Demographic and clinical characteristics associated with anxiety and depressive symptom outcomes in users of a digital mental health intervention incorporating a relational agent.

BACKGROUND: Digital mental health interventions (DMHIs) may reduce treatment access issues for those experiencing depressive and/or anxiety symptoms. DMHIs that incorporate relational agents may offer unique ways to engage and respond to users and to potentially help reduce provider burden. This study tested Woebot for Mood & Anxiety (W-MA-02), a DMHI that employs Woebot, a relational agent that incorporates elements of several evidence-based psychotherapies, among those with baseline clinical levels of depressive or anxiety symptoms. Changes in self-reported depressive and anxiety symptoms over 8 weeks were measured, along with the association between each of these outcomes and demographic and clinical characteristics. METHODS: This exploratory, single-arm, 8-week study of 256 adults yielded non-mutually exclusive subsamples with either clinical levels of depressive or anxiety symptoms at baseline. Week 8 Patient Health Questionnaire-8 (PHQ-8) changes were measured in the depressive subsample (PHQ-8 ≥ 10). Week 8 Generalized Anxiety Disorder-7 (GAD-7) changes were measured in the anxiety subsample (GAD-7 ≥ 10). Demographic and clinical characteristics were examined in association with symptom changes via bivariate and multiple regression models adjusted for W-MA-02 utilization. Characteristics included age, sex at birth, race/ethnicity, marital status, education, sexual orientation, employment status, health insurance, baseline levels of depressive and anxiety symptoms, and concurrent psychotherapeutic or psychotropic medication treatments during the study. RESULTS: Both the depressive and anxiety subsamples were predominantly female, educated, non-Hispanic white, and averaged 38 and 37 years of age, respectively. The depressive subsample had significant reductions in depressive symptoms at Week 8 (mean change =-7.28, SD = 5.91, Cohen's d = -1.23, p < 0.01); the anxiety subsample had significant reductions in anxiety symptoms at Week 8 (mean change = -7.45, SD = 5.99, Cohen's d = -1.24, p < 0.01). No significant associations were found between sex at birth, age, employment status, educational background and Week 8 symptom changes. Significant associations between depressive and anxiety symptom outcomes and sexual orientation, marital status, concurrent mental health treatment, and baseline symptom severity were found. CONCLUSIONS: The present study suggests early promise for W-MA-02 as an intervention for depression and/or anxiety symptoms. Although exploratory in nature, this study revealed potential user characteristics associated with outcomes that can be investigated in future studies. TRIAL REGISTRATION: This study was retrospectively registered on ClinicalTrials.gov (#NCT05672745) on January 5th, 2023.

Anatomy of a Woebot® (WB001): agent guided CBT for women with postpartum depression.

INTRODUCTION: Postpartum depression (PPD) is common, persistent, and stigmatized. There are insufficient trained professionals to deliver appropriate screening, diagnosis, and treatment. AREAS COVERED: WB001 is a Software as a Medical Device (SaMD) based Agent-Guided Cognitive-Behavioral Therapy (AGCBT) program for the treatment of PPD, for which Breakthrough Device Designation was recently granted by the US Food and Drug Administration. WB001 combines therapeutic alliance, human-centered design, machine learning techniques, and established principles from CBT and interpersonal therapy (IPT). We introduce AGCBT as a new model of service delivery, whilst describing Woebot, the agent technology that enables guidance through the replication of some elements of human relationships. The profile describes the device's design principles, enabling technology, risk handling, and efficacy data in PPD. EXPERT OPINION: WB001 is a dynamic and personalized tool with which patients may establish a therapeutic bond. Woebot is designed to augment (rather than replace) human healthcare providers, unlocking the therapeutic potency associated with guidance, whilst retaining the scalability and agency that characterizes self-help approaches. WB001 has the potential to improve both the quality and the scalability of care through providing support to patients on waiting lists, in between clinical encounters, and enabling automation of measurement-based-care.

User Engagement Clusters of an 8-Week Digital Mental Health Intervention Guided by a Relational Agent (Woebot): Exploratory Study.

