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BACKGROUND Patients’ informed consent to participate is fundamental to ethical clinical research. Giving informed consent means agreeing to take part in the trial and having understood what is involved, including the risks and benefits of participation. Flawed informed consent processes, including missing dates and signatures, are common regulatory audit findings. Electronic consent (eConsent) uses digital technologies to enable the consenting process. It aims to improve participant comprehension and engagement with study information, and to address data quality concerns. OBJECTIVE This systematic literature review aimed to assess the effectiveness of eConsent in terms of patient comprehension, acceptability, usability, and study enrollment and retention rates, as well as effects on time taken to consent (“cycle time”) and site workload, in comparison with traditional, paper-based consenting. METHODS The systematic review was conducted and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. OVID Embase and OVID Medline were systematically searched for publications reporting original, comparative data on the effectiveness of eConsent in terms of patient comprehension, acceptability, usability, enrollment and retention rates, cycle time, and site workload. The methodological validity of studies that compared outcomes for comprehension, acceptability, and usability across paper consent and eConsent was assessed. Study methodologies were categorized as “high” if comprehensive assessments, such as established instruments, were used. RESULTS A total of 37 publications describing 35 studies (13,281 participants in total) were included. All studies comparing eConsenting and paper-based consenting for comprehension (20 studies [10 with “high” validity]), acceptability (8 studies [1 with “high” validity]), and usability (5 studies [1 with “high” validity]) reported either significantly better results with eConsent, better results but without significance testing, or no significant differences in overall results. No study reported better results with paper than eConsent. Among “high” validity studies, six studies on comprehension reported significantly better understanding for at least some concepts, the study on acceptability reported statistically significant higher satisfaction scores, and the study on usability reported statistically significant higher usability scores with eConsent than paper (all P CONCLUSIONS This systematic review showed that, compared with patients using paper-based consenting, patients using eConsent had a better understanding of the clinical trial information, showed greater engagement with content, and rated the consenting process as more acceptable and usable. eConsent solutions thus have the potential to enhance understanding, acceptability, and usability of the consenting process while inherently being able to address data quality concerns, including those related to flawed consenting processes.

As we seek to gain richer insights to understand intervention effects, and increasingly decentralise aspects of clinical trials to simplify participation, there is a growing interest in leveraging wearables and sensors to generate novel and informative clinical outcome measures for at-home assessment. The sensors embedded within smartphone technology provide one approach to capture of this data, and may be particularly useful when patients are already using mobile devices for at-home capture of other clinical trials data, such as patient-reported outcomes. We describe the results of an initial algorithm development study to determine whether the atmospheric pressure data provided by an onboard smartphone sensor is sufficiently informative to enable detection of a small height gain, such as that achieved during a short stair climb performance test. We were able to sufficiently distinguish height changes of 0.6 m in indoor conditions, representing around 4 stairs on an average staircase. This suggests that the smartphone barometer may indeed be suitable for inclusion within future work developing a stair-climbing performance outcome test instrumented using a mobile application.

Aim: We undertook qualitative research to understand more about older people and their interactions with technology, specifically to evidence the question “ can older people (seniors) manage electronic patient-reported outcomes solutions in clinical trials? ”. Methods: We undertook qualitative research interviews with older people and investigated the findings. Results: Seven of the ten participants had a smartphone and 3/10 had a feature phone (a mobile phone with buttons and no touchscreen). There was a shift from smartphone use by the younger participants to feature phone use by the oldest participants. Conclusion: The younger group of older individuals had similar experiences and attitudes toward touchscreen devices as the rest of the population. While the older participants expressed some reluctance toward unfamiliar technology, all participants were using technology and accepting of it.

Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively and continuously. With the accelerated adoption of DHTs in clinical trials, there is an unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable. It is not obvious how commonly used statistical methods to handle missing data in clinical trials can be directly applied to the complex data collected by DHTs. Meanwhile, current approaches used to address missing data from DHTs are of limited sophistication and focus on the exclusion of data where the quantity of missing data exceeds a given threshold. High-frequency time series data collected by DHTs are often summarized to derive epoch-level data, which are then processed to compute daily summary measures. In this article, we discuss characteristics of missing data collected by DHT, review emerging statistical approaches for addressing missingness in epoch-level data including within-patient imputations across common time periods, functional data analysis, and deep learning methods, as well as imputation approaches and robust modeling appropriate for handling missing data in daily summary measures. We discuss strategies for minimizing missing data by optimizing DHT deployment and by including the patients' perspectives in the study design. We believe that these approaches provide more insight into preventing missing data when deriving digital endpoints. We hope this article can serve as a starting point for further discussion among clinical trial stakeholders.

