Common practices for sociodemographic data reporting in digital mental health intervention research: a scoping review.

BACKGROUND: The ability of digital mental health interventions (DMHIs) to reduce mental health disparities relies on the recruitment of research participants with diverse sociodemographic and self-identity characteristics. Despite its importance, sociodemographic reporting in research is often limited, and the state of reporting practices in DMHI research in particular has not been comprehensively reviewed. OBJECTIVES: To characterise the state of sociodemographic data reported in randomised controlled trials (RCTs) of app-based DMHIs published globally from 2007 to 2022. METHODS: A scoping review of RCTs of app-based DMHIs examined reporting frequency for 16 sociodemographic domains (eg, gender) and common category options within each domain (eg, woman). The search queried five electronic databases. 5079 records were screened and 299 articles were included. RESULTS: On average, studies reported 4.64 (SD=1.79; range 0-9) of 16 sociodemographic domains. The most common were age (97%) and education (67%). The least common were housing situation (6%), residency/location (5%), veteran status (4%), number of children (3%), sexual orientation (2%), disability status (2%) and food security (<1%). Gender or sex was reported in 98% of studies: gender only (51%), sex only (28%), both (<1%) and gender/sex reported but unspecified (18%). Race or ethnicity was reported in 48% of studies: race only (14%), ethnicity only (14%), both (10%) and race/ethnicity reported but unspecified (10%). CONCLUSIONS: This review describes the widespread underreporting of sociodemographic information in RCTs of app-based DMHIs published from 2007 to 2022. Reporting was often incomplete (eg, % female only), unclear (eg, the conflation of gender/sex) and limited (eg, only options representing majority groups were reported). Trends suggest reporting has somewhat improved in recent years. Diverse participant populations must be welcomed and described in DMHI research to broaden learning and the generalisability of results, a prerequisite of DMHI's potential to reduce disparities in mental healthcare.

Rigorous and rapid evidence assessment in digital health with the evidence DEFINED framework.

Dozens of frameworks have been proposed to assess evidence for digital health interventions (DHIs), but existing frameworks may not facilitate DHI evidence reviews that meet the needs of stakeholder organizations including payers, health systems, trade organizations, and others. These organizations may benefit from a DHI assessment framework that is both rigorous and rapid. Here we propose a framework to assess Evidence in Digital health for EFfectiveness of INterventions with Evaluative Depth (Evidence DEFINED). Designed for real-world use, the Evidence DEFINED Quick Start Guide may help streamline DHI assessment. A checklist is provided summarizing high-priority evidence considerations in digital health. Evidence-to-recommendation guidelines are proposed, specifying degrees of adoption that may be appropriate for a range of evidence quality levels. Evidence DEFINED differs from prior frameworks in its inclusion of unique elements designed for rigor and speed. Rigor is increased by addressing three gaps in prior frameworks. First, prior frameworks are not adapted adequately to address evidence considerations that are unique to digital health. Second, prior frameworks do not specify evidence quality criteria requiring increased vigilance for DHIs in the current regulatory context. Third, extant frameworks rarely leverage established, robust methodologies that were developed for non-digital interventions. Speed is achieved in the Evidence DEFINED Framework through screening optimization and deprioritization of steps that may have limited value. The primary goals of Evidence DEFINED are to a) facilitate standardized, rapid, rigorous DHI evidence assessment in organizations and b) guide digital health solutions providers who wish to generate evidence that drives DHI adoption.

SIBAT-A Computerized Assessment Tool for Suicide Ideation and Behavior: Development and Psychometric Properties.

