Research with older adults has hinted at the ways that elements beyond the interface play a role in technology use, including videoconferencing. To further understand the range of materials and resources involved, we studied videoconferencing use by ten older individuals with cognitive concerns in a week-long study of interviews, observations, and a modified diary study. Our analysis identified that objects extending beyond software and hardware play a role in videoconferencing, including paper-based objects, personal items, and objects in the built environment. These objects support participants by externalizing information difficult to recall, distributing cognitive effort across time, and lowering cognitive load through their spatial placement and affordances. These insights point to opportunities for researchers working with older people to focus on the work happening outside of today's interfaces. We also discuss how the lens of distributed cognition could help us design better technologies to support age-related cognitive impairment.

Much research on older people with memory concerns is focused on tracking and informed by the priorities of others. In this paper, we seek to understand the potential that people with memory concerns see in tracking. We conducted interviews with 29 participants with concerns about their memory and engaged in an affective writing approach. We find a range of potentials that can be traced to how participants are already self-tracking. Emotions associated with these potentials vary: from acceptance to resistance, and positive anticipation to aversion. Participants are emotionally motivated to foreclose possibilities in some instances and keep them open in others. While individual and unique, potential is structured by forces that include individual routines, relationships with others, and macro-level institutions and cultural contexts. We reflect on these findings in the context of research on self-tracking with older adults, designing with ambiguity, and forces that structure the experience of living with memory concerns.

Spreadsheets are widely used for storing, manipulating, analyzing, and visualizing data. Features such as conditional formatting, formulas, sorting, and filtering play an important role when understanding and analyzing data in spreadsheets. They employ visual cues, but we have little understanding of the experiences of blind screen reader (SR) users with such features. We conducted a study with 12 blind SR users to gain insights into their challenges, workarounds, and strategies in understanding and extracting information from a spreadsheet consisting of multiple tables that incorporated data analysis features. We identified five factors that impact blind SR users’ experiences: cognitive overload, time-information trade-off, lack of awareness and expertise, inadequate system feedback, and delayed and absent SR responses. Drawn from these findings, we discuss design suggestions and future research agenda to improve SR users’ spreadsheet experiences.

Activity tracking has the potential to promote active lifestyles among older adults. However, current activity tracking technologies may inadvertently perpetuate ageism by focusing on age-related health risks. Advocating for a personalized approach in activity tracking technology, we sought to understand what activities older adults find meaningful to track and the underlying values of those activities. We conducted a reflective interview study following a 7-day activity journaling with 13 participants. We identified various underlying values motivating participants to track activities they deemed meaningful. These values, whether competing or aligned, shape the desirability of activities. Older adults appreciate low-exertion activities, but they are difficult to track. We discuss how these activities can become central in designing activity tracking systems. Our research offers insights for creating value-driven, personalized activity trackers that resonate more fully with the meaningful activities of older adults.

Ambient Information Systems (AIS) have shown some success when used as a notification towards users’ health-related activities. But in the actual busy lives of users, ambient notifications might be forgotten or even missed, nullifying the original notification. Could a system use multiple levels of noticeability to ensure its message is received, and how could this concept be effectively portrayed? To examine these questions, we took a Research through Design approach and created plant-mimicking Shape-Changing Interface (S-CI) artifacts, then conducted interviews with 10 participants who currently used a reminder system for health-related activities. We report findings on acceptable scenarios to disrupting people for health-related activities, and participants’ reactions to our design choices, including how using naturalistic aesthetics led to interpretations of the uncanny and morose, and which ways system physicality affected imagined uses. We offer design suggestions in health-related notification systems and S-CIs, and discuss future work in ambient-to-disruptive technology.

Current activity tracking technologies are largely trained on younger adults’ data, which can lead to solutions that are not well-suited for older adults. To build activity trackers for older adults, it is crucial to collect training data with them. To this end, we examine the feasibility and challenges with older adults in collecting activity labels by leveraging speech. Specifically, we built MyMove, a speech-based smartwatch app to facilitate the in-situ labeling with a low capture burden. We conducted a 7-day deployment study, where 13 older adults collected their activity labels and smartwatch sensor data, while wearing a thigh-worn activity monitor. Participants were highly engaged, capturing 1,224 verbal reports in total. We extracted 1,885 activities with corresponding effort level and timespan, and examined the usefulness of these reports as activity labels. We discuss the implications of our approach and the collected dataset in supporting older adults through personalized activity tracking technologies.

