Regulatory considerations for developing remote measurement technologies for Alzheimer's disease research.
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ABSTRACT:
SUBMITTER: Erdemli G
PROVIDER: S-EPMC11375004 | biostudies-literature | 2024 Sep
REPOSITORIES: biostudies-literature
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Regulatory considerations for developing remote measurement technologies for Alzheimer's disease research.
Erdemli Gül G Grammatikopoulou Margarita M Wagner Bertil B Vairavan Srinivasan S Curcic Jelena J Aarsland Dag D Wittenberg Gayle G Nikolopoulos Spiros S Muurling Marijn M Froehlich Holger H de Boer Casper C Shanbhag Niraj M NM Nies Vera J M VJM Coello Neva N Gove Dianne D Diaz Ana A Foy Suzanne S Dartee Wim W Brem Anna-Katharine AK
NPJ digital medicine 20240904 1
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Project description:Despite the importance of function in early Alzheimer's disease (AD), current measures are outdated and insensitive. Moreover, COVID-19 has heighted the need for remote assessment in older people, who are at higher risk of being infection and are particularly advised to use social distancing measures, yet the importance of diagnosis and treatment of dementia remains unchanged. The emergence of remote measurement technologies (RMTs) allows for more precise and objective measures of function. However, RMT selection is a critical challenge. Therefore, this case study outlines the processes through which we identified relevant functional domains, engaged with stakeholder groups to understand participants' perspectives and worked with technical experts to select relevant RMTs to examine function. After an extensive literature review to select functional domains relevant to AD biomarkers, quality of life, rate of disease progression and loss of independence, functional domains were ranked and grouped by the empirical evidence for each. For all functional domains, we amalgamated feedback from a patient advisory board. The results were prioritized into: highly relevant, relevant, neutral, and less relevant. This prioritized list of functional domains was then passed onto a group of experts in the use of RMTs in clinical and epidemiological studies to complete the selection process, which consisted of: (i) identifying relevant functional domains and RMTs; (ii) synthesizing proposals into final RMT selection, and (iii) verifying the quality of these decisions. Highly relevant functional domains were, "difficulties at work," "spatial navigation and memory," and "planning skills and memory required for task completion." All functional domains were successfully allocated commercially available RMTs that make remote measurement of function feasible. This case study provides a set of prioritized functional domains sensitive to the early stages of AD and a set of RMTs capable of targeting them. RMTs have huge potential to transform the way we assess function in AD-monitoring for change and stability continuously within the home environment, rather than during infrequent clinic visits. Our decomposition of RMT and functional domain selection into identify, synthesize, and verify activities, provides a pragmatic structure with potential to be adapted for use in future RMT selection processes.
| S-EPMC7674649 | biostudies-literature
Project description:BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills. Timely detection of these symptoms can facilitate early intervention, potentially slowing disease progression and enabling appropriate treatment and care.MethodsThe RADAR-AD study was designed to evaluate the accuracy and validity of multiple RMTs in detecting functional decline across various stages of AD in a real-world setting, compared to standard clinical rating scales. Our approach involved a univariate analysis using Analysis of Covariance (ANCOVA) to analyze individual features of six RMTs while adjusting for variables such as age, sex, years of education, clinical site, BMI and season. Additionally, we employed four machine learning classifiers - Logistic Regression, Decision Tree, Random Forest, and XGBoost - using a nested cross-validation approach to assess the discriminatory capabilities of the RMTs.ResultsThe ANCOVA results indicated significant differences between healthy and AD subjects regarding reduced physical activity, less REM sleep, altered gait patterns, and decreased cognitive functioning. The machine-learning-based analysis demonstrated that RMT-based models could identify subjects in the prodromal stage with an Area Under the ROC Curve of 73.0 %. In addition, our findings show that the Amsterdam iADL questionnaire has high discriminatory abilities.ConclusionsRMTs show promise in AD detection already in the prodromal stage. Using them could allow for earlier detection and intervention, thereby improving patients' quality of life. Furthermore, the Amsterdam iADL questionnaire holds high potential when employed remotely.