Objective: Most assessments of suicidal ideation and behavior (SIB) are limited by reliance on a single assessor, typically a clinician or patient, with scant detail on patient-related drivers of SIB and inability to detect rapid change in SIB. Furthermore, many techniques do not include a semistructured interview, increasing rater variability. The Suicide Ideation and Behavior Assessment Tool (SIBAT) addresses these limitations. Design: More than 30 experts in scale development, statistics, and clinical management of suicidal patients collaborated over a greater than four-year period to develop the SIBAT. Input for content and validity was received from patients, clinicians, and regulatory authorities in the United States (US) and Europe. Psychometric properties of the SIBAT were evaluated in validation studies. Results: The SIBAT is organized into eight independent patient- or clinician-rated modules with branching logic and scoring algorithms, which necessitates computerization. Patient-reported information is first captured in Modules 1 to 5. Thereafter, an experienced clinician reviews the patient's report, conducts a semistructured interview (Module 6), and assesses the patient's suicide risk (Module 7) and optimal antisuicide management (Module 8). Input from cognitive interviews of diverse adult, adolescent, and clinician participants was incorporated into the final version of the SIBAT. Psychometric testing demonstrated good inter-rater reliability (intraclass coefficient range: 0.68-0.82), intra-rater reliability (weighted-kappa range: 0.64-0.76), and concurrent validity with other instruments for assessing SIB. Conclusion: Patient- and clinician-based assessments and the psychometric studies summarized in this report support the validity and reliability of the SIBAT for capturing critical information related to assessment of SIB in adolescents and adults at risk for suicide.

Safety of Triage Self-assessment Using a Symptom Assessment App for Walk-in Patients in the Emergency Care Setting: Observational Prospective Cross-sectional Study.

BACKGROUND: Increasing use of emergency departments (EDs) by patients with low urgency, combined with limited availability of medical staff, results in extended waiting times and delayed care. Technological approaches could possibly increase efficiency by providing urgency advice and symptom assessments. OBJECTIVE: The purpose of this study is to evaluate the safety of urgency advice provided by a symptom assessment app, Ada, in an ED. METHODS: The study was conducted at the interdisciplinary ED of Marburg University Hospital, with data collection performed between August 2019 and March 2020. This study had a single-center cross-sectional prospective observational design and included 378 patients. The app's urgency recommendation was compared with an established triage concept (Manchester Triage System [MTS]), including patients from the lower 3 MTS categories only. For all patients who were undertriaged, an expert physician panel assessed the case to detect potential avoidable hazardous situations (AHSs). RESULTS: Of 378 participants, 344 (91%) were triaged the same or more conservatively and 34 (8.9%) were undertriaged by the app. Of the 378 patients, 14 (3.7%) had received safe advice determined by the expert panel and 20 (5.3%) were considered to be potential AHS. Therefore, the assessment could be considered safe in 94.7% (358/378) of the patients when compared with the MTS assessment. From the 3 lowest MTS categories, 43.4% (164/378) of patients were not considered as emergency cases by the app, but could have been safely treated by a general practitioner or would not have required a physician consultation at all. CONCLUSIONS: The app provided urgency advice after patient self-triage that has a high rate of safety, a rate of undertriage, and a rate of triage with potential to be an AHS, equivalent to telephone triage by health care professionals while still being more conservative than direct ED triage. A large proportion of patients in the ED were not considered as emergency cases, which could possibly relieve ED burden if used at home. Further research should be conducted in the at-home setting to evaluate this hypothesis. TRIAL REGISTRATION: German Clinical Trial Registration DRKS00024909; https://www.drks.de/drks_web/navigate.do? navigationId=trial.HTML&TRIAL_ID=DRKS00024909.

Quality of condition suggestions and urgency advice provided by the Ada symptom assessment app evaluated with vignettes optimised for Australia.

When people face a health problem, they often first ask, 'Is there an app for that?'. We investigated the quality of advice provided by the Ada symptom assessment application to address the question, 'How do I know the app on my phone is safe and provides good advice?'. The app was tested with 48 independently created vignettes developed for a previous study, including 18 specifically developed for the Australian setting, using an independently developed methodology to evaluate the accuracy of condition suggestions and urgency advice. The correct condition was listed first in 65% of vignettes, and in the Top 3 results in 83% of vignettes. The urgency advice in the app exactly matched the gold standard 63% of vignettes. The app's accuracy of condition suggestion and urgency advice is higher than that of the best-performing symptom assessment app reported in a previous study (61%, 77% and 52% for conditions suggested in the Top 1, Top 3 and exactly matching urgency advice respectively). These results are relevant to the application of symptom assessment in primary and community health, where medical quality and safety should determine app choice.

How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs.