BACKGROUND: With the proliferation of digital mental health interventions (DMHIs) guided by relational agents, little is known about the behavioral, cognitive, and affective engagement components associated with symptom improvement over time. Obtaining a better understanding could lend clues about recommended use for particular subgroups of the population, the potency of different intervention components, and the mechanisms underlying the intervention's success. OBJECTIVE: This exploratory study applied clustering techniques to a range of engagement indicators, which were mapped to the intervention's active components and the connect, attend, participate, and enact (CAPE) model, to examine the prevalence and characterization of each identified cluster among users of a relational agent-guided DMHI. METHODS: We invited adults aged 18 years or older who were interested in using digital support to help with mood management or stress reduction through social media to participate in an 8-week DMHI guided by a natural language processing-supported relational agent, Woebot. Users completed assessments of affective and cognitive engagement, working alliance as measured by goal and task working alliance subscale scores, and enactment (ie, application of therapeutic recommendations in real-world settings). The app passively collected data on behavioral engagement (ie, utilization). We applied agglomerative hierarchical clustering analysis to the engagement indicators to identify the number of clusters that provided the best fit to the data collected, characterized the clusters, and then examined associations with baseline demographic and clinical characteristics as well as mental health outcomes at week 8. RESULTS: Exploratory analyses (n=202) supported 3 clusters: (1) "typical utilizers" (n=81, 40%), who had intermediate levels of behavioral engagement; (2) "early utilizers" (n=58, 29%), who had the nominally highest levels of behavioral engagement in week 1; and (3) "efficient engagers" (n=63, 31%), who had significantly higher levels of affective and cognitive engagement but the lowest level of behavioral engagement. With respect to mental health baseline and outcome measures, efficient engagers had significantly higher levels of baseline resilience (P<.001) and greater declines in depressive symptoms (P=.01) and stress (P=.01) from baseline to week 8 compared to typical utilizers. Significant differences across clusters were found by age, gender identity, race and ethnicity, sexual orientation, education, and insurance coverage. The main analytic findings remained robust in sensitivity analyses. CONCLUSIONS: There were 3 distinct engagement clusters found, each with distinct baseline demographic and clinical traits and mental health outcomes. Additional research is needed to inform fine-grained recommendations regarding optimal engagement and to determine the best sequence of particular intervention components with known potency. The findings represent an important first step in disentangling the complex interplay between different affective, cognitive, and behavioral engagement indicators and outcomes associated with use of a DMHI incorporating a natural language processing-supported relational agent. TRIAL REGISTRATION: ClinicalTrials.gov NCT05672745; https://classic.clinicaltrials.gov/ct2/show/NCT05672745.

A Relational Agent Intervention for Adolescents Seeking Mental Health Treatment: Protocol for a Randomized Controlled Trial.

BACKGROUND: Unmet pediatric mental health (MH) needs are growing as rates of pediatric depression and anxiety dramatically increase. Access to care is limited by multiple factors, including a shortage of clinicians trained in developmentally specific, evidence-based services. Novel approaches to MH care delivery, including technology-leveraged and readily accessible options, need to be evaluated in service of expanding evidence-based services to youths and their families. Preliminary evidence supports the use of Woebot, a relational agent that digitally delivers guided cognitive behavioral therapy (CBT) through a mobile app, for adults with MH concerns. However, no studies have evaluated the feasibility and acceptability of such app-delivered relational agents specifically for adolescents with depression and/or anxiety within an outpatient MH clinic, nor compared them to other MH support services. OBJECTIVE: This paper describes the protocol for a randomized controlled trial evaluating the feasibility and acceptability of an investigational device, Woebot for Adolescents (W-GenZD), within an outpatient MH clinic for youths presenting with depression and/or anxiety. The study's secondary aim will compare the clinical outcomes of self-reported depressive symptoms with W-GenZD and a telehealth-delivered CBT-based skills group (CBT-group). Tertiary aims will evaluate additional clinical outcomes and therapeutic alliance between adolescents in W-GenZD and the CBT-group. METHODS: Participants include youths aged 13-17 years with depression and/or anxiety seeking care from an outpatient MH clinic at a children's hospital. Eligible youths will have no recent safety concerns or complex comorbid clinical diagnoses; have no concurrent individual therapy; and, if on medications, are on stable doses, based on clinical screening and as well as study-specific criteria. RESULTS: Recruitment began in May 2022. As of December 8, 2022, we have randomized 133 participants. CONCLUSIONS: Establishing the feasibility and acceptability of W-GenZD within an outpatient MH clinical setting will add to the field's current understanding of the utility and implementation considerations of this MH care service modality. The study will also evaluate the noninferiority of W-GenZD against the CBT-group. Findings may also have implications for patients, families, and providers looking for additional MH support options for adolescents seeking help for their depression and/or anxiety. Such options expand the menu of supports for youths with lower-intensity needs as well as possibly reduce waitlists and optimize clinician deployment toward more severe cases. TRIAL REGISTRATION: ClinicalTrials.gov NCT05372913; https://clinicaltrials.gov/ct2/show/NCT05372913. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44940.