Background Visual analogue scales (VASs) are used in a variety of patient-, observer- and clinician-reported outcome measures. While typically included in measures originally developed for pen-and-paper completion, a greater number of clinical trials currently use electronic approaches to their collection. This leads researchers to question whether the measurement properties of the scale have been conserved during the migration to an electronic format, particularly because electronic formats often use a different scale length than the 100 mm paper standard. Methods We performed a review of published studies investigating the measurement comparability of paper and electronic formats of the VAS. Results Our literature search yielded 26 studies published between 1997 and 2018 that reported comparison of paper and electronic formats using the VAS. After excluding 2 publications, 23 of the remaining 24 studies included in this review reported electronic formats of the VAS (eVAS) and paper formats (pVAS) to be equivalent. A further study concluded that eVAS and pVAS were both acceptable but should not be interchanged. eVAS length varied from 21 to 200 mm, indicating that 100 mm length is not a requirement. Conclusions The literature supports the hypothesis that eVAS and pVAS provide comparable results regardless of the VAS length. When implementing a VAS on a screen-based electronic mode, we recommend following industry best practices for faithful migration to minimise the likelihood of non-comparability with pVAS.

A growing number of clinical trials employ electronic media, in particular smartphones and tablets, to collect patient-reported outcome data. This is driven by the ubiquity of the technology, and an increased awareness of associated improvements in data integrity, quality and timeliness. Despite this, there remains a lingering question relating to the measurement equivalence of an instrument when migrated from paper to a screen-based format. As a result, researchers often must provide evidence demonstrating the measurement equivalence of paper and electronic versions, such as that recommended by the ISPOR ePRO Good Research Practices Task Force. In the last decade, a considerable body of work has emerged that overwhelmingly supports the measurement equivalence of instruments using screen-based electronic formats. Our review of key works derives recommendations on evidence needed to support electronic implementation. We recommend application of best practice recommendations is sufficient to conclude measurement equivalence with paper PROMs. In addition, we recommend that previous usability evidence in a representative group is sufficient, as opposed to per-study testing. Further, we conclude that this also applies to studies using multiple screen-based devices, including bring-your-own-device, if a minimum device specification can be ensured and the instrument is composed of standard response scale types.

Background Scrolling is a perceived barrier in the use of bring your own device (BYOD) to capture electronic patient reported outcomes (ePROs). This study explored the impact of scrolling on the measurement equivalence of electronic patient-reported outcome measures (ePROMs) in the presence and absence of scrolling. Methods Adult participants with a chronic condition involving daily pain completed ePROMs on four devices with different scrolling properties: a large provisioned device not requiring scrolling; two provisioned devices requiring scrolling – one with a “smart-scrolling” feature that disabled the “next” button until all information was viewed, and a second without this feature; and BYOD with smart-scrolling. The ePROMs included were the SF-12, EQ-5D-5L, and three pain measures: a visual analogue scale, a numeric response scale and a Likert scale. Participants completed English or Spanish versions according to their first language. Associations between ePROM scores were assessed using intraclass correlation coefficients (ICCs), with lower bound of 95% confidence interval (CI) > 0.7 indicating comparability. Results One hundred fifteen English- or Spanish-speaking participants (21-75y) completed all four administrations. High associations between scrolling and non-scrolling were observed (ICCs: 0.71–0.96). The equivalence threshold was met for all but one SF-12 domain score (bodily pain; lower 95% CI: 0.65) and two EQ-5D-5L item scores (pain/discomfort, usual activities; lower 95% CI: 0.64/0.67). Age, language, and device size produced insignificant differences in scores. Conclusions The measurement properties of PROMs are preserved even in the presence of scrolling on a handheld device. Further studies that assess scrolling impact over long-term, repeated use are recommended.

