transients averaging about 10 N were sometimes present at heel-strike/toe-off. For five of six patients, these forces increased with treadmill speed during stance (R2 = .99; p < 0.001) and increased with patient height during swing (R2 = .71; p = 0.073). During assisted walking, the trainers applied 3.0 ± 2.8 N (mean ± standard deviation across patients) and 14.1 ± 3.4 N of force anteriorly and upwards, respectively. The patients exhibited a 20 ± 21% increase in unassisted swing length between Days 1-6 (p = 0.058). CONCLUSIONS: The results support the feasibility of locomotor assistance with a telerobotics approach. Simultaneous measurement of trainer manipulative actions, patient motor responses, and the forces associated with these interactions may prove useful for testing sensorimotor rehabilitation hypotheses. Further research with clinicians as operators and randomized controlled trials are needed before conclusions regarding efficacy can be made.

NUCare: Advancing research on technological integration for self-management in the aging population.

BACKGROUND: The Center for Technology in Support of Self-Management and Health (NUCare) is an exploratory research center funded by the National Institute of Nursing Research's P20 mechanism positioned to conduct rigorous research on the integration of technology in the self-management of the older adult population. PURPOSE: The purpose of this paper is to describe the development and application of an evaluation plan and preliminary evaluation results from the first year of implementation. METHODS: This evaluation plan is derived from and is consistent with Dorsey et al.'s (2014) logic model. Dorsey's model provided guidelines for evaluating sustainability, leveraging of resources, and interdisciplinary collaboration within the center. DISCUSSION: Preliminary results and strategies for addressing findings from the first year of evaluation are discussed. A secondary aim of this paper is to showcase the relevance of this center to the advancement and maintenance of health in the aging population.

Sleep habits in mild cognitive impairment.

We explored the relationship between sleep disturbances and mild cognitive impairment (MCI) in community-dwelling seniors. Recent evidence suggests that sleep habits are differentially compromised in different subtypes of MCI, but the relationship between sleep disruption and MCI remains poorly understood. We gathered daily objective measures of sleep disturbance from 45 seniors, including 16 with MCI (mean age, 86.9±4.3 y), over a 6-month period. We also collected self-report measures of sleep disturbance. Although there were no differences between groups in any of our self-report measures, we found that amnestic MCI (aMCI) volunteers had less disturbed sleep than both nonamnestic MCI (naMCI) and cognitively intact volunteers, as measured objectively by movement in bed at night (F2,1078=4.30, P=0.05), wake after sleep onset (F2,1078=41.6, P<0.001), and number of times up at night (F2,1078=26.7, P<0.001). The groups did not differ in total sleep time. In addition, the aMCI group had less day-to-day variability in these measures than the intact and naMCI volunteers. In general, the naMCI volunteers showed a level of disturbed sleep that was intermediate to that of aMCI and intact volunteers. These differences in sleep disruption between aMCI and naMCI may be related to differences in the pathology underlying these MCI subtypes.

Improving the study of brain-behavior relationships by revisiting basic assumptions.

Neuroimaging research has been at the forefront of concerns regarding the failure of experimental findings to replicate. In the study of brain-behavior relationships, past failures to find replicable and robust effects have been attributed to methodological shortcomings. Methodological rigor is important, but there are other overlooked possibilities: most published studies share three foundational assumptions, often implicitly, that may be faulty. In this paper, we consider the empirical evidence from human brain imaging and the study of non-human animals that calls each foundational assumption into question. We then consider the opportunities for a robust science of brain-behavior relationships that await if scientists ground their research efforts in revised assumptions supported by current empirical evidence.

The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols.

The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN's cross-study data harmonization efforts. We review the process through which harmonization challenges and opportunities motivated the development of tools and collection of metadata within the ILHBN. A variety of strategies have been adopted within the ILHBN to facilitate harmonization of ecological momentary assessment, location, accelerometer, and participant engagement data while preserving theory-driven heterogeneity and data privacy considerations. Several tools have been developed by the ILHBN to resolve challenges in integrating ILD across multiple data streams and time scales both within and across studies. Harmonization of distinct longitudinal measures, measurement tools, and sampling rates across studies is challenging, but also opens up new opportunities to address cross-cutting scientific themes of interest.