Anatomy of a Woebot® (WB001): agent guided CBT for women with postpartum depression.

INTRODUCTION: Postpartum Depression (PPD) is common, persistent, and stigmatized. There are insufficient trained professionals to deliver appropriate screening, diagnosis, and treatment. AREAS COVERED: WB001 is a Software as a Medical Device (SaMD) based Agent-Guided Cognitive Behavioral Therapy (AGCBT) program for the treatment of PPD, for which Breakthrough Device Designation was recently granted by the US Food and Drug Administration. WB001 combines therapeutic alliance, human-centered design, machine learning techniques, and established principles from CBT and interpersonal therapy (IPT). We introduce AGCBT as a new model of service delivery, whilst describing Woebot, the agent technology that enables guidance through the replication of some elements of human relationships. The profile describes the device's design principles, enabling technology, risk handling, and efficacy data in PPD. EXPERT OPINION: WB001 is a dynamic and personalized tool with which patients may establish a therapeutic bond. Woebot is designed to augment (rather than replace) human healthcare providers, unlocking the therapeutic potency associated with guidance, whilst retaining the scalability and agency that characterizes self-help approaches. WB001 has the potential to improve both the quality and the scalability of care through providing support to patients on waiting lists, in between clinical encounters, and enabling automation of measurement-based care.

Beyond the Therapist's Office: Merging Measurement-Based Care and Digital Medicine in the Real World.

This viewpoint focuses on the ways in which digital medicine and measurement-based care can be utilized in tandem to promote better assessment, patient engagement, and an improved quality of psychiatric care. To date, there has been an underutilization of digital measurement in psychiatry, and there is little discussion of the feedback and patient engagement process in digital medicine. Measurement-based care is a recognized evidence-based strategy that engages patients in an understanding of their outcome data. When implemented as designed, providers review the scores and trends in outcome immediately and then provide feedback to their patients. However, the process is typically confined to office visits, which does not provide a complete picture of a patient's progress and functioning. The process is labor intensive, even with digital feedback systems, but the integration of passive metrics obtained through wearables and apps can supplement office-based observations. This enhanced measurement-based care process can provide a picture of real-world patient functioning through passive metrics (activity, sleep, etc.). This can potentially engage patients more in their health data and involve a critically needed therapeutic alliance component in digital medicine.

Videoconferencing-Based Telemental Health: Important Questions for the COVID-19 Era From Clinical and Patient-Centered Perspectives.

The COVID-19 pandemic has intensified the search for digital approaches in mental health treatment, particularly due to patients and clinicians practicing social distancing. This has resulted in the dramatic growth of videoconferencing-based telemental health (V-TMH) services. It is critical for behavioral health providers and those in the mental health field to understand the implications of V-TMH expansion on the stakeholders who use such services, such as patients and clinicians, to provide the service that addresses both patient and clinical needs. Several key questions arise as a result, such as the following: (1) in what ways does V-TMH affect the practice of psychotherapy (ie, clinical needs), (2) to what extent are ethical and patient-centered concerns warranted in terms of V-TMH services (ie, patient needs), and (3) how do factors related to user experience affect treatment dynamics for both the patient and therapist (ie, patient and clinical needs)? We discuss how behavioral health providers can consider the future delivery of mental health care services based on these questions, which pose strong implications for technological innovation, the adaptation of treatments to new technologies, and training professionals in the delivery of V-TMH services and other digital health interventions.

