The Evolution of Personalized Behavioral Intervention Technology

Dobkin, Bruce H.; Dorsch, Andrew

doi:10.1161/strokeaha.117.016620

Cited by 22 publications

(10 citation statements)

References 36 publications

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“…Several studies [ 70 , 71 , 72 , 73 ] have already shown practical perspectives of wearable sensors use in the home environment to remotely monitor patients’ clinical state and improve therapeutic strategies. The objective and long-term measures by means of wearable sensors in free living-like conditions would support patients’ physical activity by giving feedback about motor performance and tailored instructions [ 74 ], such as lengthen the stride length during walking to limit the sequence effect (step-to-step decrease in amplitude) and thus potentially reduce FOG occurrence [ 75 ].…”

Section: Discussionmentioning

confidence: 99%

Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals

Mazzetta

Zampogna

Suppa

et al. 2019

Sensors

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We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson’s disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art.

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Section: Discussionmentioning

confidence: 99%

Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals

Mazzetta

Zampogna

Suppa

et al. 2019

Sensors

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“…As a consequence of differences in the factors found between end-users, the used strategies must be different for patients/informal caregivers and healthcare professionals. For patients, this means that it is important that future eRehabilitation programs increase the ease of use, especially for of impaired body functions, to ensure eRehabilitation is applicable for as many patients as possible [ 37 ]. For uptake among healthcare professionals, it seems crucial that the eRehabilitation program is attractive, but also fits well into existing process of care.…”

Section: Discussionmentioning

confidence: 99%

Why the uptake of eRehabilitation programs in stroke care is so difficult—a focus group study in the Netherlands

et al. 2018

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BackgroundThe uptake of eRehabilitation programs in stroke care is insufficient, despite the growing availability. The aim of this study was to explore which factors influence the uptake of eRehabilitation in stroke rehabilitation, among stroke patients, informal caregivers, and healthcare professionals.MethodsA qualitative focus group study with eight focus groups (6–8 participants per group) was conducted: six with stroke patients/informal caregivers and two with healthcare professionals involved in stroke rehabilitation (rehabilitation physicians, physical therapists, occupational therapists, psychologists, managers). Focus group interviews were audiotaped, transcribed in full, and analyzed by direct content analysis using the implementation model of Grol.ResultsThirty-two patients, 15 informal caregivers, and 13 healthcare professionals were included. A total of 14 influencing factors were found, grouped to 5 of the 6 levels of the implementation model of Grol (Innovation, Organizational context, Individual patient, Individual professional, and Economic and political context). Most quotes of patients, informal caregivers, and healthcare professionals were classified to factors at the level of the Innovation (e.g., content, attractiveness, and feasibility of eRehabilitation programs). In addition, for patients, relatively many quotes were classified to factors at the level of the individual patient (e.g., patients characteristics as fatigue and the inability to understand ICT-devices), and for healthcare professionals at the level of the organizational context (e.g., having sufficient time and the fit with existing processes of care).ConclusionAlthough there was a considerable overlap in reported factors between patients/informal caregivers and healthcare professionals when it concerns eRehabilitation as innovation, its seems that patients/informal caregivers give more emphasis to factors related to the individual patient, whereas healthcare professionals emphasize the importance of factors related to the organizational context. This difference should be considered when developing an implementation strategy for patients and healthcare professionals separately.

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“…Movement sensors are another means of providing feedback with the goal of motivating greater skills practice and thus improving rehabilitation. In this regard, lower extremity sensors providing simple measures of physical activity and gait are more developed than upper extremity sensors, for which accurately characterizing reaching and grasping movements represents an ongoing computational challenge 81 . In one lower extremity sensor feasibility study, participants were given feedback of walking metrics via ankle sensors.…”

Section: Virtual Reality and Movement Sensorsmentioning

confidence: 99%