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Project description:Following the unprecedented outbreak of the Coronavirus pandemic (COVID-19), educators and students have shifted from conventional face-to-face lectures to fully virtual sessions that were delivered via video conferencing software. This research investigates the facilitating conditions and the students' perceptions toward using these interactive resources to continue their learning journey. The data was gathered through a structured questionnaire among 777 students in tertiary education. The survey instrument comprised valid measures that are frequently utilized in academia, to evaluate the individuals' acceptance of interactive educational technologies. A partial least squares (PLS) approach revealed that there were very significant factors that were predicting the students' dispositions to utilize synchronous learning programs. The findings underlined the importance of providing appropriate facilitating conditions to improve perceptions and attitudes toward interactive conferencing software. These results reflect the latest developments, as COVID-19 has inevitably accelerated the digital transformation in the realms of education. This contribution implies that students adapted well to a new normal. It confirmed that they are willing to participate and engage in virtual meetings through video conferencing programs.
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Project description:Digital mental health is becoming increasingly common. This includes use of smartphones and wearables to collect data in real time during day-to-day life (remote measurement technologies, RMT). Such data could capture changes relevant to depression for use in objective screening, symptom management and relapse prevention. This approach may be particularly accessible to young people of today as the smartphone generation. However, there is limited research on how such a complex intervention would work in the real world. We conducted a collaborative realist review of RMT for depression in young people. Here we describe how, why, for whom and in what contexts RMT appear to work or not work for depression in young people and make recommendations for future research and practice. Ethical, data protection and methodological issues need to be resolved and standardized; without this, RMT may be currently best used for self-monitoring and feedback to the healthcare professional where possible, to increase emotional self-awareness, enhance the therapeutic relationship and monitor the effectiveness of other interventions.
| S-EPMC10963268 | biostudies-literature
Project description:SignificanceStudies using simultaneous functional near-infrared spectroscopy (fNIRS)-electroencephalography (EEG) during natural sleep in infancy are rare. Developments for combined fNIRS-EEG for sleep research that ensure optimal comfort as well as good coupling and data quality are needed.AimWe describe the steps toward developing a comfortable, wearable NIRS-EEG headgear adapted specifically for sleeping infants ages 5 to 9 months and present the experimental procedures and data quality to conduct infant sleep research using combined fNIRS-EEG.ApproachN=49 5- to 9-month-old infants participated. In phase 1, N=26 (10 = slept) participated using the non-wearable version of the NIRS-EEG headgear with 13-channel-wearable EEG and 39-channel fiber-based NIRS. In phase 2, N=23 infants (21 = slept) participated with the wireless version of the headgear with 20-channel-wearable EEG and 47-channel wearable NIRS. We used QT-NIRS to assess the NIRS data quality based on the good time window percentage, included channels, nap duration, and valid EEG percentage.ResultsThe infant nap rate during phase 1 was ∼40% (45% valid EEG data) and increased to 90% during phase 2 (100% valid EEG data). Infants slept significantly longer with the wearable system than the non-wearable system. However, there were more included good channels based on QT-NIRS in study phase 1 (61%) than phase 2 (50%), though this difference was not statistically significant.ConclusionsWe demonstrated the usability of an integrated NIRS-EEG headgear during natural infant sleep with both non-wearable and wearable NIRS systems. The wearable NIRS-EEG headgear represents a good compromise between data quality, opportunities of applications (home visits and toddlers), and experiment success (infants' comfort, longer sleep duration, and opportunities for caregiver-child interaction).
| S-EPMC10519625 | biostudies-literature
Project description:IntroductionDuring the COVID-19 pandemic, in-person research study visits were moved to an online format using a variety of communication platforms (e.g., Webex and Zoom). Increased technology use among older adults allowed for greater insight regarding the remote research study visit format.MethodsA survey developed by the National Alzheimer's Coordinating Center (NACC) was distributed among 12 Alzheimer's Disease Research Centers (ADRCs). The COVID-19 Technology Accessibility Survey aimed to understand preferences of older adults regarding their research study visits and how they accessed the internet.ResultsAmong 12 ADRCs, 2070 responses were received (mean age: 72.8 years [standard deviation (SD) = 10.4], mean education: 16.6 years [SD = 2.6], race/ethnicity: 85% White/non-Hispanic). Among respondents, those with some form of cognitive impairment were more likely to prefer remote research study visits (mild cognitive impairment [MCI] vs. normal [odds ratio (OR) = 1.40, P = 0.02] and dementia vs. normal [OR = 1.48, P < 0.01]). Respondents with cognitive impairment were also less likely to have interest in smartphone use during at-home study visits (MCI vs. normal [OR = 0.71, P = 0.02] and dementia vs. normal [OR = 0.63, P < 0.001]). Results were similar regarding tablet use (MCI vs. normal [OR = 0.73, P = 0.04] and dementia vs. normal [OR = 0.72, P = 0.01]). Geographical location was analyzed in terms of the percentage of respondents in each region who preferred remote research study visits: West, 51%; Midwest, 34%; South, 41%; and Northeast, 57% (P < 0.0001).DiscussionResults from the study suggest that there is a growing interest in the remote research study visit format. Further studies will allow for greater understanding and development of this research format.