Information seeking is crucial for people's self-care and wellbeing in times of public crises. Extensive research has investigated empirical understandings as well as technical solutions to facilitate information seeking by domestic citizens of affected regions. However, limited knowledge is established to support international migrants who need to survive a crisis in their host countries. The current paper presents an interview study with two cohorts of Chinese migrants living in Japan (N=14) and the United States (N=14). Participants reflected on their information seeking experiences during the COVID pandemic. The reflection was supplemented by two weeks of self-tracking where participants maintained records of their COVID-related information seeking practice. Our data indicated that participants often took language detours, or visits to Mandarin resources for information about the COVID outbreak in their host countries. They also made strategic use of the Mandarin information to perform selective reading, cross-checking, and contextualized interpretation of COVID-related information in Japanese or English. While such practices enhanced participants' perceived effectiveness of COVID-related information gathering and sensemaking, they disadvantaged people through sometimes incognizant ways. Further, participants lacked the awareness or preference to review migrant-oriented information that was issued by the host country's public authorities despite its availability. Building upon these findings, we discussed solutions to improve international migrants' COVID-related information seeking in their non-native language and cultural environment. We advocated inclusive crisis infrastructures that would engage people with diverse levels of local language fluency, information literacy, and experience in leveraging public services.

Speech as a natural and low-burden input modality has great potential to support personal data capture. However, little is known about how people use speech input, together with traditional touch input, to capture different types of data in self-tracking contexts. In this work, we designed and developed NoteWordy, a multimodal self-tracking application integrating touch and speech input, and deployed it in the context of productivity tracking for two weeks (N = 17). Our participants used the two input modalities differently, depending on the data type as well as personal preferences, error tolerance for speech recognition issues, and social surroundings. Additionally, we found speech input reduced participants' diary entry time and enhanced the data richness of the free-form text. Drawing from the findings, we discuss opportunities for supporting efficient personal data capture with multimodal input and implications for improving the user experience with natural language input to capture various self-tracking data.

Patient-generated data (PGD) show great promise for informing the delivery of personalized and patient-centered care. However, patients' data tracking does not automatically lead to data sharing and discussion with clinicians, which can make it difficult to utilize and derive optimal benefit from PGD. In this paper, we investigate whether and how patients share their PGD with clinicians and the types of challenges that arise within this context. We describe patients' immediate experiences of PGD sharing with clinicians, based on our short onsite interviews with 57 patients who had just met with a clinician at a university health center. Our analyses identified overarching patterns in patients' PGD sharing practices and the associated challenges that arise from the information asymmetry between patients and clinicians and from patients' reliance on their memory to share their PGD. We discuss the implications of our findings for designing PGD-integrated health IT systems in ways to support patients' tracking of relevant PGD, clinicians' effective engagement with patients around PGD, and the efficient sharing and review of PGD within clinical settings.

The factors influencing people’s food decisions, such as one’s mood and eating environment, are important information to foster self-reflection and to develop personalized healthy diet. But, it is difficult to consistently collect them due to the heavy data capture burden. In this work, we examine how speech input supports capturing everyday food practice through a week-long data collection study (N = 11). We deployed FoodScrap, a speech-based food journaling app that allows people to capture food components, preparation methods, and food decisions. Using speech input, participants detailed their meal ingredients and elaborated their food decisions by describing the eating moments, explaining their eating strategy, and assessing their food practice. Participants recognized that speech input facilitated self-reflection, but expressed concerns around re-recording, mental load, social constraints, and privacy. We discuss how speech input can support low-burden and reflective food journaling and opportunities for effectively processing and presenting large amounts of speech data.

Polycystic Ovary Syndrome (PCOS) is a condition that causes hormonal imbalance and infertility in women and people with female reproductive organs. PCOS causes different symptoms for different people, with no singular or universal cure. Being a stigmatized and enigmatic condition, it is challenging to discover, diagnose, and manage PCOS. This work aims to inform the design of inclusive health technologies through an understanding of people’s lived experiences and challenges with PCOS. We conducted semi-structured interviews with 10 women diagnosed with PCOS and analyzed a PCOS-specific subreddit forum. We report people’s support-seeking, sense-making, and self-experimentation practices, and find uncertainty and stigma to be key in shaping their unique experiences of the condition. We further identify potential avenues for designing technology to support their diverse needs, such as personalized and contextual tracking, accelerated self-discovery, and co-management, contributing to a growing body of HCI literature on stigmatized topics in women’s health and well-being.

