User acceptance of location-tracking technologies in health research: Implications for study design and data quality

Recruitment and retention of adolescents for an ecological momentary assessment measurement burst mental health study: The MHIM engagement strategy

INTRODUCTION

Recruitment and long-term retention of adolescent participants in longitudinal research are challenging and may be especially so in studies involving remote measurement and biosampling components. The ability to effectively recruit and retain participants can be supported by the use of specific evidence-based engagement strategies that are built in from the earliest stages.

METHODS

Informed by a review of the evidence on effective engagement strategies and consultations with adolescents (via two Young Person Advisory Groups [YPAGs]; ages 11-13 and 14-17), the current protocol describes the planned participant engagement strategy for the Mental Health in the Moment Study: a multimodal measurement burst study of adolescent mental health across ages 11-19.

RESULTS

The protocol incorporates engagement strategies in four key domains: consultations/co-design with the target population, incentives, relationship-building and burden/barrier reduction. In addition to describing general engagement strategies in longitudinal studies, we also discuss specific concerns regarding engagement in data collection methods such as biosampling and ecological momentary assessment where a paucity of evidence exists.

CONCLUSION

Engagement strategies for adolescent mental health studies should be based on existing evidence and consultations with adolescents. We present our approach in developing the planned engagement strategies and also discuss limitations and future directions in engaging adolescents in longitudinal research.

PATIENT OR PUBLIC CONTRIBUTION

The study design for this project places a strong emphasis on the active engagement of adolescents throughout its development. Specifically, the feedback and suggestions provided by the YPAGs have been instrumental in refining our strategies for maximising the recruitment and retention of participants.

Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review

BACKGROUND

Wearable devices, mobile technologies, and their combination have been accepted into clinical use to better assess the physical fitness and quality of life of patients and as preventive measures. Usability is pivotal for overcoming constraints and gaining users' acceptance of technology such as wearables and their companion mobile health (mHealth) apps. However, owing to limitations in design and evaluation, interactive wearables and mHealth apps have often been restricted from their full potential.

OBJECTIVE

This study aims to identify studies that have incorporated wearable devices and determine their frequency of use in conjunction with mHealth apps or their combination. Specifically, this study aims to understand the attributes and evaluation techniques used to evaluate usability in the health care domain for these technologies and their combinations.

METHODS

We conducted an extensive search across 4 electronic databases, spanning the last 30 years up to December 2021. Studies including the keywords "wearable devices," "mobile apps," "mHealth apps," "physiological data," "usability," "user experience," and "user evaluation" were considered for inclusion. A team of 5 reviewers screened the collected publications and charted the features based on the research questions. Subsequently, we categorized these characteristics following existing usability and wearable taxonomies. We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

RESULTS

A total of 382 reports were identified from the search strategy, and 68 articles were included. Most of the studies (57/68, 84%) involved the simultaneous use of wearables and connected mobile apps. Wrist-worn commercial consumer devices such as wristbands were the most prevalent, accounting for 66% (45/68) of the wearables identified in our review. Approximately half of the data from the medical domain (32/68, 47%) focused on studies involving participants with chronic illnesses or disorders. Overall, 29 usability attributes were identified, and 5 attributes were frequently used for evaluation: satisfaction (34/68, 50%), ease of use (27/68, 40%), user experience (16/68, 24%), perceived usefulness (18/68, 26%), and effectiveness (15/68, 22%). Only 10% (7/68) of the studies used a user- or human-centered design paradigm for usability evaluation.

CONCLUSIONS

Our scoping review identified the types and categories of wearable devices and mHealth apps, their frequency of use in studies, and their implementation in the medical context. In addition, we examined the usability evaluation of these technologies: methods, attributes, and frameworks. Within the array of available wearables and mHealth apps, health care providers encounter the challenge of selecting devices and companion apps that are effective, user-friendly, and compatible with user interactions. The current gap in usability and user experience in health care research limits our understanding of the strengths and limitations of wearable technologies and their companion apps. Additional research is necessary to overcome these limitations.

Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research

Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data vary from study to study, making comparisons and reproducibility challenging.

