Data Contribution Summaries for Patient Engagement in Multi-Device Health Monitoring Research

Rainey, Jay; Verweij, David; Dodds, Colin; Graeber, Johanna; Farhadi, Farzaneh; Ali, Ridita; Zhang, Viana; Bull, Christopher; Smeddinck, Jan David

doi:10.1145/3460418.3479371

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…Performance expectancy . As pointed out by Rainey et al, 20 participants want to know that the devices actually work and that using them has a benefit for the study. Many devices do not have indications of whether they work.…”

Section: Discussionmentioning

confidence: 99%

Technology acceptance of digital devices for home use: Qualitative results of a mixed methods study

et al. 2023

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Objective Digital devices have demonstrated benefits to patients with chronic and neurodegenerative diseases. But when patients use medical devices in their homes, the technologies have to fit into their lives. We investigated the technology acceptance of seven digital devices for home use. Methods We conducted 60 semi-structured interviews with participants of a larger device study on their views on the acceptability of seven devices. Transcriptions were analysed using qualitative content analysis. Results Based on the unified theory of acceptance and use of technology, we evaluated effort, facilitating conditions, performance expectancy and social influence of each device. In the effort category, five themes emerged: (a) the hassle to use the device; (b) its usability; (c) comfort; (d) disturbance to daily life; and (e) problems during usage. Facilitating conditions consisted of five themes: (a) expectations regarding a device; (b) quality of the instructions; (c) insecurities with usage; (d) possibilities of optimization; and (e) possibilities to use the device longer. Regarding performance expectancy, we identified three themes: (a) insecurities with the performance of a device; (b) feedback; and (c) motivation for using a device. In the social influence category, three themes emerged: (a) reactions of peers; (b) concerns with the visibility of a device; and (c) concerns regarding data privacy. Conclusions We identify key factors that determine the acceptability of medical devices for home use from the participants’ perspective. These include low effort of use, minor disruptions to their daily lives and good support from the study team.

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Section: Discussionmentioning

confidence: 99%

Technology acceptance of digital devices for home use: Qualitative results of a mixed methods study

et al. 2023

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“…These data were collected between August 2020 and August 2021, therefore during the COVID-19 pandemic. More information can be found in [42] and a table of the participants' demographics can be found in Table 1 in the Results section.…”

Section: Experimental Protocolmentioning

confidence: 99%

Evaluation of walking activity and gait to identify physical and mental fatigue in neurodegenerative and immune disorders: preliminary insights from the IDEA-FAST feasibility study

Hinchliffe,

Rehman,

Pinaud

et al. 2024

J NeuroEngineering Rehabil

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Background Many individuals with neurodegenerative (NDD) and immune-mediated inflammatory disorders (IMID) experience debilitating fatigue. Currently, assessments of fatigue rely on patient reported outcomes (PROs), which are subjective and prone to recall biases. Wearable devices, however, provide objective and reliable estimates of gait, an essential component of health, and may present objective evidence of fatigue. This study explored the relationships between gait characteristics derived from an inertial measurement unit (IMU) and patient-reported fatigue in the IDEA-FAST feasibility study. Methods Participants with IMIDs and NDDs (Parkinson's disease (PD), Huntington's disease (HD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), primary Sjogren’s syndrome (PSS), and inflammatory bowel disease (IBD)) wore a lower-back IMU continuously for up to 10 days at home. Concurrently, participants completed PROs (physical fatigue (PF) and mental fatigue (MF)) up to four times a day. Macro (volume, variability, pattern, and acceleration vector magnitude) and micro (pace, rhythm, variability, asymmetry, and postural control) gait characteristics were extracted from the accelerometer data. The associations of these measures with the PROs were evaluated using a generalised linear mixed-effects model (GLMM) and binary classification with machine learning. Results Data were recorded from 72 participants: PD = 13, HD = 9, RA = 12, SLE = 9, PSS = 14, IBD = 15. For the GLMM, the variability of the non-walking bouts length (in seconds) with PF returned the highest conditional R2, 0.165, and with MF the highest marginal R2, 0.0018. For the machine learning classifiers, the highest accuracy of the current analysis was returned by the micro gait characteristics with an intrasubject cross validation method and MF as 56.90% (precision = 43.9%, recall = 51.4%). Overall, the acceleration vector magnitude, bout length variation, postural control, and gait rhythm were the most interesting characteristics for future analysis. Conclusions Counterintuitively, the outcomes indicate that there is a weak relationship between typical gait measures and abnormal fatigue. However, factors such as the COVID-19 pandemic may have impacted gait behaviours. Therefore, further investigations with a larger cohort are required to fully understand the relationship between gait and abnormal fatigue.

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“…In IDEA-FAST's feasibility study utilizing multiple sensing devices (measuring physical activity, cognition and other health metrics), a summary of health data was provided back to patients at different time points during the study. To understand the importance of returning data and the best mechanisms to do so, the study explored patients' perceived benefits of access to data, as well as use and interpretability of the data 50 . The preliminary findings suggested that patients reported using the information to monitor their health and performance, and felt confident about their adherence to the devices, which also fostered communications with clinicians.…”

Section: Establishing Dht Acceptability and Feasibility Through Real-...mentioning

confidence: 99%

Patient-centricity in digital measure development: co-evolution of best practice and regulatory guidance

Aryal,

Blankenship,

Bachman

et al. 2024

npj Digit. Med.

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Digital health technologies (DHTs) have the potential to modernize drug development and clinical trial operations by remotely, passively, and continuously collecting ecologically valid evidence that is meaningful to patients’ lived experiences. Such evidence holds potential for all drug development stakeholders, including regulatory agencies, as it will help create a stronger evidentiary link between approval of new therapeutics and the ultimate aim of improving patient lives. However, only a very small number of novel digital measures have matured from exploratory usage into regulatory qualification or efficacy endpoints. This shows that despite the clear potential, actually gaining regulatory agreement that a new measure is both fit-for-purpose and delivers value remains a serious challenge. One of the key stumbling blocks for developers has been the requirement to demonstrate that a digital measure is meaningful to patients. This viewpoint aims to examine the co-evolution of regulatory guidance in the United States (U.S.) and best practice for integration of DHTs into the development of clinical outcome assessments. Contextualizing guidance on meaningfulness within the larger shift towards a patient-centric drug development approach, this paper reviews the U.S. Food and Drug Administration (FDA) guidance and existing literature surrounding the development of meaningful digital measures and patient engagement, including the recent examples of rejections by the FDA that further emphasize patient-centricity in digital measures. Finally, this paper highlights remaining hurdles and provides insights into the established frameworks for development and adoption of digital measures in clinical research.

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Data Contribution Summaries for Patient Engagement in Multi-Device Health Monitoring Research

Cited by 8 publications

References 26 publications

Technology acceptance of digital devices for home use: Qualitative results of a mixed methods study

Technology acceptance of digital devices for home use: Qualitative results of a mixed methods study

Evaluation of walking activity and gait to identify physical and mental fatigue in neurodegenerative and immune disorders: preliminary insights from the IDEA-FAST feasibility study

Patient-centricity in digital measure development: co-evolution of best practice and regulatory guidance

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