Jeana Frost

Social Uses of Personal Health Information Within PatientsLikeMe, an Online Patient Community: What Can Happen When Patients Have Access to One Another’s Data

Background This project investigates the ways in which patients respond to the shared use of what is often considered private information: personal health data. There is a growing demand for patient access to personal health records. The predominant model for this record is a repository of all clinically relevant health information kept securely and viewed privately by patients and their health care providers. While this type of record does seem to have beneficial effects for the patient–physician relationship, the complexity and novelty of these data coupled with the lack of research in this area means the utility of personal health information for the primary stakeholders—the patients—is not well documented or understood. Objective PatientsLikeMe is an online community built to support information exchange between patients. The site provides customized disease-specific outcome and visualization tools to help patients understand and share information about their condition. We begin this paper by describing the components and design of the online community. We then identify and analyze how users of this platform reference personal health information within patient-to-patient dialogues. Methods Patients diagnosed with amyotrophic lateral sclerosis (ALS) post data on their current treatments, symptoms, and outcomes. These data are displayed graphically within personal health profiles and are reflected in composite community-level symptom and treatment reports. Users review and discuss these data within the Forum, private messaging, and comments posted on each other’s profiles. We analyzed member communications that referenced individual-level personal health data to determine how patient peers use personal health information within patient-to-patient exchanges. Results Qualitative analysis of a sample of 123 comments (about 2% of the total) posted within the community revealed a variety of commenting and questioning behaviors by patient members. Members referenced data to locate others with particular experiences to answer specific health-related questions, to proffer personally acquired disease-management knowledge to those most likely to benefit from it, and to foster and solidify relationships based on shared concerns. Conclusions Few studies examine the use of personal health information by patients themselves. This project suggests how patients who choose to explicitly share health data within a community may benefit from the process, helping them engage in dialogues that may inform disease self-management. We recommend that future designs make each patient’s health information as clear as possible, automate matching of people with similar conditions and using similar treatments, and integrate data into online platforms for health conversations.

Patient-reported outcomes as a source of evidence in off-label prescribing: analysis of data from PatientsLikeMe.

Background Evaluating a new use for an existing drug can be expensive and time consuming. Providers and patients must all too often rely upon their own individual-level experience to inform clinical practice, which generates only anecdotal and unstructured data. While academic-led clinical trials are occasionally conducted to test off-label uses of drugs with expired patents, this is relatively rare. In this work, we explored how a patient-centered online research platform could supplement traditional trials to create a richer understanding of medical products postmarket by efficiently aggregating structured patient-reported data. PatientsLikeMe is a tool for patients, researchers, and caregivers (currently 82,000 members across 11 condition-based communities) that helps users make treatment decisions, manage symptoms, and improve outcomes. Members enter demographic information, longitudinal treatment, symptoms, outcome data, and treatment evaluations. These are reflected back as longitudinal health profiles and aggregated reports. Over the last 3 years, patients have entered treatment histories and evaluations on thousands of medical products. These data may aid in evaluating the effectiveness and safety of some treatments more efficiently and over a longer period of time course than is feasible through traditional trials. Objective The objective of our study was to examine the illustrative cases of amitriptyline and modafinil – drugs commonly used off-label. Methods We analyzed patient-reported treatment histories and drug evaluations for each drug, examining prevalence, treatment purpose, and evaluations of effectiveness, side effects, and burden. Results There were 1948 treatment histories for modafinil and 1394 treatment reports for amitriptyline reported across five PatientsLikeMe communities (multiple sclerosis, Parkinsons disease, mood conditions, fibromyalgia/chronic fatigue syndrome, and amyotrophic lateral sclerosis). In these reports, the majority of members reported taking the drug for off-label uses. Only 34 of the 1755 (1%) reporting purpose used modafinil for an approved purpose (narcolepsy or sleep apnea). Only 104 out of 1197 members (9%) reported taking amitriptyline for its approved indication, depression. Members taking amitriptyline for off-label purposes rated the drug as more effective than those who were taking it for its approved indication. While dry mouth is a commonly reported side effect of amitriptyline for most patients, 88 of 220 (40%) of people with amyotrophic lateral sclerosis on the drug reported taking advantage of this side effect to treat their symptom of excess saliva. Conclusions Patient-reported outcomes, like those entered within PatientsLikeMe, offer a unique real-time approach to understand utilization and performance of treatments across many conditions. These patient-reported data can provide a new source of evidence about secondary uses and potentially identify targets for treatments to be studied systematically in traditional efficacy trials.