In search of a daily physical activity "sweet spot": Piloting a digital tracking intervention for people with multiple sclerosis.

OBJECTIVE: This pilot study tested a course-based intervention to help people with multiple sclerosis (MS) match their daily activity to symptom severity ("sweet spot") using wearable activity trackers. METHODS: This two-phase study recruited online research network members reporting MS and who were utilizing Fitbit One™ activity trackers. In the first phase, participant interviews assessed demand based on physical activity and the use of behavior-change techniques. The second phase assessed the demand, limited efficacy, acceptability, and practicality of a "Wearables 101" course that integrated behavior change and self-experimentation principles. Tracker data were used to determine the percent of matches between daily symptom-based step goals and step counts. RESULTS: Participants expressed demand in the form of interest in gaining insights about a possible "sweet spot" behavioral target, if a system could be produced to support that. Limited efficacy results were mixed, with approximately one-third of participants dropping out and only half matching their daily target goals for at least 50% of days. In terms of practicality, participants commented on the burden of daily measurement and the need for a longer baseline period. Participants noted that tracking helped support an understanding of the link between activities and symptom severity, suggesting acceptability. CONCLUSIONS: Results suggested that the intervention demand and acceptability criteria were demonstrated more strongly than limited efficacy and practicality. The matching intervention tested in this study will require refinement in baseline measurement, goal definition, and reduced data-gathering burden. Such changes may improve efficacy and practicality requirements and, by extension, later impact of the intervention on MS outcomes. Overall, these results provide justification for additional work on refining the intervention to increase practicality and efficacy.

Mental Health Apps in Psychiatric Treatment: A Patient Perspective on Real World Technology Usage.

For many people who use mobile apps, the primary motivations are entertainment, news, gaming, social connections, or productivity. For those experiencing health problems, particularly those with chronic conditions such as psychiatric disorders, the stakes are much higher. The digital tools that they select may be the difference between improvement and decompensation or even life and death. Although there has been a wide expansion of mental health apps with promise as well as hype, the current means of researching, evaluating, and deploying effective tools have been problematic. As a means of gaining a perspective that moves beyond usability testing, surveys, and app ratings, the primary objective of this patient perspective is to question the killer app and condition-specific mentality of current mental health app development. We do this by reviewing the current mobile mental health app literature, identifying ways in which psychiatric patients use apps in their lives, and then exploring how these issues are experienced by a software engineer who has struggled with her bipolar disorder for many years. Her lived experience combined with a technology perspective offers potential avenues for using technology productively in psychiatric treatment. We believe that this responds to JMIR Publications' call for patient perspective papers and provides encouragement for patients to share their views on mental health and technology.

Digital Trespass: Ethical and Terms-of-Use Violations by Researchers Accessing Data From an Online Patient Community.

With the expansion and popularity of research on websites such as Facebook and Twitter, there has been increasing concern about investigator conduct and social media ethics. The availability of large data sets has attracted researchers who are not traditionally associated with health data and its associated ethical considerations, such as computer and data scientists. Reliance on oversight by ethics review boards is inadequate and, due to the public availability of social media data, there is often confusion between public and private spaces. In addition, social media participants and researchers may pay little attention to traditional terms of use. In this paper, we review four cases involving ethical and terms-of-use violations by researchers seeking to conduct social media studies in an online patient research network. These violations involved unauthorized scraping of social media data, entry of false information, misrepresentation of researcher identities of participants on forums, lack of ethical approval and informed consent, use of member quotations, and presentation of findings at conferences and in journals without verifying accurate potential biases and limitations of the data. The correction of these ethical lapses often involves much effort in detecting and responding to violators, addressing these lapses with members of an online community, and correcting inaccuracies in the literature (including retraction of publications and conference presentations). Despite these corrective actions, we do not regard these episodes solely as violations. Instead, they represent broader ethical issues that may arise from potential sources of confusion, misinformation, inadequacies in applying traditional informed consent procedures to social media research, and differences in ethics training and scientific methodology across research disciplines. Social media research stakeholders need to assure participants that their studies will not compromise anonymity or lead to harmful outcomes while preserving the societal value of their health-related studies. Based on our experience and published recommendations by social media researchers, we offer potential directions for future prevention-oriented measures that can be applied by data producers, computer/data scientists, institutional review boards, research ethics committees, and publishers.