OBJECTIVES

The objectives of this systematic review were to (1) identify best practices for GPS data collection and processing; (2) quantify reporting of best practices in published studies; and (3) discuss examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage of GPS data in health studies.

DESIGN

A systematic review.

DATA SOURCES

Electronic databases searched (24 October 2023) were PubMed, Scopus and Web of Science (PROSPERO ID: CRD42022322166).

ELIGIBILITY CRITERIA

Included peer-reviewed studies published in English met at least one of the criteria: (1) protocols involving GPS for exposure/context and human health research purposes and containing empirical data; (2) linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health; (4) derived variable methods using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary).

DATA EXTRACTION AND SYNTHESIS

We examined 157 manuscripts for reporting of best practices including wear time, sampling frequency, data validity, noise/signal loss and data linkage to assess risk of bias.

RESULTS

We found that 6% of the studies did not disclose the GPS device model used, only 12.1% reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and only 68.2% reported the inclusion criteria for their data.

CONCLUSIONS

Our recommendations for reporting on GPS usage, processing and linkage may be transferrable to other geospatial devices, with the hope of promoting transparency and reproducibility in this research.

PROSPERO REGISTRATION NUMBER

CRD42022322166.

Engaging Adolescents in Contemporary Longitudinal Health Research: Strategies for Promoting Participation and Retention

PURPOSE

High (and nonselective) recruitment and retention rates in longitudinal studies of adolescence are essential for illuminating health trajectories and determinants during this critical period. Knowledge of optimal recruitment and retention strategies must keep pace with emerging challenges and opportunities, such as the shifts towards digitally-based data collection.

METHODS

We used a narrative review approach to synthesize research on promising recruitment and retention strategies for optimizing engagement in the next generation of longitudinal adolescent health studies.

RESULTS

We identified a small number of well-evidenced strategies, emerging challenges and opportunities for recruitment and retention in contemporary studies, and key evidence gaps. Core recommendations include the use of well-evidenced strategies (e.g., incentivizing participation, reducing barriers and burden, and investing in building positive relationships with participants) and coproducing recruitment and retention strategies with adolescents and parents of adolescents.

DISCUSSION

More research is needed into successful recruitment/retention strategies for digital/remote data collection methods, but initial evidence suggests that adopting principles and adapting well-evidenced strategies from traditional longitudinal studies is promising.

Usability and ease of use of long-term remote monitoring of physical activity for individuals with acquired brain injury in community: a qualitative analysis

Introduction

Objective and continuous monitoring of physical activity over the long-term in the community is perhaps the most important step in the paradigm shift toward evidence-based practice and personalized therapy for successful community integration. With the advancement in technology, physical activity monitors have become the go-to tools for objective and continuous monitoring of everyday physical activity in the community. While these devices are widely used in many patient populations, their use in individuals with acquired brain injury is slowly gaining traction. The first step before using activity monitors in this population is to understand the patient perspective on usability and ease of use of physical activity monitors at different wear locations. However, there are no studies that have looked at the feasibility and patient perspectives on long-term utilization of activity monitors in individuals with acquired brain injury.

Methods

This pilot study aims to fill this gap and understand patient-reported aspects of the feasibility of using physical activity monitors for long-term use in community-dwelling individuals with acquired brain injury.

Results

This pilot study found that patients with acquired brain injury faced challenges specific to their functional limitations and that the activity monitors worn on the waist or wrist may be better suited in this population.

Discussion

The unique wear location-specific challenges faced by individuals with ABI need to be taken into account when selecting wearable activity monitors for long term use in this population.

Personal Location as Health-Related Data: Public Knowledge, Public Concern, and Personal Action

OBJECTIVES

Personal health information (PHI), including health status and behaviors, are often associated with personal locations. Smart devices and other technologies routinely collect personal location. Therefore, technologies collecting personal location do not just create generic questions of privacy, but specific concerns related to PHI.

METHODS

To assess public opinion on the relationship between health, personal location, and privacy, a national survey of US residents was administered online in March 2020. Respondents answered questions about their use of smart devices and knowledge of location tracking. They also identified which of the locations they could visit were most private and how to balance possibilities that locations may be private but can also be useful to share.