Our current regulatory framework identifies the steps needed to develop and validate clinical endpoints. These approaches can be used to ensure that new digital endpoints derived using novel technologies can be implemented appropriately, and with sufficient supporting evidence, in clinical development programs. New digital endpoints from novel technologies provide opportunity to (i) measure constructs that cannot be measured feasibly using existing methods, (ii) measure objectively and with the potential of greater precision compared to some traditional methods, and (iii) measure more frequently and provide additional richness to the insights that can be gained compared to traditional measurement approaches. New technologies may also facilitate greater remote measurement which may enhance patient monitoring and make study participation more convenient. New technologies should be used to measure clinical endpoints not because it would be trendy or interesting to do so, but when they offer a viable approach to appropriately measure the endpoints of interest. Sponsors should select a “fit-for-purpose” technology with respect to the construct being studied, the protocol, and the study population, and use this evaluation to support their approach. Ensuring that the properties of clinical endpoints derived from novel technologies are well understood in reference to the target population is essential for their use in clinical development to support labeling claims and regulatory decision making. We understand enough to begin using these approaches where appropriate and to begin gaining acceptance for the clinical endpoints they can measure.

Aim: To understand the impact of anticancer treatment on oncology patients’ ability to use electronic solutions for completing patient-reported outcomes (ePRO). Materials & methods: Semi-structured interviews were conducted with seven individuals who had experienced a cancer diagnosis and treatment. Results: Participants reported that the following would impact the ability to interact with an ePRO solution: peripheral neuropathy of the hands (4/7), fatigue and/or concentration and memory issues (6/7), where they are in a treatment cycle (5/7). Approaches to improve usability included: larger, well-spaced buttons to deal with finger numbness, the ability to pause a survey and complete at a later point and presenting the recall period with every question to reduce reliance on memory. Conclusion: Symptoms associated with cancers and anticancer treatments can impact the use of technologies. The recommendations for optimizing the electronic implementation of patient-reported outcome instruments in this population provides the potential to improve data quality in oncology trials and places patient needs at the forefront to ensure ‘fit-for-purpose’ solutions.

This proof-of-concept study aimed to assess the ability of a mobile application and cloud analytics software solution to extract facial expression information from participant selfie videos. This is one component of a solution aimed at extracting possible health outcome measures based on expression, voice acoustics and speech sentiment from video diary data provided by patients. Forty healthy volunteers viewed 21 validated images from the International Affective Picture System database through a mobile app which simultaneously captured video footage of their face using the selfie camera. Images were intended to be associated with the following emotional responses: anger, disgust, sadness, contempt, fear, surprise and happiness. Both valence and arousal scores estimated from the video footage associated with each image were adequate predictors of the IAPS image scores (p

Part 1 in the DIA Study Endpoint Community Working Group on Mobile Sensor Technology (MST) series addresses considerations that may be useful when determining the minimum wear time associated with mobile sensor use to ensure reliable estimation of the clinical endpoint under consideration. What constitutes a minimum valid data set is a dilemma facing those using MSTs in clinical studies. If this alignment does not occur, the integrity of the data collected and conclusions drawn from these data may be in incorrect. While study participants should consent to engage with MSTs as defined in a protocol, participant behavior or technology lapses may result in capturing incomplete data. Drawing from the literature, we review what constitutes a minimum data set, the risks associated with missing data, alignment with the clinical endpoint(s) and goals of a study, as well as managing patient burden.

Objectives The aim of this study was to assess the measurement equivalence of individual response scale types by using a patient reported outcome measure (PROM) collected on paper and migrated into electronic format for use on the subject's own mobile device (BYOD) and on a provisioned device (site device). Methods Subjects suffering from chronic health conditions causing daily pain or discomfort were invited to participate in this single-site, single visit, three-way crossover study. Association between individual item and instrument subscale scores was assessed by using the intraclass correlation coefficient (ICC) and its CI. Participant attitudes toward the use of BYOD in a clinical trial were assessed through use of a questionnaire. Results In this study, 155 subjects (females 83 [54%]; males 72 [46%]) ages 19 to 69 years (mean ± SD: 48.6 ± 13.1) were recruited. High association between the modes of administration (paper, BYOD, site device) was shown with analysis of ICCs (0.79–0.98) for each response scale type, including visual analogue scale, numeric rating scale, verbal response scale, and Likert scale. Of the subjects, 94% (146 of 155) stated that they would definitely or probably be willing to download an app onto their own mobile device for a forthcoming clinical trial. Forty-five percent of subjects felt BYOD would be more convenient compared with 15% preferring a provisioned device (40% had no preference). Conclusions This study provides strong evidence supporting the use of BYOD for PROM collection in terms of the conservation of instrument measurement equivalence across the most widely used response scale types, and high patient acceptance of the approach.