Understanding 'Good Health care' from the Patient's Perspective: Development of a Conceptual Model Using Group Concept Mapping.

BACKGROUND: There is an increasing focus on measuring performance indicators of health care providers, but there is a lack of patient input into what defines 'good care.' OBJECTIVE: The primary objective was to develop a conceptual model of 'good health care' from the patient's perspective. Exploratory analyses were also conducted to investigate (1) differences in patient priorities based on demographic and clinical factors, and (2) differences between patients and health stakeholders (e.g., clinicians, researchers) with respect to patient health care priorities. METHOD: These objectives were accomplished using group concept mapping. Following statement generation, PatientsLikeMe members, Baltimore community members recruited through a university-affiliated clinic, and stakeholders individually sorted the statements into meaningful categories and rated the statements with respect to importance. Qualitative and quantitative analyses generated a final conceptual model. RESULTS: One hundred and fifty-seven patients and 17 stakeholders provided input during statement generation. The 1779-statement pool was reduced to 79 statements for the structuring (sorting and rating) activities. In total, 221 patients and 16 stakeholders completed structuring activities through group concept mapping software. Results yielded a 10-cluster solution, and patient priorities were found to be relatively invariant across demographic/clinical groups. Results were also similar between patients and stakeholders. CONCLUSIONS: This comprehensive qualitative and quantitative investigation is an important first step in developing patient-reported outcome performance measures that capture the aspects of health care that are most important and relevant for patients. Limitations and future directions are discussed.

Patient Perspective of Cognitive Symptoms in Major Depressive Disorder: Retrospective Database and Prospective Survey Analyses.

BACKGROUND: Major depressive disorder (MDD) is a common and burdensome condition. The clinical understanding of MDD is shaped by current research, which lacks insight into the patient perspective. OBJECTIVE: This two-part study aimed to generate data from PatientsLikeMe, an online patient network, on the perception of cognitive symptoms and their prioritization in MDD. METHODS: A retrospective data analysis (study 1) was used to analyze data from the PatientsLikeMe community with self-reported MDD. Information on patient demographics, comorbidities, self-rated severity of MDD, treatment effectiveness, and specific symptoms of MDD was analyzed. A prospective electronic survey (study 2) was emailed to longstanding and recently active members of the PatientsLikeMe MDD community. Study 1 analysis informed the objectives of the study 2 survey, which were to determine symptom perception and prioritization, cognitive symptoms of MDD, residual symptoms, and medication effectiveness. RESULTS: In study 1 (N=17,166), cognitive symptoms were frequently reported, including "severe" difficulty in concentrating (28%). Difficulty in concentrating was reported even among patients with no/mild depression (80%) and those who considered their treatment successful (17%). In study 2 (N=2525), 23% (118/508) of patients cited cognitive symptoms as a treatment priority. Cognitive symptoms correlated with depression severity, including difficulty in making decisions, concentrating, and thinking clearly (rs=0.32, 0.36, and 0.34, respectively). Cognitive symptoms interfered with meaningful relationships and daily life tasks and had a profound impact on patients' ability to work and recover from depression. CONCLUSIONS: Patients acknowledge that cognitive dysfunction in MDD limits their ability to recover fully and return to a normal level of social and occupational functioning. Further clinical understanding and characterization of MDD for symptom prioritization and relapse risk due to residual cognitive impairment are required to help patients return to normal cognitive function and aid their overall recovery.

Free-Living Physical Activity Monitoring in Adult US Patients with Multiple Sclerosis Using a Consumer Wearable Device.