OBJECTIVES: To compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of eight popular symptom assessment apps. DESIGN: Vignettes study. SETTING: 200 primary care vignettes. INTERVENTION/COMPARATOR: For eight apps and seven general practitioners (GPs): breadth of coverage and condition-suggestion and urgency advice accuracy measured against the vignettes' gold-standard. PRIMARY OUTCOME MEASURES: (1) Proportion of conditions 'covered' by an app, that is, not excluded because the user was too young/old or pregnant, or not modelled; (2) proportion of vignettes with the correct primary diagnosis among the top 3 conditions suggested; (3) proportion of 'safe' urgency advice (ie, at gold standard level, more conservative, or no more than one level less conservative). RESULTS: Condition-suggestion coverage was highly variable, with some apps not offering a suggestion for many users: in alphabetical order, Ada: 99.0%; Babylon: 51.5%; Buoy: 88.5%; K Health: 74.5%; Mediktor: 80.5%; Symptomate: 61.5%; Your.MD: 64.5%; WebMD: 93.0%. Top-3 suggestion accuracy was GPs (average): 82.1%±5.2%; Ada: 70.5%; Babylon: 32.0%; Buoy: 43.0%; K Health: 36.0%; Mediktor: 36.0%; Symptomate: 27.5%; WebMD: 35.5%; Your.MD: 23.5%. Some apps excluded certain user demographics or conditions and their performance was generally greater with the exclusion of corresponding vignettes. For safe urgency advice, tested GPs had an average of 97.0%±2.5%. For the vignettes with advice provided, only three apps had safety performance within 1 SD of the GPs-Ada: 97.0%; Babylon: 95.1%; Symptomate: 97.8%. One app had a safety performance within 2 SDs of GPs-Your.MD: 92.6%. Three apps had a safety performance outside 2 SDs of GPs-Buoy: 80.0% (p<0.001); K Health: 81.3% (p<0.001); Mediktor: 87.3% (p=1.3×10-3). CONCLUSIONS: The utility of digital symptom assessment apps relies on coverage, accuracy and safety. While no digital tool outperformed GPs, some came close, and the nature of iterative improvements to software offers scalable improvements to care.

Patients' Utilization and Perception of an Artificial Intelligence-Based Symptom Assessment and Advice Technology in a British Primary Care Waiting Room: Exploratory Pilot Study.

BACKGROUND: When someone needs to know whether and when to seek medical attention, there are a range of options to consider. Each will have consequences for the individual (primarily considering trust, convenience, usefulness, and opportunity costs) and for the wider health system (affecting clinical throughput, cost, and system efficiency). Digital symptom assessment technologies that leverage artificial intelligence may help patients navigate to the right type of care with the correct degree of urgency. However, a recent review highlighted a gap in the literature on the real-world usability of these technologies. OBJECTIVE: We sought to explore the usability, acceptability, and utility of one such symptom assessment technology, Ada, in a primary care setting. METHODS: Patients with a new complaint attending a primary care clinic in South London were invited to use a custom version of the Ada symptom assessment mobile app. This exploratory pilot study was conducted between November 2017 and January 2018 in a practice with 20,000 registered patients. Participants were asked to complete an Ada self-assessment about their presenting complaint on a study smartphone, with assistance provided if required. Perceptions on the app and its utility were collected through a self-completed study questionnaire following completion of the Ada self-assessment. RESULTS: Over a 3-month period, 523 patients participated. Most were female (n=325, 62.1%), mean age 39.79 years (SD 17.7 years), with a larger proportion (413/506, 81.6%) of working-age individuals (aged 15-64) than the general population (66.0%). Participants rated Ada's ease of use highly, with most (511/522, 97.8%) reporting it was very or quite easy. Most would use Ada again (443/503, 88.1%) and agreed they would recommend it to a friend or relative (444/520, 85.3%). We identified a number of age-related trends among respondents, with a directional trend for more young respondents to report Ada had provided helpful advice (50/54, 93%, 18-24-year olds reported helpful) than older respondents (19/32, 59%, adults aged 70+ reported helpful). We found no sex differences on any of the usability questions fielded. While most respondents reported that using the symptom checker would not have made a difference in their care-seeking behavior (425/494, 86.0%), a sizable minority (63/494, 12.8%) reported they would have used lower-intensity care such as self-care, pharmacy, or delaying their appointment. The proportion was higher for patients aged 18-24 (11/50, 22%) than aged 70+ (0/28, 0%). CONCLUSIONS: In this exploratory pilot study, the digital symptom checker was rated as highly usable and acceptable by patients in a primary care setting. Further research is needed to confirm whether the app might appropriately direct patients to timely care, and understand how this might save resources for the health system. More work is also needed to ensure the benefits accrue equally to older age groups.

Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom.

Big data, coupled with the use of advanced analytical approaches, such as artificial intelligence (AI), have the potential to improve medical outcomes and population health. Data that are routinely generated from, for example, electronic medical records and smart devices have become progressively easier and cheaper to collect, process, and analyze. In recent decades, this has prompted a substantial increase in biomedical research efforts outside traditional clinical trial settings. Despite the apparent enthusiasm of researchers, funders, and the media, evidence is scarce for successful implementation of products, algorithms, and services arising that make a real difference to clinical care. This article collection provides concrete examples of how "big data" can be used to advance healthcare and discusses some of the limitations and challenges encountered with this type of research. It primarily focuses on real-world data, such as electronic medical records and genomic medicine, considers new developments in AI and digital health, and discusses ethical considerations and issues related to data sharing. Overall, we remain positive that big data studies and associated new technologies will continue to guide novel, exciting research that will ultimately improve healthcare and medicine-but we are also realistic that concerns remain about privacy, equity, security, and benefit to all.

A Modular Health-Related Quality of Life Instrument for Electronic Assessment and Treatment Monitoring: Web-Based Development and Psychometric Validation of Core Thrive Items.

BACKGROUND: Patient-reported outcome (PRO) measures describe natural history, manage disease, and measure the effects of interventions in trials. Patients themselves increasingly use Web-based PRO tools to track their progress, share their data, and even self-experiment. However, existing PROs have limitations such as being: designed for paper (not screens), long and burdensome, negatively framed, under onerous licensing restrictions, either too generic or too specific. OBJECTIVE: This study aimed to develop and validate the core items of a modular, patient-centric, PRO system (Thrive) that could measure health status across a range of chronic conditions with minimal burden. METHODS: Thrive was developed in 4 phases, largely consistent with Food and Drug Administration guidance regarding PRO development. First, preliminary core items (common across multiple conditions: core Thrive items) were developed through literature review, analysis of approximately 20 existing PROs on PatientsLikeMe, and feedback from psychometric and content experts. Second, 2 rounds of cognitive interviews were iteratively conducted with patients (N=14) to obtain feedback on the preliminary items. Third, core Thrive items were administered electronically along with comparator measures, including 20-item Short-Form General Health Survey (SF)-20 and Patient Health Questionnaire (PHQ)-9, to a large sample (N=2002) of adults with chronic diseases through the PatientsLikeMe platform. On the basis of theoretical and empirical rationale, items were revised or removed. Fourth, the revised core Thrive items were administered to another sample of patients (N=704) with generic and condition-specific comparator measures. A psychometric evaluation, which included both modern and classical test theory approaches, was conducted on these items, and several more items were removed. RESULTS: Cognitive interviews helped to remove confusing or redundant items. Empirical testing of subscales revealed good internal consistency (Cronbach alpha=.712-.879), test-retest reliability (absolute intraclass correlations=.749-.912), and convergent validity with legacy PRO scales (eg, Pearson r=.5-.75 between Thrive subscales and PHQ-9 total). The finalized instrument consists of a 19-item core including 5 multi-item subscales: Core symptoms, Abilities, Mobility, Sleep, and Thriving. Results provide evidence of construct (content, convergent) validity, high levels of test-retest and internal consistency reliability, and the ability to detect change over time. The items did not exhibit bias based on gender or age, and the items generally functioned similarly across conditions. These results support the use of Thrive Core items across diverse chronic patient populations. CONCLUSIONS: Thrive appears to be a useful approach for capturing important domains for patients with chronic conditions. This core set serves as a foundation to begin developing modular condition-specific versions in the near future. Cross-walking against traditional PROs from the PatientsLikeMe platform is underway, in addition to clinical validation and comparison with biomarkers. Thrive is licensed under Creative Commons Attribution ShareAlike 4.0.