OBJECTIVES: We have developed a mobile application and cloud analytics software solution to extract possible health outcome measures based on expression, voice acoustics and speech sentiment analysis from video diary footage collected using an Android app. This study aimed to assess the validity of expression detection. METHODS: Healthy volunteers were requested to view 21 validated images from the International Affective Picture System (IAPS) database through a mobile app which simultaneously captured video footage of their face using the selfie camera. Images were selected to generate the following emotional responses: anger, disgust, sadness, contempt, fear, surprise and happiness. Expression analysis was performed using Microsoft Azure Cognitive services and the association with IAPS data was assessed for each image. RESULTS: Forty participants (ages 21-57 years; Sex: M (20), F (19), Not stated (1)) provided informed consent and participated in this proof-of-concept study. Emotion was summarised into valence (unpleasant to pleasant) and arousal (calm to exciting) values. Both valence and arousal scores estimated by the average emotion estimated by the analysis of video footage were adequate predictors of the IAPS image scores (p < 0.001 and p=0.04 respectively), based on linear mixed-effects multi-level modelling using a chi-squared test to assess the significance of video analysis estimates of emotional response. CONCLUSIONS: This proof-of-concept study provides early encouraging findings that facial expression derived from video footage may provide appropriate measures of expression. In combination with voice acoustical measures and speech sentiment analysis, this may lead to novel measures of health status in patients using a video diary in indications including depression, schizophrenia, autism spectrum disorder and PTSD amongst other conditions.

Background Mobile sensors offer enormous potential for the collection of informative clinical endpoints in clinical trials to support regulatory decision making and product labelling. There are currently no specific guidelines on the information needed to enable regulators to review and accept proposed endpoints derived from mobile sensors for use in drug development trials. Objective The purpose of this working group report is to recommend the structure and content of an evidence dossier intended to support whether a clinical endpoint derived from mobile sensor data is fit-for-purpose for use in regulatory submissions for drug approvals. Evidence dossier The structure and content of a dossier to provide evidence supporting the use of a sensor-derived clinical endpoint is described. Sections include clinical endpoint definition and positioning, the concept of interest, the context of use, clinical validation and interpretation, study implementation, and analytical validity with sensor performance verification in support of the selected sensor. Conclusions In the absence of definitive regulatory guidance, this report provides a considered approach to compiling a comprehensive body of evidence to justify acceptance of mobile sensors for support of new drug applications.

Immersive, interactive and mHealth technologies are increasingly being used in clinical research, healthcare and rehabilitation solutions. Leveraging technology solutions to derive new and novel clinical outcome measures is important to the ongoing assessment of clinical interventions. While demonstrating statistically significant changes is an important element of intervention assessment, understanding whether changes detected reflect changes of a magnitude that are considered meaningful to patients is equally important. We describe methodologies used to determine meaningful change and recommend that these techniques are routinely included in the development and testing of clinical assessment and rehabilitation technology solutions.

Background: Sedentary behaviour (SB) is an important risk factor for a number of chronic diseases. Although gaps remain in our knowledge of the elements of SB most associated with reduced health outcomes, measuring SB is important, especially in less active patient populations where treatment-related changes may be seen first in changes in SB. Methods: We review current published work in the measurement of SB to make recommendations for SB measurement in clinical studies. Results: To help move our understanding of the area forward, we propose a set of derived measures of SB that can be easily understood and interpreted. Conclusion: Although there is more work required to determine and validate the most clinically relevant and sensitive measures of SB, there is enough understanding of how to measure SB to enable its inclusion in study protocols.