Health behavior changes, such as prevention of suicidal thoughts and behaviors, smoking, drug use, and alcohol use; and the promotion of mental health, sleep, and physical activities, and decreases in sedentary behavior, are difficult to sustain. The ILHBN is a cooperative agreement network funded jointly by seven participating units within the National Institutes of Health to collaboratively study how factors that occur in individuals' everyday life and in their natural environment influence the success of positive health behavior changes. This article discusses how information collected using smartphones, wearables, and other devices can provide helpful active and passive reflections of the participants' extent of risk and resources at the moment for an extended period of time. However, successful engagement and retention of participants also require tailored adaptations of study designs, measurement tools, measurement intervals, study span, and device choices that create hurdles in integrating (harmonizing) data from multiple studies. We describe some of the challenges, opportunities, and solutions that emerged from harmonizing intensive longitudinal data under heterogeneous study and participant characteristics within the ILHBN, and share some tools and recommendations to facilitate future data harmonization efforts.

Advancing the science of mHealth.

Mobile health (mHealth) technologies have the potential to greatly impact health research, health care, and health outcomes, but the exponential growth of the technology has outpaced the science. This article outlines two initiatives designed to enhance the science of mHealth. The mHealth Evidence Workshop used an expert panel to identify optimal methodological approaches for mHealth research. The NIH mHealth Training Institutes address the silos among the many academic and technology areas in mHealth research and is an effort to build the interdisciplinary research capacity of the field. Both address the growing need for high quality mobile health research both in the United States and internationally. mHealth requires a solid, interdisciplinary scientific approach that pairs the rapid change associated with technological progress with a rigorous evaluation approach. The mHealth Evidence Workshop and the NIH mHealth Training Institutes were both designed to address and further develop this scientific approach to mHealth.

Blinding efficacy and adverse events following repeated transcranial alternating current, direct current, and random noise stimulation.

As transcranial electrical stimulation (tES) protocols advance, assumptions underlying the technique need to be retested to ensure they still hold. Whilst the safety of stimulation has been demonstrated mainly for a small number of sessions, and small sample size, adverse events (AEs) following multiple sessions remain largely untested. Similarly, whilst blinding procedures are typically assumed to be effective, the effect of multiple stimulation sessions on the efficacy of blinding procedures also remains under question. This is especially relevant in multisite projects where small unintentional variations in protocol could lead to inter-site difference. We report AE and blinding data from 1,019 participants who received up to 11 semi-consecutive sessions of active or sham transcranial alternating current stimulation (tACS), direct current stimulation (tDCS), and random noise stimulation (tRNS), at 4 sites in the UK and US. We found that AEs were often best predicted by factors other than tES, such as testing site or session number. Results from the blinding analysis suggested that blinding was less effective for tDCS and tACS than tRNS. The occurrence of AEs did not appear to be linked to tES despite the use of smaller electrodes or repeated delivery. However, blinding efficacy was impacted in tES conditions with higher cutaneous sensation, highlighting a need for alternative stimulation blinding protocols. This may be increasingly necessary in studies wishing to deliver stimulation with higher intensities.

Advancing Behavioral Intervention and Theory Development for Mobile Health: The HeartSteps II Protocol.

Background: Recent advances in mobile and wearable technologies have led to new forms of interventions, called "Just-in-Time Adaptive Interventions" (JITAI). JITAIs interact with the individual at the most appropriate time and provide the most appropriate support depending on the continuously acquired Intensive Longitudinal Data (ILD) on participant physiology, behavior, and contexts. These advances raise an important question: How do we model these data to better understand and intervene on health behaviors? The HeartSteps II study, described here, is a Micro-Randomized Trial (MRT) intended to advance both intervention development and theory-building enabled by the new generation of mobile and wearable technology. Methods: The study involves a year-long deployment of HeartSteps, a JITAI for physical activity and sedentary behavior, with 96 sedentary, overweight, but otherwise healthy adults. The central purpose is twofold: (1) to support the development of modeling approaches for operationalizing dynamic, mathematically rigorous theories of health behavior; and (2) to serve as a testbed for the development of learning algorithms that JITAIs can use to individualize intervention provision in real time at multiple timescales. Discussion and Conclusions: We outline an innovative modeling paradigm to model and use ILD in real- or near-time to individually tailor JITIAs.

BayesLDM: A Domain-specific Modeling Language for Probabilistic Modeling of Longitudinal Data.

In this paper we present BayesLDM, a library for Bayesian longitudinal data modeling consisting of a high-level modeling language with specific features for modeling complex multivariate time series data coupled with a compiler that can produce optimized probabilistic program code for performing inference in the specified model. BayesLDM supports modeling of Bayesian network models with a specific focus on the efficient, declarative specification of dynamic Bayesian Networks (DBNs). The BayesLDM compiler combines a model specification with inspection of available data and outputs code for performing Bayesian inference for unknown model parameters while simultaneously handling missing data. These capabilities have the potential to significantly accelerate iterative modeling workflows in domains that involve the analysis of complex longitudinal data by abstracting away the process of producing computationally efficient probabilistic inference code. We describe the BayesLDM system components, evaluate the efficiency of representation and inference optimizations and provide an illustrative example of the application of the system to analyzing heterogeneous and partially observed mobile health data.

