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Introduction: Patient and Public Involvement and Engagement (PPIE) involves working ' with ' or ' by ' patients and the public, rather than ' to ,' ' about ,' or ' for ' them, and is integral to neurological and digital health research. This rapid review examined PPIE integration in the development and implementation of digital health technologies for neurological conditions., Methods: Key terms were input into six databases. Included articles were qualitative studies or PPIE activities involving patient perspectives in shaping digital health technologies for neurological conditions. Bias was evaluated using the NICE qualitative checklist, with reporting following PRISMA guidelines., Results: 2,140 articles were identified, with 28 included. Of these, 25 were qualitative studies, and only three were focused PPIE activities. Patient involvement was mostly limited to one-off consultations during development.There was little evidence of PPIE during implementation, and minimal reporting on its impact., Conclusions: PPIE has been inconsistently reported in this research area, highlighting the need for more guidance and best-practice examples This review used a UK-based definition of PPIE, which may have excluded relevant activities from other countries. Future reviews should broaden terminology to capture PPIE integration globally.

Background: Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies., Objective: The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back-worn inertial sensors., Methods: Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back-worn inertial sensors., Results: The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids., Conclusions: Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health., Trial Registration: ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987., International Registered Report Identifier (irrid): RR2-10.1136/bmjopen-2021-050785., (©Felix Kluge, Yonatan E Brand, M Encarna Micó-Amigo, Stefano Bertuletti, Ilaria D'Ascanio, Eran Gazit, Tecla Bonci, Cameron Kirk, Arne Küderle, Luca Palmerini, Anisoara Paraschiv-Ionescu, Francesca Salis, Abolfazl Soltani, Martin Ullrich, Lisa Alcock, Kamiar Aminian, Clemens Becker, Philip Brown, Joren Buekers, Anne-Elie Carsin, Marco Caruso, Brian Caulfield, Andrea Cereatti, Lorenzo Chiari, Carlos Echevarria, Bjoern Eskofier, Jordi Evers, Judith Garcia-Aymerich, Tilo Hache, Clint Hansen, Jeffrey M Hausdorff, Hugo Hiden, Emily Hume, Alison Keogh, Sarah Koch, Walter Maetzler, Dimitrios Megaritis, Martijn Niessen, Or Perlman, Lars Schwickert, Kirsty Scott, Basil Sharrack, David Singleton, Beatrix Vereijken, Ioannis Vogiatzis, Alison Yarnall, Lynn Rochester, Claudia Mazzà, Silvia Del Din, Arne Mueller. Originally published in JMIR Formative Research (https://formative.jmir.org), 01.05.2024.)

Background: Digital tools may support people to self-manage their heart failure (HF). Having previously outlined the human-centered design development of a digital tool to support self-care of HF, the next step was to pilot the tool over a period of time to establish people's acceptance of it in practice., Objective: This study aims to conduct an observational pilot study to examine the usability, adherence, and feasibility of a digital health tool for HF within the Irish health care system., Methods: A total of 19 participants with HF were provided with a digital tool comprising a mobile app and the Fitbit Charge 4 and Aria Air smart scales for a period of 6 months. Changes to their self-care were assessed before and after the study with the 9-item European HF Self-care Behavior Scale (EHFScBS) and the Minnesota Living with HF Questionnaire (MLwHFQ) using a Wilcoxon signed rank test. After the study, 3 usability questionnaires were implemented and descriptively analyzed: the System Usability Scale (SUS), Wearable Technology Motivation Scale (WTMS), and Comfort Rating Scale (CRS). Participants also undertook a semistructured interview regarding their experiences with the digital tool. Interviews were analyzed deductively using the Theoretical Domains Framework., Results: Participants wore their devices for an average of 86.2% of the days in the 6-month testing period ranging from 40.6% to 98%. Although improvements in the EHFScBS and MLwHFQ were seen, these changes were not significant (P=.10 and P=.70, respectively, where P>.03, after a Bonferroni correction). SUS results suggest that the usability of this system was not acceptable with a median score of 58.8 (IQR 55.0-60.0; range 45.0-67.5). Participants demonstrated a strong motivation to use the system according to the WTMS (median 6.0, IQR 5.0-7.0; range 1.0-7.0), whereas the Fitbit was considered very comfortable as demonstrated by the low CRS results (median 0.0, IQR 0.0-0.0; range 0.0-2.0). According to participant interviews, the digital tool supported self-management through increased knowledge, improved awareness, decision-making, and confidence in their own data, and improving their social support through a feeling of comfort in being watched., Conclusions: The digital health tool demonstrated high levels of adherence and acceptance among participants. Although the SUS results suggest low usability, this may be explained by participants uncertainty that they were using it fully, rather than it being unusable, especially given the experiences documented in their interviews. The digital tool targeted key self-management behaviors and feelings of social support. However, a number of changes to the tool, and the health service, are required before it can be implemented at scale. A full-scale feasibility trial conducted at a wider level is required to fully determine its potential effectiveness and wider implementation needs., (©Alison Keogh, Carol Brennan, William Johnston, Jane Dickson, Stephen J Leslie, David Burke, Peter Megyesi, Brian Caulfield. Originally published in JMIR Formative Research (https://formative.jmir.org), 01.03.2024.)

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987., (© 2024. The Author(s).)