Integrating glucometers and digital photography as experience capture tools to enhance patient understanding and communication of diabetes self-management practices

Glucometers measure the accumulation of glucose in the bloodstream and are essential for avoiding health complications related to diabetes. Despite their value as tools to record and present physiological data, they lack the ability to capture the behaviors that cause fluctuations in blood glucose levels, activities that ultimately need to be understood and managed in order to maintain good health. In this paper, we describe an intervention that introduces digital photography into diabetes self-management routines to augment glucometer data and facilitate the sharing of experiences that affect long-term health. Two studies of the approach are presented to illustrate the ways that diabetics use visualizations of past activities to reflect on their health. We also discuss design suggestions for augmented memory systems based on our findings, focusing on ways to enhance learning with repositories of past experiences collected automatically and/or manually.

Anonymity Versus Privacy: Selective Information Sharing in Online Cancer Communities

Background Active sharing in online cancer communities benefits patients. However, many patients refrain from sharing health information online due to privacy concerns. Existing research on privacy emphasizes data security and confidentiality, largely focusing on electronic medical records. Patient preferences around information sharing in online communities remain poorly understood. Consistent with the privacy calculus perspective adopted from e-commerce research, we suggest that patients approach online information sharing instrumentally, weighing privacy costs against participation benefits when deciding whether to share certain information. Consequently, we argue that patients prefer sharing clinical information over daily life and identity information that potentially compromises anonymity. Furthermore, we explore whether patients’ prior experiences, age, health, and gender affect perceived privacy costs and thus willingness to share information. Objective The goal of the present study is to document patient preferences for sharing information within online health platforms. Methods A total of 115 cancer patients reported sharing intentions for 15 different types of information, demographics, health status, prior privacy experiences, expected community utility, and privacy concerns. Results Factor analysis on the 15 information types revealed 3 factors coinciding with 3 proposed information categories: clinical, daily life, and identity information. A within-subject ANOVA showed a strong preference for sharing clinical information compared to daily life and identity information (F 1,114=135.59, P=.001, η2=.93). Also, adverse online privacy experiences, age, and health status negatively affected information-sharing intentions. Female patients shared information less willingly. Conclusions Respondents’ information-sharing intentions depend on dispositional and situational factors. Patients share medical details more willingly than daily life or identity information. The results suggest the need to focus on anonymity rather than privacy in online communities.

Using feedback through digital technology to disrupt and change habitual behavior: A critical review of current literature

Abstract Habitual behavior is often hard to change because of a lack of self-monitoring skills. Digital technologies offer an unprecedented chance to facilitate self-monitoring by delivering feedback on undesired habitual behavior. This review analyzed the results of 72 studies in which feedback from digital technology attempted to disrupt and change undesired habits. A vast majority of these studies found that feedback through digital technology is an effective way to disrupt habits, regardless of target behavior or feedback technology used. Unfortunately, methodological issues limit our confidence in the findings of all but 14 of the 50 studies with quantitative measurements in this review. Furthermore, only 4 studies tested for (and only 3 of those 4 found) sustained habit change, and it remains unclear how feedback from digital technology is moderated by receiver states and traits, as well as feedback characteristics such as feedback sign, comparison, tailoring, modality, frequency, timing and duration. We conclude with recommendations for new research directions.