INTRODUCTION: Wearable devices have been used to characterize physical activity in multiple sclerosis (MS). The objectives of this study were to advance the literature on the utility of free-living physical activity tracking from secondary analyses of a pilot study in MS patients. METHOD: The original observational study was conducted in participants with MS at PatientsLikeMe (PatientsLikeMe (www.PatientsLikeMe.com), an online network of patients with chronic diseases. Participants completed a baseline self-assessment, and received a Fitbit OneTM wearable device with instructions to upload data. Eligible participants (1) self-reported MS, (2) logged on to the PatientsLikeMe website 90 days prior to enrollment, and (3) consented to participate electronically. Participants (1) < 18 years, (2) living outside the United States, and (3) requiring wheelchair assistance for most daily activities were excluded. The secondary analyses were limited to participants with complete data on MS type, disease duration, and Multiple Sclerosis Rating Scale (MSRS) and at least 7 days of wearable data. Step count was used as a measure of physical activity. RESULTS: The analysis cohort of 114 participants uploaded a mean of 20.1 days of wearable data over the 23-day study (87% adherence); participants averaged 4,393 steps per day. The mean age of participants was 52 years, predominantly female (75%), relapsing-remitting type (79%), with mean disease duration of 16 years. Mean MSRS score within 30-day of baseline was 32; 72% reported mild-moderate walking disability. The reliability of step count measured by intraclass correlation was 0.55 for a single day, ≥0.7 for 2-day average, and ≥0.9 for 7-day average. After controlling for covariates, self-reported disease severity (MSRS quartile) was an independent predictor of step count (p < 0.001). Least square means (LS means) for participants that were least disabled (lowest quartile) was 5,937 steps, which was significantly higher than participants in the second, third, and fourth quartiles (4,570, 3,490, and 3,272, respectively). Similarly, LS means of participants with no ambulatory disability (measured by MSRS walk component) was 6,931 steps, significantly higher than participants with greater disability (4,743, 4,394, 2,727 steps for symptomatic, mild, and moderate disability, respectively, p < 0.001). DISCUSSION: Using an interactive platform, this study captured free-living mobility data in MS patients. Important metrics such as the use of a minimum of 2-day estimates and self-reported disability were found to be robust indicators and correlates, respectively, of participant activity levels. Further triangulation of such metrics may reduce the burden on patients, clinicians, and researchers when monitoring clinical status.

A Randomized Controlled Trial of the Effects of Online Pain Management Education on Primary Care Providers.

Objective: To improve pain management practices, we developed an online interactive continuing education (CE) program for primary care providers (PCPs). This program follows the flow of clinical decision-making through simulated cases at critical pain treatment points along the pain treatment continuum. Design: A randomized controlled trial was conducted to test the efficacy of this program. Participants were randomized to either the experimental condition or the control condition (online, text-based CE program). Subjects: A total of 238 primary care providers were recruited through hospitals, professional newsletters, and pain conferences. Results: Participants in both conditions reported significantly improved scores on knowledge (KNOW-PAIN 50), attitudes (CAOS), and pain practice behaviors (PPBS) scales over the four-month study. The experimental condition showed significantly greater change over time on the tamper-resistant formulations (TRFs) of opioids and dosing CAOS subscale compared with the control condition. Post hoc comparisons suggested that participants in the experimental condition were less likely to endorse use of opioid TRFs over time compared with the control condition. Exploratory analyses for potential moderators indicated a significant three-way interaction with time, condition, and discipline (i.e., physician vs other) for the impediments and concerns attitudes subscale and the early refill behaviors subscale. Post hoc comparisons indicated that physicians in the experimental condition exhibited the greatest change in attitudes and the nonphysicians exhibited the greatest change in reported behaviors in response to requests for early refills. Conclusions: Findings suggest online CE programs may positively impact PCPs' knowledge, attitudes, and pain practice behaviors but provide minimal evidence for the value of including interactivity.

What Do Ovarian Cancer Patients Expect From Treatment?: Perspectives From an Online Patient Community.