National randomized controlled trial of virtual house calls for Parkinson disease.

OBJECTIVE: To determine whether providing remote neurologic care into the homes of people with Parkinson disease (PD) is feasible, beneficial, and valuable. METHODS: In a 1-year randomized controlled trial, we compared usual care to usual care supplemented by 4 virtual visits via video conferencing from a remote specialist into patients' homes. Primary outcome measures were feasibility, as measured by the proportion who completed at least one virtual visit and the proportion of virtual visits completed on time; and efficacy, as measured by the change in the Parkinson's Disease Questionnaire-39, a quality of life scale. Secondary outcomes included quality of care, caregiver burden, and time and travel savings. RESULTS: A total of 927 individuals indicated interest, 210 were enrolled, and 195 were randomized. Participants had recently seen a specialist (73%) and were largely college-educated (73%) and white (96%). Ninety-five (98% of the intervention group) completed at least one virtual visit, and 91% of 388 virtual visits were completed. Quality of life did not improve in those receiving virtual house calls (0.3 points worse on a 100-point scale; 95% confidence interval [CI] -2.0 to 2.7 points; p = 0.78) nor did quality of care or caregiver burden. Each virtual house call saved patients a median of 88 minutes (95% CI 70-120; p < 0.0001) and 38 miles per visit (95% CI 36-56; p < 0.0001). CONCLUSIONS: Providing remote neurologic care directly into the homes of people with PD was feasible and was neither more nor less efficacious than usual in-person care. Virtual house calls generated great interest and provided substantial convenience. CLINICALTRIALSGOV IDENTIFIER: NCT02038959. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that for patients with PD, virtual house calls from a neurologist are feasible and do not significantly change quality of life compared to in-person visits. The study is rated Class III because it was not possible to mask patients to visit type.

Increasing Patient Involvement in Drug Development.

BACKGROUND: To ensure the creation of treatments that maximize value at the lowest cost, all aspects of the health care system need to align with patient needs and preferences. Despite growing efforts to engage patients in research and regulatory activities, the pharmaceutical industry has yet to maximize patient involvement in the drug development process. OBJECTIVE: To gain a better understanding of the present state of patient involvement in drug development. METHODS: Through a semistructured interview methodology, we sought to identify opportunities, barriers, and examples of patient involvement in the drug development process. Telephone interviews were conducted with six senior leaders of evidence generation within the pharmaceutical industry and four patients. These interviews were supplemented with interviews with a research funder, a regulator, a patient advocacy group, and a caregiver. RESULTS: Although our interviewees spoke of the potential benefits of aligning research around the needs of patients, there were few examples they could share to suggest this was occurring at scale. A number of barriers were identified including the added burden associated with adverse event reporting, concerns about patient representativeness or their ability to participate in drug development conversations, and the costs in time and resources involved relative to returns on investment. CONCLUSIONS: As health care systems continue to evolve and establish patients as the primary stakeholder in their health care decision making, the pharmaceutical industry will need to be innovative to demonstrate the value of their products relative to the outcomes experienced by patients. Pharmaceutical companies should recognize the value of involving patients across the entire product life cycle and work to transform present perceptions and practices throughout their organizations.

Medicine and the future of health: reflecting on the past to forge ahead.

The development of new therapies has a rich history, evolves quickly with societal trends, and will have an exciting future. The last century has seen an exponential increase in complex interactions between medical practitioners, pharmaceutical companies, governments and patients. We believe technology and societal expectations will open up the opportunity for more individuals to participate as information becomes more freely available and inequality less acceptable. Corporations must recognize that usual market forces do not function ideally in a setting where health is regarded as a human right, and as modern consumers, patients will increasingly take control of their own data, wellbeing, and even the means of production for developing their own treatments. Ethics and legislation will increasingly impact the processes that facilitate drug development, distribution and administration. This article collection is a cross-journal collaboration, between the Journal of Pharmaceutical Policy and Practice (JoPPP) and BMC Medicine that seeks to cover recent advances in drug development, medicines use, policy and access with high clinical and public health relevance in the future.The Medicine and the Future of Health article collection is a joint collection between BMC Medicine and Journal of Pharmaceutical Policy and Practice. Therefore, this Editorial by the guest editors has been published in both journals.