Executive function predicts risk of falls in older adults without balance impairment.

BACKGROUND: Executive dysfunction has previously been found to be a risk factor for falls. The aim of this study is to investigate the association between executive dysfunction and risk of falling and to determine if this association is independent of balance. METHODS: Participants were 188 community-dwelling individuals aged 65 and older. All participants underwent baseline and annual evaluations with review of health history, standardized neurologic examination, neuropsychological testing, and qualitative and quantitative assessment of motor function. Falls were recorded prospectively using weekly online health forms. RESULTS: During 13 months of follow-up, there were 65 of 188 participants (34.6%) who reported at least one fall. Univariate analysis showed that fallers were more likely to have lower baseline scores in executive function than non-fallers (p = 0.03). Among participants without balance impairment we found that higher executive function z-scores were associated with lower fall counts (p = 0.03) after adjustment for age, sex, health status and prior history of falls using negative binomial regression models. This relationship was not present among participants with poor balance. CONCLUSIONS: Lower scores on executive function tests are a risk factor for falls in participants with minimal balance impairment. However, this effect is attenuated in individuals with poor balance where physical or more direct motor systems factors may play a greater role in fall risk.

Measuring motor speed through typing: a surrogate for the finger tapping test.

Motor speed is an important indicator and predictor of both cognitive and physical function. One common assessment of motor speed is the finger-tapping test (FTT), which is typically administered as part of a neurological or neuropsychological assessment. However, the FTT suffers from several limitations, including infrequent in-person administration, the need for a trained assessor and dedicated equipment, and potential short-term sensory-motor fatigue. In this article, we propose an alternative method of measuring motor speed, with face validity to the FTT, that addresses these limitations by measuring the interkeystroke intervals (IKI) of familiar and repeated login data collected in the home during a subject's regular computer use. We show significant correlations between the mean tapping speeds from the FTT and the median IKIs of the nondominant (r = .77) and dominant (r = .70) hands, respectively, in an elderly cohort of subjects living independently. Finally, we discuss how the proposed method for measuring motor speed fits well into the framework of unobtrusive and continuous in-home assessment.

Adaptive Health Coaching Technology for Tailored Interventions.

Recent advances in sensor and communications technology have enabled scalable methods for providing continuity of care to the home for patients with chronic conditions and older adults wanting to age in place. In this article we describe our framework for a health coaching platform with a dynamic user model that enables tailored health coaching messages. We have shown that this can improve coach efficiency without a loss of message quality. We also discovered many lessons for coaching technology, most demonstrating the need for more coach input on sample message content, perhaps even requiring that individual coaches be able to modify the message database directly. Overall, coaches felt that the structure of the automated message generation was useful in remembering what to say, easy to edit if necessary and especially helpful for training new health coaches.

Reducing Motion Artifacts of Pulse Intervals from Photoplethysmogram of a Commercial Wristband for Heart Rate Variability Analysis.

Heart rate monitoring based on photoplethysmography (PPG) is a noninvasive and inexpensive way of measuring many important cardiovascular metrics such as heart rate and heart rate variability, and has been used in many wearable devices. Unfortunately, the accuracy of the measurements is compromised by motion artifacts. We propose a theoretically sound method to reduce the motion artifacts of heart rate sensed by a commercial wristband. This method is based on outlier detection and singular spectrum analysis which enables us to reduce the movement-related noise in non-stationary signals. The results suggest that this method exhibits high correspondence to the simultaneously measured heart rate using ECG. Several metrics of heart rate variability computed from cleaned data also indicate high agreement with those obtained from ECG.

Investigating a classical neuropsychological test in a real world context.

This study was performed to investigate the validity of a real world version of the Trail Making Test (TMT) across age strata, compared to the current standard TMT which is delivered using a pen-paper protocol. We developed a real world version of the TMT, the Can-TMT, that involves the retrieval of food cans, with numeric or alphanumerical labels, from a shelf in ascending order. Eye tracking data was acquired during the Can-TMT to calculate task completion time and compared to that of the Paper-TMT. Results indicated a strong significant correlation between the real world and paper tasks for both TMTA and TMTB versions of the tasks, indicative of the validity of the real world task. Moreover, the two age groups exhibited significant differences on the TMTA and TMTB versions of both task modalities (paper and can), further supporting the validity of the real world task. This work will have a significant impact on our ability to infer skill or impairment with visual search, spatial reasoning, working memory, and motor proficiency during complex real-world tasks. Thus, we hope to fill a critical need for an exam with the resolution capable of determining deficits which subjective or reductionist assessments may otherwise miss.