Most mobile health apps employ data visualization to help people view their health and activity data, but these apps provide limited support for visual data exploration. Furthermore, despite its huge potential benefits, mobile visualization research in the personal data context is sparse. This work aims to empower people to easily navigate and compare their personal health data on smartphones by enabling flexible time manipulation with speech. We designed and developed [email protected], a mobile app that leverages the synergy of two complementary modalities: speech and touch. Through an exploratory study with 13 long-term Fitbit users, we examined how multimodal interaction helps participants explore their own health data. Participants successfully adopted multimodal interaction (i.e., speech and touch) for convenient and fluid data exploration. Based on the quantitative and qualitative findings, we discuss design implications and opportunities with multimodal interaction for better supporting visual data exploration on mobile devices.

Digital intervention tools against problematic smartphone usage help users control their consumption on smartphones, for example, by setting a time limit on an app. However, today's social media apps offer a mix of quasiessential and addictive features in an app (e.g., Instagram has following feeds, recommended feeds, stories, and direct messaging features), which makes it hard to apply a uniform logic for all uses of an app without a nuanced understanding of feature-level usage behaviors. We study when and why people regret using different features of social media apps on smartphones. We examine regretful feature uses in four smartphone social media apps (Facebook, Instagram, YouTube, and KakaoTalk) by utilizing feature usage logs, ESM surveys on regretful use collected for a week, and retrospective interviews from 29 Android users. In determining whether a feature use is regretful, users considered different types of rewards they obtained from using a certain feature (i.e., social, informational, personal interests, and entertainment) as well as alternative rewards they could have gained had they not used the smartphone (e.g., productivity). Depending on the types of rewards and the way rewards are presented to users, probabilities to regret vary across features of the same app. We highlight three patterns of features with different characteristics that lead to regretful use. First, "following"-based features (e.g., Facebook's News Feed and Instagram's Following Posts and Stories) induce habitual checking and quickly deplete rewards from app use. Second, recommendation-based features situated close to actively used features (e.g., Instagram's Suggested Posts adjacent to Search) cause habitual feature tour and sidetracking from the original intention of app use. Third, recommendation-based features with bite-sized contents (e.g., Facebook's Watch Videos) induce using "just a bit more," making people fall into prolonged use. We discuss implications of our findings for how social media apps and intervention tools can be designed to reduce regretful use and how feature-level usage information can strengthen self-reflection and behavior changes.

Smart speakers such as Amazon Echo present promising opportunities for exploring voice interaction in the domain of in-home exercise tracking. In this work, we examine if and how voice interaction complements and augments a mobile app in promoting consistent exercise. We designed and developed TandemTrack, which combines a mobile app and an Alexa skill to support exercise regimen, data capture, feedback, and reminder. We then conducted a four-week between-subjects study deploying TandemTrack to 22 participants who were instructed to follow a short daily exercise regimen: one group used only the mobile app and the other group used both the app and the skill. We collected rich data on individuals' exercise adherence and performance, and their use of voice and visual interactions, while examining how TandemTrack as a whole influenced their exercise experience. Reflecting on these data, we discuss the benefits and challenges of incorporating voice interaction to assist daily exercise, and implications for designing effective multimodal systems to support self-tracking and promote consistent exercise.

In many families, there are obstacles to support and collaborate with one another around positive health outcomes. One obstacle in providing support to one another occurs when family members are unaware of the need. In this study, we examine aspects of family conversations about health that affect family members’ decision to share (or not to share) information within the family, specifically information about sleep behaviors and medication intake. We conducted an interview study with independent living elderly parents (n = 11) and adult children (n = 14). We present factors that motivate and discourage family members from talking about sleep and medication within the family. We identified that some family members shift sharing behaviors following life changes that we characterize as “inciting incidents.” We elaborate the concept of inciting incidents as a resource for design ideas and contribute with a synthesis of design insights for developing family-centered health technologies.