Understanding the perceived burden of clinical trial participation is an important element of patient-centric trial design and conduct.We report the results of a study to gain preliminary insights into the perceived burden associated with patient-reported outcome (PRO) data collection among a sample (n = 61) of volunteers from the general population including people with various health conditions resulting in chronic pain.Participants identified morning completion as more burdensome than completion of PRO measures in the evening. Weekly completion was perceived as less burdensome than daily, and twice-a-day more burdensome than once-a-day.Our results, while not generalizable in isolation, provide a valuable starting point to understand the complex construct of subject burden. This preliminary work is intended to be a catalyst for more in-depth research to better understand and predict burden and acceptable burden thresholds in clinical trials. Understanding subject burden is a vital component of human subject research that will be valuable in helping to inform future clinical trial designs.

Applications utilising 3D Camera technologies for the measurement of health outcomes in the health and wellness sector continues to expand. The Intel® RealSense™ is one of the leading 3D depth sensing cameras currently available on the market and aligns itself for use in many applications, including robotics, automation, and medical systems. One of the most prominent areas is the production of interactive solutions for rehabilitation which includes gait analysis and facial tracking. Advancements in depth camera technology has resulted in a noticeable increase in the integration of these technologies into portable platforms, suggesting significant future potential for pervasive in-clinic and field based health assessment solutions. This paper reviews the Intel RealSense technology’s technical capabilities and discusses its application to clinical research and includes examples where the Intel RealSense camera range has been used for the measurement of health outcomes. This review supports the use of the technology to develop robust, objective movement and mobility-based endpoints to enable accurate tracking of the effects of treatment interventions in clinical trials.

BACKGROUND Despite an aging population, older adults are typically underrecruited in clinical trials, often because of the perceived burden associated with participation, particularly travel associated with clinic visits. Conducting a clinical trial remotely presents an opportunity to leverage mobile and wearable technologies to bring the research to the patient. However, the burden associated with shifting clinical research to a remote site requires exploration. While a remote trial may reduce patient burden, the extent to which this shifts burden on the other stakeholders needs to be investigated. OBJECTIVE The aim of this study was to explore the burden associated with a remote trial in a nursing home setting on both staff and residents. METHODS Using results from a grounded analysis of qualitative data, this study explored and characterized the burden associated with a remote trial conducted in a nursing home in Dublin, Ireland. A total of 11 residents were recruited to participate in this trial (mean age: 80 years; age range: 67-93 years). To support research activities, we also recruited 10 nursing home staff members, including health care assistants, an activities co-ordinator, and senior nurses. This study captured the lived experience of this remote trial among staff and residents and explored the burden associated with participation. At the end of the trial, a total of 6 residents and 8 members of staff participated in semistructured interviews (n=14). They reviewed clinical data generated by mobile and wearable devices and reflected upon their trial-related experiences. RESULTS Staff reported extensive burden in fulfilling their roles and responsibilities to support activities of the trial. Among staff, we found eight key characteristics of burden: (1) comprehension, (2) time, (3) communication, (4) emotional load, (5) cognitive load, (6) research engagement, (7) logistical burden, and (8) product accountability. Residents reported comparatively less burden. Among residents, we found only four key characteristics of burden: (1) comprehension, (2) adherence, (3) emotional load, and (4) personal space. CONCLUSIONS A remote trial in a nursing home setting can minimize the burden on residents and enable inclusive participation. However, it arguably creates additional burden on staff, particularly where they have a role to play in locally supporting and maintaining technology as part of data collection. Future research should examine how to measure and minimize the burden associated with data collection in remote trials.

The increasing miniaturization and affordability of sensors and circuitry has led to the current level of innovation in the area of wearable and microsensor solutions for health monitoring. This facilitates the development of solutions that can be used to measure complex health outcomes in nonspecialist and remote settings. In this article, we review a number of innovations related to brain monitoring including portable and wearable solutions to directly measure brain electrical activity, and solutions measuring aspects related to brain function such as sleep patterns, gait, cognition, voice acoustics, and gaze analysis. Despite the need for more scientific validation work, we conclude that there is enough understanding of how to implement these approaches as exploratory tools that may provide additional valuable insights due to the rich and frequent data they produce, to justify their inclusion in clinical study protocols.