Optimizing medication reminders using a decision-theoretic framework.

We discuss a new approach to patients' adherence to enhance to their medication-taking regimen by developing a context-aware alerting system that would optimize the expected utility of alerts. Each patient's instantaneous context is assessed using a real-time sensor network deploying a variety of sensors. The alerts are generated to optimize the expected value to the patient. This paper is focused on the initial assessment of the utility of alerts, including the tradeoff between effectiveness and annoyance.

A study of medication-taking and unobtrusive, intelligent reminding.

Poor medication adherence is one of the major causes of illness and of treatment failure in the United States. The objective of this study was to conduct an initial evaluation of a context-aware reminder system, which generated reminders at an opportune time to take the medication. Ten participants aged 65 or older, living alone and managing their own medications, participated in the study. Participants took a low-dose vitamin C tablet twice daily at times that they specified. Participants were considered adherent if they took the vitamin within 90 minutes (before or after) of the prescribed time. Adherence and activity in the home was measured using a system of sensors, including an instrumented pillbox. There were three phases of the study: baseline, in which there was no prompting; time-based, in which there was prompting at the prescribed times for pill-taking; and context-aware, in which participants were only prompted if they forgot to take their pills and were likely able to take their pills. The context-based prompting resulted in significantly better adherence (92.3%) as compared to time-based (73.5%) or no prompting (68.1%) conditions (p < 0.0002, chi(2) = 17.0). In addition, subjects had better adherence in the morning than in the evening. We have shown in this study that a system that generates reminders at an opportune time to take the medication significantly improves adherence. This study indicates that context-aware prompting may provide improved adherence over standard time-based reminders.

Exploring Associations Between the Self-Reported Values, Well-Being, and Health Behaviors of Finnish Citizens: Cross-Sectional Analysis of More Than 100,000 Web-Survey Responses.

BACKGROUND: Understanding the relationship between personal values, well-being, and health-related behavior could facilitate the development of engaging, effective digital interventions for promoting well-being and the healthy lifestyles of citizens. Although the associations between well-being and values have been quite extensively studied, the knowledge about the relationship between health behaviors and values is less comprehensive. OBJECTIVE: The aim of this study was to assess retrospectively the associations between self-reported values and commitment to values combined with self-reported well-being and health behaviors from a large cross-sectional dataset. METHODS: We analyzed 101,130 anonymous responses (mean age 44.78 years [SD 13.82]; 78.88%, 79,770/101,130 women) to a Finnish Web survey, which were collected as part of a national health promotion campaign. The data regarding personal values were unstructured, and the self-reported value items were classified into value types based on the Schwartz value theory and by applying principal component analysis. Logistic and multiple linear regression were used to explore the associations of value types and commitment to values with well-being factors (happiness, communal social activity, work, and family-related distress) and health behaviors (exercise, eating, smoking, alcohol consumption, and sleep). RESULTS: Commitment to personal values was positively related to happiness (part r2=0.28), communal social activity (part r2=0.09), and regular exercise (part r2=0.06; P<.001 for all). Health, Power (social status and dominance), and Mental balance (self-acceptance) values had the most extensive associations with health behaviors. Regular exercise, healthy eating, and nonsmoking increased the odds of valuing Health by 71.7%, 26.8%, and 40.0%, respectively (P<.001 for all). Smoking, unhealthy eating, irregular exercise, and increased alcohol consumption increased the odds of reporting Power values by 27.80%, 27.78%, 24.66%, and 17.35%, respectively (P<.001 for all). Smoking, unhealthy eating, and irregular exercise increased the odds of reporting Mental balance values by 20.79%, 16.67%, and 15.37%, respectively (P<.001 for all). In addition, lower happiness levels increased the odds of reporting Mental balance and Power values by 24.12% and 20.69%, respectively (P<.001 for all). CONCLUSIONS: The findings suggest that commitment to values is positively associated with happiness and highlight various, also previously unexplored, associations between values and health behaviors.

FAST: A Novel, Executive Function-Based Approach to Cognitive Enhancement.