We compare the efficacy of animated and small multiples variants of scatterplots on mobile phones for comparing trends in multivariate datasets. Visualization is increasingly prevalent in mobile applications and mobile-first websites, yet there is little prior visualization research dedicated to small displays. In this paper, we build upon previous experimental research carried out on larger displays that assessed animated and non-animated variants of scatterplots. Incorporating similar experimental stimuli and tasks, we conducted an experiment where 96 crowdworker participants performed nine trend comparison tasks using their mobile phones. We found that those using a small multiples design consistently completed tasks in less time, albeit with slightly less confidence than those using an animated design. The accuracy results were more task-dependent, and we further interpret our results according to the characteristics of the individual tasks, with a specific focus on the trajectories of target and distractor data items in each task. We identify cases that appear to favor either animation or small multiples, providing new questions for further experimental research and implications for visualization design on mobile devices. Lastly, we provide a reflection on our evaluation methodology.

Productivity tracking tools often determine productivity based on the time interacting with work-related applications. To deconstruct productivity's diverse and nebulous nature, we investigate how knowledge workers conceptualize personal productivity and delimit productive tasks in both work and non-work contexts. We report a 2-week diary study followed by a semi-structured interview with 24 knowledge workers. Participants captured productive activities and provided the rationale for why the activities were assessed to be productive. They reported a wide range of productive activities beyond typical desk-bound work-ranging from having a personal conversation with dad to getting a haircut. We found six themes that characterize the productivity assessment-work product, time management, worker's state, attitude toward work, impact & benefit, and compound task and identified how participants interleaved multiple facets when assessing their productivity. We discuss how these findings could inform the design of a comprehensive productivity tracking system that covers a wide range of productive activities.

We report co-design workshops with registered dietitians conducted to identify opportunities for designing customizable food trackers. Dietitians typically see patients who have different dietary problems, thus having different information needs. However, existing food trackers such as paper-based diaries and mobile apps are rarely customizable, making it difficult to capture necessary data for both patients and dietitians. During the co-design sessions, dietitians created representative patient personas and designed food trackers for each persona. We found a wide range of potential tracking items such as food, reflection, symptom, activity, and physical state. Depending on patients' dietary problems and dietitians' practice, the necessity and importance of these tracking items vary. We identify opportunities for patients and healthcare providers to collaborate around data tracking and sharing through customization. We also discuss how to structure co-design workshops to solicit the design considerations of self-tracking tools for patients with specific health problems.

OBJECTIVE: Despite the potential values self-tracking data could offer, we have little understanding of how much access people have to "their" data. Our goal of this article is to unveil the current state of the data accessibility-the degree to which people can access their data-of personal health apps in the market. MATERIALS AND METHODS: We reviewed 240 personal health apps from the App Store and selected 45 apps that support semi-automated tracking. We characterized the data accessibility of these apps using two dimensions-data access methods and data types. RESULTS: More than 90% of our sample apps (n = 41) provide some types of data access support, which include synchronizing data with a health platform (ie, Apple Health), file download, and application program interfaces. However, the two approachable data access methods for laypeople-health platform and file download-typically put a significant limit on data format, granularity, and amount, which constrains people from easily repurposing the data. DISCUSSION: Personal data should be accessible to the people who collect them, but existing methods lack sufficient support for people in accessing the fine-grained data. Lack of standards in personal health data schema as well as frequent changes in market conditions are additional hurdles to data accessibility. CONCLUSIONS: Many stakeholders including patients, healthcare providers, researchers, third-party developers, and the general public rely on data accessibility to utilize personal data for various goals. As such, improving data accessibility should be considered as an important factor in designing personal health apps and health platforms.

In the first crowdsourced visualization experiment conducted exclusively on mobile phones, we compare approaches to visualizing ranges over time on small displays. People routinely consume such data via a mobile phone, from temperatures in weather forecasting apps to sleep and blood pressure readings in personal health apps. However, we lack guidance on how to effectively visualize ranges on small displays in the context of different value retrieval and comparison tasks, or with respect to different data characteristics such as periodicity, seasonality, or the cardinality of ranges. Central to our experiment is a comparison between two ways to lay out ranges: a more conventional linear layout strikes a balance between quantitative and chronological scale resolution, while a less conventional radial layout emphasizes the cyclicality of time and may prioritize discrimination between values at its periphery. With results from 87 crowd workers, we found that while participants completed tasks more quickly with linear layouts than with radial ones, there were few differences in terms of error rate between layout conditions. We also found that participants performed similarly with both layouts in tasks that involved comparing superimposed observed and average ranges.