Exploring Concordance of Patient-Reported Information on PatientsLikeMe and Medical Claims Data at the Patient Level.

BACKGROUND: With the emergence of data generated by patient-powered research networks, it is informative to characterize their correspondence with health care system-generated data. OBJECTIVES: This study explored the linking of 2 disparate sources of real-world data: patient-reported data from a patient-powered research network (PatientsLikeMe) and insurance claims. METHODS: Active patients within the PatientsLikeMe community, residing in the United States, aged 18 years or older, with a self-reported diagnosis of multiple sclerosis or Parkinson's disease (PD) were invited to participate during a 2-week period in December 2014. Patient-reported data were anonymously matched and compared to IMS Health medical and pharmacy claims data with dates of service between December 2009 and December 2014. Patient-level match (identity), diagnosis, and usage of disease-modifying therapies (DMTs) were compared between data sources. RESULTS: Among 603 consenting patients, 94% had at least 1 record in the IMS Health dataset; of these, there was 93% agreement rate for multiple sclerosis diagnosis. Concordance on the use of any treatment was 59%, and agreement on reports of specific treatment usage (within an imputed 5-year period) ranged from 73.5% to 100%. CONCLUSIONS: It is possible to match patient identities between the 2 data sources, and the high concordance at multiple levels suggests that the matching process was accurate. Likewise, the high degree of concordance suggests that these patients were able to accurately self-report their diagnosis and, to a lesser degree, their treatment usage. Further studies of linked data types are warranted to evaluate the use of enriched datasets to generate novel insights.

Can we use social media to support content validity of patient-reported outcome instruments in medical product development?

We report a panel designed to open a dialog between pharmaceutical sponsors, regulatory reviewers, and other stakeholders regarding the use of social media to collect data to support the content validity of patient-reported outcome instruments in the context of medical product labeling. Multiple stakeholder perspectives were brought together to better understand the issues encountered in pursuing social media as a form of data collection to support content validity. Presenters represented a pharmaceutical sponsor of clinical trials, a regulatory reviewer from the Food and Drug Administration, and an online data platform provider. Each presenter shared its perspective on the advantages and disadvantages of using social media to collect this type of information. There was consensus that there is great potential for using social media for this purpose. There remain, however, unanswered questions that need to be addressed such as identifying which type of social media is most appropriate for data collection and ensuring that participants are representative of the target population while maintaining the advantages of anonymity provided by online platforms. The use of social media to collect evidence of content validity holds much promise. Clarification of issues that need to be addressed and accumulation of empirical evidence to address these questions are essential to moving forward.

Adaptation and validation of the Treatment Burden Questionnaire (TBQ) in English using an internet platform.

BACKGROUND: Treatment burden refers to the workload imposed by healthcare on patients, and the effect this has on quality of life. The Treatment Burden Questionnaire (TBQ) aims to assess treatment burden in different condition and treatment contexts. Here, we aimed to evaluate the validity and reliability of an English version of the TBQ, a scale that was originally developed in French. METHODS: The TBQ was translated into English by a forward-backward translation method. Wording and possible missing items were assessed during a pretest involving 200 patients with chronic conditions. Measurement properties of the instrument were assessed online with a patient network, using the PatientsLikeMe website. Dimensional structure of the questionnaire was assessed by factor analysis. Construct validity was assessed by associating TBQ global score with clinical variables, adherence to medication assessed by Morisky's Medication Adherence Scale (MMAS-8), quality of life (QOL) assessed by the PatientsLikeMe Quality of Life Scale (PLMQOL), and patients' confidence in their knowledge of their conditions and treatments. Reliability was determined by a test-retest method. RESULTS: In total, 610 patients with chronic conditions, mainly from the USA, UK, Canada, Australia, or New Zealand, completed the TBQ between September and October 2013. The English TBQ showed a unidimensional structure with Cronbach α of 0.90. The TBQ global score was negatively correlated with the PLMQOL score (rs = -0.50; p < 0.0001). Low rather than moderate or high adherence to medication was associated with high TBQ score (mean [SD] TBQ score 61.8 [30.5] vs. 37.7 [27.5]; P < 0.0001). The treatment burden was higher for patients who had insufficient knowledge compared with those who had sufficient knowledge about their treatments (mean ± SD TBQ score 62.3 ± 31.3 vs. 47.8 ± 30.4; P < 0.0001) and conditions (63.0 ± 31.6 vs. 49.3 ± 30.7; P < 0.0001). The intraclass correlation coefficient for the retest (n = 282) was 0.77 (95% CI 0.70 to 0.82). CONCLUSIONS: We found that the English TBQ is a reliable instrument in this population, and provide evidence supporting the construct validity for its use to assess treatment burden for patients with one or more chronic conditions in English-speaking countries.