BACKGROUND: Advances in ovarian cancer treatment have improved outcomes. However, the gap between patients' hopes and expectations and their actual outcomes remains an understudied aspect of treatment decision making. This gap has been noted to be a predictor of poorer health-related quality of life. Incorporating patient hopes and expectations for treatment into clinical care may improve patient experiences. OBJECTIVES: The aim of this study is to describe the hopes and expectations of ovarian cancer patients at diagnosis and throughout treatment transitions. METHODS: This study sampled from an online community of patients with ovarian cancer. Thirty members completed an online qualitative survey about their diagnostic and treatment journey, including hopes and expectations around treatment. RESULTS: Initially, ovarian cancer patients hoped for a complete cure or removal of cancer. As they progressed through treatment, hopes and expectations centered on issues related to living with ovarian cancer. A subset of patients emphasized a lack of information about treatment side effects. CONCLUSIONS: The shift in expectations from survival to living with ovarian cancer may demonstrate an enhanced understanding of ovarian cancer prognosis as patients learn more about their condition. Patients underscored that a more involved discussion of side effect profiles with clinicians would enhance their treatment decision making and expectation setting process. IMPLICATIONS FOR PRACTICE: Expectation setting may be improved by contextualizing treatment in terms of the transitions ovarian cancer patients experience. Providing relevant information that aligns with patient needs, desires, and concerns at critical transitions may improve clinical care and decision making.

Communicating laboratory test results for rheumatoid factor: what do patients and physicians want?

OBJECTIVE: This study aimed to explore patient and physician perspectives on current laboratory test reporting practices and to elicit ideas for improvement. METHODS: Two independent studies were conducted. The first solicited members of an online physician community for opinions on current laboratory test reporting practices and possible improvements. The second addressed the same topic, but solicited patient feedback, and included an evaluation of a mock laboratory test report for the rheumatoid factor blood test. RESULTS: Both physicians and patients expressed a desire for patient-friendly information on laboratory reports. Physicians expressed a need for education for patients around false-positive and false-negative results within laboratory reports, while patients sought context around the meaning of results, relevance to other tests, and follow-up steps. CONCLUSION: Physicians and patients see value in enhancing laboratory test reports to improve communication. While reports should include the context that patients value, they should also contain cautionary interpretation emphasized by physicians. Patient consultation on improving laboratory reports may help improve such patient-focused communication and promote greater patient understanding of health information, thereby increasing patient participation in their own health care and improving outcomes. PRACTICE IMPLICATIONS: Laboratory reports are typically designed by experts. Including patients in laboratory report design may facilitate communication and improve outcomes through better patient engagement.

Factors in Patient Empowerment: A Survey of an Online Patient Research Network.

BACKGROUND: Providers and healthcare organizations have begun recognizing the importance of patient empowerment as a driver of patient-centered care. Unfortunately, most studies have investigated empowerment with single diseases. Identifying factors of empowerment across conditions and populations would enable a greater understanding of this construct. OBJECTIVE: The purpose of this study was to understand empowerment in relation to health information-seeking, interactions with providers and peers, and healthcare access in chronic disease patients. This study also sought to identify key empowerment factors and their association with patient characteristics. METHODS: Participants were recruited through PatientsLikeMe, an online research platform where patients share their personal and medical history data. Patients completed an online survey that assessed self-reported health behavior (e.g. knowledge-seeking, experiences with healthcare providers, and peer interactions) and healthcare access. An exploratory factor analysis identified key empowerment domains. Domain level sum scores and sum of all domains (total score) were compared across patient characteristics and diseases. RESULTS: Overall, 3988 participants were included in the study, with the majority actively involved in their healthcare, but many cited difficulties with matching their treatment goals with those of their physician (34 %) and spending sufficient time with the physician (36 %). Factor analysis identified two domains-Positive Patient-Provider Interaction, and Knowledge and Personal Control-that explained >60 % of the overall variance in the observed variables. Mean total empowerment scores for patients with a primary complaint of Parkinson's disease (61.8) and multiple sclerosis (60.3) were significantly greater than fibromyalgia (55.3) and chronic fatigue syndrome (54.8). Patients who were older, male, more educated, and insured also reported significantly greater levels of empowerment. CONCLUSIONS: The two domains of empowerment identified in this study are consistent with previous studies, but the differences in empowerment levels across diseases suggest a need for further studies on disease-related attributes of empowerment. Future research should examine the pathways for empowerment, as well as the relationship between empowerment domains and clinical outcomes.

New approach for analyzing self-reporting of insomnia symptoms reveals a high rate of comorbid insomnia across a wide spectrum of chronic diseases.