Background Wearable devices offer huge potential to collect rich sources of data to provide insights into the effects of treatment interventions. Despite this, at the time of writing this report, limited regulatory guidance on the use of wearables in clinical trial programs has been published. Objectives To present recommendations from the Critical Path Institute's Electronic Patient-Reported Outcome Consortium regarding the selection and evaluation of wearable devices and their measurements for use in regulatory trials and to support labeling claims. Methods The evaluation group was composed of Critical Path Institute's clinical outcome assessment (COA) scientists and COA specialists from pharmaceutical trial eCOA solution providers, including COA development and validation specialists. The resulting recommendations were drawn from a broad range of backgrounds, perspectives, and expertise that enriched the development of this report. Recommendations were developed through analysis of existing regulatory guidance relating to COA development and use in clinical trials, medical device certification/clearance regulations, literature-reported best practice, and practical experience of wearable technology application in clinical trials. Results We identify the essential properties of fit-for-purpose wearables and propose evidence needed to support their use. In addition, we overview the activities required to establish clinical endpoints derived from wearables data. Conclusions Using this framework, we believe there is enough current understanding to promote the appropriate use of wearables in study protocols. We hope this will provide a basis for discussion among clinical trial stakeholders and catalyze the development of more robust regulatory guidance.

There is a growing body of applications leveraging Microsoft Kinect and the associated Windows Software Development Kit in health and wellness. In particular, this platform has been valuable in developing interactive solutions for rehabilitation including creating more engaging exercise regimens and ensuring that exercises are performed correctly for optimal outcomes. \ud Clinical trials rely upon robust and validated methodologies to measure health status and to detect treatment-related changes over time to enable the efficacy and safety of new drug treatments to be assessed and measured. In many therapeutic areas, traditional outcome measures rely on subjective investigator and patient ratings. Subjective ratings are not always sensitive to detecting small improvements, are subject to inter- and intra-rater variability and limited in their ability to record detailed or subtle aspects of movement and mobility. For these reasons, objective measurements may provide greater sensitivity to detect treatment-related changes where they exist. \ud In this review paper, we explore the use of the Kinect platform to develop low-cost approaches to objectively measure aspects of movement. We consider published applications that measure aspects of gait and balance, upper extremity movement, chest wall motion and facial analysis. In each case, we explore the utility of the approach for clinical trials, and the precision and accuracy of estimates derived from the Kinect output. \ud We conclude that the use of games platforms such as Microsoft Kinect to measure clinical outcomes offer a versatile, easy to use and low-cost approach that may add significant value and utility to clinical drug development, in particular in replacing conventional subjective measures and providing richer information about movement than previously possible in large scale clinical trials, especially in the measurement of gross spatial movements. Regulatory acceptance of clinical outcomes collected in this way will be subject to comprehensive assessment of validity and clinical relevance, and this will require good quality peer-reviewed publications of scientific evidence.

This article presents a review of the research literature to identify the methodology used and outcome measures derived in the use of accelerometers to measure free-living activity in patients with COPD. Using this and existing empirical validity evidence we further identify standards for use, and recommended clinical outcome measures from continuous accelerometer data to describe pertinent measures of sedentary behaviour and physical activity in this and similar patient populations. We provide measures of the strength of evidence to support our recommendations and identify areas requiring continued research. Our findings support the use of accelerometry in clinical trials to understand and measure treatment-related changes in free-living physical activity and sedentary behaviour in patient populations with limited activity.