RESULTS

Of respondents that used smart devices (n = 688), a majority (71.1%) indicated they knew they had applications tracking their location, with respondents who were younger (P < .001) and male (P = .002) and with more education (P = .045) more likely to indicate "yes." When all respondents (N = 828) identified the locations on a hypothetical map they felt were most private, health-related locations (substance use treatment center, hospital, urgent care) were the most selected.

CONCLUSIONS

The historical notion of PHI is no longer adequate and the public need greater education on how data from smart devices may be used to predict health status and behaviors. The COVID-19 pandemic brought increased attention to personal location as a tool for public health. Given healthcare's dependence upon trust, the field needs to lead the conversation and be viewed as protecting privacy while usefully leveraging location data.

The Implementation of a GPS-Based Location-Tracking Smartphone App in South Africa to Improve Engagement in HIV Care: Randomized Controlled Trial

BACKGROUND

Mobile health interventions are common in public health settings in Africa, and our preliminary work showed that smartphones are increasing in South Africa. We developed a novel smartphone app-CareConekta-that used GPS location data to characterize personal mobility to improve engagement in HIV care among pregnant and postpartum women living with HIV in South Africa. The app also used the user's location to map nearby clinics.

OBJECTIVE

We aimed to describe the feasibility, acceptability, and initial efficacy of using the app in a real-world setting.

METHODS

We conducted a prospective randomized controlled trial at a public sector clinic near Cape Town, South Africa. We enrolled 200 pregnant (third trimester) women living with HIV who owned a smartphone that met the required specifications. All participants installed the app, designed to collect 2 GPS heartbeats per day to geolocate the participant within a random 1-km fuzzy radius (for privacy). We randomized (1:1) participants to a control arm to receive the app with no additional support or an intervention arm to receive supportive phone calls, WhatsApp (Meta Platforms, Inc) messages, or both from the study team when traveling >50 km from the study area for >7 days. In addition to mobility data collected daily through the phone, participants completed questionnaires at enrollment and follow-up (approximately 6 months post partum).

RESULTS

A total of 7 participants were withdrawn at enrollment or shortly after because of app installation failure (6/200, 3%) or changing to an unsuitable phone (1/200, 0.50%). During the study period, no participant's smartphone recorded at least 1 heartbeat per day, which was our primary feasibility measure. Of the 171 participants who completed follow-up, only half (91/171, 53.2%) reported using the same phone as that used at enrollment, with the CareConekta app still installed on the phone and GPS usually enabled. The top reasons reported for the lack of heartbeat data were not having mobile data, uninstalling the app, and no longer having a smartphone. Acceptability measures were positive, but participants at follow-up demonstrated a lack of understanding of the app's purpose and function. The clinic finder was a popular feature. Owing to the lack of consistent GPS heartbeats throughout the study, we were unable to assess the efficacy of the intervention.

CONCLUSIONS

Several key challenges impeded our study feasibility. Although the app was designed to reverse bill participants for any data use, the lack of mobile data was a substantial barrier to our study success. Participants reported purchasing WhatsApp data, which could not support the app. Problems with the web-based dashboard meant that we could not consistently monitor mobility. Our study provides important lessons about implementing an ambitious GPS-based study under real-world conditions in a limited-resource setting.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03836625; https://clinicaltrials.gov/ct2/show/NCT03836625.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s13063-020-4190-x.

Targeting Patients' Cognitive Load for Telehealth Video Visits Through Student-Delivered Helping Sessions at a United States Federally Qualified Health Center: Equity-Focused, Mixed Methods Pilot Intervention Study

BACKGROUND

The task complexity involved in connecting to telehealth video visits may disproportionately impact health care access in populations already experiencing inequities. Human intermediaries can be a strategy for addressing health care access disparities by acting as technology helpers to reduce the cognitive load demands required to learn and use patient-facing telehealth technologies.