BACKGROUND: Insomnia is increasingly recognized to be comorbid with one or more medical conditions. This study used an online research platform to characterize insomnia across different mental and physical conditions. METHODS: A custom cross-sectional survey was fielded online to 31,208 users of the patient community PatientsLikeMe. The survey queried members on National Sleep Foundation-defined insomnia risk (waking up feeling unrefreshed, difficulty falling asleep, waking in the middle of the night, or waking too early). RESULTS: Complete results were obtained from 5256 patients with 11 comorbid conditions. Seventy-six percent of US-based respondents were at risk for insomnia. Patients who reported difficulty falling asleep were found to have nearly twice the odds of self-reporting insomnia (odds ratio [OR]: 1.84; 95% confidence interval [CI]: 1.5-2.1) when compared to those who do not have difficulty falling asleep, whereas those who reported waking during the night or waking up unrefreshed were no more likely (OR: 1.025 and 1.032, respectively) to report that they suffered from insomnia than those who did not experience these issues. Although insomnia was self-reported as severe or very severe across most conditions, few respondents had actually been diagnosed with insomnia by a physician. After adjustment for age and gender, there was an independent and strong effect of primary condition severity on insomnia risk, and those with severe epilepsy (0.93), depressive disorders (0.92), and fibromyalgia (0.92) occupied the highest risk probabilities. CONCLUSIONS: The high rate of severity and frequency of insomnia across a multitude of mental and physical conditions reveals an opportunity for better disease management through enhanced insomnia awareness.

'Trust but verify'--five approaches to ensure safe medical apps.

Mobile health apps are health and wellness programs available on mobile devices such as smartphones or tablets. In three systematic assessments published in BMC Medicine, Huckvale and colleagues demonstrate that widely available health apps meant to help patients calculate their appropriate insulin dosage, educate themselves about asthma, or perform other important functions are methodologically weak. Insulin dose calculators lacked user input validation and made inappropriate dose recommendations, with a lack of documentation throughout. Since 2011, asthma apps have become more interactive, but have not improved in quality; peak flow calculators have the same issues as the insulin calculators. A review of the accredited National Health Service Health Apps Library found poor and inconsistent implementation of privacy and security, with 28% of apps lacking a privacy policy and one even transmitting personally identifying data the policy claimed would be anonymous. Ensuring patient safety might require a new approach, whether that be a consumer education program at one extreme or government regulation at the other. App store owners could ensure transparency of algorithms (whiteboxing), data sharing, and data quality. While a proper balance must be struck between innovation and caution, patient safety must be paramount.Please see related articles: http://dx.doi.org/10.1186/s12916-015-0444-y , http://www.biomedcentral.com/1741-7015/13/106 and http://www.biomedcentral.com/1741-7015/13/58.

Patient-centered activity monitoring in the self-management of chronic health conditions.

BACKGROUND: As activity tracking devices become smaller, cheaper, and more consumer-accessible, they will be used more extensively across a wide variety of contexts. The expansion of activity tracking and personal data collection offers the potential for patient engagement in the management of chronic diseases. Consumer wearable devices for activity tracking have shown promise in post-surgery recovery in cardiac patients, pulmonary rehabilitation, and activity counseling in diabetic patients, among others. Unfortunately, the data generated by wearable devices is seldom integrated into programmatic self-management chronic disease regimens. In addition, there is lack of evidence supporting sustained use or effects on health outcomes, as studies have primarily focused on establishing the feasibility of monitoring activity and the association of measured activity with short-term benefits. DISCUSSION: Monitoring devices can make a direct and real-time impact on self-management, but the validity and reliability of measurements need to be established. In order for patients to become engaged in wearable data gathering, key patient-centered issues relating to usefulness in care, motivation, the safety and privacy of information, and clinical integration need to be addressed. Because the successful usage of wearables requires an ability to comprehend and utilize personal health data, the user experience should account for individual differences in numeracy skills and apply evidence-based behavioral science principles to promote continued engagement. SUMMARY: Activity monitoring has the potential to engage patients as advocates in their personalized care, as well as offer health care providers real world assessments of their patients' daily activity patterns. This potential will be realized as the voice of the chronic disease patients is accounted for in the design of devices, measurements are validated against existing clinical assessments, devices become part of the treatment 'prescription', behavior change programs are used to engage patients in self-management, and best practices for clinical integration are defined.