Background: Despite an aging population, older adults are typically underrecruited in clinical trials, often because of the perceived burden associated with participation, particularly travel associated with clinic visits. Conducting a clinical trial remotely presents an opportunity to leverage mobile and wearable technologies to bring the research to the patient. However, the burden associated with shifting clinical research to a remote site requires exploration. While a remote trial may reduce patient burden, the extent to which this shifts burden on the other stakeholders needs to be investigated. Objective: The aim of this study was to explore the burden associated with a remote trial in a nursing home setting on both staff and residents. Methods: Using results from a grounded analysis of qualitative data, this study explored and characterized the burden associated with a remote trial conducted in a nursing home in Dublin, Ireland. A total of 11 residents were recruited to participate in this trial (mean age: 80 years; age range: 67-93 years). To support research activities, we also recruited 10 nursing home staff members, including health care assistants, an activities co-ordinator, and senior nurses. This study captured the lived experience of this remote trial among staff and residents and explored the burden associated with participation. At the end of the trial, a total of 6 residents and 8 members of staff participated in semistructured interviews (n=14). They reviewed clinical data generated by mobile and wearable devices and reflected upon their trial-related experiences. Results: Staff reported extensive burden in fulfilling their roles and responsibilities to support activities of the trial. Among staff, we found eight key characteristics of burden: (1) comprehension, (2) time, (3) communication, (4) emotional load, (5) cognitive load, (6) research engagement, (7) logistical burden, and (8) product accountability. Residents reported comparatively less burden. Among residents, we found only four key characteristics of burden: (1) comprehension, (2) adherence, (3) emotional load, and (4) personal space. Conclusions: A remote trial in a nursing home setting can minimize the burden on residents and enable inclusive participation. However, it arguably creates additional burden on staff, particularly where they have a role to play in locally supporting and maintaining technology as part of data collection. Future research should examine how to measure and minimize the burden associated with data collection in remote trials. Enterprise Ireland Science Foundation Ireland Applied Research for Connected Health IDA

OBJECTIVES: Motion-based video game platforms provide the capability to track 3D body movements and may offer a versatile, easy to use and low-cost approach to measuring objective clinical outcomes. We reviewed published validation studies comparing clinical outcomes derived from video game platforms to gold-standard approaches. METHODS: We categorized studies in our review into three areas of application and summarized validation findings. We confined our review to studies using the Microsoft Kinect platform due to the volume of work in this area. RESULTS: Gait and balance: Five validation studies reported varied findings. One study in MS reported good correlation of most parameters with ClinROs; and a second study reported good validation of walk test parameters in Stroke patients. A treadmill test in healthy volunteers found Kinect underestimated joint flexion and over-estimated extension; and a further study was able to detect gait disturbances in MS during a speed-walking test compared to healthy volunteers although correlation to clinician assessment was modest (r=0.447). Kinect use during a battery of balance and dexterity tests in PD accurately measured the timing (ICCs: 0.940- 0.999) and gross spatial characteristics of clinically relevant movements, but spatial accuracy for smaller movements, such as toe tapping (ICC = 0.038), was poor. Upper extremity movement: Eight studies reported good validity in measurement of shoulder range of motion (r’s > 0.8, ICCs > 0.864). Spirometry: One study reported strong correlation of spirometry parameters (r>0.866) estimated using multiple sensors to generate a 3D image of the chest. CONCLUSIONS: Motion-based video gaming platforms offer potential for low-cost assessment of movement and mobility in large-scale clinical trials without reliance on specialist centers. Studies report good validity in some application areas. The ability to provide the level of accuracy needed in more rapid and finer movements requires more validation work.

Background: Older adults, particularly those with physical and cognitive impairments, are typically under-recruited in clinical trials, despite the fact that they experience the greatest need for healthcare services. Reasons for underrepresentation are disparate but may relate to comorbidities, communication difficulties (e.g. hearing and vision impairments) and physical immobility that constrains transportation to a research site. Remote trials supported by mobile and wearable health technologies have the potential to make clinical research participation more accessible for these groups. In order to determine the feasibility of this model, it is essential to understand the burden remote data collection places on the participants involved. Objective: The REACHES study (Remote Assessment of Older People in a Care Home Setting) explored the burden experienced by participants in a remote trial supported by mobile and wearable technology in a care home setting. Methods: The remote trial focused on implementing a falls prevention programme in a single care home over an eight-week period from March to May 2017. The following technological solutions were selected to support the activities of the trial: QTUG™ (Kinesis Health Technologies, Ireland), a sensor-based medical device that assesses gait, mobility, falls risk and frailty; Aging Research App (ICON Clinical Research, Ireland in partnership with mPROVE Health, US), a tablet version of the Age-Related Muscle Loss Questionnaire that assesses the impact of muscle loss on activities of daily living; and vivosmart® HR (Garmin Ltd., US) a wrist-worn device that tracks daily activity, heart rate and sleep patterns. These devices provided outcome measures for falls risk and mobility in older adults; offer a variety of data collection methods; and are conducive to remote data collection. A participatory design was used to define the study procedures from the outset. A range of qualitative methods were used to capture the “lived experience” of staff and residents participating in the trial. These included semi-structured interviews, ethnographic observations and diaries. Qualitative analytical procedures were employed using thematic analysis supported by NVivo software (QSR International). Results: A total of 6 residents and 8 members of staff participated in the semi-structured interviews (n=14). Results showed that staff experienced extensive burden in fulfilling their roles and responsibilities to support the remote trial, whereas residents reported limited burden. For both groups, the burden of comprehending the research and associated tasks was prominent. Additionally, for staff a lack of time emerged as a substantial burden. Findings suggest that the experience of burden was not mitigated by the perceived value of the trial. Conclusions: These findings provide insight into the experience of burden of a remote clinical trial among staff and residents in a care home setting. Understanding and mitigating the burden created by remote trials is vital to researchers and companies attempting to scale such a model. Future research could build on the lessons learned from the REACHES study to develop a method to measure the burden remote clinical trial protocols place on patients and other stakeholders involved.

