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Motivated by the need to support those self-managing chronic pain, we report on the development and evaluation of a novel pressure-based tangible user interface (TUI) for the self-report of scalar values representing pain intensity. Our TUI consists of a conductive foam-based, force-sensitive resistor (FSR) covered in a soft rubber with embedded signal conditioning, an ARM Cortex-M0 microprocessor, and Bluetooth Low Energy (BLE). In-lab usability and feasibility studies with 28 participants found that individuals were able to use the device to make reliable reports with four degrees of freedom as well map squeeze pressure to pain level and visual feedback. Building on insights from these studies, we further redesigned the FSR into a wearable device with multiple form factors, including a necklace, bracelet, and keychain. A usability study with an additional 7 participants from our target population, elderly individuals with chronic pain, found high receptivity to the wearable design, which offered a number of participant-valued characteristics (e.g., discreetness) along with other design implications that serve to inform the continued refinement of tangible devices that support pain self-assessment.

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BACKGROUND: Chronic pain is a globally prevalent condition. It is closely linked with psychological well-being, and it is often concomitant with anxiety, negative affect, and in some cases even depressive disorders. In the case of musculoskeletal chronic pain, frequent physical activity is beneficial. However, reluctance to engage in physical activity is common due to negative psychological associations (eg, fear) between movement and pain. It is known that encouragement, self-efficacy, and positive beliefs are effective to bolster physical activity. However, given that the majority of time is spent away from personnel who can give such encouragement, there is a great need for an automated ubiquitous solution. OBJECTIVE: MyBehaviorCBP is a mobile phone app that uses machine learning on sensor-based and self-reported physical activity data to find routine behaviors and automatically generate physical activity recommendations that are similar to existing behaviors. Since the recommendations are based on routine behavior, they are likely to be perceived as familiar and therefore likely to be actualized even in the presence of negative beliefs. In this paper, we report the preliminary efficacy of MyBehaviorCBP based on a pilot trial on individuals with chronic back pain. METHODS: A 5-week pilot study was conducted on people with chronic back pain (N=10). After a week long baseline period with no recommendations, participants received generic recommendations from an expert for 2 weeks, which served as the control condition. Then, in the next 2 weeks, MyBehaviorCBP recommendations were issued. An exit survey was conducted to compare acceptance toward the different forms of recommendations and map out future improvement opportunities. RESULTS: In all, 90% (9/10) of participants felt positive about trying the MyBehaviorCBP recommendations, and no participant found the recommendations unhelpful. Several significant differences were observed in other outcome measures. Participants found MyBehaviorCBP recommendations easier to adopt compared to the control (ßint=0.42, P<.001) on a 5-point Likert scale. The MyBehaviorCBP recommendations were actualized more (ßint=0.46, P<.001) with an increase in approximately 5 minutes of further walking per day (ßint=4.9 minutes, P=.02) compared to the control. For future improvement opportunities, participants wanted push notifications and adaptation for weather, pain level, or weekend/weekday. CONCLUSIONS: In the pilot study, MyBehaviorCBP's automated approach was found to have positive effects. Specifically, the recommendations were actualized more, and perceived to be easier to follow. To the best of our knowledge, this is the first time an automated approach has achieved preliminary success to promote physical activity in a chronic pain context. Further studies are needed to examine MyBehaviorCBP's efficacy on a larger cohort and over a longer period of time.

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The rapid growth of mobile health (mHealth) devices holds substantial potential for improving care and care outcomes in all patient populations, including older adults with pain. However, existing research reflects a substantial gap in knowledge about how to design, evaluate, and disseminate devices to optimally address the many challenges associated with managing pain in older persons. Given these knowledge gaps, we sought to develop a set of practice-based research priorities to facilitate innovation in this field. We employed the Cornell Research-Practice Consensus Workshop Model, an evidence-based approach to generating research priorities. Sixty participants attended the conference, where stakeholder groups included older adults with pain and their caregivers, behavioral and social scientists, healthcare providers, pain experts, and specialists in mHealth and health policy. Participants generated 13 recommendations classified into 2 categories: 1) implications for designing research on mHealth among older adults (eg, conduct research on ways to enhance accessibility of mHealth tools among diverse groups of older adults with pain, expand research on mHealth sensing applications), and 2) implementation of mHealth technology into practice and associated regulatory issues (eg, promote research on ways to initiate/sustain patient behavior change, expand research on mHealth cybersecurity and privacy issues). PERSPECTIVE: This report highlights a set of research priorities in the area of mHealth and later-life pain derived from the joint perspectives of researchers and key stakeholder groups. Addressing these priorities could help to improve the quality of care delivered to older adults with pain.