OBJECTIVE

We conducted a cognitive load theory-informed pilot intervention involving warm accompaniment telehealth helping sessions with patients at a Federally Qualified Health Center (FQHC). We demonstrate how to design and report recruitment methods, reach, delivery process, and the preliminary impact of a novel equity-focused intervention.

METHODS

Early into the COVID-19 pandemic a telehealth helping session was offered to patients at FQHC via phone. Graduate students led the sessions on conducting a telehealth video test run or helping with patient portal log-in. They systematically recorded their recruitment efforts, intervention observations, and daily reflection notes. Following the intervention, we asked the intervention participants to participate in an interview and all patients who had telehealth visits during and 4 weeks before and after the intervention period to complete a survey. Electronic health records were reviewed to assess telehealth visit format changes. Descriptive and inferential statistical analyses of the recruitment records, electronic health record data, and surveys were performed. Through integrative analysis, we developed process-related themes and recommendations for future equity-focused telehealth interventions.

RESULTS

Of the 239 eligible patients, 34 (14.2%) completed the intervention and 3 (1.2%) completed subsequent interviews. The intervention participants who completed the survey (n=15) had lower education and less technological experience than the nonintervention survey participants (n=113). We identified 3 helping strategies for cognitive load reduction: providing step-by-step guidance for configuring and learning, building rapport to create confidence while problem-solving, and being on the same page to counter informational distractions. Intervention participants reported increased understanding but found that learning the video visit software was more difficult than nonintervention participants. A comparison of visit experiences did not find differences in difficulty (cognitive load measure) using telehealth-related technologies, changes to visit modality, or reported technical problems during the visit. However, the intervention participants were significantly less satisfied with the video visits.

CONCLUSIONS

Although a limited number of people participated in the intervention, it may have reached individuals more likely to need technology assistance. We postulate that significant differences between intervention and nonintervention participants were rooted in baseline differences between the groups' education level, technology experience, and technology use frequency; however, small sample sizes limit conclusions. The barriers encountered during the intervention suggest that patients at FQHC may require both improved access to web-based technologies and human intermediary support to make telehealth video visits feasible. Future large, randomized, equity-focused studies should investigate blended strategies to facilitate video visit access.

A review of the factors influencing adoption of digital health applications for people living with dementia

Objective

Researchers have used various theories and models to understand technology adoption, however, with the growing interest and availability of mobile applications (apps) for people living with dementia, it is desirable to have a broader insight into how technology adoption may be further improved. This paper aims to explore the factors influencing the adoption of digital health applications for people living with dementia and add to the current literature on this topic.

Methods

Searches were conducted in CINHAL, Web of Science, Psych Info, ProQuest Health and Medical, IEEE Xplore and Scopus. Citation searching and handsearching were used in the identification of other studies.

Results

Following an assessment of relevancy, nine studies remained and are included within this review. Methodological quality was assessed using The Mixed Methods Appraisal Tool (MMAT). A thematic analysis was used for the data synthesis of included studies. Each study reported on different types of apps.

Conclusion

From the synthesis of included studies, four analytic themes were identified; Theme 1: Personal and contextual factors; Theme 2: Perceived value and benefit; Theme 3: Design and content of app; and Theme 4: Digital Literacy and Confidence. People are diverse and so are their reasons for the adoption of apps. These findings provide an insight into the range of factors that impact the adoption of apps for people living with dementia. Understanding the factors that impact the adoption of mobile applications is critical to their success. These findings can be beneficial for app developers and for people living with dementia and their carers.

Community-dwelling older adults' acceptance of smartwatches for health and location tracking

BACKGROUND

Despite rapid growth in the popularity of smartwatches, evidence lacks regarding older adults' acceptance of smartwatches. Since most wearable sensors are not designed specifically for older adults, there is a need to examine wearability and usability challenges of wearable sensing devices faced by older adults to facilitate the use of objective measurements of health and mobility.

OBJECTIVES

We aimed to examine older adults' perceptions of GPS-enabled smartwatches and to identify potential barriers and facilitators of smartwatch and sensor data use.