The present study introduces a novel cognitive intervention aimed at improving fluid intelligence (Gf), based on a framework we refer to as FAST: Flexible, Adaptive, Synergistic Training. FAST leverages a combination of novel game-based executive function (EF) training-designed specifically to enhance the likelihood of transfer-and transcranial electrical stimulation (tES), with aims to synergistically activate and strengthen mechanisms of cognitive control critical to Gf. To test our intervention, we collected three Gf measures from 113 participants [the advanced short Bochumer Matrizen-Test (BOMAT), Raven's Advanced Progressive Matrices (APM), and matrices similar to Raven's generated by Sandia labs], prior to and following one of three interventions: (1) the FAST + tRNS intervention, a combination of 30 min of daily training with our novel training game, Robot Factory, and 20 min of concurrent transcranial random noise stimulation applied to bilateral dorsolateral prefrontal cortex (DLPFC); (2) an adaptively difficult Active Control intervention comprised of visuospatial tasks that specifically do not target Gf; or (3) a no-contact control condition. Analyses of changes in a Gf factor from pre- to post-test found numerical increases for the FAST + tRNS group compared to the two control conditions, with a 0.3 SD increase relative to Active Control (p = 0.07), and a 0.19 SD increase relative to a No-contact control condition (p = 0.26). This increase was found to be largely driven by significant differences in pre- and post-test Gf as measured on the BOMAT test. Progression through the FAST training game (Robot Factory) was significantly correlated with changes in Gf. This is in contrast with progress in the Active Control condition, as well as with changes in individual EFs during FAST training, which did not significantly correlate with changes in Gf. Taken together, this research represents a useful step forward in providing new insights into, and new methods for studying, the nature of Gf and its malleability. Though our results await replication and extension, they provide preliminary evidence that the crucial characteristic of Gf may, in fact, be the ability to combine EFs rapidly and adaptively according to changing demand, and that Gf may be susceptible to targeted training.

Effect of low concentrations of apomorphine on parkinsonism in a randomized, placebo-controlled, crossover study.

OBJECTIVE: To determine whether low concentrations of a dopamine agonist worsen parkinsonism, which would suggest that activation of presynaptic dopamine autoreceptors causes a super-off state. DESIGN: Randomized, double-blind, placebo-controlled, crossover clinical trial. SETTING: Academic movement disorders center. PATIENTS: Patients with Parkinson disease and motor fluctuations. INTERVENTION: Fourteen patients with Parkinson disease and motor fluctuations were randomized to receive 1 of 6 possible sequences of placebo, low-dose (subthreshold) apomorphine hydrochloride, and high-dose (threshold to suprathreshold) apomorphine hydrochloride infusions. Subthreshold doses of apomorphine hydrochloride (12.5 microg/kg/h every 2 hours and 25 microg/kg/h every 2 hours), threshold to suprathreshold doses of apomorphine hydrochloride (50 microg/kg/h every 2 hours and 100 microg/kg/h every 2 hours), and placebo were infused for 4 hours daily for 3 consecutive days. MAIN OUTCOME MEASURES: Finger and foot tapping rates. RESULTS: There was no decline in finger or foot tapping rates during the low-dose apomorphine hydrochloride infusions relative to placebo. The high-dose infusions increased foot tapping (P < .001) and trended toward increasing finger tapping compared with placebo infusions. CONCLUSIONS: Subthreshold concentrations of apomorphine did not worsen parkinsonism, suggesting that presynaptic dopamine autoreceptors are not important to the motor response in moderate to advanced Parkinson disease. Trial Registration clinicaltrials.gov Identifier: NCT00472355.

Unobtrusive assessment of activity patterns associated with mild cognitive impairment.

BACKGROUND: Timely detection of early cognitive impairment is difficult. Measures taken in the clinic reflect a single snapshot of performance that might be confounded by the increased variability typical in aging and disease. We evaluated the use of continuous, long-term, and unobtrusive in-home monitoring to assess neurologic function in healthy and cognitively impaired elders. METHODS: Fourteen older adults 65 years and older living independently in the community were monitored in their homes by using an unobtrusive sensor system. Measures of walking speed and amount of activity in the home were obtained. Wavelet analysis was used to examine variance in activity at multiple time scales. RESULTS: More than 108,000 person-hours of continuous activity data were collected during periods as long as 418 days (mean, 315 +/- 82 days). The coefficient of variation in the median walking speed was twice as high in the mild cognitive impairment (MCI) group (0.147 +/- 0.074) as compared with the healthy group (0.079 +/- 0.027; t(11) = 2.266, P < .03). Furthermore, the 24-hour wavelet variance was greater in the MCI group (MCI, 4.07 +/- 0.14; healthy elderly, 3.79 +/- 0.23; F = 7.58, P