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BACKGROUND: Technological advances in personal informatics allow people to track their own health in a variety of ways, representing a dramatic change in individuals' control of their own wellness. However, research regarding patient interpretation of traditional medical tests highlights the risks in making complex medical data available to a general audience. OBJECTIVE: This study aimed to explore how people interpret medical test results, examined in the context of a mobile blood testing system developed to enable self-care and health management. METHODS: In a preliminary investigation and main study, we presented 27 and 303 adults, respectively, with hypothetical results from several blood tests via one of the several mobile interface designs: a number representing the raw measurement of the tested biomarker, natural language text indicating whether the biomarker's level was low or high, or a one-dimensional chart illustrating this level along a low-healthy axis. We measured respondents' correctness in evaluating these results and their confidence in their interpretations. Participants also told us about any follow-up actions they would take based on the result and how they envisioned, generally, using our proposed personal health system. RESULTS: We find that a majority of participants (242/328, 73.8%) were accurate in their interpretations of their diagnostic results. However, 135 of 328 participants (41.1%) expressed uncertainty and confusion about their ability to correctly interpret these results. We also find that demographics and interface design can impact interpretation accuracy, including false confidence, which we define as a respondent having above average confidence despite interpreting a result inaccurately. Specifically, participants who saw a natural language design were the least likely (421.47 times, P=.02) to exhibit false confidence, and women who saw a graph design were less likely (8.67 times, P=.04) to have false confidence. On the other hand, false confidence was more likely among participants who self-identified as Asian (25.30 times, P=.02), white (13.99 times, P=.01), and Hispanic (6.19 times, P=.04). Finally, with the natural language design, participants who were more educated were, for each one-unit increase in education level, more likely (3.06 times, P=.02) to have false confidence. CONCLUSIONS: Our findings illustrate both promises and challenges of interpreting medical data outside of a clinical setting and suggest instances where personal informatics may be inappropriate. In surfacing these tensions, we outline concrete interface design strategies that are more sensitive to users' capabilities and conditions.

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BACKGROUND: In today's health care environment, increasing costs and inadequate medical resources have created a worldwide need for more affordable diagnostic tools that are also portable, fast, and easy to use. To address this issue, numerous research and commercial efforts have focused on developing rapid diagnostic technologies; however, the efficacy of existing systems has been hindered by usability problems or high production costs, making them infeasible for deployment in at-home, point-of-care (POC), or resource-limited settings. OBJECTIVE: The aim of this study was to create a low-cost optical reader system that integrates with any smart device and accepts any type of rapid diagnostic test strip to provide fast and accurate data collection, sample analysis, and diagnostic result reporting. METHODS: An iterative design methodology was employed by a multidisciplinary research team to engineer three versions of a portable diagnostic testing device that were evaluated for usability and overall user receptivity. RESULTS: Repeated design critiques and usability studies identified a number of system requirements and considerations (eg, software compatibility, biomatter contamination, and physical footprint) that we worked to incrementally incorporate into successive system variants. Our final design phase culminated in the development of Tidbit, a reader that is compatible with any Wi-Fi-enabled device and test strip format. The Tidbit includes various features that support intuitive operation, including a straightforward test strip insertion point, external indicator lights, concealed electronic components, and an asymmetric shape, which inherently signals correct device orientation. Usability testing of the Tidbit indicates high usability for potential user communities. CONCLUSIONS: This study presents the design process, specification, and user reception of the Tidbit, an inexpensive, easy-to-use, portable optical reader for fast, accurate quantification of rapid diagnostic test results. Usability testing suggests that the reader is usable among and can benefit a wide group of potential users, including in POC contexts. Generally, the methodology of this study demonstrates the importance of testing these types of systems with potential users and exemplifies how iterative design processes can be employed by multidisciplinary research teams to produce compelling technological solutions.