Distributed knowledge in an online patient support community: Authority and discovery

Amyotrophic lateral sclerosis (ALS) is a progressively debilitating neurodegenerative condition that occurs in adulthood and targets the motor neurons. Social support is crucial to the well‐being and quality of life of people with unpredictable and incurable diseases such as ALS. Members of the PatientsLikeMe (PLM) ALS online support community share social support but also exchange and build distributed knowledge within their discussion forum. This qualitative analysis of 1,000 posts from the PLM ALS online discussion examines the social support within the PLM ALS online community and explores ways community members share and build knowledge. The analysis responds to 3 research questions: RQ1: How and why is knowledge shared among the distributed participants in the PLM‐ALS threaded discussion forum?; RQ2: How do the participants in the PLM‐ALS threaded discussion forum work together to discover knowledge about treatments and to keep knowledge discovered over time?; and RQ3: How do participants in the PLM‐ALS forum co‐create and treat authoritative knowledge from multiple sources including the medical literature, healthcare professionals, lived experiences of patients and “other” sources of information such as lay literature and alternative health providers? The findings have implications for supporting knowledge sharing and discovery in addition to social support for patients.

Does familiarity breed contempt or liking? Comment on Reis, Maniaci, Caprariello, Eastwick, and Finkel

Reis, Maniaci, Caprariello, Eastwick, and Finkel (see record 2011-04644-001) conducted 2 studies that demonstrate that in certain cases, familiarity can lead to liking--in seeming contrast to the results of our earlier article (see record 2006-23056-008). We believe that Reis et al. (a) utilized paradigms far removed from spontaneous, everyday social interactions that were particularly likely to demonstrate a positive link between familiarity and liking and (b) failed to include and incorporate other sources of data-both academic and real-world-showing that familiarity breeds contempt. We call for further research exploring when and why familiarity is likely to lead to contempt or liking, and we suggest several factors that are likely to inform this debate.

When Does Familiarity Promote Versus Undermine Interpersonal Attraction? A Proposed Integrative Model From Erstwhile Adversaries

This article began as an adversarial collaboration between two groups of researchers with competing views on a longstanding question: Does familiarity promote or undermine interpersonal attraction? As we explored our respective positions, it became clear that the limitations of our conceptualizations of the familiarity–attraction link, as well as the limitations of prior research, were masking a set of higher order principles capable of integrating these diverse conceptualizations. This realization led us to adopt a broader perspective, which focuses on three distinct relationship stages—awareness, surface contact, and mutuality—and suggests that the influence of familiarity on attraction depends on both the nature and the stage of the relationship between perceivers and targets. This article introduces the framework that emerged from our discussions and suggests directions for research to investigate its validity.

Evaluation of a Smart Fork to Decelerate Eating Rate.

Overweight is associated with a range of negative health consequences, such as type 2 diabetes, cardiovascular disease, gastrointestinal disorders, and premature mortality. One means to combat overweight is through encouraging people to eat more slowly. People who eat quickly tend to consume more and have a higher body mass index, whereas people who eat more slowly feel satiated sooner and eat less. Unfortunately, eating rate is difficult to modify, because of its highly automatic nature. In clinical settings, researchers have had some success changing behavior by using devices that deliver feedback in real time. However, existing technologies are either too cumbersome or not engaging enough for use in daily life contexts. Training people to eat more slowly in everyday eating contexts, therefore, requires creative and engaging solutions. This article presents a qualitative evaluation of the feasibility of a smart fork to decelerate eating rate in daily life contexts. Furthermore, we outline the planned research to test the efficacy of this device in both laboratory and community settings.

The case for using social media to aggregate patient experiences with off-label prescriptions

Off-label prescribing is a common and legal practice in the USA, with an estimated 21% of all prescriptions written for off-label indications. Although off-label prescribing can be immensely useful, with patients and providers taking advantage of observed drug effects for novel applications [1], insights gained from anecdotal evidence can be confounded by placebo effects or the tendency for patients to improve on their own. In fact, almost three quarters of off-label prescriptions are unsupported by scientific evidence [2]. Cost is the major factor for the lack of evidence underlying off-label prescribing. In particular, running a randomized controlled trial (RCT) to establish the efficacy of a drug for a new purpose costs between US