Extended periods of uninterrupted sedentary behavior are detrimental to long-term health. While prolonged sitting is prevalent among information workers, it is difficult for them to break prolonged sedentary behavior due to the nature of their work. This work aims to understand information workers' intentions & practices around standing or moving breaks. We developed Time for Break, a break prompting system that enables people to set their desired work duration and prompts them to stand up or move. We conducted an exploratory field study (N = 25) with Time for Break to collect participants' work & break intentions and behaviors for three weeks, followed by semi-structured interviews. We examined rich contexts affecting participants' receptiveness to standing or moving breaks, and identified how their habit strength and self-regulation are related to their break-taking intentions & practices. We discuss design implications for interventions to break up periods of prolonged sedentary behavior in workplaces.

Parental controls allow parents to set limits on children's use of technology, but prior work suggests that controlling children alone is unlikely to foster the development of healthy media habits. We took elements from evidence-based preschool curricula that teach self-regulation and translated them to the digital space by creating a tool for preschoolers and parents to plan their device-based playtime. In an observational lab study with 11 parent-child dyads and follow-up interviews with 14 parents, we found that children demonstrated intentionality and made goal-directed choices as they planned, the mediating factor in developing self-regulation. We observed that parents prompted their child to be intentional and solicited children's input. When children played through their plan, they transitioned to the next activity without intervention 93% of the time. Our results suggest that evidence-based practices for teaching self-regulation in a non-digital context can be applied productively to children's use of technology. As parents supported children in trying the tool for the first time, a further contribution of this work is a hierarchical model of parents' approaches to scaffolding children's use of a novel technology.

Annotation plays an important role in conveying key points in visual data-driven storytelling; it helps presenters explain and emphasize core messages and specific data. However, the visualization research community has a limited understanding of annotation and its role in data-driven storytelling, and existing charting software provides limited support for creating annotations. In this paper, we characterize a design space of chart annotations, one informed by a survey of 106 annotated charts published by six prominent news graphics desks. Using this design space, we designed and developed ChartAccent, a tool that allows people to quickly and easily augment charts via a palette of annotation interactions that generate manual and data-driven annotations. We also report on a study in which participants reproduced a series of annotated charts using ChartAccent, beginning with unadorned versions of the same charts. Finally, we discuss the lessons learned during the process of designing and evaluating ChartAccent, and suggest directions for future research.

Insomnia can drastically affect individuals' overall well-being and work performance, with substantial costs to society and industry. Cognitive behavioral therapy for insomnia (CBT-I) is a psychotherapeutic treatment, which requires patients to track sleep and share the data with CBT-I clinicians. However, the number of specialists who can provide CBT-I limits the number of patients who can receive it. In this paper, we aim to identify opportunities to leverage technology to assist clinicians in delivering quality and effective CBT-I services to broader populations. Toward this goal, we conducted formative studies, including 11 CBT-I clinic observations and 17 semi-structured interviews, to understand the current workflow of CBT-I and associated challenges. We discuss how technology can assist clinicians and patients throughout the various steps of CBT-I workflow while addressing some of the identified challenges, and more broadly, how technology can make space for clinicians and patients to build quality therapeutic relationships.

To help people enhance their personal productivity by providing effective feedback, we designed and developed TimeAware, a self-monitoring system for capturing and reflecting on personal computer usage behaviors. TimeAware employs an ambient widget to promote self-awareness and to lower the feedback access burden, and web-based information dashboard to visualize people's detailed computer usage. To examine the effect of framing on individual's productivity, we designed two versions of TimeAware, each with a different framing setting-one emphasizing productive activities (positive framing) and the other emphasizing distracting activities (negative framing), and conducted an eight-week deployment study (N = 24). We found a significant effect of framing on participants' productivity: only participants in the negative framing condition improved their productivity. The ambient widget seemed to help sustain engagement with data and enhance self-awareness. We discuss how to leverage framing effects to help people enhance their productivity, and how to design successful productivity monitoring tool.

Manual tracking of health behaviors affords many benefits, including increased awareness and engagement. However, the capture burden makes long-term manual tracking challenging. In this study on sleep tracking, we examine ways to reduce the capture burden of manual tracking while leveraging its benefits. We report on the design and evaluation of SleepTight, a low-burden, self-monitoring tool that leverages the Android's widgets both to reduce the capture burden and to improve access to information. Through a four-week deployment study (N = 22), we found that participants who used SleepTight with the widgets enabled had a higher sleep diary compliance rate (92%) than participants who used SleepTight without the widgets (73%). In addition, the widgets improved information access and encouraged self-reflection. We discuss how to leverage widgets to help people collect more data and improve access to information, and more broadly, how to design successful manual self-monitoring tools that support self-reflection.