Schizophrenia affects around 1% of the population but accounts for a much larger proportion of healthcare costs. New atypical antipsychotics are perceived as expensive because of higher drug costs, even though such drug costs account for only a small percentage of the total cost of care. However, if these agents reduce other types of treatment costs they could prove to be highly cost-effective. This paper presents a health economic model for the treatment of an acute episode of chronic schizophrenia and its subsequent control through maintenance treatment. Simulation experiments, using cost data reported for England, indicate that the most important treatment cost is care setting: treatments enabling patients to move from expensive inpatient and sheltered accommodation to less expensive settings represent the largest potential cost saving. Atypical antipsychotics with improved efficacy in negative symptoms and/or cognitive function may facilitate this and thus lead to substantial savings. Enhanced efficacy in positive symptoms, and improvements in compliance and relapse rates also have an influence on the cost of treating schizophrenia. Model predictions suggest that reported clinical profiles of atypical antipsychotics could lead to significant savings and large improvements in effectiveness over conventional therapy. Prospective studies are required to confirm these findings.

To assess the efficacy of the antipsychotic drug zotepine in the treatment of the global psychopathology of schizophrenia.Fifteen randomized placebo- or antipsychotic comparator- controlled trials were analysed, using the Brief Psychiatric Rating Scale (BPRS) or other improvement scales. A meta-analysis of standardized treatment differences and a test for homogeneity were performed on four comparator groups: all placebo-controlled trials, all antipsychotic comparator-controlled trials, conventional antipsychotic comparator-controlled trials, and antipsychotic comparator-controlled trials excluding those in which the dosage for zotepine exceeded 75-300 mg/day (the recommended dose in the UK). The outcome measure used in the meta-analysis was the change in rating score.Meta-analysis of the placebo-controlled trials showed that a significantly greater reduction in BPRS occurred with zotepine therapy than with placebo therapy. The reduction in rating score was also greater with zotepine therapy than with antipsychotic comparators. Exclusion of the high-dose zotepine studies did not alter these conclusions. Only in one trial, in which zotepine was compared with the atypical antipsychotic clozapine, was there a greater reduction in rating score with the comparator than with zotepine (not statistically significant). All tests of homogeneity failed to reach significance, demonstrating that the data were not influenced by inter-study heterogeneity.Zotepine is at least as effective against all psychopathological symptoms of schizophrenia as conventional antipsychotics. (Int J Psych Clin Pract 2000; 4:19-27).

There are a number of different approaches to the assessment of dose proportionality, but most of these are directed toward hypothesis testing. The analysis of dose proportionality studies, however, requires estimation rather than significance testing in order that the pharmacokinetic and clinical significance of any nonproportionality can be assessed.The methods of statistical analysis of these studies have been reviewed. An empirical model relating the log of the pharmacokinetic parameter linearly to the log of the dose (the “power model”) provides a readily interpreted measure of the degree of nonproportionality. The potential utility of this model has been demonstrated using a number of observed dataseis from different drugs. If there is doubt over the goodness of fit of the power model then analysis of variance (after log transformation), together with pairwise comparisons between doses, provides a suitable alternative approach.