| S-EPMC9677364 | biostudies-literature
Project description: Not available
| S-EPMC527697 | biostudies-literature
Project description:The development of novel digital endpoints (NDEs) using digital health technologies (DHTs) may provide opportunities to transform drug development. It requires a multidisciplinary, multi-study approach with strategic planning and a regulatory-guided pathway to achieve regulatory and clinical acceptance. Many NDEs have been explored; however, success has been limited. To advance industry use of NDEs to support drug development, we outline a theoretical, methodological study as a use-case proposal to describe the process and considerations when developing and obtaining regulatory acceptance for an NDE to assess sleep in patients with rheumatoid arthritis (RA). RA patients often suffer joint pain, fatigue, and sleep disturbances (SDs). Although many researchers have investigated the mobility of joint functions using wearable technologies, the research of SD in RA has been limited due to the availability of suitable technologies. We proposed measuring the improvement of sleep as the novel endpoint for an anti-TNF therapy and described the meaningfulness of the measure, considerations of tool selection, and the design of clinical validation. The recommendations from the FDA patient-focused drug development guidance, the Clinical Trials Transformation Initiative (CTTI) pathway for developing novel endpoints from DHTs, and the V3 framework developed by the Digital Medicine Society (DiMe) have been incorporated in the proposal. Regulatory strategy and engagement pathways are also discussed.
| S-EPMC8460981 | biostudies-literature
Project description:BackgroundRemote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment.ObjectiveA mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement.MethodsA total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device.ResultsThe overall retention rate was 60%. Higher-intensity treatment (χ21=4.6; P=.03) and higher baseline anxiety (t56.28=-2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t50.4=-0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition (U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90%-30% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20%-40% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list.ConclusionsDifferent data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term.
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Project description:Digital technologies, especially if used in novel ways, provide a number of potential advantages to clinical research in trials related to human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) and may greatly facilitate operations as well as data collection and analysis. These technologies may even allow answering questions that are not answerable with older technologies. However, they come with a variety of potential concerns for both the participants and the trial sponsors. The exact challenges and means for alleviation depend on the technology and on the population in which it is deployed, and the rapidly changing landscape of digital technologies presents a challenge for creating future-proof guidelines for technology application.The aim of this study was to identify and summarize some common themes that are frequently encountered by researchers in this context and highlight those that should be carefully considered before making a decision to include these technologies in their research.In April 2016, the Global HIV Vaccine Enterprise surveyed the field for research groups with recent experience in novel applications of digital technologies in HIV clinical research and convened these groups for a 1-day meeting. Real-world uses of various technologies were presented and discussed by 46 attendees, most of whom were researchers involved in the design and conduct of clinical trials of biomedical HIV prevention and treatment approaches. After the meeting, a small group of organizers reviewed the presentations and feedback obtained during the meeting and categorized various lessons-learned to identify common themes. A group of 9 experts developed a draft summary of the findings that was circulated via email to all 46 attendees for review. Taking into account the feedback received, the group finalized the considerations that are presented here.Meeting presenters and attendees discussed the many successful applications of digital technologies to improve research outcomes, such as those for recruitment and enrollment, participant identification, informed consent, data collection, data quality, and protocol or treatment adherence. These discussions also revealed unintended consequence of technology usage, including risks to study participants and risks to study integrity.Key lessons learned from these discussions included the need to thoroughly evaluate systems to be used, the idea that early success may not be sustained throughout the study, that some failures will occur, and considerations for study-provided devices. Additionally, taking these key lessons into account, the group generated recommendations on how to move forward with the use of technology in HIV vaccine and biomedical prevention trials.
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