Researchers have studied how people use self-tracking technologies and discovered a long list of barriers including lack of time and motivation as well as difficulty in data integration and interpretation. Despite the barriers, an increasing number of Quantified-Selfers diligently track many kinds of data about themselves, and some of them share their best practices and mistakes through Meetup talks, blogging, and conferences. In this work, we aim to gain insights from these "extreme users," who have used existing technologies and built their own workarounds to overcome different barriers. We conducted a qualitative and quantitative analysis of 52 video recordings of Quantified Self Meetup talks to understand what they did, how they did it, and what they learned. We highlight several common pitfalls to self-tracking, including tracking too many things, not tracking triggers and context, and insufficient scientific rigor. We identify future research efforts that could help make progress toward addressing these pitfalls. We also discuss how our findings can have broad implications in designing and developing self-tracking technologies.

In-home sensing and inference systems impose privacy risks and social tensions, which can be substantial barriers for the wide adoption of these systems. To understand what might affect people's perceptions and acceptance of in-home sensing and inference systems, we conducted an empirical study with 22 participants from 11 households. The study included in-lab activities, four weeks using sensor proxies in situ, and exit interviews. We report on participants' perceived benefits and concerns of in-home sensing applications and the observed changes of their perceptions throughout the study. We also report on tensions amongst stakeholders around the adoption and use of such systems. We conclude with a discussion on how the ubicomp design space might be sensitized to people's perceived concerns and tensions regarding sensing and inference in the home.

The bedroom environment can have a significant impact on the quality of a person's sleep. Experts recommend sleeping in a room that is cool, dark, quiet, and free from disruptors to ensure the best quality sleep. However, it is sometimes difficult for a person to assess which factors in the environment may be causing disrupted sleep. In this paper, we present the design, implementation, and initial evaluation of a capture and access system, called Lullaby. Lullaby combines temperature, light, and motion sensors, audio and photos, and an off-the-shelf sleep sensor to provide a comprehensive recording of a person's sleep. Lullaby allows users to review graphs and access recordings of factors relating to their sleep quality and environmental conditions to look for trends and potential causes of sleep disruptions. In this paper, we report results of a feasibility study where participants (N=4) used Lullaby in their homes for two weeks. Based on our experiences, we discuss design insights for sleep technologies, capture and access applications, and personal informatics tools.

Getting the right amount of quality sleep is a key aspect of good health, along with a healthy diet and regular exercise. Human-computer interaction (HCI) researchers have recently designed systems to support diet and exercise, but sleep has been relatively under-studied in the HCI community. We conducted a literature review and formative study aimed at uncovering opportunities for computing to support the important area of promoting healthy sleep. We present results from interviews with sleep experts, as well as a survey (N = 230) and interviews with potential users (N = 16) to indicate what people would find practical and useful for sleep. Based on these results, we identify a number of design considerations, challenges, and opportunities for using computing to support healthy sleep behaviors, as well as a design framework for mapping the design space of technologies for sleep.

Getting the sufficient amount of quality sleep is a key aspect of good health along with a healthy diet and regular exercise. Despite its importance, sleep has been considerably underexplored in the area of human-computer interaction. In this proposal, I describe my research in understanding the need to help improve people's sleep habits and creating a persuasive sleep application to help them achieve their sleep-related goals. The persuasive sleep application involves self-monitoring and feedback features to help people be aware of their sleep habits. My dissertation research investigates a design of a self-monitoring system focusing on how information is presented as a persuasive means accounting for user emotions in the context of receiving concerning health news.

As advances in technology accelerate, sensors and recording devices are increasingly being integrated into homes. Although the added benefit of sensing is often clear (e.g., entertainment, security, encouraging sustainable behaviors, etc.), the home is a private and intimate place, with multiple stakeholders who may have competing priorities and tolerances for what is acceptable and useful. In an effort to develop systems that account for the needs and concerns of householders, we conducted an anonymous survey (N = 475) focusing on the activities and habits that people do at home that they would not want to be recorded. In this paper, we discuss those activities and where in the home they are performed, and offer suggestions for the design of UbiComp systems that rely on sensing and recording.