Feasibility of a web-based survey of hallucinations and assessment of visual function in patients with Parkinson's disease.

BACKGROUND: Patients with Parkinson's disease (PD) experience visual hallucinations, which may be related to decreased contrast sensitivity (ie, the ability to discern shades of grey). OBJECTIVE: The objective of this study was to investigate if an online research platform can be used to survey patients with Parkinson's disease regarding visual hallucinations, and also be used to assess visual contrast perception. METHODS: From the online patient community, PatientsLikeMe, 964 members were invited via email to participate in this study. Participants completed a modified version of the University of Miami Parkinson's disease hallucinations questionnaire and an online vision test. RESULTS: The study was completed by 27.9% (269/964) of those who were invited: 56.9% of this group had PD (153/269) and 43.1% (116/269) were non-Parkinson's controls. Hallucinations were reported by 18.3% (28/153) of the Parkinson's group. Although 10 subjects (9%) in the control group reported experiencing hallucinations, only 2 of them actually described formed hallucinations. Participants with Parkinson's disease with a mean of 1.75 (SD 0.35) and the control group with a mean of 1.85 (SD 0.36) showed relatively good contrast perception as measured with the online letter test (P=.07). People who reported hallucinations showed contrast sensitivity levels that did not differ from levels shown by people without hallucinations (P=.96), although there was a trend towards lower contrast sensitivity in hallucinators. CONCLUSIONS: Although more Parkinson's responders reported visual hallucinations, a significant number of non-Parkinson's control group responders also reported visual hallucinations. The online survey method may have failed to distinguish between formed hallucinations, which are typical in Parkinson's disease, and non-formed hallucinations that have less diagnostic specificity. Multiple questions outlining the nature of the hallucinations are required. In a clinical interview, the specific nature of the hallucination would be further refined to rule out a vague description that does not indicate a true, formed visual hallucination. Contrary to previous literature, both groups showed relatively good contrast sensitivity, perhaps representing a ceiling effect or limitations of online testing conditions that are difficult to standardize. Steps can be taken in future trials to further standardize online visual function testing, to refine control group parameters and to take steps to rule out confounding variables such as comorbid disease that could be associated with hallucinations. Contacting subjects via an online health social network is a novel, cost-effective method of conducting vision research that allows large numbers of individuals to be contacted quickly, and refinement of questionnaires and visual function testing may allow more robust findings in future research.

Innovations in e-health.

The theme of ISOQOL's 19th Annual Conference in Budapest, Hungary, was The Journey of Quality of Life Research: A Path Towards Personalized Medicine. Innovations in e-health was one of four plenary panels. E-health is changing the landscape of clinical practice and health care, but the best way to leverage the many promised benefits of emerging e-health technologies is still not clear. The Innovations in e-health panel presented emerging changes in technologies and applications that will facilitate clinical decision making, improve quality and efficiency of care, engage individuals in clinical decision making, and empower them to adopt healthy behaviors. The purpose of this paper was to present emerging trends in e-health and considerations for successful adoption of new technologies, and an overview of each of the presentations in the e-health plenary. The presentations included a personal perspective on the use of technology for self-monitoring in Parkinson's disease, an overview of online social networks and emerging technologies, and the collection of patient-reported outcomes through web-based systems in clinical practice. The common thread across all the talks was the application of e-health tools to empower individuals with chronic disease to be actively engaged in the management of their health. Considerations regarding data ownership and privacy, universal access to e-health, interactivity between different types of e-health technologies, and tailoring applications to individual needs were explored.