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To better support the self-management of chronic pain, this paper investigates how those living with the condition prefer to self-assess their pain levels using smartphones. Our work consists of three stages: design ideation and review, an in-lab user study with 10 participants resulting in nine candidate interfaces, and a 3 week field trial of two further honed measures with 12 participants. This research firstly yields a better understanding of participants' strong and sometimes contrasting preferences regarding their self-assessment of pain intensity. We additionally contribute two novel interfaces that support accurate, quick, and repeated use along with other participant-valued interactions (e.g., familiar, relatable, and highly usable). In particular, we focus on designing tailored measures that both enhance respondent motivation as well as minimize the difficulty of meaningful self-assessment by supporting the cog-nitive effort in translating a subjective experience into a single numerical value.

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Dynamic psychological processes are most often assessed using self-report instruments. This places a constraint on how often and for how long data can be collected due to the burden placed on human participants. Smartphones are ubiquitous and highly personal devices, equipped with sensors that offer an opportunity to measure and understand psychological processes in real-world contexts over the long term. In this article, we present a novel smartphone approach to address the limitations of self-report in bipolar disorder where mood and activity are key constructs. We describe the development of MoodRhythm, a smartphone application that incorporates existing self-report elements from interpersonal and social rhythm therapy, a clinically validated treatment, and combines them with novel inputs from smartphone sensors. We reflect on lessons learned in transitioning from an existing self-report instrument to one that involves smartphone sensors and discuss the potential impact of these changes on the future of psychological assessment.

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OBJECTIVE: To evaluate the feasibility of automatically assessing the Social Rhythm Metric (SRM), a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), using passively-sensed data from smartphones. METHODS: Seven patients with BD used smartphones for 4 weeks passively collecting sensor data including accelerometer, microphone, location, and communication information to infer behavioral and contextual patterns. Participants also completed SRM entries using a smartphone app. RESULTS: We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and non-stationary duration as inputs, our generalized model achieves root-mean-square-error of 1.40, a reasonable performance given the range of SRM score (0-7). Personalized models further improve performance with mean root-mean-square-error of 0.92 across users. Classifiers using sensor streams can predict stable (SRM score ≥3.5) and unstable (SRM score <3.5) states with high accuracy (precision: 0.85 and recall: 0.86). CONCLUSIONS: Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.

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OBJECTIVE: To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management. MATERIALS AND METHODS: Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses. RESULTS: Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data. DISCUSSION: Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor. CONCLUSION: This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management.

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Our body clock causes considerable variations in our behavioral, mental, and physical processes, including alertness, throughout the day. While much research has studied technology usage patterns, the potential impact of underlying biological processes on these patterns is under-explored. Using data from 20 participants over 40 days, this paper presents the first study to connect patterns of mobile application usage with these contributing biological factors. Among other results, we find that usage patterns vary for individuals with different body clock types, that usage correlates with rhythms of alertness, that app use features such as duration and switching can distinguish periods of low and high alertness, and that app use reflects sleep interruptions as well as sleep duration. We conclude by discussing how our findings inform the design of biologically-friendly technology that can better support personal rhythms of performance.

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As millions of people turn to social media for health information, better understanding the factors that guide health-related judgments and perceptions in this context is imperative. We report on two Web experiments (n>400 total) examining the power of society's widespread weight bias and related stereotypes to influence nutrition judgments in social media spaces. In Experiment 1, meals were judged as lower in nutritional quality when the person who recommended them (the source) was depicted as obese rather than of normal weight, an effect mediated by stereotypic beliefs about the source as a generally unhealthy person. Experiment 2 replicated this effect, which--notably--remained significant when controlling for objective nutritional information (calories and fat content). Results highlight spillover effects of weight bias that extend beyond person perception to color impressions of objects (here, food) that are associated with stigmatized attributes. Implications for everyday nutrition judgments and public health are considered.