METHODS

As part of a larger feasibility study, we conducted a mixed-methods study that included a descriptive content analysis of interviews and a brief usability survey with 30 participants aged 60 years and older after they had used a smartwatch for 3 days.

RESULTS

Most participants perceived wearable activity trackers including smartwatches and sensor-based data as useful for tracking health, finding activity patterns and promoting healthy behaviours. Privacy was of little concern, leading to willingness to share activity and location data with others. Participants identified barriers to usability as clumsy design, lack of aesthetic appeal, and difficulty reading the display and using the GPS tracking function. In contrast, identified facilitators of adoption included a big display, high-tech look, self-awareness and possible behaviour change.

CONCLUSIONS

Smartwatches have the potential of personalised detection of health deterioration and disability prevention, based on analysis of older adults' activities in free-living environments. The usefulness of this technology for older adults can be significantly increased by addressing usability issues and providing instructions on challenging features.

IMPLICATIONS FOR PRACTICE

To support sustained self-monitoring behaviours through wearable sensor devices in older adults, it is critical to examine how they perceive those devices and identify factors affecting technology acceptance that can maximise adoption.

Wireless muometric navigation system

While satellite-based global navigation systems have become essential tools in our daily lives, their effectiveness is often hampered by the fact that the signals cannot be accessed in underground, indoor, or underwater environments. Recently, a novel navigation system has been invented to address this issue by utilizing the characteristics of the ubiquitous and highly penetrative cosmic-ray muons. This technique, muometric navigation, does not require active signal generation and enables positioning in the aforementioned environments within a reference coordinate defined by the three-dimensional positions of multiple detectors. In its first phase of development, these reference detectors had to be connected to the receivers via a wired configuration to guarantee precise time synchronization. This work describes more versatile, wireless muometric navigation system (MuWNS), which was designed in conjunction with a cost-effective, crystal-oscillator-based grandmaster clock and a performance evaluation is reported for shallow underground/indoor, deep underground and undersea environments. It was confirmed that MuWNS offers a navigation quality almost equivalent to aboveground GPS-based handheld navigation by determining the distance between the reference frame and the receivers within a precision range between 1 and 10 m.

How consumer digital signals are reshaping the customer journey

Marketers are adopting increasingly sophisticated ways to engage with customers throughout their journeys. We extend prior perspectives on the customer journey by introducing the role of digital signals that consumers emit throughout their activities. We argue that the ability to detect and act on consumer digital signals is a source of competitive advantage for firms. Technology enables firms to collect, interpret, and act on these signals to better manage the customer journey. While some consumers' desire for privacy can restrict the opportunities technology provides marketers, other consumers' desire for personalization can encourage the use of technology to inform marketing efforts. We posit that this difference in consumers' willingness to emit observable signals may hinge on the strength of their relationship with the firm. We next discuss factors that may shift consumer preferences and consequently affect the technology-enabled opportunities available to firms. We conclude with a research agenda that focuses on consumers, firms, and regulators.

Psychosocial determinants of healthcare personnel's willingness to carry real-time locating system tags during daily inpatient care in hospital managing COVID-19 patients: insights from a mixed-methods analysis

Objective

Real-time locating systems (RTLS) enable contact tracing and hand hygiene reminders, to improve hospital safety. Successful implementation requires healthcare personnel (HCP) to carry RTLS tags continuously. We assessed for determinants of HCP’s willingness to use RTLS tags during routine inpatient care, and evaluated concerns using mixed-methods analysis.

Materials and Methods

We conducted a cross-sectional study in the 330-bed purpose-built National Centre for Infectious Diseases in Singapore, from January 15 through February 4, 2020. The anonymous survey comprised 24 questions based on constructs from behavioral models and an open-ended question. Principal component analysis was performed to derive the latent factor structure applied in the multivariable logistic regression analysis. Concerns were analyzed using thematic analysis.

Results

Of 260 HCP (nurses [40.8%], ancillary and administrative staff [23.1%], allied health professionals [18.5%], and physicians [17.7%]), 75% were willing to use the RTLS tag. After adjusting for age, gender, healthcare professional group, and duration of practice, the acceptance of the use of the RTLS tag (adjusted OR 11.28 [95% CI 4.39–29.00], P < .001) was highly associated with the willingness to use the RTLS tag. HCP who perceived the tag to be easy to use (adjusted OR 2.80 [95% CI 1.37–5.72], P = .005), were also more willing to use the tag. HCP were willing to carry the RTLS tag for the purpose of contact tracing despite privacy concerns.

Conclusion

More communications on the intentions and data protection standards of the RTLS, and accessory enhancements for HCP’s convenient and sustained use of the RTLS tag are crucial, to optimize RTLS’s usefulness during the COVID-19 pandemic.

Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review

Background

There is increasing interest in reusing person-generated wearable device data for research purposes, which raises concerns about data quality. However, the amount of literature on data quality challenges, specifically those for person-generated wearable device data, is sparse.

Objective

This study aims to systematically review the literature on factors affecting the quality of person-generated wearable device data and their associated intrinsic data quality challenges for research.

Methods

The literature was searched in the PubMed, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Google Scholar databases by using search terms related to wearable devices and data quality. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, studies were reviewed to identify factors affecting the quality of wearable device data. Studies were eligible if they included content on the data quality of wearable devices, such as fitness trackers and sleep monitors. Both research-grade and consumer-grade wearable devices were included in the review. Relevant content was annotated and iteratively categorized into semantically similar factors until a consensus was reached. If any data quality challenges were mentioned in the study, those contents were extracted and categorized as well.

Results

A total of 19 papers were included in this review. We identified three high-level factors that affect data quality—device- and technical-related factors, user-related factors, and data governance-related factors. Device- and technical-related factors include problems with hardware, software, and the connectivity of the device; user-related factors include device nonwear and user error; and data governance-related factors include a lack of standardization. The identified factors can potentially lead to intrinsic data quality challenges, such as incomplete, incorrect, and heterogeneous data. Although missing and incorrect data are widely known data quality challenges for wearable devices, the heterogeneity of data is another aspect of data quality that should be considered for wearable devices. Heterogeneity in wearable device data exists at three levels: heterogeneity in data generated by a single person using a single device (within-person heterogeneity); heterogeneity in data generated by multiple people who use the same brand, model, and version of a device (between-person heterogeneity); and heterogeneity in data generated from multiple people using different devices (between-person heterogeneity), which would apply especially to data collected under a bring-your-own-device policy.

Conclusions

Our study identifies potential intrinsic data quality challenges that could occur when analyzing wearable device data for research and three major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is needed on how to address the data quality challenges of wearable devices.

Innovations in research and clinical care using patient-generated health data

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

Investigating the concept of participant burden in aging technology research

BACKGROUND

Research participation burden, despite being an integral concept in research ethics, is not well-conceptualized in the context of the use of technology in research. This knowledge gap is especially critical for the older adult population as new technology solutions are increasingly embedded in clinical trials for this demographic. Our objective was to investigate how older adults conceptualize participation burden in contact for research participation and research trials using technology.

METHODS

We developed and conducted an Internet-based survey consisting of 22 multiple choice and Likert-scale type questions investigating older adults' preferred means and frequency of being contacted about research opportunities, their willingness to use specific kinds of technology and their concerns regarding technology use in clinical trials. We received a total of 273 completed surveys from eligible participants aged 50 or older.

RESULTS

Older adults preferred to be contacted about research opportunities monthly, over email. Survey participants were least willing to use monitoring devices and their biggest concern was the security of the storage of information gathered by technology. This concern was positively correlated with age. Participants indicated a preference to use technology daily, in short sessions, preferably in a way that can be incorporated into their daily routine.

CONCLUSIONS

Results from this work provide insights for the design of effective recruitment campaigns as well as technology interventions in clinical trials through minimizing the burden of research participation.