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Physical and psychological changes that occur during pregnancy present a unique challenge for women's physical activity. Using a theory-based prospective design, this study examines the effects of pregnant women's (a) physical activity cognitions (self-efficacy, outcome expectancy, and safety beliefs) and (b) online self-regulation activities (goal-setting and self-monitoring) on subsequent changes in their physical activity intentions and behavior during pregnancy and immediately postpartum. The authors used data from three panel surveys administered to pregnant women enrolled in a web-based intervention to promote healthy pregnancy and postpartum weight, as well as log data on their use of self-regulatory features on the intervention website. Perceived self-efficacy and perceived safety of physical activity in pregnancy enhanced subsequent intentions to be physically active. Repeated goal-setting and monitoring of those goals helped to maintain positive intentions during pregnancy, but only repeated self-monitoring transferred positive intentions into actual behavior. Theoretically, this study offers a better understanding of the roles of self-regulation activities in the processes of goal-striving. The authors also discuss practical implications for encouraging physical activity among pregnant and early postpartum women.

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OBJECTIVES: To examine preliminarily the effectiveness of a short message service (SMS) text message-based social support intervention for reducing daily pain and pain interference levels, improving affect and perceptions of social support in patients with chronic noncancer pain, and exploring the feasibility of a novel mobile application to track perceptions of pain and pain interference. MATERIALS AND METHODS: Participants (17 men, 51 women) from 2 pain clinics in New York City downloaded a pain tracking application (App) on their Smartphone and used it to record twice-daily pain, pain interference, and affect scores over the 4-week study period. Participants were randomly assigned to receive standard care (control) or standard care along with receipt of twice-daily supportive SMS text messages delivered during the second and third week of the study (intervention). Demographic and clinical data were obtained at baseline, and social support measures were administered at baseline and at 4 weeks. Statistical analysis was carried out using general linear mixed models, taking into account variances associated with time of assessments and with patients. RESULTS: The social support intervention reduced perceptions of pain and pain interference and improved positive affect for chronic noncancer pain patients assigned to the intervention condition in comparison with controls. Participants completed approximately 80% of the daily measurements requested. DISCUSSION: These findings establish the feasibility of collecting daily pain data using a mobile tracking App and provide significant implications and insight into a nuanced approach to reducing the daily experience of pain through mobile technology, especially because of its accessibility.

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MoodLight is an interactive ambient lighting system that responds to biosensor input related to an individual's current level of arousal. Changes in levels of arousal correspond to fluctuations in the color of light provided by the system, altering the immediate environment in ways intimately related to the user's private internal state. We use this intervention to explore personal and social implications of the ambient display of biosensor data. This study provides greater understanding of the ways in which the representations of personal informatics, with a focus on ambient feedback, influence our perceptions of ourselves and those around us.

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This study examined psychosocial and sociodemographic factors associated with pregnant women's use of Web-based tools to set and monitor personal goals for healthy diet and physical activity. These tools were made available to women participating in a randomized trial testing a Web-based intervention to promote appropriate gestational weight gain. We used data from a baseline survey of pregnant women assigned to the intervention group and log data on women's use of various intervention features (N = 873). Women who believed that appropriate gestational weight gain would lead to healthy outcomes for their child were more likely to engage in online goal-setting and self-monitoring. Less positive outcome expectancy beliefs about the relationship between their own weight and baby's health partially explains why some at risk subpopulations (e.g., African-American women) were less likely to utilize online self-regulatory tools. This study specifies key psychosocial and motivational factors that guide the construction and monitoring of goals among pregnant women. These findings offer guidance for the design of interventions to promote self-regulatory techniques by identifying groups for whom those features are most likely to be useful, as well as psychological determinants of their use.

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BACKGROUND: Gaining more weight during pregnancy than is recommended by the Institute of Medicine is prevalent and contributes to the development of obesity in women. This article describes the development and use of e-Moms of Rochester (e-Moms Roc), an electronic intervention (e-intervention), to address this health issue in a socioeconomically diverse sample of pregnant women. MATERIALS AND METHODS: Formative research in the form of intercept interviews, in-depth interviews, and focus groups was conducted to inform the design of the e-intervention. The Web site continuously tracked each participant's use of e-intervention features. RESULTS: An e-intervention, including Web site and mobile phone components, was developed and implemented in a randomized control trial. Formative research informed the design. Participants in all arms accessed blogs, local resources, articles, frequently asked questions, and events. Participants in the intervention arms also accessed the weight gain tracker and diet and physical activity goal-setting tools. Overall, 80% of women logged into the Web site and used a tool or feature at least twice. Among those in the intervention arm, 70% used the weight gain tracker, but only 40% used the diet and physical activity goal-setting tools. CONCLUSIONS: To maximize and sustain potential usage of e-Moms Roc over time, the e-intervention included customized reminders, tailored content, and community features such as blogs and resources. Usage was comparable to those in other weight studies with young adults and higher than reported in a published study with pregnant women. This e-intervention specifically designed for pregnant women was used by the majority of women.