Social determinants of health in mental health care and research: a case for greater inclusion

Social determinants of health (SDOH) are known to influence mental health outcomes, which are independent risk factors for poor health status and physical illness. Currently, however, existing SDOH data collection methods are ad hoc and inadequate, and SDOH data are not systematically included in clinical research or used to inform patient care. Social contextual data are rarely captured prospectively in a structured and comprehensive manner, leaving large knowledge gaps. Extraction methods are now being developed to facilitate the collection, standardization, and integration of SDOH data into electronic health records. If successful, these efforts may have implications for health equity, such as reducing disparities in access and outcomes. Broader use of surveys, natural language processing, and machine learning methods to harness SDOH may help researchers and clinical teams reduce barriers to mental health care.

Smartphone GPS accuracy study in an urban environment

An iPhone 6 using the Avenza software for capturing horizontal positions was employed to understand relative positional accuracy in an urban environment, during two seasons of the year, two times of day, and two perceived WiFi usage periods. On average, time of year did not seem to influence the average error observed in horizontal positions when GPS-only (no WiFi) capability was enabled, nor when WiFi was enabled. Observations of average horizontal position error only seemed to improve with time of day (afternoon) during the leaf-off season. During each season and during each time of day, horizontal position error seemed to improve in general during perceived high WiFi usage periods (when more people were present). Overall average horizontal position accuracy of the iPhone 6 (7–13 m) is consistent with the general accuracy levels observed of recreation-grade GPS receivers in potential high multi-path environments.

Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study

Background

Mobile devices are increasingly used to collect location-based information from individuals about their physical activities, dietary intake, environmental exposures, and mental well-being. Such research, which typically uses wearable devices or mobile phones to track location, benefits from the growing availability of fine-grained data regarding human mobility. However, little is known about the comparative geospatial accuracy of such devices.

Objective

In this study, we compared the data quality of location information collected from two mobile devices that determine location in different ways—a global positioning system (GPS) watch and a mobile phone with Google’s Location History feature enabled.

Methods

A total of 21 chronically ill participants carried both devices, which generated digital traces of locations, for 28 days. A mobile phone–based brief ecological momentary assessment (EMA) survey asked participants to manually report their location at 4 random times throughout each day. Participants also took part in qualitative interviews and completed surveys twice during the study period in which they reviewed recent mobile phone and watch trace data to compare the devices’ trace data with their memory of their activities on those days. Trace data from the devices were compared on the basis of (1) missing data days, (2) reasons for missing data, (3) distance between the route data collected for matching day and the associated EMA survey locations, and (4) activity space total area and density surfaces.

Results

The watch resulted in a much higher proportion of missing data days (P<.001), with missing data explained by technical differences between the devices as well as participant behaviors. The mobile phone was significantly more accurate in detecting home locations (P=.004) and marginally more accurate (P=.07) for all types of locations combined. The watch data resulted in a smaller activity space area and more accurately recorded outdoor travel and recreation.

Conclusions

The most suitable mobile device for location-based health research depends on the particular study objectives. Furthermore, data generated from mobile devices, such as GPS phones and smartwatches, require careful analysis to ensure quality and completeness. Studies that seek precise measurement of outdoor activity and travel, such as measuring outdoor physical activity or exposure to localized environmental hazards, would benefit from the use of GPS devices. Conversely, studies that aim to account for time within buildings at home or work, or those that document visits to particular places (such as supermarkets, medical facilities, or fast food restaurants), would benefit from the greater precision demonstrated by the mobile phone in recording indoor activities.

Good intentions are not enough: how informatics interventions can worsen inequality

Health informatics interventions are designed to help people avoid, recover from, or cope with disease and disability, or to improve the quality and safety of healthcare. Unfortunately, they pose a risk of producing intervention-generated inequalities (IGI) by disproportionately benefiting more advantaged people. In this perspective paper, we discuss characteristics of health-related interventions known to produce IGI, explain why health informatics interventions are particularly vulnerable to this phenomenon, and describe safeguards that can be implemented to improve health equity. We provide examples in which health informatics interventions produced inequality because they were more accessible to, heavily used by, adhered to, or effective for those from socioeconomically advantaged groups. We provide a brief outline of precautions that intervention developers and implementers can take to guard against creating or worsening inequality through health informatics. We conclude by discussing evaluation approaches that will ensure that IGIs are recognized and studied.