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We examine how social support (perceived support and support from a spouse, or committed partner) may influence pregnant women's information seeking behaviors on a pregnancy website. We assess information seeking behavior among participants in a trial testing the effectiveness of a web-based intervention for appropriate gestational weight gain. Participants were pregnant women (N = 1,329) recruited from clinics and private practices in one county in the Northeast United States. We used logistic regression models to estimate the likelihood of viewing articles, blogs, frequently asked questions (FAQs), and resources on the website as a function of perceived social support, and support from a spouse or relationship partner. All models included socio-demographic controls (income, education, number of adults and children living at home, home Internet use, and race/ethnicity). Compared to single women, women who were married or in a committed relationship were more likely to information seek online by viewing articles (OR 1.95, 95 % CI [1.26-3.03]), FAQs (OR 1.64 [1.00-2.67]), and blogs (OR 1.88 [1.24-2.85]). Women who felt loved and valued (affective support) were more likely to seek information by viewing articles on the website (OR 1.19 [1.00-1.42]). While the Internet provides a space for people who have less social support to access health information, findings from this study suggest that for pregnant women, women who already had social support were most likely to seek information online. This finding has important implications for designing online systems and content to encourage pregnant women with fewer support resources to engage with content.

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A player can learn many things from playing a game for health. Some of these learnings were deliberately designed for the player to use in his or her real life, outside of any game. The effective ways to enable players to generalize what they learn in the game to their real lives (and thereby benefit from playing the game) are not clear. We have convened a group of expert health game designers and researchers to discuss this important issue.

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This study extends a risk information seeking and processing model to explore the relative effect of cognitive processing strategies, positive and negative emotions, and normative beliefs on individuals' decision making about potential health risks. Most previous research based on this theoretical framework has examined environmental risks. Applying this risk communication model to study health decision making presents an opportunity to explore theoretical boundaries of the model, while also bringing this research to bear on a pressing medical issue: low enrollment in clinical trials. Comparative analysis of data gathered from 2 telephone surveys of a representative national sample (n = 500) and a random sample of cancer patients (n = 411) indicated that emotions played a more substantive role in cancer patients' decisions to enroll in a potential trial, whereas cognitive processing strategies and normative beliefs had greater influences on the decisions of respondents from the national sample.

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BACKGROUND: Emotion is a ubiquitous aspect of humanity that governs behavior in a number of ways and is linked inextricably with health. Pausing to evaluate one's emotional state in the face of decisions and reflecting on past patterns of emotion have been shown to improve behaviors. Further, social expression of emotion has been shown to directly improve health outcomes. While the virtual reality research community does not ignore emotion on the whole, there does exist a need to explore what roles emotional awareness and emotion sharing can play in this domain. METHODS: A mobile-phone-based social emotion recording and sharing system, Aurora, was developed to provide individuals with a means to pause and evaluate their emotional state, reflect on past emotions, share emotions with others, and participate in socially supportive activities with peers. A study was conducted with 65 subjects to evaluate Aurora as a tool to encourage emotional reflection and awareness as well as social sharing of emotion. RESULTS: Users of Aurora reported an increased comfort in socially expressing emotion and were encouraged to share emotions, even with strangers. Subjects also reported liking reflecting on their emotional state and found it valuable. Subjects' behavior also suggested that the system encouraged individuals to reach out to one another in acts of social support. CONCLUSIONS: The Aurora system offers a tool for encouraging emotional awareness, emotion sharing, and socially supportive behavior. Such a tool could be impactful in numerous health settings where emotion is considered to be an important indicator of or influence on outcome, such as for weight loss, alcohol cessation, or cancer sufferers.

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